Add circular edges sample.

* Adding support for events & shared state in durable workflows.

* PR feedback fixes

* PR feedback fixes.

* Add YieldOutputAsync calls to 05_WorkflowEvents sample executors

The integration test asserts that WorkflowOutputEvent is found in the
stream, but the sample executors only used AddEventAsync for custom
events and never called YieldOutputAsync. Since WorkflowOutputEvent is
only emitted via explicit YieldOutputAsync calls, the assertion would
fail. Added YieldOutputAsync to each executor to match the test
expectation and demonstrate the API in the sample.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix deserialization to use shared serializer options.

* PR feedback updates.

* Sample cleanup

* PR feedback fixes

* Addressing PR review feedback for DurableStreamingWorkflowRun

   - Use -1 instead of 0 for taskId in TaskFailedException when task ID is not relevant.
   - Add [NotNullWhen(true)] to TryParseWorkflowResult out parameter following .NET TryXXX conventions.

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

.devcontainer/dotnet/devcontainer.json

@@ -1,9 +1,12 @@
 {
   "name": "C# (.NET)",
-  "image": "mcr.microsoft.com/devcontainers/dotnet",
+  //"image": "mcr.microsoft.com/devcontainers/dotnet",
+  // Workaround for https://github.com/devcontainers/images/issues/1752
+  "build": {
+    "dockerfile": "dotnet.Dockerfile"
+  },
   "features": {
     "ghcr.io/devcontainers/features/azure-cli:1.2.9": {},
-    "ghcr.io/devcontainers/features/docker-in-docker:2": {},
     "ghcr.io/devcontainers/features/github-cli:1": {
       "version": "2"
     },

.devcontainer/dotnet/dotnet.Dockerfile

@@ -0,0 +1,5 @@
+FROM mcr.microsoft.com/devcontainers/universal:latest
+
+# Remove Yarn repository with expired GPG key to prevent apt-get update failures
+# Tracking issue: https://github.com/devcontainers/images/issues/1752
+RUN rm -f /etc/apt/sources.list.d/yarn.list

.github/.linkspector.yml

@@ -20,7 +20,6 @@ ignorePatterns:
   - pattern: "https://your-resource.openai.azure.com/"
   - pattern: "http://host.docker.internal"
   - pattern: "https://openai.github.io/openai-agents-js/openai/agents/classes/"
-  - pattern: "https:\/\/dotnet.microsoft.com\/download"
 # excludedDirs:
   # Folders which include links to localhost, since it's not ignored with regular expressions
 baseUrl: https://github.com/microsoft/agent-framework/

.github/actions/python-setup/action.yml

@@ -8,10 +8,6 @@ inputs:
     os:
       description: The operating system to set up
       required: true
-    exclude-packages:
-      description: Space-separated list of packages to exclude from uv sync
-      required: false
-      default: ''
 
 runs:
     using: "composite"
@@ -23,20 +19,6 @@ runs:
             enable-cache: true
             cache-suffix: ${{ inputs.os }}-${{ inputs.python-version }}
             cache-dependency-glob: "**/uv.lock"
-      - name: Exclude incompatible workspace packages
-        if: ${{ inputs.exclude-packages != '' }}
-        shell: bash
-        run: |
-          for pkg in ${{ inputs.exclude-packages }}; do
-            for f in python/packages/*/pyproject.toml; do
-              if grep -q "name = \"$pkg\"" "$f"; then
-                pkg_dir=$(dirname "$f" | sed 's|python/||')
-                echo "Excluding workspace package: $pkg ($pkg_dir)"
-                sed -i.bak '/\[tool\.uv\.workspace\]/a\exclude = ["'"$pkg_dir"'"]' python/pyproject.toml
-                sed -i.bak '/'"$pkg"' = { workspace = true }/d' python/pyproject.toml
-              fi
-            done
-          done
       - name: Install the project
         shell: bash
         run: |

.github/actions/sample-validation-setup/action.yml

@@ -1,50 +0,0 @@
-name: Sample Validation Setup
-description: Sets up the environment for sample validation (checkout, Node.js, Copilot CLI, Azure login, Python)
-
-inputs:
-  azure-client-id:
-    description: Azure Client ID for OIDC login
-    required: true
-  azure-tenant-id:
-    description: Azure Tenant ID for OIDC login
-    required: true
-  azure-subscription-id:
-    description: Azure Subscription ID for OIDC login
-    required: true
-  python-version:
-    description: The Python version to set up
-    required: false
-    default: "3.12"
-  os:
-    description: The operating system to set up
-    required: false
-    default: "Linux"
-
-runs:
-  using: "composite"
-  steps:
-    - name: Set up Node.js environment
-      uses: actions/setup-node@v6
-      with:
-        node-version: 22
-
-    - name: Install Copilot CLI
-      shell: bash
-      run: npm install -g @github/copilot
-
-    - name: Test Copilot CLI
-      shell: bash
-      run: copilot --version && copilot -p "What can you do in one sentence?"
-
-    - name: Azure CLI Login
-      uses: azure/login@v2
-      with:
-        client-id: ${{ inputs.azure-client-id }}
-        tenant-id: ${{ inputs.azure-tenant-id }}
-        subscription-id: ${{ inputs.azure-subscription-id }}
-
-    - name: Set up python and install the project
-      uses: ./.github/actions/python-setup
-      with:
-        python-version: ${{ inputs.python-version }}
-        os: ${{ inputs.os }}

.github/actions/setup-local-mcp-server/action.yml

@@ -1,166 +0,0 @@
-name: Setup Local MCP Server
-description: Start and validate a local streamable HTTP MCP server for integration tests
-
-inputs:
-  fallback_url:
-    description: Existing LOCAL_MCP_URL value to keep as a fallback if local startup fails
-    required: false
-    default: ''
-  host:
-    description: Host interface to bind the local MCP server
-    required: false
-    default: '127.0.0.1'
-  port:
-    description: Port to bind the local MCP server
-    required: false
-    default: '8011'
-  mount_path:
-    description: Mount path for the local streamable HTTP MCP endpoint
-    required: false
-    default: '/mcp'
-
-outputs:
-  effective_url:
-    description: Local MCP URL when startup succeeds, otherwise the provided fallback URL
-    value: ${{ steps.start.outputs.effective_url }}
-  local_url:
-    description: URL of the local MCP server
-    value: ${{ steps.start.outputs.local_url }}
-  started:
-    description: Whether the local MCP server started and passed validation
-    value: ${{ steps.start.outputs.started }}
-  pid:
-    description: PID of the local MCP server process when startup succeeded
-    value: ${{ steps.start.outputs.pid }}
-
-runs:
-  using: composite
-  steps:
-    - name: Start and validate local MCP server
-      id: start
-      shell: bash
-      run: |
-        set -euo pipefail
-
-        host="${{ inputs.host }}"
-        port="${{ inputs.port }}"
-        mount_path="${{ inputs.mount_path }}"
-        fallback_url="${{ inputs.fallback_url }}"
-
-        if [[ ! "$mount_path" =~ ^/ ]]; then
-          mount_path="/$mount_path"
-        fi
-
-        local_url="http://${host}:${port}${mount_path}"
-        health_url="http://${host}:${port}/healthz"
-        log_file="$RUNNER_TEMP/local-mcp-server.log"
-        pid_file="$RUNNER_TEMP/local-mcp-server.pid"
-        rm -f "$log_file" "$pid_file"
-
-        server_pid="$(
-          python3 - "$GITHUB_WORKSPACE/python" "$log_file" "$host" "$port" "$mount_path" <<'PY'
-        from __future__ import annotations
-
-        import subprocess
-        import sys
-
-        workspace, log_file, host, port, mount_path = sys.argv[1:]
-
-        with open(log_file, "w", encoding="utf-8") as log:
-            process = subprocess.Popen(
-                [
-                    "uv",
-                    "run",
-                    "python",
-                    "scripts/local_mcp_streamable_http_server.py",
-                    "--host",
-                    host,
-                    "--port",
-                    port,
-                    "--mount-path",
-                    mount_path,
-                ],
-                cwd=workspace,
-                stdout=log,
-                stderr=subprocess.STDOUT,
-                start_new_session=True,
-            )
-
-        print(process.pid)
-        PY
-        )"
-        echo "$server_pid" > "$pid_file"
-
-        started=false
-        for _ in $(seq 1 30); do
-          if curl --silent --fail "$health_url" >/dev/null; then
-            started=true
-            break
-          fi
-          if ! kill -0 "$server_pid" 2>/dev/null; then
-            break
-          fi
-          sleep 1
-        done
-
-        if [[ "$started" == "true" ]]; then
-          if ! (
-            cd "$GITHUB_WORKSPACE/python"
-            LOCAL_MCP_URL="$local_url" uv run python - <<'PY'
-        from __future__ import annotations
-
-        import asyncio
-        import os
-
-        from agent_framework import Content, MCPStreamableHTTPTool
-
-
-        def result_to_text(result: str | list[Content]) -> str:
-            if isinstance(result, str):
-                return result
-            return "\n".join(content.text for content in result if content.type == "text" and content.text)
-
-
-        async def main() -> None:
-            tool = MCPStreamableHTTPTool(
-                name="local_ci_mcp",
-                url=os.environ["LOCAL_MCP_URL"],
-                approval_mode="never_require",
-            )
-
-            async with tool:
-                assert tool.functions, "Local MCP server did not expose any tools."
-                result = result_to_text(await tool.functions[0].invoke(query="What is Agent Framework?"))
-                assert result, "Local MCP server returned an empty response."
-
-
-        asyncio.run(main())
-        PY
-          ); then
-            started=false
-          fi
-        fi
-
-        effective_url="$local_url"
-        pid="$server_pid"
-
-        if [[ "$started" != "true" ]]; then
-          effective_url="$fallback_url"
-          pid=""
-          if kill -0 "$server_pid" 2>/dev/null; then
-            kill -TERM -- "-$server_pid" 2>/dev/null || kill -TERM "$server_pid" || true
-            sleep 1
-            kill -KILL -- "-$server_pid" 2>/dev/null || kill -KILL "$server_pid" || true
-          fi
-          echo "Local MCP server was unavailable; continuing with fallback LOCAL_MCP_URL."
-          if [[ -f "$log_file" ]]; then
-            tail -n 100 "$log_file" || true
-          fi
-        else
-          echo "Using local MCP server at $local_url"
-        fi
-
-        echo "started=$started" >> "$GITHUB_OUTPUT"
-        echo "local_url=$local_url" >> "$GITHUB_OUTPUT"
-        echo "effective_url=$effective_url" >> "$GITHUB_OUTPUT"
-        echo "pid=$pid" >> "$GITHUB_OUTPUT"

.github/copilot-instructions.md

@@ -1,19 +1,69 @@
 # GitHub Copilot Instructions
 
-Microsoft Agent Framework - a multi-language framework for building, orchestrating, and deploying AI agents.
+This repository contains both Python and C# code.
+All python code resides under the `python/` directory.
+All C# code resides under the `dotnet/` directory.
 
-## Repository Structure
+The purpose of the code is to provide a framework for building AI agents.
 
-- `python/` - Python implementation → see [python/AGENTS.md](../python/AGENTS.md)
-- `dotnet/` - C#/.NET implementation → see [dotnet/AGENTS.md](../dotnet/AGENTS.md)
-- `docs/` - Design documents and architectural decision records
+When contributing to this repository, please follow these guidelines:
 
-## Architectural Decision Records (ADRs)
+## C# Code Guidelines
 
-ADRs in `docs/decisions/` capture significant design decisions and their rationale. They document considered alternatives, trade-offs, and the reasoning behind choices.
+Here are some general guidelines that apply to all code.
 
-**Templates:**
-- `adr-template.md` - Full template with detailed sections
-- `adr-short-template.md` - Abbreviated template for simpler decisions
+- The top of all *.cs files should have a copyright notice: `// Copyright (c) Microsoft. All rights reserved.`
+- All public methods and classes should have XML documentation comments.
+- After adding, modifying or deleting code, run `dotnet build`, and then fix any reported build errors.
+- After adding or modifying code, run `dotnet format` to automatically fix any formatting errors.
 
-When proposing architectural changes, create an ADR to capture options considered and the decision rationale. See [docs/decisions/README.md](../docs/decisions/README.md) for the full process.
+### C# Sample Code Guidelines
+
+Sample code is located in the `dotnet/samples` directory.
+
+When adding a new sample, follow these steps:
+
+- The sample should be a standalone .net project in one of the subdirectories of the samples directory.
+- The directory name should be the same as the project name.
+- The directory should contain a README.md file that explains what the sample does and how to run it.
+- The README.md file should follow the same format as other samples.
+- The csproj file should match the directory name.
+- The csproj file should be configured in the same way as other samples.
+- The project should preferably contain a single Program.cs file that contains all the sample code.
+- The sample should be added to the solution file in the samples directory.
+- The sample should be tested to ensure it works as expected.
+- A reference to the new samples should be added to the README.md file in the parent directory of the new sample.
+
+The sample code should follow these guidelines:
+
+- Configuration settings should be read from environment variables, e.g. `var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");`.
+- Environment variables should use upper snake_case naming convention.
+- Secrets should not be hardcoded in the code or committed to the repository.
+- The code should be well-documented with comments explaining the purpose of each step.
+- The code should be simple and to the point, avoiding unnecessary complexity.
+- Prefer inline literals over constants for values that are not reused. For example, use `new ChatClientAgent(chatClient, instructions: "You are a helpful assistant.")` instead of defining a constant for "instructions".
+- Ensure that all private classes are sealed
+- Use the Async suffix on the name of all async methods that return a Task or ValueTask.
+- Prefer defining variables using types rather than var, to help users understand the types involved.
+- Follow the patterns in the samples in the same directories where new samples are being added.
+- The structure of the sample should be as follows:
+  - The top of the Program.cs should have a copyright notice: `// Copyright (c) Microsoft. All rights reserved.`
+  - Then add a comment describing what the sample is demonstrating.
+  - Then add the necessary using statements.
+  - Then add the main code logic.
+  - Finally, add any helper methods or classes at the bottom of the file.
+
+### C# Unit Test Guidelines
+
+Unit tests are located in the `dotnet/tests` directory in projects with a `.UnitTests.csproj` suffix.
+
+Unit tests should follow these guidelines:
+
+- Use `this.` for accessing class members
+- Add Arrange, Act and Assert comments for each test
+- Ensure that all private classes, that are not subclassed, are sealed
+- Use the Async suffix on the name of all async methods
+- Use the Moq library for mocking objects where possible
+- Validate that each test actually tests the target behavior, e.g. we should not have tests that creates a mock, calls the mock and then verifies that the mock was called, without the target code being involved. We also shouldn't have tests that test language features, e.g. something that the compiler would catch anyway.
+- Avoid adding excessive comments to tests. Instead favour clear easy to understand code.
+- Follow the patterns in the unit tests in the same project or classes to which new tests are being added

.github/labeler.yml

@@ -23,7 +23,7 @@ workflows:
   - any-glob-to-any-file:
     - dotnet/src/Microsoft.Agents.AI.Workflows/**
     - dotnet/src/Microsoft.Agents.AI.Workflows.Declarative/**
-    - dotnet/samples/03-workflows/**
+    - dotnet/samples/GettingStarted/Workflow/**
     - python/packages/main/agent_framework/_workflow/**
     - python/samples/getting_started/workflow/**
 

.github/scripts/stale_issue_pr_ping.py

@@ -1,216 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-"""Scan open issues and PRs labeled 'waiting-for-author' for stale follow-ups.
-
-Team members manually add the 'waiting-for-author' label when they need a
-response from the external author.  If the author hasn't replied within
-DAYS_THRESHOLD days of the last team comment, post a reminder and add the
-'requested-info' label to prevent duplicate pings.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import time
-from datetime import datetime, timezone
-
-from github import Auth, Github, GithubException
-from github.Issue import Issue
-from github.IssueComment import IssueComment
-
-
-PING_COMMENT = (
-    "@{author}, friendly reminder — this issue is waiting on your response. "
-    "Please share any updates when you get a chance. (This is an automated message.)"
-)
-TRIGGER_LABEL = "waiting-for-author"
-PINGED_LABEL = "requested-info"
-
-
-def get_team_members(g: Github, org: str, team_slug: str) -> set[str]:
-    """Fetch active team member usernames."""
-    try:
-        org_obj = g.get_organization(org)
-        team = org_obj.get_team_by_slug(team_slug)
-        return {m.login for m in team.get_members()}
-    except GithubException as exc:
-        if exc.status in (403, 404):
-            print(
-                f"ERROR: Failed to fetch team members for {org}/{team_slug} "
-                f"(HTTP {exc.status}). Check that the token has the 'read:org' "
-                f"scope and that the team slug '{team_slug}' is correct."
-            )
-        else:
-            print(f"ERROR: Failed to fetch team members for {org}/{team_slug}: {exc}")
-        sys.exit(1)
-    except Exception as exc:
-        print(f"ERROR: Failed to fetch team members for {org}/{team_slug}: {exc}")
-        sys.exit(1)
-
-
-def find_last_team_comment(
-    comments: list[IssueComment], team_members: set[str]
-) -> IssueComment | None:
-    """Return the most recent comment from a team member, or None."""
-    for comment in reversed(comments):
-        if comment.user and comment.user.login in team_members:
-            return comment
-    return None
-
-
-def author_replied_after(
-    comments: list[IssueComment], author: str, after: datetime
-) -> bool:
-    """Check if the issue author commented after the given timestamp."""
-    for comment in comments:
-        if (
-            comment.user
-            and comment.user.login == author
-            and comment.created_at > after
-        ):
-            return True
-    return False
-
-
-def should_ping(
-    issue: Issue,
-    team_members: set[str],
-    days_threshold: int,
-    now: datetime,
-) -> bool:
-    """Determine whether this issue/PR should be pinged.
-
-    Only issues/PRs carrying the 'waiting-for-author' label are candidates.
-    """
-    author = issue.user.login
-
-    # Skip if the trigger label is not present
-    if not any(label.name == TRIGGER_LABEL for label in issue.labels):
-        return False
-    # Skip if author is a team member
-    if author in team_members:
-        return False
-
-    # Skip if already pinged
-    if any(label.name == PINGED_LABEL for label in issue.labels):
-        return False
-
-    # Skip if no comments at all
-    if issue.comments == 0:
-        return False
-
-    # Fetch comments once for both lookups
-    comments = list(issue.get_comments())
-
-    # Find last team member comment
-    last_team_comment = find_last_team_comment(comments, team_members)
-    if last_team_comment is None:
-        return False
-
-    # Skip if author replied after the last team comment
-    if author_replied_after(comments, author, last_team_comment.created_at):
-        return False
-
-    # Check if enough days have passed
-    days_since = (now - last_team_comment.created_at.astimezone(timezone.utc)).days
-    if days_since < days_threshold:
-        return False
-
-    return True
-
-
-def ping(issue: Issue, dry_run: bool) -> bool:
-    """Post a reminder comment and add the 'requested-info' label. Returns True on success."""
-    author = issue.user.login
-    kind = "PR" if issue.pull_request else "Issue"
-
-    if dry_run:
-        print(f"  [DRY RUN] Would ping {kind} #{issue.number} (@{author})")
-        return True
-
-    max_retries = 3
-    commented = False
-    labeled = False
-    for attempt in range(1, max_retries + 1):
-        try:
-            if not commented:
-                issue.create_comment(PING_COMMENT.format(author=author))
-                commented = True
-            if not labeled:
-                issue.add_to_labels(PINGED_LABEL)
-                labeled = True
-            print(f"  Pinged {kind} #{issue.number} (@{author})")
-            return True
-        except Exception as exc:
-            if attempt < max_retries:
-                wait = 2 ** attempt  # 2s, 4s
-                print(f"  WARN: Attempt {attempt}/{max_retries} failed for {kind} #{issue.number}: {exc}. Retrying in {wait}s...")
-                time.sleep(wait)
-            else:
-                print(f"  ERROR: Failed to ping {kind} #{issue.number} after {max_retries} attempts: {exc}")
-                return False
-
-
-def main() -> None:
-    token = os.environ.get("GITHUB_TOKEN")
-    if not token:
-        print("ERROR: GITHUB_TOKEN environment variable is required")
-        sys.exit(1)
-
-    repository = os.environ.get("GITHUB_REPOSITORY")
-    if not repository:
-        print("ERROR: GITHUB_REPOSITORY environment variable is required")
-        sys.exit(1)
-
-    team_slug = os.environ.get("TEAM_SLUG")
-    if not team_slug:
-        print("ERROR: TEAM_SLUG environment variable is required")
-        sys.exit(1)
-
-    days_threshold_raw = os.environ.get("DAYS_THRESHOLD", "4")
-    try:
-        days_threshold = int(days_threshold_raw)
-    except ValueError:
-        print(f"ERROR: DAYS_THRESHOLD must be a numeric value, got '{days_threshold_raw}'")
-        sys.exit(1)
-    dry_run = os.environ.get("DRY_RUN", "false").lower() == "true"
-
-    org = repository.split("/")[0]
-
-    if dry_run:
-        print("Running in DRY RUN mode — no comments or labels will be applied.\n")
-
-    g = Github(auth=Auth.Token(token))
-    repo = g.get_repo(repository)
-
-    print(f"Fetching team members for {org}/{team_slug}...")
-    team_members = get_team_members(g, org, team_slug)
-    print(f"Found {len(team_members)} team members.\n")
-
-    now = datetime.now(timezone.utc)
-    pinged = []
-    failed = []
-    scanned = 0
-
-    print(f"Scanning open issues and PRs labeled '{TRIGGER_LABEL}' (threshold: {days_threshold} days)...\n")
-
-    for issue in repo.get_issues(state="open", labels=[TRIGGER_LABEL]):
-        scanned += 1
-
-        if should_ping(issue, team_members, days_threshold, now):
-            if ping(issue, dry_run):
-                pinged.append(issue.number)
-            else:
-                failed.append(issue.number)
-
-    print(f"\nDone. Scanned {scanned} items, pinged {len(pinged)}, failed {len(failed)}.")
-    if pinged:
-        print(f"Pinged: {', '.join(f'#{n}' for n in pinged)}")
-    if failed:
-        print(f"Failed: {', '.join(f'#{n}' for n in failed)}")
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()

.github/tests/test_stale_issue_pr_ping.py

@@ -1,297 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-"""Tests for stale_issue_pr_ping.py."""
-
-from __future__ import annotations
-
-import os
-import sys
-from datetime import datetime, timezone, timedelta
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-# Ensure the script directory is importable
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "scripts"))
-
-from stale_issue_pr_ping import (
-    PINGED_LABEL,
-    PING_COMMENT,
-    TRIGGER_LABEL,
-    author_replied_after,
-    find_last_team_comment,
-    get_team_members,
-    main,
-    ping,
-    should_ping,
-)
-
-TEAM = {"alice", "bob"}
-NOW = datetime(2026, 3, 15, 12, 0, 0, tzinfo=timezone.utc)
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _make_comment(login: str | None, created_at: datetime) -> MagicMock:
-    """Create a mock IssueComment."""
-    c = MagicMock()
-    if login is None:
-        c.user = None
-    else:
-        c.user = MagicMock()
-        c.user.login = login
-    c.created_at = created_at
-    return c
-
-
-def _make_label(name: str) -> MagicMock:
-    lbl = MagicMock()
-    lbl.name = name
-    return lbl
-
-
-def _make_issue(
-    author: str = "external",
-    labels: list[str] | None = None,
-    comment_count: int = 1,
-    comments: list[MagicMock] | None = None,
-    pull_request: bool = False,
-    number: int = 42,
-) -> MagicMock:
-    issue = MagicMock()
-    issue.user = MagicMock()
-    issue.user.login = author
-    issue.number = number
-    # Default to having the trigger label, since the API query pre-filters.
-    if labels is None:
-        labels = [TRIGGER_LABEL]
-    issue.labels = [_make_label(n) for n in labels]
-    issue.comments = comment_count
-    issue.pull_request = MagicMock() if pull_request else None
-    if comments is not None:
-        issue.get_comments.return_value = comments
-    return issue
-
-
-# ---------------------------------------------------------------------------
-# find_last_team_comment
-# ---------------------------------------------------------------------------
-
-class TestFindLastTeamComment:
-    def test_returns_last_team_comment(self):
-        c1 = _make_comment("alice", datetime(2026, 3, 1, tzinfo=timezone.utc))
-        c2 = _make_comment("external", datetime(2026, 3, 2, tzinfo=timezone.utc))
-        c3 = _make_comment("bob", datetime(2026, 3, 3, tzinfo=timezone.utc))
-        assert find_last_team_comment([c1, c2, c3], TEAM) is c3
-
-    def test_returns_none_when_no_team_comments(self):
-        c1 = _make_comment("external", datetime(2026, 3, 1, tzinfo=timezone.utc))
-        assert find_last_team_comment([c1], TEAM) is None
-
-    def test_returns_none_for_empty_list(self):
-        assert find_last_team_comment([], TEAM) is None
-
-    def test_skips_deleted_user(self):
-        c1 = _make_comment(None, datetime(2026, 3, 1, tzinfo=timezone.utc))
-        c2 = _make_comment("alice", datetime(2026, 3, 2, tzinfo=timezone.utc))
-        assert find_last_team_comment([c1, c2], TEAM) is c2
-
-    def test_only_deleted_users(self):
-        c1 = _make_comment(None, datetime(2026, 3, 1, tzinfo=timezone.utc))
-        assert find_last_team_comment([c1], TEAM) is None
-
-
-# ---------------------------------------------------------------------------
-# author_replied_after
-# ---------------------------------------------------------------------------
-
-class TestAuthorRepliedAfter:
-    def test_author_replied(self):
-        after = datetime(2026, 3, 1, tzinfo=timezone.utc)
-        c1 = _make_comment("external", datetime(2026, 3, 2, tzinfo=timezone.utc))
-        assert author_replied_after([c1], "external", after) is True
-
-    def test_author_not_replied(self):
-        after = datetime(2026, 3, 5, tzinfo=timezone.utc)
-        c1 = _make_comment("external", datetime(2026, 3, 2, tzinfo=timezone.utc))
-        assert author_replied_after([c1], "external", after) is False
-
-    def test_different_user_replied(self):
-        after = datetime(2026, 3, 1, tzinfo=timezone.utc)
-        c1 = _make_comment("someone_else", datetime(2026, 3, 2, tzinfo=timezone.utc))
-        assert author_replied_after([c1], "external", after) is False
-
-    def test_deleted_user_comment(self):
-        after = datetime(2026, 3, 1, tzinfo=timezone.utc)
-        c1 = _make_comment(None, datetime(2026, 3, 2, tzinfo=timezone.utc))
-        assert author_replied_after([c1], "external", after) is False
-
-
-# ---------------------------------------------------------------------------
-# should_ping
-# ---------------------------------------------------------------------------
-
-class TestShouldPing:
-    def test_should_ping_stale_issue(self):
-        team_comment = _make_comment("alice", NOW - timedelta(days=5))
-        issue = _make_issue(comments=[team_comment], comment_count=1)
-        assert should_ping(issue, TEAM, 4, NOW) is True
-
-    def test_skip_team_member_author(self):
-        issue = _make_issue(author="alice", labels=[TRIGGER_LABEL], comment_count=1)
-        assert should_ping(issue, TEAM, 4, NOW) is False
-
-    def test_skip_already_pinged(self):
-        issue = _make_issue(labels=[TRIGGER_LABEL, PINGED_LABEL], comment_count=1)
-        assert should_ping(issue, TEAM, 4, NOW) is False
-
-    def test_skip_no_comments(self):
-        issue = _make_issue(comment_count=0)
-        assert should_ping(issue, TEAM, 4, NOW) is False
-
-    def test_skip_no_team_comment(self):
-        c = _make_comment("external", NOW - timedelta(days=5))
-        issue = _make_issue(comments=[c], comment_count=1)
-        assert should_ping(issue, TEAM, 4, NOW) is False
-
-    def test_skip_author_replied(self):
-        team_c = _make_comment("alice", NOW - timedelta(days=5))
-        author_c = _make_comment("external", NOW - timedelta(days=3))
-        issue = _make_issue(comments=[team_c, author_c], comment_count=2)
-        assert should_ping(issue, TEAM, 4, NOW) is False
-
-    def test_skip_not_enough_days(self):
-        team_comment = _make_comment("alice", NOW - timedelta(days=2))
-        issue = _make_issue(comments=[team_comment], comment_count=1)
-        assert should_ping(issue, TEAM, 4, NOW) is False
-
-    def test_aware_datetime_handled(self):
-        """Timezone-aware datetimes should not be mangled by astimezone."""
-        aware_dt = (NOW - timedelta(days=5)).replace(tzinfo=timezone.utc)
-        team_comment = _make_comment("alice", aware_dt)
-        issue = _make_issue(comments=[team_comment], comment_count=1)
-        assert should_ping(issue, TEAM, 4, NOW) is True
-
-    def test_naive_datetime_handled(self):
-        """Naive datetimes (pre-PyGithub 2.x) should be handled by astimezone."""
-        naive_dt = (NOW - timedelta(days=5)).replace(tzinfo=None)
-        team_comment = _make_comment("alice", naive_dt)
-        issue = _make_issue(comments=[team_comment], comment_count=1)
-        # astimezone on naive datetime treats it as local time; just verify no crash
-        should_ping(issue, TEAM, 4, NOW)
-
-
-# ---------------------------------------------------------------------------
-# ping
-# ---------------------------------------------------------------------------
-
-class TestPing:
-    def test_dry_run(self, capsys):
-        issue = _make_issue()
-        assert ping(issue, dry_run=True) is True
-        issue.create_comment.assert_not_called()
-        assert "DRY RUN" in capsys.readouterr().out
-
-    def test_success(self, capsys):
-        issue = _make_issue()
-        assert ping(issue, dry_run=False) is True
-        issue.create_comment.assert_called_once()
-        issue.add_to_labels.assert_called_once_with(PINGED_LABEL)
-
-    @patch("stale_issue_pr_ping.time.sleep")
-    def test_retry_on_failure(self, mock_sleep):
-        issue = _make_issue()
-        issue.create_comment.side_effect = [Exception("net error"), None]
-        assert ping(issue, dry_run=False) is True
-        assert issue.create_comment.call_count == 2
-        mock_sleep.assert_called_once()
-
-    @patch("stale_issue_pr_ping.time.sleep")
-    def test_idempotent_retry_skips_comment_on_label_failure(self, mock_sleep):
-        """If create_comment succeeds but add_to_labels fails, retry should not re-comment."""
-        issue = _make_issue()
-        issue.add_to_labels.side_effect = [Exception("label error"), None]
-        assert ping(issue, dry_run=False) is True
-        # Comment should only be created once even though there were 2 attempts
-        assert issue.create_comment.call_count == 1
-        assert issue.add_to_labels.call_count == 2
-
-    @patch("stale_issue_pr_ping.time.sleep")
-    def test_all_retries_fail(self, mock_sleep):
-        issue = _make_issue()
-        issue.create_comment.side_effect = Exception("permanent error")
-        assert ping(issue, dry_run=False) is False
-        assert issue.create_comment.call_count == 3
-
-
-# ---------------------------------------------------------------------------
-# get_team_members
-# ---------------------------------------------------------------------------
-
-class TestGetTeamMembers:
-    def test_success(self):
-        g = MagicMock()
-        member = MagicMock()
-        member.login = "alice"
-        g.get_organization.return_value.get_team_by_slug.return_value.get_members.return_value = [member]
-        assert get_team_members(g, "org", "my-team") == {"alice"}
-
-    def test_403_error_message(self, capsys):
-        from github import GithubException
-
-        g = MagicMock()
-        g.get_organization.return_value.get_team_by_slug.side_effect = GithubException(
-            403, {"message": "Forbidden"}, None
-        )
-        with pytest.raises(SystemExit):
-            get_team_members(g, "org", "my-team")
-        out = capsys.readouterr().out
-        assert "read:org" in out
-        assert "403" in out
-
-    def test_404_error_message(self, capsys):
-        from github import GithubException
-
-        g = MagicMock()
-        g.get_organization.return_value.get_team_by_slug.side_effect = GithubException(
-            404, {"message": "Not Found"}, None
-        )
-        with pytest.raises(SystemExit):
-            get_team_members(g, "org", "bad-slug")
-        out = capsys.readouterr().out
-        assert "read:org" in out
-        assert "bad-slug" in out
-
-    def test_generic_error(self, capsys):
-        g = MagicMock()
-        g.get_organization.side_effect = RuntimeError("boom")
-        with pytest.raises(SystemExit):
-            get_team_members(g, "org", "team")
-
-
-# ---------------------------------------------------------------------------
-# main – env var validation
-# ---------------------------------------------------------------------------
-
-class TestMain:
-    @patch.dict(os.environ, {
-        "GITHUB_TOKEN": "tok",
-        "GITHUB_REPOSITORY": "org/repo",
-        "TEAM_SLUG": "my-team",
-        "DAYS_THRESHOLD": "abc",
-    }, clear=True)
-    def test_invalid_days_threshold(self, capsys):
-        with pytest.raises(SystemExit):
-            main()
-        assert "numeric" in capsys.readouterr().out
-
-    @patch.dict(os.environ, {
-        "GITHUB_TOKEN": "tok",
-        "GITHUB_REPOSITORY": "org/repo",
-    }, clear=True)
-    def test_missing_team_slug(self, capsys):
-        with pytest.raises(SystemExit):
-            main()
-        assert "TEAM_SLUG" in capsys.readouterr().out

.github/workflows/dotnet-build-and-test.yml

@@ -59,20 +59,20 @@ jobs:
         if: steps.filter.outputs.dotnet != 'true'
         run: echo "NOT dotnet file"
 
-  # Build the full solution (including samples) on all TFMs. No tests.
-  dotnet-build:
+  dotnet-build-and-test:
     needs: paths-filter
     if: needs.paths-filter.outputs.dotnetChanges == 'true'
     strategy:
       fail-fast: false
       matrix:
         include:
-          - { targetFramework: "net10.0", os: "ubuntu-latest", configuration: Release }
+          - { targetFramework: "net10.0", os: "ubuntu-latest", configuration: Release, integration-tests: true, environment: "integration" }
           - { targetFramework: "net9.0", os: "windows-latest", configuration: Debug }
           - { targetFramework: "net8.0", os: "ubuntu-latest", configuration: Release }
-          - { targetFramework: "net472", os: "windows-latest", configuration: Release }
+          - { targetFramework: "net472", os: "windows-latest", configuration: Release, integration-tests: true, environment: "integration" }
 
     runs-on: ${{ matrix.os }}
+    environment: ${{ matrix.environment }}
     steps:
       - uses: actions/checkout@v6
         with:
@@ -84,8 +84,18 @@ jobs:
               python
               workflow-samples
 
+      # Start Cosmos DB Emulator for all integration tests and only for unit tests when CosmosDB changes happened)
+      - name: Start Azure Cosmos DB Emulator
+        if: ${{ runner.os == 'Windows' && (needs.paths-filter.outputs.cosmosDbChanges == 'true' || (github.event_name != 'pull_request' && matrix.integration-tests)) }}
+        shell: pwsh
+        run: |
+          Write-Host "Launching Azure Cosmos DB Emulator"
+          Import-Module "$env:ProgramFiles\Azure Cosmos DB Emulator\PSModules\Microsoft.Azure.CosmosDB.Emulator"
+          Start-CosmosDbEmulator -NoUI -Key "C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw=="
+          echo "COSMOS_EMULATOR_AVAILABLE=true" >> $env:GITHUB_ENV
+
       - name: Setup dotnet
-        uses: actions/setup-dotnet@v5.2.0
+        uses: actions/setup-dotnet@v5.1.0
         with:
           global-json-file: ${{ github.workspace }}/dotnet/global.json
       - name: Build dotnet solutions
@@ -130,98 +140,25 @@ jobs:
           popd
           rm -rf "$TEMP_DIR"
 
-  # Build src+tests only (no samples) for a single TFM and run tests.
-  dotnet-test:
-    needs: paths-filter
-    if: needs.paths-filter.outputs.dotnetChanges == 'true'
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - { targetFramework: "net10.0", os: "ubuntu-latest", configuration: Release, integration-tests: true, environment: "integration" }
-          - { targetFramework: "net472", os: "windows-latest", configuration: Release, integration-tests: true, environment: "integration" }
-
-    runs-on: ${{ matrix.os }}
-    environment: ${{ matrix.environment }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-            persist-credentials: false
-            sparse-checkout: |
-              .
-              .github
-              dotnet
-              python
-              workflow-samples
-
-      # Start Cosmos DB Emulator for all integration tests and only for unit tests when CosmosDB changes happened)
-      - name: Start Azure Cosmos DB Emulator
-        if: ${{ runner.os == 'Windows' && (needs.paths-filter.outputs.cosmosDbChanges == 'true' || (github.event_name != 'pull_request' && matrix.integration-tests)) }}
-        shell: pwsh
-        run: |
-          Write-Host "Launching Azure Cosmos DB Emulator"
-          Import-Module "$env:ProgramFiles\Azure Cosmos DB Emulator\PSModules\Microsoft.Azure.CosmosDB.Emulator"
-          Start-CosmosDbEmulator -NoUI -Key "C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw=="
-          echo "COSMOSDB_EMULATOR_AVAILABLE=true" >> $env:GITHUB_ENV
-
-      - name: Setup dotnet
-        uses: actions/setup-dotnet@v5.2.0
-        with:
-          global-json-file: ${{ github.workspace }}/dotnet/global.json
-
-      - name: Generate test solution (no samples)
-        shell: pwsh
-        run: |
-          ./dotnet/eng/scripts/New-FilteredSolution.ps1 `
-            -Solution dotnet/agent-framework-dotnet.slnx `
-            -TargetFramework ${{ matrix.targetFramework }} `
-            -Configuration ${{ matrix.configuration }} `
-            -ExcludeSamples `
-            -OutputPath dotnet/filtered.slnx `
-            -Verbose
-
-      - name: Build src and tests
-        shell: bash
-        run: dotnet build dotnet/filtered.slnx -c ${{ matrix.configuration }} -f ${{ matrix.targetFramework }} --warnaserror
-
-      - name: Generate test-type filtered solutions
-        shell: pwsh
-        run: |
-          $commonArgs = @{
-            Solution       = "dotnet/filtered.slnx"
-            TargetFramework = "${{ matrix.targetFramework }}"
-            Configuration   = "${{ matrix.configuration }}"
-            Verbose         = $true
-          }
-          ./dotnet/eng/scripts/New-FilteredSolution.ps1 @commonArgs `
-            -TestProjectNameFilter "*UnitTests*" `
-            -OutputPath dotnet/filtered-unit.slnx
-          ./dotnet/eng/scripts/New-FilteredSolution.ps1 @commonArgs `
-            -TestProjectNameFilter "*IntegrationTests*" `
-            -OutputPath dotnet/filtered-integration.slnx
-
       - name: Run Unit Tests
-        shell: pwsh
-        working-directory: dotnet
+        shell: bash
         run: |
-          $coverageSettings = Join-Path $PWD "tests/coverage.runsettings"
-          $coverageArgs = @()
-          if ("${{ matrix.targetFramework }}" -eq "${{ env.COVERAGE_FRAMEWORK }}") {
-            $coverageArgs = @(
-              "--coverage",
-              "--coverage-output-format", "cobertura",
-              "--coverage-settings", $coverageSettings,
-              "--results-directory", "../TestResults/Coverage/"
-            )
-          }
+          export UT_PROJECTS=$(find ./dotnet -type f -name "*.UnitTests.csproj" | tr '\n' ' ')
+          for project in $UT_PROJECTS; do
+            # Query the project's target frameworks using MSBuild with the current configuration
+            target_frameworks=$(dotnet msbuild $project -getProperty:TargetFrameworks -p:Configuration=${{ matrix.configuration }} -nologo 2>/dev/null | tr -d '\r')
 
-          dotnet test --solution ./filtered-unit.slnx `
-            -f ${{ matrix.targetFramework }} `
-            -c ${{ matrix.configuration }} `
-            --no-build -v Normal `
-            --report-xunit-trx `
-            --ignore-exit-code 8 `
-            @coverageArgs
+            # Check if the project supports the target framework
+            if [[ "$target_frameworks" == *"${{ matrix.targetFramework }}"* ]]; then
+              if [[ "${{ matrix.targetFramework }}" == "${{ env.COVERAGE_FRAMEWORK }}" ]]; then
+                dotnet test -f ${{ matrix.targetFramework }} -c ${{ matrix.configuration }} $project --no-build -v Normal --logger trx --collect:"XPlat Code Coverage" --results-directory:"TestResults/Coverage/" -- DataCollectionRunSettings.DataCollectors.DataCollector.Configuration.ExcludeByAttribute=GeneratedCodeAttribute,CompilerGeneratedAttribute,ExcludeFromCodeCoverageAttribute
+              else
+                dotnet test -f ${{ matrix.targetFramework }} -c ${{ matrix.configuration }} $project --no-build -v Normal --logger trx
+              fi
+            else
+              echo "Skipping $project - does not support target framework ${{ matrix.targetFramework }} (supports: $target_frameworks)"
+            fi
+          done
         env:
           # Cosmos DB Emulator connection settings
           COSMOSDB_ENDPOINT: https://localhost:8081
@@ -248,48 +185,53 @@ jobs:
         id: azure-functions-setup
 
       - name: Run Integration Tests
-        shell: pwsh
-        working-directory: dotnet
+        shell: bash
         if: github.event_name != 'pull_request' && matrix.integration-tests
         run: |
-          dotnet test --solution ./filtered-integration.slnx `
-            -f ${{ matrix.targetFramework }} `
-            -c ${{ matrix.configuration }} `
-            --no-build -v Normal `
-            --report-xunit-trx `
-            --ignore-exit-code 8 `
-            --filter-not-trait "Category=IntegrationDisabled" `
-            --parallel-algorithm aggressive `
-            --max-threads 2.0x
+          export INTEGRATION_TEST_PROJECTS=$(find ./dotnet -type f -name "*IntegrationTests.csproj" | tr '\n' ' ')
+          for project in $INTEGRATION_TEST_PROJECTS; do
+            # Query the project's target frameworks using MSBuild with the current configuration
+            target_frameworks=$(dotnet msbuild $project -getProperty:TargetFrameworks -p:Configuration=${{ matrix.configuration }} -nologo 2>/dev/null | tr -d '\r')
+
+            # Check if the project supports the target framework
+            if [[ "$target_frameworks" == *"${{ matrix.targetFramework }}"* ]]; then
+              dotnet test -f ${{ matrix.targetFramework }} -c ${{ matrix.configuration }} $project --no-build -v Normal --logger trx --filter "Category!=IntegrationDisabled"
+            else
+              echo "Skipping $project - does not support target framework ${{ matrix.targetFramework }} (supports: $target_frameworks)"
+            fi
+          done
         env:
           # Cosmos DB Emulator connection settings
           COSMOSDB_ENDPOINT: https://localhost:8081
           COSMOSDB_KEY: C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw==
           # OpenAI Models
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          OPENAI_CHAT_MODEL_NAME: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
-          OPENAI_REASONING_MODEL_NAME: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
+          OpenAI__ApiKey: ${{ secrets.OPENAI__APIKEY }}
+          OpenAI__ChatModelId: ${{ vars.OPENAI__CHATMODELID }}
+          OpenAI__ChatReasoningModelId: ${{ vars.OPENAI__CHATREASONINGMODELID }}
           # Azure OpenAI Models
-          AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_ENDPOINT: ${{ vars.AZURE_OPENAI_ENDPOINT }}
+          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+          AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
           # Azure AI Foundry
-          AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-          AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZURE_AI_MODEL_DEPLOYMENT_NAME }}
-          AZURE_AI_BING_CONNECTION_ID: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}
+          AzureAI__Endpoint: ${{ secrets.AZUREAI__ENDPOINT }}
+          AzureAI__DeploymentName: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
+          AzureAI__BingConnectionId: ${{ vars.AZUREAI__BINGCONECTIONID }}
+          FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
+          FOUNDRY_MEDIA_DEPLOYMENT_NAME: ${{ vars.FOUNDRY_MEDIA_DEPLOYMENT_NAME }}
+          FOUNDRY_MODEL_DEPLOYMENT_NAME: ${{ vars.FOUNDRY_MODEL_DEPLOYMENT_NAME }}
+          FOUNDRY_CONNECTION_GROUNDING_TOOL: ${{ vars.FOUNDRY_CONNECTION_GROUNDING_TOOL }}
 
       # Generate test reports and check coverage
       - name: Generate test reports
         if: matrix.targetFramework == env.COVERAGE_FRAMEWORK
-        uses: danielpalme/ReportGenerator-GitHub-Action@5.5.3
+        uses: danielpalme/ReportGenerator-GitHub-Action@5.5.1
         with:
-          reports: "./TestResults/Coverage/**/*.cobertura.xml"
+          reports: "./TestResults/Coverage/**/coverage.cobertura.xml"
           targetdir: "./TestResults/Reports"
           reporttypes: "HtmlInline;JsonSummary"
 
       - name: Upload coverage report artifact
         if: matrix.targetFramework == env.COVERAGE_FRAMEWORK
-        uses: actions/upload-artifact@v7
+        uses: actions/upload-artifact@v6
         with:
           name: CoverageReport-${{ matrix.os }}-${{ matrix.targetFramework }}-${{ matrix.configuration }} # Artifact name
           path: ./TestResults/Reports # Directory containing files to upload
@@ -297,13 +239,13 @@ jobs:
       - name: Check coverage
         if: matrix.targetFramework == env.COVERAGE_FRAMEWORK
         shell: pwsh
-        run: ./dotnet/eng/scripts/dotnet-check-coverage.ps1 -JsonReportPath "TestResults/Reports/Summary.json" -CoverageThreshold $env:COVERAGE_THRESHOLD
+        run: .github/workflows/dotnet-check-coverage.ps1 -JsonReportPath "TestResults/Reports/Summary.json" -CoverageThreshold $env:COVERAGE_THRESHOLD
 
   # This final job is required to satisfy the merge queue. It must only run (or succeed) if no tests failed
   dotnet-build-and-test-check:
     if: always()
     runs-on: ubuntu-latest
-    needs: [dotnet-build, dotnet-test]
+    needs: [dotnet-build-and-test]
     steps:
       - name: Get Date
         shell: bash

.github/workflows/dotnet-format.yml

@@ -86,10 +86,11 @@ jobs:
         run: docker pull mcr.microsoft.com/dotnet/sdk:${{ matrix.dotnet }}
 
       # This step will run dotnet format on each of the unique csproj files and fail if any changes are made
+      # exclude-diagnostics should be removed after fixes for IL2026 and IL3050 are out: https://github.com/dotnet/sdk/issues/51136
       - name: Run dotnet format
         if: steps.find-csproj.outputs.csproj_files != ''
         run: |
           for csproj in ${{ steps.find-csproj.outputs.csproj_files }}; do
             echo "Running dotnet format on $csproj"
-            docker run --rm -v $(pwd):/app -w /app mcr.microsoft.com/dotnet/sdk:${{ matrix.dotnet }} /bin/sh -c "dotnet format $csproj --verify-no-changes --verbosity diagnostic"
+            docker run --rm -v $(pwd):/app -w /app mcr.microsoft.com/dotnet/sdk:${{ matrix.dotnet }} /bin/sh -c "dotnet format $csproj --verify-no-changes --verbosity diagnostic --exclude-diagnostics IL2026 IL3050"
           done

.github/workflows/dotnet-integration-tests.yml

@@ -1,102 +0,0 @@
-#
-# Dedicated .NET integration tests workflow, called from the manual integration test orchestrator.
-# Only runs integration test matrix entries (net10.0 and net472).
-#
-
-name: dotnet-integration-tests
-
-on:
-  workflow_call:
-    inputs:
-      checkout-ref:
-        description: "Git ref to checkout (e.g., refs/pull/123/head)"
-        required: true
-        type: string
-
-permissions:
-  contents: read
-  id-token: write
-
-jobs:
-  dotnet-integration-tests:
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - { targetFramework: "net10.0", os: "ubuntu-latest", configuration: Release }
-          - { targetFramework: "net472", os: "windows-latest", configuration: Release }
-    runs-on: ${{ matrix.os }}
-    environment: integration
-    timeout-minutes: 60
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
-          persist-credentials: false
-          sparse-checkout: |
-            .
-            .github
-            dotnet
-            python
-            workflow-samples
-
-      - name: Start Azure Cosmos DB Emulator
-        if: runner.os == 'Windows'
-        shell: pwsh
-        run: |
-          Write-Host "Launching Azure Cosmos DB Emulator"
-          Import-Module "$env:ProgramFiles\Azure Cosmos DB Emulator\PSModules\Microsoft.Azure.CosmosDB.Emulator"
-          Start-CosmosDbEmulator -NoUI -Key "C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw=="
-          echo "COSMOS_EMULATOR_AVAILABLE=true" >> $env:GITHUB_ENV
-
-      - name: Setup dotnet
-        uses: actions/setup-dotnet@v5.2.0
-        with:
-          global-json-file: ${{ github.workspace }}/dotnet/global.json
-
-      - name: Build dotnet solutions
-        shell: bash
-        run: |
-          export SOLUTIONS=$(find ./dotnet/ -type f -name "*.slnx" | tr '\n' ' ')
-          for solution in $SOLUTIONS; do
-            dotnet build $solution -c ${{ matrix.configuration }} --warnaserror
-          done
-
-      - name: Azure CLI Login
-        uses: azure/login@v2
-        with:
-          client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-
-      - name: Set up Durable Task and Azure Functions Integration Test Emulators
-        if: matrix.os == 'ubuntu-latest'
-        uses: ./.github/actions/azure-functions-integration-setup
-
-      - name: Run Integration Tests
-        shell: bash
-        run: |
-          export INTEGRATION_TEST_PROJECTS=$(find ./dotnet -type f -name "*IntegrationTests.csproj" | tr '\n' ' ')
-          for project in $INTEGRATION_TEST_PROJECTS; do
-            target_frameworks=$(dotnet msbuild $project -getProperty:TargetFrameworks -p:Configuration=${{ matrix.configuration }} -nologo 2>/dev/null | tr -d '\r')
-            if [[ "$target_frameworks" == *"${{ matrix.targetFramework }}"* ]]; then
-              dotnet test -f ${{ matrix.targetFramework }} -c ${{ matrix.configuration }} $project --no-build -v Normal --logger trx --filter "Category!=IntegrationDisabled"
-            else
-              echo "Skipping $project - does not support target framework ${{ matrix.targetFramework }} (supports: $target_frameworks)"
-            fi
-          done
-        env:
-          COSMOSDB_ENDPOINT: https://localhost:8081
-          COSMOSDB_KEY: C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw==
-          OpenAI__ApiKey: ${{ secrets.OPENAI__APIKEY }}
-          OpenAI__ChatModelId: ${{ vars.OPENAI__CHATMODELID }}
-          OpenAI__ChatReasoningModelId: ${{ vars.OPENAI__CHATREASONINGMODELID }}
-          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-          AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-          AzureAI__Endpoint: ${{ secrets.AZUREAI__ENDPOINT }}
-          AzureAI__DeploymentName: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
-          AzureAI__BingConnectionId: ${{ vars.AZUREAI__BINGCONECTIONID }}
-          FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-          FOUNDRY_MEDIA_DEPLOYMENT_NAME: ${{ vars.FOUNDRY_MEDIA_DEPLOYMENT_NAME }}
-          FOUNDRY_MODEL_DEPLOYMENT_NAME: ${{ vars.FOUNDRY_MODEL_DEPLOYMENT_NAME }}
-          FOUNDRY_CONNECTION_GROUNDING_TOOL: ${{ vars.FOUNDRY_CONNECTION_GROUNDING_TOOL }}

.github/workflows/integration-tests-manual.yml

@@ -1,134 +0,0 @@
-#
-# This workflow allows manually running integration tests against an open PR or a branch.
-# Go to Actions → "Integration Tests (Manual)" → Run workflow → enter a PR number or branch name.
-#
-# It calls dedicated integration-only workflows (dotnet-integration-tests and python-integration-tests),
-# passing a ref so they check out and test the correct code.
-# Changed paths are detected here so only the relevant test suites run.
-#
-
-name: Integration Tests (Manual)
-
-on:
-  workflow_dispatch:
-    inputs:
-      pr-number:
-        description: "PR number to run integration tests against (leave empty if using branch)"
-        required: false
-        type: string
-        default: ""
-      branch:
-        description: "Branch name to run integration tests against (leave empty if using PR number)"
-        required: false
-        type: string
-        default: ""
-
-permissions:
-  contents: read
-  pull-requests: read
-  id-token: write
-
-concurrency:
-  group: integration-tests-manual-${{ github.event.inputs.pr-number || github.event.inputs.branch }}
-  cancel-in-progress: true
-
-jobs:
-  resolve-ref:
-    name: Resolve ref
-    runs-on: ubuntu-latest
-    outputs:
-      checkout-ref: ${{ steps.resolve.outputs.checkout-ref }}
-      dotnet-changes: ${{ steps.detect-changes.outputs.dotnet }}
-      python-changes: ${{ steps.detect-changes.outputs.python }}
-    steps:
-      - name: Resolve checkout ref
-        id: resolve
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          PR_NUMBER: ${{ github.event.inputs.pr-number }}
-          BRANCH: ${{ github.event.inputs.branch }}
-          REPO: ${{ github.repository }}
-        run: |
-          if [ -n "$PR_NUMBER" ] && [ -n "$BRANCH" ]; then
-            echo "::error::Please provide either a PR number or a branch name, not both."
-            exit 1
-          fi
-
-          if [ -z "$PR_NUMBER" ] && [ -z "$BRANCH" ]; then
-            echo "::error::Please provide either a PR number or a branch name."
-            exit 1
-          fi
-
-          if [ -n "$PR_NUMBER" ]; then
-            if ! echo "$PR_NUMBER" | grep -Eq '^[0-9]+$'; then
-              echo "::error::Invalid PR number. Only numeric values are allowed."
-              exit 1
-            fi
-
-            PR_DATA=$(gh pr view "$PR_NUMBER" --repo "$REPO" --json state)
-            PR_STATE=$(echo "$PR_DATA" | jq -r '.state')
-
-            if [ "$PR_STATE" != "OPEN" ]; then
-              echo "::error::PR #$PR_NUMBER is not open (state: $PR_STATE)"
-              exit 1
-            fi
-
-            echo "checkout-ref=refs/pull/$PR_NUMBER/head" >> "$GITHUB_OUTPUT"
-            echo "Running integration tests for PR #$PR_NUMBER"
-          else
-            if ! echo "$BRANCH" | grep -Eq '^[a-zA-Z0-9_./-]+$'; then
-              echo "::error::Invalid branch name. Only alphanumeric characters, hyphens, underscores, dots, and slashes are allowed."
-              exit 1
-            fi
-
-            echo "checkout-ref=$BRANCH" >> "$GITHUB_OUTPUT"
-            echo "Running integration tests for branch $BRANCH"
-          fi
-
-      - name: Detect changed paths
-        id: detect-changes
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          PR_NUMBER: ${{ github.event.inputs.pr-number }}
-          BRANCH: ${{ github.event.inputs.branch }}
-          REPO: ${{ github.repository }}
-        run: |
-          if [ -n "$PR_NUMBER" ]; then
-            CHANGED_FILES=$(gh pr diff "$PR_NUMBER" --repo "$REPO" --name-only)
-          else
-            # For branches, compare against main using the GitHub API
-            CHANGED_FILES=$(gh api "repos/$REPO/compare/main...$BRANCH" --jq '.files[].filename')
-          fi
-
-          DOTNET_CHANGES=false
-          PYTHON_CHANGES=false
-
-          if echo "$CHANGED_FILES" | grep -q '^dotnet/'; then
-            DOTNET_CHANGES=true
-          fi
-
-          if echo "$CHANGED_FILES" | grep -q '^python/'; then
-            PYTHON_CHANGES=true
-          fi
-
-          echo "dotnet=$DOTNET_CHANGES" >> "$GITHUB_OUTPUT"
-          echo "python=$PYTHON_CHANGES" >> "$GITHUB_OUTPUT"
-          echo "Detected changes — dotnet: $DOTNET_CHANGES, python: $PYTHON_CHANGES"
-
-  dotnet-integration-tests:
-    name: .NET Integration Tests
-    needs: resolve-ref
-    if: needs.resolve-ref.outputs.dotnet-changes == 'true'
-    uses: ./.github/workflows/dotnet-integration-tests.yml
-    with:
-      checkout-ref: ${{ needs.resolve-ref.outputs.checkout-ref }}
-    secrets: inherit
-
-  python-integration-tests:
-    name: Python Integration Tests
-    needs: resolve-ref
-    if: needs.resolve-ref.outputs.python-changes == 'true'
-    uses: ./.github/workflows/python-integration-tests.yml
-    with:
-      checkout-ref: ${{ needs.resolve-ref.outputs.checkout-ref }}
-    secrets: inherit

.github/workflows/merge-gatekeeper.yml

@@ -29,7 +29,4 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           timeout: 3600
           interval: 30
-          # "Cleanup artifacts", "Agent", "Prepare", and "Upload results" are check runs
-          # created by an org-level GitHub App (MSDO), not by any workflow in this repo.
-          # They are outside our control and their transient failures should not block merges.
-          ignored: CodeQL,CodeQL analysis (csharp),Cleanup artifacts,Agent,Prepare,Upload results
+          ignored: CodeQL,CodeQL analysis (csharp)

.github/workflows/python-check-coverage.py

@@ -1,13 +1,10 @@
 #!/usr/bin/env python3
 # Copyright (c) Microsoft. All rights reserved.
-"""Check Python test coverage against threshold for enforced targets.
+"""Check Python test coverage against threshold for enforced modules.
 
 This script parses a Cobertura XML coverage report and enforces a minimum
-coverage threshold on specific targets. Targets can be package names
-(e.g., "packages.core.agent_framework") or individual Python file paths
-(e.g., "packages/core/agent_framework/observability.py").
-
-Non-enforced targets are reported for visibility but don't block the build.
+coverage threshold on specific modules. Non-enforced modules are reported
+for visibility but don't block the build.
 
 Usage:
     python python-check-coverage.py <coverage-xml-path> <threshold>
@@ -21,30 +18,22 @@ import xml.etree.ElementTree as ET
 from dataclasses import dataclass
 
 # =============================================================================
-# ENFORCED TARGETS CONFIGURATION
+# ENFORCED MODULES CONFIGURATION
 # =============================================================================
-# Add or remove entries from this set to control which targets must meet
-# the coverage threshold. Only these targets will fail the build if below
-# threshold. Other targets are reported for visibility only.
+# Add or remove modules from this set to control which packages must meet
+# the coverage threshold. Only these modules will fail the build if below
+# threshold. Other modules are reported for visibility only.
 #
-# Target values can be:
-# - Package paths as they appear in the coverage report
-#   (e.g., "packages.azure-ai.agent_framework_azure_ai")
-# - Python source file paths as they appear in the coverage report
-#   (e.g., "packages/core/agent_framework/observability.py")
+# Module paths should match the package paths as they appear in the coverage
+# report (e.g., "packages.azure-ai.agent_framework_azure_ai" for packages/azure-ai).
+# Sub-modules can be included by specifying their full path.
 # =============================================================================
-ENFORCED_TARGETS: set[str] = {
-    # Packages
+ENFORCED_MODULES: set[str] = {
     "packages.azure-ai.agent_framework_azure_ai",
-    "packages.core.agent_framework",
-    "packages.core.agent_framework._workflows",
-    "packages.purview.agent_framework_purview",
-    "packages.anthropic.agent_framework_anthropic",
-    "packages.azure-ai-search.agent_framework_azure_ai_search",
-    "packages.openai.agent_framework_openai",
-    # Individual files (if you want to enforce specific files instead of whole packages)
-    "packages/core/agent_framework/observability.py",
-    # Add more targets here as coverage improves
+    # Add more modules here as coverage improves:
+    # "packages.core.agent_framework",
+    # "packages.core.agent_framework._workflows",
+    # "packages.anthropic.agent_framework_anthropic",
 }
 
 
@@ -71,21 +60,14 @@ class PackageCoverage:
         return self.branch_rate * 100
 
 
-def normalize_coverage_path(path: str) -> str:
-    """Normalize coverage paths for reliable matching."""
-    return path.replace("\\", "/").lstrip("./")
-
-
-def parse_coverage_xml(
-    xml_path: str,
-) -> tuple[dict[str, PackageCoverage], dict[str, PackageCoverage], float, float]:
+def parse_coverage_xml(xml_path: str) -> tuple[dict[str, PackageCoverage], float, float]:
     """Parse Cobertura XML and extract per-package coverage data.
 
     Args:
         xml_path: Path to the Cobertura XML coverage report.
 
     Returns:
-        A tuple of (packages_dict, files_dict, overall_line_rate, overall_branch_rate).
+        A tuple of (packages_dict, overall_line_rate, overall_branch_rate).
     """
     tree = ET.parse(xml_path)
     root = tree.getroot()
@@ -95,7 +77,6 @@ def parse_coverage_xml(
     overall_branch_rate = float(root.get("branch-rate", 0))
 
     packages: dict[str, PackageCoverage] = {}
-    file_stats: dict[str, dict[str, int]] = {}
 
     for package in root.findall(".//package"):
         package_path = package.get("name", "unknown")
@@ -110,43 +91,19 @@ def parse_coverage_xml(
         branches_covered = 0
 
         for class_elem in package.findall(".//class"):
-            file_path = normalize_coverage_path(class_elem.get("filename", ""))
-            if file_path and file_path not in file_stats:
-                file_stats[file_path] = {
-                    "lines_valid": 0,
-                    "lines_covered": 0,
-                    "branches_valid": 0,
-                    "branches_covered": 0,
-                }
-
             for line in class_elem.findall(".//line"):
                 lines_valid += 1
                 if int(line.get("hits", 0)) > 0:
                     lines_covered += 1
-
-                if file_path:
-                    file_stats[file_path]["lines_valid"] += 1
-                    if int(line.get("hits", 0)) > 0:
-                        file_stats[file_path]["lines_covered"] += 1
-
                 # Branch coverage from line elements
                 if line.get("branch") == "true":
                     condition_coverage = line.get("condition-coverage", "")
                     if condition_coverage:
                         # Parse "X% (covered/total)" format
                         try:
-                            coverage_parts = (
-                                condition_coverage.split("(")[1].rstrip(")").split("/")
-                            )
+                            coverage_parts = condition_coverage.split("(")[1].rstrip(")").split("/")
                             branches_covered += int(coverage_parts[0])
                             branches_valid += int(coverage_parts[1])
-                            if file_path:
-                                file_stats[file_path]["branches_covered"] += int(
-                                    coverage_parts[0]
-                                )
-                                file_stats[file_path]["branches_valid"] += int(
-                                    coverage_parts[1]
-                                )
                         except (IndexError, ValueError):
                             # Ignore malformed condition-coverage strings; treat this line as having no branch data.
                             pass
@@ -155,33 +112,14 @@ def parse_coverage_xml(
         packages[package_path] = PackageCoverage(
             name=package_path,
             line_rate=line_rate if lines_valid == 0 else lines_covered / lines_valid,
-            branch_rate=branch_rate
-            if branches_valid == 0
-            else branches_covered / branches_valid,
+            branch_rate=branch_rate if branches_valid == 0 else branches_covered / branches_valid,
             lines_valid=lines_valid,
             lines_covered=lines_covered,
             branches_valid=branches_valid,
             branches_covered=branches_covered,
         )
 
-    files: dict[str, PackageCoverage] = {}
-    for file_path, stats in file_stats.items():
-        lines_valid = stats["lines_valid"]
-        lines_covered = stats["lines_covered"]
-        branches_valid = stats["branches_valid"]
-        branches_covered = stats["branches_covered"]
-
-        files[file_path] = PackageCoverage(
-            name=file_path,
-            line_rate=0 if lines_valid == 0 else lines_covered / lines_valid,
-            branch_rate=0 if branches_valid == 0 else branches_covered / branches_valid,
-            lines_valid=lines_valid,
-            lines_covered=lines_covered,
-            branches_valid=branches_valid,
-            branches_covered=branches_covered,
-        )
-
-    return packages, files, overall_line_rate, overall_branch_rate
+    return packages, overall_line_rate, overall_branch_rate
 
 
 def format_coverage_value(coverage: float, threshold: float, is_enforced: bool) -> str:
@@ -190,7 +128,7 @@ def format_coverage_value(coverage: float, threshold: float, is_enforced: bool)
     Args:
         coverage: Coverage percentage (0-100).
         threshold: Minimum required coverage percentage.
-        is_enforced: Whether this target is enforced.
+        is_enforced: Whether this module is enforced.
 
     Returns:
         Formatted string like "85.5%" or "85.5% ✅" or "75.0% ❌".
@@ -204,7 +142,6 @@ def format_coverage_value(coverage: float, threshold: float, is_enforced: bool)
 
 def print_coverage_table(
     packages: dict[str, PackageCoverage],
-    files: dict[str, PackageCoverage],
     threshold: float,
     overall_line_rate: float,
     overall_branch_rate: float,
@@ -213,7 +150,6 @@ def print_coverage_table(
 
     Args:
         packages: Dictionary of package name to coverage data.
-        files: Dictionary of file path to coverage data, used for per-file enforcement.
         threshold: Minimum required coverage percentage.
         overall_line_rate: Overall line coverage rate (0-1).
         overall_branch_rate: Overall branch coverage rate (0-1).
@@ -227,25 +163,21 @@ def print_coverage_table(
     print(f"Overall Branch Coverage: {overall_branch_rate * 100:.1f}%")
     print(f"Threshold:               {threshold}%")
 
-    enforced_targets = {normalize_coverage_path(t) for t in ENFORCED_TARGETS}
-
     # Package table
     print("\n" + "-" * 110)
     print(f"{'Package':<80} {'Lines':<15} {'Line Cov':<15}")
     print("-" * 110)
 
-    # Sort: enforced package targets first, then alphabetically
+    # Sort: enforced modules first, then alphabetically
     sorted_packages = sorted(
         packages.values(),
-        key=lambda p: (p.name not in ENFORCED_TARGETS, p.name),
+        key=lambda p: (p.name not in ENFORCED_MODULES, p.name),
     )
 
     for pkg in sorted_packages:
-        is_enforced = normalize_coverage_path(pkg.name) in enforced_targets
+        is_enforced = pkg.name in ENFORCED_MODULES
         enforced_marker = "[ENFORCED] " if is_enforced else ""
-        line_cov = format_coverage_value(
-            pkg.line_coverage_percent, threshold, is_enforced
-        )
+        line_cov = format_coverage_value(pkg.line_coverage_percent, threshold, is_enforced)
         lines_info = f"{pkg.lines_covered}/{pkg.lines_valid}"
         package_label = f"{enforced_marker}{pkg.name}"
 
@@ -253,98 +185,50 @@ def print_coverage_table(
 
     print("-" * 110)
 
-    # Enforced file/model entries (if configured)
-    enforced_files = [
-        files[target]
-        for target in sorted(enforced_targets)
-        if target in files and target.endswith(".py")
-    ]
-
-    if enforced_files:
-        print("\nEnforced Files/Models")
-        print("-" * 110)
-        print(f"{'File':<80} {'Lines':<15} {'Line Cov':<15}")
-        print("-" * 110)
-
-        for file_cov in enforced_files:
-            line_cov = format_coverage_value(
-                file_cov.line_coverage_percent, threshold, True
-            )
-            lines_info = f"{file_cov.lines_covered}/{file_cov.lines_valid}"
-            print(f"[ENFORCED] {file_cov.name:<69} {lines_info:<15} {line_cov:<15}")
-
-        print("-" * 110)
-
 
 def check_coverage(xml_path: str, threshold: float) -> bool:
-    """Check if all enforced targets meet the coverage threshold.
+    """Check if all enforced modules meet the coverage threshold.
 
     Args:
         xml_path: Path to the Cobertura XML coverage report.
         threshold: Minimum required coverage percentage.
 
     Returns:
-        True if all enforced targets pass, False otherwise.
+        True if all enforced modules pass, False otherwise.
     """
-    packages, files, overall_line_rate, overall_branch_rate = parse_coverage_xml(
-        xml_path
-    )
+    packages, overall_line_rate, overall_branch_rate = parse_coverage_xml(xml_path)
 
-    print_coverage_table(
-        packages, files, threshold, overall_line_rate, overall_branch_rate
-    )
+    print_coverage_table(packages, threshold, overall_line_rate, overall_branch_rate)
 
-    # Check enforced targets
-    failed_targets: list[str] = []
-    missing_targets: list[str] = []
+    # Check enforced modules
+    failed_modules: list[str] = []
+    missing_modules: list[str] = []
 
-    for target_name in ENFORCED_TARGETS:
-        normalized_target = normalize_coverage_path(target_name)
-        package_alias = normalized_target.replace("/", ".")
-
-        target_coverage = None
-        if target_name in packages:
-            target_coverage = packages[target_name]
-        elif normalized_target in files:
-            target_coverage = files[normalized_target]
-        elif package_alias in packages:
-            target_coverage = packages[package_alias]
-
-        if target_coverage is None:
-            missing_targets.append(target_name)
+    for module_name in ENFORCED_MODULES:
+        if module_name not in packages:
+            missing_modules.append(module_name)
             continue
 
-        if target_coverage.line_coverage_percent < threshold:
-            failed_targets.append(
-                f"{target_name} ({target_coverage.line_coverage_percent:.1f}%)"
-            )
+        pkg = packages[module_name]
+        if pkg.line_coverage_percent < threshold:
+            failed_modules.append(f"{module_name} ({pkg.line_coverage_percent:.1f}%)")
 
     # Report results
-    if missing_targets:
-        print(
-            f"\n❌ FAILED: Enforced targets not found in coverage report: {', '.join(missing_targets)}"
-        )
+    if missing_modules:
+        print(f"\n❌ FAILED: Enforced modules not found in coverage report: {', '.join(missing_modules)}")
         return False
 
-    if failed_targets:
-        print(
-            f"\n❌ FAILED: The following enforced targets are below {threshold}% coverage threshold:"
-        )
-        for target in failed_targets:
-            print(f"   - {target}")
-        print("\nTo fix: Add more tests to improve coverage for the failing targets.")
+    if failed_modules:
+        print(f"\n❌ FAILED: The following enforced modules are below {threshold}% coverage threshold:")
+        for module in failed_modules:
+            print(f"   - {module}")
+        print("\nTo fix: Add more tests to improve coverage for the failing modules.")
         return False
 
-    if ENFORCED_TARGETS:
-        found_enforced = [
-            target
-            for target in ENFORCED_TARGETS
-            if target in packages or normalize_coverage_path(target) in files
-        ]
+    if ENFORCED_MODULES:
+        found_enforced = [m for m in ENFORCED_MODULES if m in packages]
         if found_enforced:
-            print(
-                f"\n✅ PASSED: All enforced targets meet the {threshold}% coverage threshold."
-            )
+            print(f"\n✅ PASSED: All enforced modules meet the {threshold}% coverage threshold.")
 
     return True
 

.github/workflows/python-code-quality.yml

@@ -12,13 +12,13 @@ env:
   UV_CACHE_DIR: /tmp/.uv-cache
 
 jobs:
-  pre-commit-hooks:
-    name: Pre-commit Hooks
+  pre-commit:
+    name: Checks
     if: "!cancelled()"
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.11"]
+        python-version: ["3.10", "3.14"]
     runs-on: ubuntu-latest
     continue-on-error: true
     defaults:
@@ -37,105 +37,17 @@ jobs:
           python-version: ${{ matrix.python-version }}
           os: ${{ runner.os }}
         env:
+          # Configure a constant location for the uv cache
           UV_CACHE_DIR: /tmp/.uv-cache
       - uses: actions/cache@v5
         with:
-          path: ~/.cache/prek
-          key: prek|${{ matrix.python-version }}|${{ hashFiles('python/.pre-commit-config.yaml') }}
-      - uses: j178/prek-action@v1
-        name: Run Pre-commit Hooks (excluding poe-check)
-        env:
-          SKIP: poe-check
+          path: ~/.cache/pre-commit
+          key: pre-commit|${{ matrix.python-version }}|${{ hashFiles('python/.pre-commit-config.yaml') }}
+      - uses: pre-commit/action@v3.0.1
+        name: Run Pre-Commit Hooks
         with:
-          extra-args: --cd python --all-files
-
-  package-checks:
-    name: Package Checks
-    if: "!cancelled()"
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.11"]
-    runs-on: ubuntu-latest
-    continue-on-error: true
-    defaults:
-      run:
-        working-directory: ./python
-    env:
-      UV_PYTHON: ${{ matrix.python-version }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ matrix.python-version }}
-          os: ${{ runner.os }}
-        env:
-          UV_CACHE_DIR: /tmp/.uv-cache
-      - name: Run syntax and pyright across packages
-        run: uv run poe check-packages
-
-  samples-markdown:
-    name: Samples & Markdown
-    if: "!cancelled()"
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.11"]
-    runs-on: ubuntu-latest
-    continue-on-error: true
-    defaults:
-      run:
-        working-directory: ./python
-    env:
-      UV_PYTHON: ${{ matrix.python-version }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ matrix.python-version }}
-          os: ${{ runner.os }}
-        env:
-          UV_CACHE_DIR: /tmp/.uv-cache
-      - name: Run samples checks
-        run: uv run poe check -S
-      - name: Run markdown code lint
-        run: uv run poe markdown-code-lint
-
-  mypy:
-    name: Mypy Checks
-    if: "!cancelled()"
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.11"]
-    runs-on: ubuntu-latest
-    continue-on-error: true
-    defaults:
-      run:
-        working-directory: ./python
-    env:
-      UV_PYTHON: ${{ matrix.python-version }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ matrix.python-version }}
-          os: ${{ runner.os }}
-        env:
-          UV_CACHE_DIR: /tmp/.uv-cache
+          extra_args: --config python/.pre-commit-config.yaml --all-files
       - name: Run Mypy
         env:
           GITHUB_BASE_REF: ${{ github.event.pull_request.base.ref || github.base_ref || 'main' }}
-        run: uv run python scripts/workspace_poe_tasks.py ci-mypy
+        run: uv run poe ci-mypy

.github/workflows/python-dependency-range-validation.yml

@@ -1,216 +0,0 @@
-# Probe the highest allowed dependency versions, then open issues/PRs from the passing updates.
-name: Python - Dependency Range Validation
-
-on:
-  workflow_dispatch:
-
-permissions:
-  contents: write
-  issues: write
-  pull-requests: write
-
-env:
-  UV_CACHE_DIR: /tmp/.uv-cache
-
-jobs:
-  dependency-range-validation:
-    name: Dependency Range Validation
-    runs-on: ubuntu-latest
-    env:
-      # For now only run 3.13, if we do encounter situations where there are mismatches between packages and python versions (other then 3.10 and 3.14 which are known to not be able to install everything)
-      # then we will have to reevaluate.
-      UV_PYTHON: "3.13"
-      GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-
-      - name: Set up python and install the project
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-        env:
-          UV_CACHE_DIR: /tmp/.uv-cache
-
-      - name: Run dependency range validation
-        id: validate_ranges
-        # Keep workflow running so we can still publish diagnostics from this run.
-        continue-on-error: true
-        run: uv run poe validate-dependency-bounds-project --mode upper --package "*"
-        working-directory: ./python
-
-      - name: Upload dependency range report
-        # Always publish the report so failures are inspectable even when validation fails.
-        if: always()
-        uses: actions/upload-artifact@v7
-        with:
-          name: dependency-range-results
-          path: python/scripts/dependencies/dependency-range-results.json
-          if-no-files-found: warn
-
-      - name: Create issues for failed dependency candidates
-        # Always process the report so failed candidates create actionable tracking issues.
-        if: always()
-        uses: actions/github-script@v8
-        with:
-          script: |
-            const fs = require("fs")
-            const reportPath = "python/scripts/dependencies/dependency-range-results.json"
-
-            if (!fs.existsSync(reportPath)) {
-              core.warning(`No dependency range report found at ${reportPath}`)
-              return
-            }
-
-            const report = JSON.parse(fs.readFileSync(reportPath, "utf8"))
-            const dependencyFailures = []
-
-            for (const packageResult of report.packages ?? []) {
-              for (const dependency of packageResult.dependencies ?? []) {
-                const candidateVersions = new Set(dependency.candidate_versions ?? [])
-                const failedAttempts = (dependency.attempts ?? []).filter(
-                  (attempt) => attempt.status === "failed" && candidateVersions.has(attempt.trial_upper)
-                )
-                if (!failedAttempts.length) {
-                  continue
-                }
-
-                const failuresByVersion = new Map()
-                for (const attempt of failedAttempts) {
-                  const version = attempt.trial_upper || "unknown"
-                  if (!failuresByVersion.has(version)) {
-                    failuresByVersion.set(version, attempt.error || "No error output captured.")
-                  }
-                }
-
-                dependencyFailures.push({
-                  packageName: packageResult.package_name,
-                  projectPath: packageResult.project_path,
-                  dependencyName: dependency.name,
-                  originalRequirements: dependency.original_requirements ?? [],
-                  finalRequirements: dependency.final_requirements ?? [],
-                  failedVersions: [...failuresByVersion.entries()].map(([version, error]) => ({ version, error })),
-                })
-              }
-            }
-
-            if (!dependencyFailures.length) {
-              core.info("No failing dependency candidates found.")
-              return
-            }
-
-            const owner = context.repo.owner
-            const repo = context.repo.repo
-            const openIssues = await github.paginate(github.rest.issues.listForRepo, {
-              owner,
-              repo,
-              state: "open",
-              per_page: 100,
-            })
-            const openIssueTitles = new Set(
-              openIssues.filter((issue) => !issue.pull_request).map((issue) => issue.title)
-            )
-
-            const formatError = (message) => String(message || "No error output captured.").replace(/```/g, "'''")
-
-            for (const failure of dependencyFailures) {
-              const title = `Dependency validation failed: ${failure.dependencyName} (${failure.packageName})`
-              if (openIssueTitles.has(title)) {
-                core.info(`Issue already exists: ${title}`)
-                continue
-              }
-
-              const visibleFailures = failure.failedVersions.slice(0, 5)
-              const omittedCount = failure.failedVersions.length - visibleFailures.length
-              const failureDetails = visibleFailures
-                .map(
-                  (entry) =>
-                    `- \`${entry.version}\`\n\n\`\`\`\n${formatError(entry.error).slice(0, 3500)}\n\`\`\``
-                )
-                .join("\n\n")
-
-              const body = [
-                "Automated dependency range validation found candidate versions that failed checks.",
-                "",
-                `- Package: \`${failure.packageName}\``,
-                `- Project path: \`${failure.projectPath}\``,
-                `- Dependency: \`${failure.dependencyName}\``,
-                `- Original requirements: ${
-                  failure.originalRequirements.length
-                    ? failure.originalRequirements.map((value) => `\`${value}\``).join(", ")
-                    : "_none_"
-                }`,
-                `- Final requirements after run: ${
-                  failure.finalRequirements.length
-                    ? failure.finalRequirements.map((value) => `\`${value}\``).join(", ")
-                    : "_none_"
-                }`,
-                "",
-                "### Failed versions and errors",
-                failureDetails,
-                omittedCount > 0 ? `\n_Additional failed versions omitted: ${omittedCount}_` : "",
-                "",
-                `Workflow run: ${context.serverUrl}/${owner}/${repo}/actions/runs/${context.runId}`,
-              ].join("\n")
-
-              await github.rest.issues.create({
-                owner,
-                repo,
-                title,
-                body,
-              })
-              openIssueTitles.add(title)
-              core.info(`Created issue: ${title}`)
-            }
-
-      - name: Refresh lockfile
-        # Only refresh lockfile after a clean validation to avoid committing known-bad ranges.
-        if: steps.validate_ranges.outcome == 'success'
-        run: uv lock --upgrade
-        working-directory: ./python
-
-      - name: Commit and push dependency updates
-        id: commit_updates
-        if: steps.validate_ranges.outcome == 'success'
-        run: |
-          BRANCH="automation/python-dependency-range-updates"
-
-          git config user.name "github-actions[bot]"
-          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
-          git checkout -B "${BRANCH}"
-
-          git add python/packages/*/pyproject.toml python/uv.lock
-          if git diff --cached --quiet; then
-            echo "has_changes=false" >> "$GITHUB_OUTPUT"
-            echo "No dependency updates to commit."
-            exit 0
-          fi
-
-          git commit -m "chore: update dependency ranges"
-          git push --force-with-lease --set-upstream origin "${BRANCH}"
-          echo "has_changes=true" >> "$GITHUB_OUTPUT"
-
-      - name: Create or update pull request with GitHub CLI
-        # Only open/update PRs for validated updates to keep automation branches trustworthy.
-        if: steps.validate_ranges.outcome == 'success' && steps.commit_updates.outputs.has_changes == 'true'
-        run: |
-          BRANCH="automation/python-dependency-range-updates"
-          PR_TITLE="Python: chore: update dependency ranges"
-          PR_BODY_FILE="$(mktemp)"
-
-          cat > "${PR_BODY_FILE}" <<'EOF'
-          This PR was generated by the dependency range validation workflow.
-
-          - Ran `uv run poe validate-dependency-bounds-project --mode upper --package "*"`
-          - Updated package dependency bounds
-          - Refreshed `python/uv.lock` with `uv lock --upgrade`
-          EOF
-
-          PR_NUMBER="$(gh pr list --head "${BRANCH}" --base main --state open --json number --jq '.[0].number')"
-          if [ -n "${PR_NUMBER}" ]; then
-            gh pr edit "${PR_NUMBER}" --title "${PR_TITLE}" --body-file "${PR_BODY_FILE}"
-          else
-            gh pr create --base main --head "${BRANCH}" --title "${PR_TITLE}" --body-file "${PR_BODY_FILE}"
-          fi

.github/workflows/python-dev-dependency-upgrade.yml

@@ -1,91 +0,0 @@
-name: Python - Dev Dependency Upgrade
-
-on:
-  workflow_dispatch:
-
-permissions:
-  contents: write
-  pull-requests: write
-
-env:
-  UV_CACHE_DIR: /tmp/.uv-cache
-
-jobs:
-  upgrade-dev-dependencies:
-    name: Upgrade Dev Dependencies
-    runs-on: ubuntu-latest
-    env:
-      UV_PYTHON: "3.13"
-      GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-
-      - name: Set up python and install the project
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-        env:
-          UV_CACHE_DIR: /tmp/.uv-cache
-
-      - name: Upgrade dev dependencies and validate workspace
-        run: uv run poe upgrade-dev-dependencies
-        working-directory: ./python
-
-      - name: Commit and push dev dependency updates
-        id: commit_updates
-        run: |
-          BRANCH="automation/python-dev-dependency-updates"
-
-          git config user.name "github-actions[bot]"
-          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
-          git checkout -B "${BRANCH}"
-
-          git add python/pyproject.toml python/packages/*/pyproject.toml python/uv.lock
-          if git diff --cached --quiet; then
-            echo "has_changes=false" >> "$GITHUB_OUTPUT"
-            echo "No dev dependency updates to commit."
-            exit 0
-          fi
-
-          git commit -F- <<'EOF'
-          Python: chore: upgrade dev dependencies
-          EOF
-          git push --force-with-lease --set-upstream origin "${BRANCH}"
-          echo "has_changes=true" >> "$GITHUB_OUTPUT"
-
-      - name: Create or update pull request with GitHub CLI
-        if: steps.commit_updates.outputs.has_changes == 'true'
-        run: |
-          BRANCH="automation/python-dev-dependency-updates"
-          PR_TITLE="Python: chore: upgrade dev dependencies"
-          PR_BODY_FILE="$(mktemp)"
-
-          cat > "${PR_BODY_FILE}" <<'EOF'
-          ### Motivation and Context
-
-          This automated update refreshes Python dev dependency pins across the workspace and reruns the repo validation gates before opening a pull request.
-
-          ### Description
-
-          - Ran `uv run poe upgrade-dev-dependencies`
-          - Refreshed dev dependency pins in workspace `pyproject.toml` files
-          - Refreshed `python/uv.lock` with `uv lock --upgrade`
-          - Reinstalled from the frozen lockfile and reran `check`, `typing`, and `test`
-
-          ### Contribution Checklist
-
-          - [x] The code builds clean without any errors or warnings
-          - [x] The PR follows the [Contribution Guidelines](https://github.com/microsoft/agent-framework/blob/main/CONTRIBUTING.md)
-          - [x] All unit tests pass, and I have added new tests where possible
-          - [ ] **Is this a breaking change?** If yes, add "[BREAKING]" prefix to the title of the PR.
-          EOF
-
-          PR_NUMBER="$(gh pr list --head "${BRANCH}" --base main --state open --json number --jq '.[0].number')"
-          if [ -n "${PR_NUMBER}" ]; then
-            gh pr edit "${PR_NUMBER}" --title "${PR_TITLE}" --body-file "${PR_BODY_FILE}"
-          else
-            gh pr create --base main --head "${BRANCH}" --title "${PR_TITLE}" --body-file "${PR_BODY_FILE}"
-          fi

.github/workflows/python-integration-tests.yml

@@ -1,364 +0,0 @@
-#
-# Dedicated Python integration tests workflow, called from the manual integration test orchestrator.
-# Runs all tests (unit + integration) split into parallel jobs by provider.
-#
-# NOTE: This workflow and python-merge-tests.yml share the same set of parallel
-# test jobs. Keep them in sync — when adding, removing, or modifying a job here,
-# apply the same change to python-merge-tests.yml.
-#
-
-name: python-integration-tests
-
-on:
-  workflow_call:
-    inputs:
-      checkout-ref:
-        description: "Git ref to checkout (e.g., refs/pull/123/head)"
-        required: true
-        type: string
-
-permissions:
-  contents: read
-  id-token: write
-
-env:
-  UV_CACHE_DIR: /tmp/.uv-cache
-  UV_PYTHON: "3.13"
-
-jobs:
-  # Unit tests: all non-integration tests across all packages
-  python-tests-unit:
-    name: Python Integration Tests - Unit
-    runs-on: ubuntu-latest
-    environment: integration
-    timeout-minutes: 60
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
-          persist-credentials: false
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Test with pytest (unit tests only)
-        run: >
-          uv run poe test -A
-          -m "not integration"
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-
-  # OpenAI integration tests
-  python-tests-openai:
-    name: Python Integration Tests - OpenAI
-    runs-on: ubuntu-latest
-    environment: integration
-    timeout-minutes: 60
-    env:
-      OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
-          persist-credentials: false
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Test with pytest (OpenAI integration)
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/openai/tests
-          -m "integration and not azure"
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-
-  # Azure OpenAI integration tests
-  python-tests-azure-openai:
-    name: Python Integration Tests - Azure OpenAI
-    runs-on: ubuntu-latest
-    environment: integration
-    timeout-minutes: 60
-    env:
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
-          persist-credentials: false
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Azure CLI Login
-        uses: azure/login@v2
-        with:
-          client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-      - name: Test with pytest (Azure OpenAI integration)
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/openai/tests/openai/test_openai_chat_completion_client_azure.py
-          packages/openai/tests/openai/test_openai_chat_client_azure.py
-          packages/openai/tests/openai/test_openai_embedding_client_azure.py
-          -m integration
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-
-  # Misc integration tests (Anthropic, Ollama, MCP)
-  python-tests-misc-integration:
-    name: Python Integration Tests - Misc
-    runs-on: ubuntu-latest
-    environment: integration
-    timeout-minutes: 60
-    env:
-      ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-      ANTHROPIC_CHAT_MODEL_ID: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
-      LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
-          persist-credentials: false
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Start local MCP server
-        id: local-mcp
-        uses: ./.github/actions/setup-local-mcp-server
-        with:
-          fallback_url: ${{ env.LOCAL_MCP_URL }}
-      - name: Prefer local MCP URL when available
-        run: echo "LOCAL_MCP_URL=${{ steps.local-mcp.outputs.effective_url }}" >> "$GITHUB_ENV"
-      - name: Test with pytest (Anthropic, Ollama, MCP integration)
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/anthropic/tests
-          packages/ollama/tests
-          packages/core/tests/core/test_mcp.py
-          -m integration
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-      - name: Stop local MCP server
-        if: always()
-        shell: bash
-        run: |
-          set -euo pipefail
-          server_pid="${{ steps.local-mcp.outputs.pid }}"
-          if [[ -z "$server_pid" ]]; then
-            exit 0
-          fi
-          if ! kill -0 "$server_pid" 2>/dev/null; then
-            exit 0
-          fi
-          kill -TERM -- "-$server_pid" 2>/dev/null || kill -TERM "$server_pid" 2>/dev/null || true
-          for _ in $(seq 1 10); do
-            if ! kill -0 "$server_pid" 2>/dev/null; then
-              exit 0
-            fi
-            sleep 1
-          done
-          kill -KILL -- "-$server_pid" 2>/dev/null || kill -KILL "$server_pid" 2>/dev/null || true
-
-  # Azure Functions + Durable Task integration tests
-  python-tests-functions:
-    name: Python Integration Tests - Functions
-    runs-on: ubuntu-latest
-    environment: integration
-    timeout-minutes: 60
-    env:
-      UV_PYTHON: "3.11"
-      OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
-      FUNCTIONS_WORKER_RUNTIME: "python"
-      DURABLE_TASK_SCHEDULER_CONNECTION_STRING: "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None"
-      AzureWebJobsStorage: "UseDevelopmentStorage=true"
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
-          persist-credentials: false
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Azure CLI Login
-        uses: azure/login@v2
-        with:
-          client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-      - name: Set up Azure Functions Integration Test Emulators
-        uses: ./.github/actions/azure-functions-integration-setup
-        id: azure-functions-setup
-      - name: Test with pytest (Functions + Durable Task integration)
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/azurefunctions/tests/integration_tests
-          packages/durabletask/tests/integration_tests
-          -m integration
-          -n logical --dist worksteal
-          -x
-          --timeout=360 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-
-  # Foundry integration tests
-  python-tests-foundry:
-    name: Python Integration Tests - Foundry
-    runs-on: ubuntu-latest
-    environment: integration
-    timeout-minutes: 60
-    env:
-      AZURE_AI_PROJECT_ENDPOINT: ${{ secrets.AZUREAI__ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
-      FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME }}
-      FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION }}
-      LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
-          persist-credentials: false
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Azure CLI Login
-        uses: azure/login@v2
-        with:
-          client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-      - name: Test with pytest
-        timeout-minutes: 15
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/foundry/tests
-          -m integration
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-
-  # Azure Cosmos integration tests
-  python-tests-cosmos:
-    name: Python Integration Tests - Cosmos
-    runs-on: ubuntu-latest
-    environment: integration
-    timeout-minutes: 60
-    services:
-      cosmosdb:
-        image: mcr.microsoft.com/cosmosdb/linux/azure-cosmos-emulator:vnext-preview
-        ports:
-          - 8081:8081
-    env:
-      AZURE_COSMOS_ENDPOINT: "http://localhost:8081/"
-      # Static Azure Cosmos DB emulator key (documented): https://learn.microsoft.com/en-us/azure/cosmos-db/emulator
-      AZURE_COSMOS_KEY: "C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw=="
-      AZURE_COSMOS_DATABASE_NAME: "agent-framework-cosmos-it-db"
-      AZURE_COSMOS_CONTAINER_NAME: "agent-framework-cosmos-it-container"
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
-          persist-credentials: false
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Wait for Cosmos DB emulator
-        run: |
-          for i in {1..60}; do
-            if curl --silent --show-error http://localhost:8081/ > /dev/null; then
-              echo "Cosmos DB emulator is ready."
-              exit 0
-            fi
-            sleep 2
-          done
-          echo "Cosmos DB emulator did not become ready in time." >&2
-          exit 1
-      - name: Test with pytest (Cosmos integration)
-        run: uv run --directory packages/azure-cosmos poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
-
-  python-integration-tests-check:
-    if: always()
-    runs-on: ubuntu-latest
-    needs:
-      [
-        python-tests-unit,
-        python-tests-openai,
-        python-tests-azure-openai,
-        python-tests-misc-integration,
-        python-tests-functions,
-        python-tests-foundry,
-        python-tests-cosmos
-      ]
-    steps:
-      - name: Fail workflow if tests failed
-        if: contains(join(needs.*.result, ','), 'failure')
-        uses: actions/github-script@v8
-        with:
-          script: core.setFailed('Integration Tests Failed!')
-
-      - name: Fail workflow if tests cancelled
-        if: contains(join(needs.*.result, ','), 'cancelled')
-        uses: actions/github-script@v8
-        with:
-          script: core.setFailed('Integration Tests Cancelled!')

.github/workflows/python-lab-tests.yml

@@ -67,7 +67,6 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           os: ${{ runner.os }}
-          exclude-packages: ${{ matrix.python-version == '3.10' && 'agent-framework-github-copilot' || '' }}
         env:
           # Configure a constant location for the uv cache
           UV_CACHE_DIR: /tmp/.uv-cache
@@ -76,9 +75,6 @@ jobs:
       - name: Run lab tests
         run: cd packages/lab && uv run poe test
 
-      - name: Run resource-intensive lab tests
-        run: cd packages/lab && uv run pytest -m "resource_intensive and not integration" --junitxml=test-results-resource-intensive.xml
-
       - name: Run lab lint
         run: cd packages/lab && uv run poe lint
 

.github/workflows/python-merge-tests.yml

@@ -1,9 +1,4 @@
 name: Python - Merge - Tests
-#
-# NOTE: This workflow and python-integration-tests.yml share the same set of
-# parallel test jobs. Keep them in sync — when adding, removing, or modifying a
-# job here, apply the same change to python-integration-tests.yml.
-#
 
 on:
   workflow_dispatch:
@@ -15,13 +10,13 @@ on:
     - cron: "0 0 * * *" # Run at midnight UTC daily
 
 permissions:
-  contents: read
+  contents: write
   id-token: write
 
 env:
   # Configure a constant location for the uv cache
   UV_CACHE_DIR: /tmp/.uv-cache
-  UV_PYTHON: "3.13"
+  RUN_INTEGRATION_TESTS: "true"
   RUN_SAMPLES_TESTS: ${{ vars.RUN_SAMPLES_TESTS }}
 
 jobs:
@@ -31,14 +26,7 @@ jobs:
       contents: read
       pull-requests: read
     outputs:
-      pythonChanges: ${{ steps.filter.outputs.python }}
-      coreChanged: ${{ steps.filter.outputs.core }}
-      openaiChanged: ${{ steps.filter.outputs.openai }}
-      azureChanged: ${{ steps.filter.outputs.azure }}
-      miscChanged: ${{ steps.filter.outputs.misc }}
-      functionsChanged: ${{ steps.filter.outputs.functions }}
-      azureAiChanged: ${{ steps.filter.outputs.azure-ai }}
-      cosmosChanged: ${{ steps.filter.outputs.cosmos }}
+      pythonChanges: ${{ steps.filter.outputs.python}}
     steps:
       - uses: actions/checkout@v6
       - uses: dorny/paths-filter@v3
@@ -47,40 +35,6 @@ jobs:
           filters: |
             python:
               - 'python/**'
-              - '.github/actions/setup-local-mcp-server/**'
-              - '.github/workflows/python-merge-tests.yml'
-              - '.github/workflows/python-integration-tests.yml'
-            core:
-              - 'python/packages/core/agent_framework/_*.py'
-              - 'python/packages/core/agent_framework/_workflows/**'
-              - 'python/packages/core/agent_framework/exceptions.py'
-              - 'python/packages/core/agent_framework/observability.py'
-            openai:
-              - 'python/packages/core/agent_framework/openai/**'
-              - 'python/packages/openai/**'
-              - 'python/samples/**/providers/openai/**'
-            azure:
-              - 'python/packages/openai/**'
-              - 'python/packages/core/agent_framework/azure/**'
-              - 'python/samples/**/providers/azure/**'
-            misc:
-              - 'python/packages/anthropic/**'
-              - 'python/packages/ollama/**'
-              - 'python/packages/core/agent_framework/_mcp.py'
-              - 'python/packages/core/tests/core/test_mcp.py'
-              - 'python/scripts/local_mcp_streamable_http_server.py'
-              - '.github/actions/setup-local-mcp-server/**'
-              - '.github/workflows/python-merge-tests.yml'
-              - '.github/workflows/python-integration-tests.yml'
-            functions:
-              - 'python/packages/azurefunctions/**'
-              - 'python/packages/durabletask/**'
-            azure-ai:
-              - 'python/packages/azure-ai/**'
-              - 'python/packages/foundry/**'
-              - 'python/samples/**/providers/foundry/**'
-            cosmos:
-              - 'python/packages/azure-cosmos/**'
       # run only if 'python' files were changed
       - name: python tests
         if: steps.filter.outputs.python == 'true'
@@ -89,260 +43,34 @@ jobs:
       - name: not python tests
         if: steps.filter.outputs.python != 'true'
         run: echo "NOT python file"
-  # Unit tests: always run all non-integration tests across all packages
-  python-tests-unit:
-    name: Python Tests - Unit
+  python-tests-core:
+    name: Python Tests - Core
     needs: paths-filter
-    if: >
-      github.event_name != 'pull_request' &&
-      needs.paths-filter.outputs.pythonChanges == 'true'
-    runs-on: ubuntu-latest
-    environment: integration
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Test with pytest (unit tests only)
-        run: >
-          uv run poe test -A
-          -m "not integration"
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-        working-directory: ./python
-      - name: Surface failing tests
-        if: always()
-        uses: pmeier/pytest-results-action@v0.7.2
-        with:
-          path: ./python/**.xml
-          summary: true
-          display-options: fEX
-          fail-on-empty: false
-          title: Unit test results
-
-  # OpenAI integration tests
-  python-tests-openai:
-    name: Python Tests - OpenAI Integration
-    needs: paths-filter
-    if: >
-      github.event_name != 'pull_request' &&
-      needs.paths-filter.outputs.pythonChanges == 'true' &&
-      (github.event_name != 'merge_group' ||
-       needs.paths-filter.outputs.openaiChanged == 'true' ||
-       needs.paths-filter.outputs.coreChanged == 'true')
-    runs-on: ubuntu-latest
-    environment: integration
+    if: github.event_name != 'pull_request' && needs.paths-filter.outputs.pythonChanges == 'true'
+    runs-on: ${{ matrix.os }}
+    environment: ${{ matrix.environment }}
+    strategy:
+      fail-fast: true
+      matrix:
+        python-version: ["3.10"]
+        os: [ubuntu-latest]
+        environment: ["integration"]
     env:
-      OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
+      UV_PYTHON: ${{ matrix.python-version }}
+      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
       OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Test with pytest (OpenAI integration)
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/openai/tests
-          -m "integration and not azure"
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-        working-directory: ./python
-      - name: Test OpenAI samples
-        timeout-minutes: 10
-        if: env.RUN_SAMPLES_TESTS == 'true'
-        run: uv run pytest tests/samples/ -m "openai"
-        working-directory: ./python
-      - name: Surface failing tests
-        if: always()
-        uses: pmeier/pytest-results-action@v0.7.2
-        with:
-          path: ./python/**.xml
-          summary: true
-          display-options: fEX
-          fail-on-empty: false
-          title: OpenAI integration test results
-
-  # Azure OpenAI integration tests
-  python-tests-azure-openai:
-    name: Python Tests - Azure OpenAI Integration
-    needs: paths-filter
-    if: >
-      github.event_name != 'pull_request' &&
-      needs.paths-filter.outputs.pythonChanges == 'true' &&
-      (github.event_name != 'merge_group' ||
-       needs.paths-filter.outputs.azureChanged == 'true' ||
-       needs.paths-filter.outputs.coreChanged == 'true')
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Azure CLI Login
-        if: github.event_name != 'pull_request'
-        uses: azure/login@v2
-        with:
-          client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-      - name: Test with pytest (Azure OpenAI integration)
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/openai/tests/openai/test_openai_chat_completion_client_azure.py
-          packages/openai/tests/openai/test_openai_chat_client_azure.py
-          packages/openai/tests/openai/test_openai_embedding_client_azure.py
-          -m integration
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-        working-directory: ./python
-      - name: Test Azure samples
-        timeout-minutes: 10
-        if: env.RUN_SAMPLES_TESTS == 'true'
-        run: uv run pytest tests/samples/ -m "azure"
-        working-directory: ./python
-      - name: Surface failing tests
-        if: always()
-        uses: pmeier/pytest-results-action@v0.7.2
-        with:
-          path: ./python/**.xml
-          summary: true
-          display-options: fEX
-          fail-on-empty: false
-          title: Azure OpenAI integration test results
-
-  # Misc integration tests (Anthropic, Ollama, MCP)
-  python-tests-misc-integration:
-    name: Python Tests - Misc Integration
-    needs: paths-filter
-    if: >
-      github.event_name != 'pull_request' &&
-      needs.paths-filter.outputs.pythonChanges == 'true' &&
-      (github.event_name != 'merge_group' ||
-       needs.paths-filter.outputs.miscChanged == 'true' ||
-       needs.paths-filter.outputs.coreChanged == 'true')
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
       ANTHROPIC_CHAT_MODEL_ID: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
-      LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Start local MCP server
-        id: local-mcp
-        uses: ./.github/actions/setup-local-mcp-server
-        with:
-          fallback_url: ${{ env.LOCAL_MCP_URL }}
-      - name: Prefer local MCP URL when available
-        run: echo "LOCAL_MCP_URL=${{ steps.local-mcp.outputs.effective_url }}" >> "$GITHUB_ENV"
-      - name: Test with pytest (Anthropic, Ollama, MCP integration)
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/anthropic/tests
-          packages/ollama/tests
-          packages/core/tests/core/test_mcp.py
-          -m integration
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-        working-directory: ./python
-      - name: Stop local MCP server
-        if: always()
-        shell: bash
-        run: |
-          set -euo pipefail
-          server_pid="${{ steps.local-mcp.outputs.pid }}"
-          if [[ -z "$server_pid" ]]; then
-            exit 0
-          fi
-          if ! kill -0 "$server_pid" 2>/dev/null; then
-            exit 0
-          fi
-          kill -TERM -- "-$server_pid" 2>/dev/null || kill -TERM "$server_pid" 2>/dev/null || true
-          for _ in $(seq 1 10); do
-            if ! kill -0 "$server_pid" 2>/dev/null; then
-              exit 0
-            fi
-            sleep 1
-          done
-          kill -KILL -- "-$server_pid" 2>/dev/null || kill -KILL "$server_pid" 2>/dev/null || true
-      - name: Surface failing tests
-        if: always()
-        uses: pmeier/pytest-results-action@v0.7.2
-        with:
-          path: ./python/**.xml
-          summary: true
-          display-options: fEX
-          fail-on-empty: false
-          title: Misc integration test results
-
-  # Azure Functions + Durable Task integration tests
-  python-tests-functions:
-    name: Python Tests - Functions Integration
-    needs: paths-filter
-    if: >
-      github.event_name != 'pull_request' &&
-      needs.paths-filter.outputs.pythonChanges == 'true' &&
-      (github.event_name != 'merge_group' ||
-       needs.paths-filter.outputs.functionsChanged == 'true' ||
-       needs.paths-filter.outputs.coreChanged == 'true')
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      UV_PYTHON: "3.11"
-      OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
       AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
+      LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
+      # For Azure Functions integration tests
       FUNCTIONS_WORKER_RUNTIME: "python"
       DURABLE_TASK_SCHEDULER_CONNECTION_STRING: "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None"
       AzureWebJobsStorage: "UseDevelopmentStorage=true"
+
     defaults:
       run:
         working-directory: python
@@ -352,8 +80,11 @@ jobs:
         id: python-setup
         uses: ./.github/actions/python-setup
         with:
-          python-version: ${{ env.UV_PYTHON }}
+          python-version: ${{ matrix.python-version }}
           os: ${{ runner.os }}
+        env:
+          # Configure a constant location for the uv cache
+          UV_CACHE_DIR: /tmp/.uv-cache
       - name: Azure CLI Login
         if: github.event_name != 'pull_request'
         uses: azure/login@v2
@@ -364,16 +95,14 @@ jobs:
       - name: Set up Azure Functions Integration Test Emulators
         uses: ./.github/actions/azure-functions-integration-setup
         id: azure-functions-setup
-      - name: Test with pytest (Functions + Durable Task integration)
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/azurefunctions/tests/integration_tests
-          packages/durabletask/tests/integration_tests
-          -m integration
-          -n logical --dist worksteal
-          -x
-          --timeout=360 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
+      - name: Test with pytest
+        timeout-minutes: 10
+        run: uv run poe all-tests -n logical --dist loadfile --dist worksteal --timeout 900 --retries 3 --retry-delay 10
+        working-directory: ./python
+      - name: Test core samples
+        timeout-minutes: 10
+        if: env.RUN_SAMPLES_TESTS == 'true'
+        run: uv run pytest tests/samples/ -m "openai" -m "azure"
         working-directory: ./python
       - name: Surface failing tests
         if: always()
@@ -383,26 +112,24 @@ jobs:
           summary: true
           display-options: fEX
           fail-on-empty: false
-          title: Functions integration test results
+          title: Test results
 
-  python-tests-foundry:
-    name: Python Integration Tests - Foundry
+  python-tests-azure-ai:
+    name: Python Tests - Azure AI
     needs: paths-filter
-    if: >
-      github.event_name != 'pull_request' &&
-      needs.paths-filter.outputs.pythonChanges == 'true' &&
-      (github.event_name != 'merge_group' ||
-       needs.paths-filter.outputs.azureAiChanged == 'true' ||
-       needs.paths-filter.outputs.coreChanged == 'true')
-    runs-on: ubuntu-latest
-    environment: integration
+    if: github.event_name != 'pull_request' && needs.paths-filter.outputs.pythonChanges == 'true'
+    runs-on: ${{ matrix.os }}
+    environment: ${{ matrix.environment }}
+    strategy:
+      fail-fast: true
+      matrix:
+        python-version: ["3.10"]
+        os: [ubuntu-latest]
+        environment: ["integration"]
     env:
+      UV_PYTHON: ${{ matrix.python-version }}
       AZURE_AI_PROJECT_ENDPOINT: ${{ secrets.AZUREAI__ENDPOINT }}
       AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
-      FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME }}
-      FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION }}
       LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
     defaults:
       run:
@@ -413,8 +140,11 @@ jobs:
         id: python-setup
         uses: ./.github/actions/python-setup
         with:
-          python-version: ${{ env.UV_PYTHON }}
+          python-version: ${{ matrix.python-version }}
           os: ${{ runner.os }}
+        env:
+          # Configure a constant location for the uv cache
+          UV_CACHE_DIR: /tmp/.uv-cache
       - name: Azure CLI Login
         if: github.event_name != 'pull_request'
         uses: azure/login@v2
@@ -423,14 +153,8 @@ jobs:
           tenant-id: ${{ secrets.AZURE_TENANT_ID }}
           subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
       - name: Test with pytest
-        timeout-minutes: 15
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/foundry/tests
-          -m integration
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
+        timeout-minutes: 10
+        run: uv run --directory packages/azure-ai poe integration-tests -n logical --dist loadfile --dist worksteal --timeout 300 --retries 3 --retry-delay 10
         working-directory: ./python
       - name: Test Azure AI samples
         timeout-minutes: 10
@@ -449,78 +173,16 @@ jobs:
 
   # TODO: Add python-tests-lab
 
-  # Azure Cosmos integration tests
-  python-tests-cosmos:
-    name: Python Tests - Cosmos Integration
-    needs: paths-filter
-    if: >
-      github.event_name != 'pull_request' &&
-      needs.paths-filter.outputs.pythonChanges == 'true' &&
-      (github.event_name != 'merge_group' ||
-       needs.paths-filter.outputs.cosmosChanged == 'true' ||
-       needs.paths-filter.outputs.coreChanged == 'true')
-    runs-on: ubuntu-latest
-    environment: integration
-    services:
-      cosmosdb:
-        image: mcr.microsoft.com/cosmosdb/linux/azure-cosmos-emulator:vnext-preview
-        ports:
-          - 8081:8081
-    env:
-      AZURE_COSMOS_ENDPOINT: "http://localhost:8081/"
-      # Static Azure Cosmos DB emulator key (documented): https://learn.microsoft.com/en-us/azure/cosmos-db/emulator
-      AZURE_COSMOS_KEY: "C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw=="
-      AZURE_COSMOS_DATABASE_NAME: "agent-framework-cosmos-it-db"
-      AZURE_COSMOS_CONTAINER_NAME: "agent-framework-cosmos-it-container"
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ env.UV_PYTHON }}
-          os: ${{ runner.os }}
-      - name: Wait for Cosmos DB emulator
-        run: |
-          for i in {1..60}; do
-            if curl --silent --show-error http://localhost:8081/ > /dev/null; then
-              echo "Cosmos DB emulator is ready."
-              exit 0
-            fi
-            sleep 2
-          done
-          echo "Cosmos DB emulator did not become ready in time." >&2
-          exit 1
-      - name: Test with pytest (Cosmos integration)
-        run: uv run --directory packages/azure-cosmos poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
-        working-directory: ./python
-      - name: Surface failing tests
-        if: always()
-        uses: pmeier/pytest-results-action@v0.7.2
-        with:
-          path: ./python/**.xml
-          summary: true
-          display-options: fEX
-          fail-on-empty: false
-          title: Cosmos integration test results
-
   python-integration-tests-check:
     if: always()
     runs-on: ubuntu-latest
     needs:
       [
-        python-tests-unit,
-        python-tests-openai,
-        python-tests-azure-openai,
-        python-tests-misc-integration,
-        python-tests-functions,
-        python-tests-foundry,
-        python-tests-cosmos,
+        python-tests-core,
+        python-tests-azure-ai
       ]
     steps:
+
       - name: Fail workflow if tests failed
         id: check_tests_failed
         if: contains(join(needs.*.result, ','), 'failure')

.github/workflows/python-sample-validation.yml

@@ -1,724 +0,0 @@
-name: Python - Sample Validation
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron: "0 0 * * *" # Run at midnight UTC daily
-
-env:
-  # Configure a constant location for the uv cache
-  UV_CACHE_DIR: /tmp/.uv-cache
-  # GitHub Copilot configuration
-  GITHUB_COPILOT_MODEL: claude-opus-4.6
-  COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_GITHUB_TOKEN }}
-
-permissions:
-  contents: read
-  id-token: write
-
-jobs:
-  validate-01-get-started:
-    name: Validate 01-get-started
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      # Required configuration for get-started samples
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 01-get-started --save-report --report-name 01-get-started
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-01-get-started
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents:
-    name: Validate 02-agents
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      # Foundry configuration
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME || vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
-      # OpenAI configuration
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-      # GitHub MCP
-      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
-      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      # Observability
-      ENABLE_INSTRUMENTATION: "true"
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=$AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME" >> .env
-          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
-          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
-          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
-          echo "GITHUB_PAT=$GITHUB_PAT" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents --exclude providers --save-report --report-name 02-agents
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-openai:
-    name: Validate 02-agents/providers/openai
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      OPENAI_MODEL: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
-          echo "OPENAI_MODEL=$OPENAI_MODEL" >> .env
-          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
-          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/openai --save-report --report-name 02-agents-openai
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-openai
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-azure:
-    name: Validate 02-agents/providers/azure
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_API_VERSION: ${{ vars.AZURE_OPENAI_API_VERSION || '' }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_API_VERSION=$AZURE_OPENAI_API_VERSION" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure --save-report --report-name 02-agents-azure
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-azure
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-anthropic:
-    name: Validate 02-agents/providers/anthropic
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-      ANTHROPIC_CHAT_MODEL_ID: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY" >> .env
-          echo "ANTHROPIC_CHAT_MODEL_ID=$ANTHROPIC_CHAT_MODEL_ID" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/anthropic --save-report --report-name 02-agents-anthropic
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-anthropic
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-github-copilot:
-    name: Validate 02-agents/providers/github_copilot
-    runs-on: ubuntu-latest
-    environment: integration
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/github_copilot --save-report --report-name 02-agents-github-copilot
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-github-copilot
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-amazon:
-    name: Validate 02-agents/providers/amazon
-    if: false # Temporarily disabled - requires AWS credentials
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      BEDROCK_CHAT_MODEL_ID: ${{ vars.BEDROCK__CHATMODELID }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/amazon --save-report --report-name 02-agents-amazon
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-amazon
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-ollama:
-    name: Validate 02-agents/providers/ollama
-    if: false # Temporarily disabled - requires local Ollama server
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      OLLAMA_MODEL: ${{ vars.OLLAMA__MODEL }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/ollama --save-report --report-name 02-agents-ollama
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-ollama
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-foundry:
-    name: Validate 02-agents/providers/foundry
-    if: false # Temporarily disabled - provider folder also contains the local Foundry sample
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME || '' }}
-      FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION || '' }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
-          echo "FOUNDRY_AGENT_NAME=$FOUNDRY_AGENT_NAME" >> .env
-          echo "FOUNDRY_AGENT_VERSION=$FOUNDRY_AGENT_VERSION" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/foundry --save-report --report-name 02-agents-foundry
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-foundry
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-copilotstudio:
-    name: Validate 02-agents/providers/copilotstudio
-    if: false # Temporarily disabled - requires Copilot Studio setup
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      COPILOTSTUDIOAGENT__ENVIRONMENTID: ${{ secrets.COPILOTSTUDIOAGENT__ENVIRONMENTID }}
-      COPILOTSTUDIOAGENT__SCHEMANAME: ${{ secrets.COPILOTSTUDIOAGENT__SCHEMANAME }}
-      COPILOTSTUDIOAGENT__TENANTID: ${{ secrets.COPILOTSTUDIOAGENT__TENANTID }}
-      COPILOTSTUDIOAGENT__AGENTAPPID: ${{ secrets.COPILOTSTUDIOAGENT__AGENTAPPID }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "COPILOTSTUDIOAGENT__ENVIRONMENTID=$COPILOTSTUDIOAGENT__ENVIRONMENTID" >> .env
-          echo "COPILOTSTUDIOAGENT__SCHEMANAME=$COPILOTSTUDIOAGENT__SCHEMANAME" >> .env
-          echo "COPILOTSTUDIOAGENT__TENANTID=$COPILOTSTUDIOAGENT__TENANTID" >> .env
-          echo "COPILOTSTUDIOAGENT__AGENTAPPID=$COPILOTSTUDIOAGENT__AGENTAPPID" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/copilotstudio --save-report --report-name 02-agents-copilotstudio
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-copilotstudio
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-custom:
-    name: Validate 02-agents/providers/custom
-    runs-on: ubuntu-latest
-    environment: integration
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/custom --save-report --report-name 02-agents-custom
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-custom
-          path: python/samples/sample_validation/reports/
-
-  validate-03-workflows:
-    name: Validate 03-workflows
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 03-workflows --save-report --report-name 03-workflows
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-03-workflows
-          path: python/samples/sample_validation/reports/
-
-  validate-04-hosting:
-    name: Validate 04-hosting
-    if: false # Temporarily disabled because of sample complexity
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # A2A configuration
-      A2A_AGENT_HOST: http://localhost:5001/
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 04-hosting --save-report --report-name 04-hosting
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-04-hosting
-          path: python/samples/sample_validation/reports/
-
-  validate-05-end-to-end:
-    name: Validate 05-end-to-end
-    if: false # Temporarily disabled because of sample complexity
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure AI Search (for evaluation samples)
-      AZURE_SEARCH_ENDPOINT: ${{ secrets.AZURE_SEARCH_ENDPOINT }}
-      AZURE_SEARCH_API_KEY: ${{ secrets.AZURE_SEARCH_API_KEY }}
-      AZURE_SEARCH_INDEX_NAME: ${{ secrets.AZURE_SEARCH_INDEX_NAME }}
-      # Evaluation sample
-      FOUNDRY_MODEL_WORKFLOW: ${{ vars.FOUNDRY_MODEL_WORKFLOW || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      FOUNDRY_MODEL_EVAL: ${{ vars.FOUNDRY_MODEL_EVAL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 05-end-to-end --save-report --report-name 05-end-to-end
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-05-end-to-end
-          path: python/samples/sample_validation/reports/
-
-  validate-autogen-migration:
-    name: Validate autogen-migration
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # OpenAI configuration
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
-          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
-          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
-          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir autogen-migration --save-report --report-name autogen-migration
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-autogen-migration
-          path: python/samples/sample_validation/reports/
-
-  validate-semantic-kernel-migration:
-    name: Validate semantic-kernel-migration
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # OpenAI configuration
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
-      # Copilot Studio
-      COPILOTSTUDIOAGENT__ENVIRONMENTID: ${{ secrets.COPILOTSTUDIOAGENT__ENVIRONMENTID }}
-      COPILOTSTUDIOAGENT__SCHEMANAME: ${{ secrets.COPILOTSTUDIOAGENT__SCHEMANAME }}
-      COPILOTSTUDIOAGENT__TENANTID: ${{ secrets.COPILOTSTUDIOAGENT__TENANTID }}
-      COPILOTSTUDIOAGENT__AGENTAPPID: ${{ secrets.COPILOTSTUDIOAGENT__AGENTAPPID }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
-          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
-          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
-          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
-          echo "COPILOTSTUDIOAGENT__ENVIRONMENTID=$COPILOTSTUDIOAGENT__ENVIRONMENTID" >> .env
-          echo "COPILOTSTUDIOAGENT__SCHEMANAME=$COPILOTSTUDIOAGENT__SCHEMANAME" >> .env
-          echo "COPILOTSTUDIOAGENT__TENANTID=$COPILOTSTUDIOAGENT__TENANTID" >> .env
-          echo "COPILOTSTUDIOAGENT__AGENTAPPID=$COPILOTSTUDIOAGENT__AGENTAPPID" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir semantic-kernel-migration --save-report --report-name semantic-kernel-migration
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-semantic-kernel-migration
-          path: python/samples/sample_validation/reports/
-
-  aggregate-results:
-    name: Aggregate Results
-    runs-on: ubuntu-latest
-    if: always()
-    needs:
-      - validate-01-get-started
-      - validate-02-agents
-      - validate-02-agents-openai
-      - validate-02-agents-azure
-      - validate-02-agents-anthropic
-      - validate-02-agents-github-copilot
-      - validate-02-agents-amazon
-      - validate-02-agents-ollama
-      - validate-02-agents-foundry
-      - validate-02-agents-copilotstudio
-      - validate-02-agents-custom
-      - validate-03-workflows
-      - validate-04-hosting
-      - validate-05-end-to-end
-      - validate-autogen-migration
-      - validate-semantic-kernel-migration
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Download all validation reports
-        uses: actions/download-artifact@v7
-        with:
-          pattern: validation-report-*
-          path: reports/
-          merge-multiple: true
-
-      - name: Restore validation history
-        id: cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: validation-history/
-          key: validation-history-${{ github.run_id }}
-          restore-keys: |
-            validation-history-
-
-      - name: Aggregate results and generate trend report
-        run: |
-          python3 python/scripts/sample_validation/aggregate.py \
-            reports/ \
-            validation-history/history.json \
-            trend-report.md
-
-      - name: Write trend report to job summary
-        run: cat trend-report.md >> "$GITHUB_STEP_SUMMARY"
-
-      - name: Save validation history
-        uses: actions/cache/save@v4
-        with:
-          path: validation-history/
-          key: validation-history-${{ github.run_id }}
-
-      - name: Upload trend report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-trend-report
-          path: trend-report.md

.github/workflows/python-test-coverage-report.yml

@@ -21,7 +21,7 @@ jobs:
     steps:
       - uses: actions/checkout@v6
       - name: Download coverage report
-        uses: actions/download-artifact@v8
+        uses: actions/download-artifact@v7
         with:
           github-token: ${{ secrets.GH_ACTIONS_PR_WRITE }}
           run-id: ${{ github.event.workflow_run.id }}
@@ -46,7 +46,7 @@ jobs:
           echo "PR_NUMBER=$PR_NUMBER" >> "$GITHUB_ENV"
       - name: Pytest coverage comment
         id: coverageComment
-        uses: MishaKav/pytest-coverage-comment@v1.6.0
+        uses: MishaKav/pytest-coverage-comment@v1.2.0
         with:
           github-token: ${{ secrets.GH_ACTIONS_PR_WRITE }}
           issue-number: ${{ env.PR_NUMBER }}

.github/workflows/python-test-coverage.yml

@@ -20,7 +20,7 @@ jobs:
       run:
         working-directory: python
     env:
-      UV_PYTHON: "3.11"
+      UV_PYTHON: "3.10"
     steps:
       - uses: actions/checkout@v6
       # Save the PR number to a file since the workflow_run event
@@ -32,17 +32,17 @@ jobs:
         id: python-setup
         uses: ./.github/actions/python-setup
         with:
-          python-version: ${{ env.UV_PYTHON }}
+          python-version: ${{ matrix.python-version }}
           os: ${{ runner.os }}
         env:
           # Configure a constant location for the uv cache
           UV_CACHE_DIR: /tmp/.uv-cache
       - name: Run all tests with coverage report
-        run: uv run poe test -A -C --cov-report=xml:python-coverage.xml -q --junitxml=pytest.xml
+        run: uv run poe all-tests-cov --cov-report=xml:python-coverage.xml -q --junitxml=pytest.xml
       - name: Check coverage threshold
         run: python ${{ github.workspace }}/.github/workflows/python-check-coverage.py python-coverage.xml ${{ env.COVERAGE_THRESHOLD }}
       - name: Upload coverage report
-        uses: actions/upload-artifact@v7
+        uses: actions/upload-artifact@v6
         with:
           path: |
             python/python-coverage.xml

.github/workflows/python-tests.yml

@@ -34,13 +34,12 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           os: ${{ runner.os }}
-          exclude-packages: ${{ matrix.python-version == '3.10' && 'agent-framework-github-copilot' || '' }}
         env:
           # Configure a constant location for the uv cache
           UV_CACHE_DIR: /tmp/.uv-cache
       # Unit tests
       - name: Run all tests
-        run: uv run poe test -A
+        run: uv run poe all-tests
         working-directory: ./python
 
       # Surface failing tests

.github/workflows/stale-issue-pr-ping.yml

@@ -1,49 +0,0 @@
-name: Stale issue and PR ping
-
-on:
-  schedule:
-    - cron: '0 0 * * *'  # Midnight UTC daily
-  workflow_dispatch:
-    inputs:
-      days_threshold:
-        description: 'Days of silence before pinging the author'
-        required: false
-        default: '4'
-      dry_run:
-        description: 'Log what would be pinged without taking action'
-        required: false
-        default: 'false'
-        type: choice
-        options:
-          - 'false'
-          - 'true'
-
-concurrency:
-  group: stale-issue-pr-ping
-  cancel-in-progress: true
-
-jobs:
-  ping_stale:
-    name: "Ping stale issues and PRs"
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      issues: write
-      pull-requests: write
-    steps:
-      - uses: actions/checkout@v6
-
-      - uses: actions/setup-python@v5
-        with:
-          python-version: '3.13'
-
-      - name: Install dependencies
-        run: pip install PyGithub==2.6.0
-
-      - name: Run stale issue/PR ping
-        run: python .github/scripts/stale_issue_pr_ping.py
-        env:
-          GITHUB_TOKEN: ${{ secrets.GH_ACTIONS_PR_WRITE }}
-          TEAM_SLUG: ${{ secrets.DEVELOPER_TEAM }}
-          DAYS_THRESHOLD: ${{ github.event.inputs.days_threshold || '4' }}
-          DRY_RUN: ${{ github.event.inputs.dry_run || 'false' }}

.gitignore

@@ -199,15 +199,14 @@ temp*/
 .tmp/
 .temp/
 
+agents.md
+
 # AI
 .claude/
 WARP.md
 **/memory-bank/
 **/projectBrief.md
 **/tmpclaude*
-# Dependency-bound validation reports
-python/scripts/dependency-*-results.json
-python/scripts/dependencies/dependency-*-results.json
 
 # Azurite storage emulator files
 */__azurite_db_blob__.json*

CONTRIBUTING.md

@@ -74,37 +74,6 @@ Contributions must maintain API signature and behavioral compatibility. Contribu
 that include breaking changes will be rejected. Please file an issue to discuss
 your idea or change if you believe that a breaking change is warranted.
 
-#### Automated API Compatibility Validation
-
-The .NET projects use [Package Validation](https://learn.microsoft.com/dotnet/fundamentals/package-validation/overview)
-to automatically detect API breaking changes. This validation runs during `dotnet build`
-(Release configuration) and `dotnet pack`, comparing the current API surface against the
-latest published NuGet baseline version.
-
-**What gets validated:** By default, packable RC packages (`IsReleaseCandidate=true`) and
-GA packages (`IsGenerallyAvailable=true`) that have a published NuGet baseline and do not
-override validation settings are automatically validated. The shared baseline version and
-default validation settings are defined in `dotnet/nuget/nuget-package.props`, but
-individual projects may opt out (for example by setting `EnablePackageValidation=false`).
-
-**If the build fails with CP errors (e.g., CP0001, CP0002):**
-
-1. **Unintentional breaking change** — Refactor your code to maintain backward compatibility.
-2. **Intentional breaking change** (approved by maintainers) — Generate a suppression file:
-   ```bash
-   dotnet build <project>.csproj -c Release /p:ApiCompatGenerateSuppressionFile=true
-   ```
-   This creates or updates a `CompatibilitySuppressions.xml` in the project directory.
-   Include this file in your PR with justification for the breaking change.
-
-**After each release:**
-
-1. Delete all `CompatibilitySuppressions.xml` files from validated projects.
-2. Update `PackageValidationBaselineVersion` in `dotnet/nuget/nuget-package.props` to the
-   newly published version.
-
-For more details, see the [Package Validation diagnostic IDs](https://learn.microsoft.com/dotnet/fundamentals/package-validation/diagnostic-ids).
-
 ### Suggested Workflow
 
 We use and recommend the following workflow:
@@ -123,30 +92,22 @@ We use and recommend the following workflow:
      "issue-123" or "githubhandle-issue".
 4. Make and commit your changes to your branch.
 5. Add new tests corresponding to your change, if applicable.
-6. Run the relevant scripts in [the section below](#development-setup) to ensure that your build is clean and all tests are passing.
+6. Run the relevant scripts in [the section below](#development-scripts) to ensure that your build is clean and all tests are passing.
 7. Create a PR against the repository's **main** branch.
    - State in the description what issue or improvement your change is addressing.
    - Verify that all the Continuous Integration checks are passing.
 8. Wait for feedback or approval of your changes from the code maintainers.
 9. When area owners have signed off, and all checks are green, your PR will be merged.
 
-### Development Setup
+### Development scripts
 
-Each language has its own dev setup guide, coding standards, and build scripts:
+The scripts below are used to build, test, and lint within the project.
 
-- **Python**: [Dev Setup](./python/DEV_SETUP.md) · [Coding Standard](./python/CODING_STANDARD.md) · [README](./python/README.md)
-  - From the `./python` directory:
-    - Build: `uv run poe build`
-    - Unit tests: `uv run poe test -A -m "not integration"`
-    - Integration tests: `uv run poe test -A -m integration` (requires API keys/endpoints)
-    - Format + lint: `uv run poe syntax`
-    - All checks: `uv run poe check`
-- **.NET**: [README](./dotnet/README.md) · [Agent Instructions](./dotnet/AGENTS.md)
-  - From the `./dotnet` directory:
-    - Build: `dotnet build`
-    - Unit tests: `dotnet test --filter-query "/*UnitTests*/*/*/*"`
-    - Integration tests: `dotnet test --filter-query "/*IntegrationTests*/*/*/*"` (requires API keys/endpoints)
-    - Linting (auto-fix): `dotnet format`
+- Python: see [python/DEV_SETUP.md](./python/DEV_SETUP.md).
+- .NET:
+  - Build: `dotnet build`
+  - Test: `dotnet test`
+  - Linting (auto-fix): `dotnet format`
 
 ### PR - CI Process
 

README.md

@@ -2,7 +2,7 @@
 
 # Welcome to Microsoft Agent Framework!
 
-[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/b5zjErwbQM?style=flat)](https://discord.gg/b5zjErwbQM)
+[![Microsoft Azure AI Foundry Discord](https://dcbadge.limes.pink/api/server/b5zjErwbQM?style=flat)](https://discord.gg/b5zjErwbQM)
 [![MS Learn Documentation](https://img.shields.io/badge/MS%20Learn-Documentation-blue)](https://learn.microsoft.com/en-us/agent-framework/)
 [![PyPI](https://img.shields.io/pypi/v/agent-framework)](https://pypi.org/project/agent-framework/)
 [![NuGet](https://img.shields.io/nuget/v/Microsoft.Agents.AI)](https://www.nuget.org/profiles/MicrosoftAgentFramework/)
@@ -53,7 +53,7 @@ Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-commu
 ### ✨ **Highlights**
 
 - **Graph-based Workflows**: Connect agents and deterministic functions using data flows with streaming, checkpointing, human-in-the-loop, and time-travel capabilities
-  - [Python workflows](./python/samples/03-workflows/) | [.NET workflows](./dotnet/samples/03-workflows/)
+  - [Python workflows](./python/samples/getting_started/workflows/) | [.NET workflows](./dotnet/samples/GettingStarted/Workflows/)
 - **AF Labs**: Experimental packages for cutting-edge features including benchmarking, reinforcement learning, and research initiatives
   - [Labs directory](./python/packages/lab/)
 - **DevUI**: Interactive developer UI for agent development, testing, and debugging workflows
@@ -73,11 +73,11 @@ Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-commu
 - **Python and C#/.NET Support**: Full framework support for both Python and C#/.NET implementations with consistent APIs
   - [Python packages](./python/packages/) | [.NET source](./dotnet/src/)
 - **Observability**: Built-in OpenTelemetry integration for distributed tracing, monitoring, and debugging
-  - [Python observability](./python/samples/02-agents/observability/) | [.NET telemetry](./dotnet/samples/02-agents/AgentOpenTelemetry/)
+  - [Python observability](./python/samples/getting_started/observability/) | [.NET telemetry](./dotnet/samples/GettingStarted/AgentOpenTelemetry/)
 - **Multiple Agent Provider Support**: Support for various LLM providers with more being added continuously
-  - [Python examples](./python/samples/02-agents/providers/) | [.NET examples](./dotnet/samples/02-agents/AgentProviders/)
+  - [Python examples](./python/samples/getting_started/agents/) | [.NET examples](./dotnet/samples/GettingStarted/AgentProviders/)
 - **Middleware**: Flexible middleware system for request/response processing, exception handling, and custom pipelines
-  - [Python middleware](./python/samples/02-agents/middleware/) | [.NET middleware](./dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/)
+  - [Python middleware](./python/samples/getting_started/middleware/) | [.NET middleware](./dotnet/samples/GettingStarted/Agents/Agent_Step14_Middleware/)
 
 ### 💬 **We want your feedback!**
 
@@ -125,33 +125,32 @@ Create a simple Agent, using OpenAI Responses, that writes a haiku about the Mic
 
 ```c#
 // dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
-using Microsoft.Agents.AI;
+using System;
 using OpenAI;
-using OpenAI.Responses;
 
 // Replace the <apikey> with your OpenAI API key.
 var agent = new OpenAIClient("<apikey>")
-    .GetResponsesClient("gpt-4o-mini")
+    .GetOpenAIResponseClient("gpt-4o-mini")
     .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
 
 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```
 
-Create a simple Agent, using Microsoft Foundry with token-based auth, that writes a haiku about the Microsoft Agent Framework
+Create a simple Agent, using Azure OpenAI Responses with token based auth, that writes a haiku about the Microsoft Agent Framework
 
 ```c#
-// dotnet add package Microsoft.Agents.AI.AzureAI --prerelease
+// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
 // dotnet add package Azure.Identity
 // Use `az login` to authenticate with Azure CLI
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
+using System;
+using OpenAI;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-var agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
-    .AsAIAgent(model: deploymentName, name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+// Replace <resource> and gpt-4o-mini with your Azure OpenAI resource name and deployment name.
+var agent = new OpenAIClient(
+    new BearerTokenPolicy(new AzureCliCredential(), "https://ai.azure.com/.default"),
+    new OpenAIClientOptions() { Endpoint = new Uri("https://<resource>.openai.azure.com/openai/v1") })
+    .GetOpenAIResponseClient("gpt-4o-mini")
+    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
 
 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```
@@ -160,43 +159,15 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram
 
 ### Python
 
-- [Getting Started](./python/samples/01-get-started): progressive tutorial from hello-world to hosting
-- [Agent Concepts](./python/samples/02-agents): deep-dive samples by topic (tools, middleware, providers, etc.)
-- [Workflows](./python/samples/03-workflows): workflow creation and integration with agents
-- [Hosting](./python/samples/04-hosting): A2A, Azure Functions, Durable Task hosting
-- [End-to-End](./python/samples/05-end-to-end): full applications, evaluation, and demos
+- [Getting Started with Agents](./python/samples/getting_started/agents): basic agent creation and tool usage
+- [Chat Client Examples](./python/samples/getting_started/chat_client): direct chat client usage patterns
+- [Getting Started with Workflows](./python/samples/getting_started/workflows): basic workflow creation and integration with agents
 
 ### .NET
 
-- [Getting Started](./dotnet/samples/01-get-started): progressive tutorial from hello agent to hosting
-- [Agent Concepts](./dotnet/samples/02-agents/Agents): basic agent creation and tool usage
-- [Agent Providers](./dotnet/samples/02-agents/AgentProviders): samples showing different agent providers
-- [Workflows](./dotnet/samples/03-workflows): advanced multi-agent patterns and workflow orchestration
-- [Hosting](./dotnet/samples/04-hosting): A2A, Durable Agents, Durable Workflows
-- [End-to-End](./dotnet/samples/05-end-to-end): full applications and demos
-
-## Troubleshooting
-
-### Authentication
-
-| Problem | Cause | Fix |
-|---------|-------|-----|
-| Authentication errors when using Azure credentials | Not signed in to Azure CLI | Run `az login` before starting your app |
-| API key errors | Wrong or missing API key | Verify the key and ensure it's for the correct resource/provider |
-
-> **Tip:** `DefaultAzureCredential` is convenient for development but in production, consider using a specific credential (e.g., `ManagedIdentityCredential`) to avoid latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-
-### Environment Variables
-
-The samples typically read configuration from environment variables. Common required variables:
-
-| Variable | Used by | Purpose |
-|----------|---------|---------|
-| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI samples | Your Azure OpenAI resource URL |
-| `AZURE_OPENAI_DEPLOYMENT_NAME` | Azure OpenAI samples | Model deployment name (e.g. `gpt-4o-mini`) |
-| `AZURE_AI_PROJECT_ENDPOINT` | Microsoft Foundry samples | Your Microsoft Foundry project endpoint |
-| `AZURE_AI_MODEL_DEPLOYMENT_NAME` | Microsoft Foundry samples | Model deployment name |
-| `OPENAI_API_KEY` | OpenAI (non-Azure) samples | Your OpenAI platform API key |
+- [Getting Started with Agents](./dotnet/samples/GettingStarted/Agents): basic agent creation and tool usage
+- [Agent Provider Samples](./dotnet/samples/GettingStarted/AgentProviders): samples showing different agent providers
+- [Workflow Samples](./dotnet/samples/GettingStarted/Workflows): advanced multi-agent patterns and workflow orchestration
 
 ## Contributor Resources
 

agent-samples/README.md

@@ -1,3 +1,3 @@
 # Declarative Agents
 
-This folder contains sample agent definitions that can be run using the declarative agent support, for python see the [declarative agent python sample folder](../python/samples/02-agents/declarative/).
+This folder contains sample agent definitions that can be run using the declarative agent support, for python see the [declarative agent python sample folder](../python/samples/getting_started/declarative/).

agent-samples/openai/OpenAIResponses.yaml

@@ -11,7 +11,7 @@ model:
         topP: 0.95
     connection:
         kind: key
-        apiKey: =Env.OPENAI_API_KEY
+        apiKey: =Env.OPENAI_APIKEY
 outputSchema:
     properties:
         language:

docs/decisions/0001-agent-run-response.md

@@ -4,8 +4,8 @@ status: accepted
 contact: westey-m
 date: 2025-07-10 {YYYY-MM-DD when the decision was last updated}
 deciders: sergeymenshykh, markwallace, rbarreto, dmytrostruk, westey-m, eavanvalkenburg, stephentoub
-consulted:
-informed:
+consulted: 
+informed: 
 ---
 
 # Agent Run Responses Design
@@ -64,7 +64,7 @@ Approaches observed from the compared SDKs:
 | AutoGen | **Approach 1** Separates messages into Agent-Agent (maps to Primary) and Internal (maps to Secondary) and these are returned as separate properties on the agent response object.  See [types of messages](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/messages.html#types-of-messages) and [Response](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.Response) | **Approach 2** Returns a stream of internal events and the last item is a Response object. See [ChatAgent.on_messages_stream](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.ChatAgent.on_messages_stream) |
 | OpenAI Agent SDK | **Approach 1** Separates new_items (Primary+Secondary) from final output (Primary) as separate properties on the [RunResult](https://github.com/openai/openai-agents-python/blob/main/src/agents/result.py#L39) | **Approach 1** Similar to non-streaming, has a way of streaming updates via a method on the response object which includes all data, and then a separate final output property on the response object which is populated only when the run is complete. See [RunResultStreaming](https://github.com/openai/openai-agents-python/blob/main/src/agents/result.py#L136) |
 | Google ADK | **Approach 2** [Emits events](https://google.github.io/adk-docs/runtime/#step-by-step-breakdown) with [FinalResponse](https://github.com/google/adk-java/blob/main/core/src/main/java/com/google/adk/events/Event.java#L232) true (Primary) / false (Secondary) and callers have to filter out those with false to get just the final response message | **Approach 2** Similar to non-streaming except [events](https://google.github.io/adk-docs/runtime/#streaming-vs-non-streaming-output-partialtrue) are emitted with [Partial](https://github.com/google/adk-java/blob/main/core/src/main/java/com/google/adk/events/Event.java#L133) true to indicate that they are streaming messages. A final non partial event is also emitted. |
-| AWS (Strands) | **Approach 3** Returns an [AgentResult](https://strandsagents.com/docs/api/python/strands.agent.agent_result/) (Primary) with messages and a reason for the run's completion. | **Approach 2** [Streams events](https://strandsagents.com/docs/api/python/strands.agent.agent/) (Primary+Secondary) including, response text, current_tool_use, even data from "callbacks" (strands plugins) |
+| AWS (Strands) | **Approach 3** Returns an [AgentResult](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent_result/) (Primary) with messages and a reason for the run's completion. | **Approach 2** [Streams events](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.stream_async) (Primary+Secondary) including, response text, current_tool_use, even data from "callbacks" (strands plugins) |
 | LangGraph | **Approach 2** A mixed list of all [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) | **Approach 2** A mixed list of all [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) |
 | Agno | **Combination of various approaches** Returns a [RunResponse](https://docs.agno.com/reference/agents/run-response) object with text content, messages (essentially chat history including inputs and instructions), reasoning and thinking text properties. Secondary events could potentially be extracted from messages. | **Approach 2** Returns [RunResponseEvent](https://docs.agno.com/reference/agents/run-response#runresponseevent-types-and-attributes) objects including tool call, memory update, etc, information, where the [RunResponseCompletedEvent](https://docs.agno.com/reference/agents/run-response#runresponsecompletedevent) has similar properties to RunResponse|
 | A2A | **Approach 3** Returns a [Task or Message](https://a2aproject.github.io/A2A/latest/specification/#71-messagesend) where the message is the final result (Primary) and task is a reference to a long running process. | **Approach 2** Returns a [stream](https://a2aproject.github.io/A2A/latest/specification/#72-messagestream) that contains task updates (Secondary) and a final message (Primary) |
@@ -496,9 +496,9 @@ We need to decide what AIContent types, each agent response type will be mapped
 |-|-|
 | AutoGen | **Approach 1** Supports [configuring an agent](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/agents.html#structured-output) at agent creation. |
 | Google ADK | **Approach 1** Both [input and output schemas can be specified for LLM Agents](https://google.github.io/adk-docs/agents/llm-agents/#structuring-data-input_schema-output_schema-output_key) at construction time. This option is specific to this agent type and other agent types do not necessarily support |
-| AWS (Strands) | **Approach 2** Supports a special invocation method called [structured_output](https://strandsagents.com/docs/api/python/strands.agent.agent/) |
+| AWS (Strands) | **Approach 2** Supports a special invocation method called [structured_output](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.structured_output) |
 | LangGraph | **Approach 1** Supports [configuring an agent](https://langchain-ai.github.io/langgraph/agents/agents/?h=structured#6-configure-structured-output) at agent construction time, and a [structured response](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) can be retrieved as a special property on the agent response |
-| Agno | **Approach 1** Supports [configuring an agent](https://docs.agno.com/input-output/structured-output/agent) at agent construction time |
+| Agno | **Approach 1** Supports [configuring an agent](https://docs.agno.com/examples/getting-started/structured-output) at agent construction time |
 | A2A | **Informal Approach 2** Doesn't formally support schema negotiation, but [hints can be provided via metadata](https://a2a-protocol.org/latest/specification/#97-structured-data-exchange-requesting-and-providing-json) at invocation time |
 | Protocol Activity | Supports returning [Complex types](https://github.com/microsoft/Agents/blob/main/specs/activity/protocol-activity.md#complex-types) but no support for requesting a type |
 
@@ -508,7 +508,7 @@ We need to decide what AIContent types, each agent response type will be mapped
 |-|-|
 | AutoGen | Supports a [stop reason](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.TaskResult.stop_reason) which is a freeform text string |
 | Google ADK | [No equivalent present](https://github.com/google/adk-python/blob/main/src/google/adk/events/event.py) |
-| AWS (Strands) | Exposes a [stop_reason](https://strandsagents.com/docs/api/python/strands.types.event_loop/) property on the [AgentResult](https://strandsagents.com/docs/api/python/strands.agent.agent_result/) class with options that are tied closely to LLM operations. |
+| AWS (Strands) | Exposes a [stop_reason](https://strandsagents.com/latest/documentation/docs/api-reference/python/types/event_loop/#strands.types.event_loop.StopReason) property on the [AgentResult](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent_result/) class with options that are tied closely to LLM operations. |
 | LangGraph | No equivalent present, output contains only [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) |
 | Agno | [No equivalent present](https://docs.agno.com/reference/agents/run-response) |
 | A2A | No equivalent present, response only contains a [message](https://a2a-protocol.org/latest/specification/#64-message-object) or [task](https://a2a-protocol.org/latest/specification/#61-task-object). |

docs/decisions/0012-python-typeddict-options.md

@@ -126,4 +126,4 @@ response = await client.get_response(
 
 Chosen option: **"Option 2: TypedDict with Generic Type Parameters"**, because it provides full type safety, excellent IDE support with autocompletion, and allows users to extend provider-specific options for their use cases. Extended this Generic to ChatAgents in order to also properly type the options used in agent construction and run methods.
 
-See [typed_options.py](../../python/samples/02-agents/typed_options.py) for a complete example demonstrating the usage of typed options with custom extensions.
+See [typed_options.py](../../python/samples/getting_started/chat_client/typed_options.py) for a complete example demonstrating the usage of typed options with custom extensions.

docs/decisions/0015-agent-run-context.md

@@ -1,147 +0,0 @@
----
-status: proposed
-contact: westey-m
-date: 2026-01-27
-deciders: sergeymenshykh, markwallace, rbarreto, dmytrostruk, westey-m, eavanvalkenburg, stephentoub, lokitoth, alliscode, taochenosu, moonbox3
-consulted: 
-informed: 
----
-
-# AgentRunContext for Agent Run
-
-## Context and Problem Statement
-
-During an agent run, various components involved in the execution (middleware, filters, tools, nested agents, etc.) may need access to contextual information about the current run, such as:
-
-1. The agent that is executing the run
-2. The session associated with the run
-3. The request messages passed to the agent
-4. The run options controlling the agent's behavior
-
-Additionally, some components may need to modify this context during execution, for example:
-
-- Replacing the session with a different one
-- Modifying the request messages before they reach the agent core
-- Updating or replacing the run options entirely
-
-Currently, there is no standardized way to access or modify this context from arbitrary code that executes during an agent run, especially from deeply nested call stacks where the context is not explicitly passed.
-
-## Sample Scenario
-
-When using an Agent as an AIFunction developers may want to pass context from the parent agent run to the child agent run. For example, the developer may want to copy chat history to the child agent, or share the same session across both agents.
-
-To enable these scenarios, we need a way to access the parent agent run context, including e.g. the parent agent itself, the parent agent session, and the parent run options from function tool calls.
-
-```csharp
-    public static AIFunction AsAIFunctionWithSessionPropagation(this ChatClientAgent agent, AIFunctionFactoryOptions? options = null)
-    {
-        Throw.IfNull(agent);
-
-        [Description("Invoke an agent to retrieve some information.")]
-        async Task<string> InvokeAgentAsync(
-            [Description("Input query to invoke the agent.")] string query,
-            CancellationToken cancellationToken)
-        {
-            // Get the session from the parent agent and pass it to the child agent.
-            var session = AIAgent.CurrentRunContext?.Session;
-
-            // Alternatively, the developer may want to create a new session but copy over the chat history from the parent agent.
-            // var parentChatHistory = AIAgent.CurrentRunContext?.Session?.GetService<IList<ChatMessage>>();
-            // if (parentChatHistory != null)
-            // {
-            //     var chp = new InMemoryChatHistoryProvider();
-            //     foreach (var message in parentChatHistory)
-            //     {
-            //         chp.Add(message);
-            //     }
-            //     session = agent.GetNewSession(chp);
-            // }
-
-            var response = await agent.RunAsync(query, session: session, cancellationToken: cancellationToken).ConfigureAwait(false);
-            return response.Text;
-        }
-
-        options ??= new();
-        options.Name ??= SanitizeAgentName(agent.Name);
-        options.Description ??= agent.Description;
-
-        return AIFunctionFactory.Create(InvokeAgentAsync, options);
-    }
-```
-
-## Decision Drivers
-
-- Components executing during an agent run need access to run context without explicit parameter passing through every layer
-- Context should flow naturally across async calls without manual propagation
-- The design should allow modification of context properties by agent decorators (e.g., replacing options or session)
-- Solution should be consistent with patterns used in similar frameworks (e.g., `FunctionInvokingChatClient.CurrentContext` `HttpContext.Current`, `Activity.Current`)
-
-## Considered Options
-
-- **Option 1**: Pass context explicitly through all method signatures
-- **Option 2**: Use `AsyncLocal<T>` to provide ambient context accessible anywhere during the run
-- **Option 3**: Use a combination of explicit parameters for `RunCoreAsync` and `AsyncLocal<T>` for ambient access
-
-## Decision Outcome
-
-Chosen option: **Option 3** - Combination of explicit parameters and AsyncLocal ambient access.
-
-This approach provides the best of both worlds:
-
-1. **Explicit parameters are passed to `RunCoreAsync`**: The core agent implementation receives the parameters explicitly, making it clear what data is available and enabling easy unit testing. Any modification of these in a decorator will require calling `RunAsync` on the inner agent with the updated parameters, which would result in the inner agent creating a new `AgentRunContext` instance.
-
-   ```csharp
-    public async Task<AgentResponse> RunAsync(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-
-        CurrentRunContext = new(this, session, messages as IReadOnlyCollection<ChatMessage> ?? messages.ToList(), options);
-        return await this.RunCoreAsync(messages, session, options, cancellationToken).ConfigureAwait(false);
-    }
-   ```
-
-2. **`AsyncLocal<AgentRunContext?>` for ambient access**: The context is stored in an `AsyncLocal<T>` field, making it accessible from any code executing during the agent run via a static property.
-
-    The main scenario for this is to allow deeply nested components (e.g., tools, chat client middleware) to access the context without needing to pass it through every method signature. These are external components that cannot easily be modified to accept additional parameters. For internal components, we prefer passing any parameters explicitly.
-
-   ```csharp
-   public static AgentRunContext? CurrentRunContext
-   {
-       get => s_currentContext.Value;
-       protected set => s_currentContext.Value = value;
-   }
-   ```
-
-### AgentRunContext Design
-
-The `AgentRunContext` class encapsulates all run-related state:
-
-```csharp
-public class AgentRunContext
-{
-    public AgentRunContext(
-        AIAgent agent,
-        AgentSession? session,
-        IReadOnlyCollection<ChatMessage> requestMessages,
-        AgentRunOptions? agentRunOptions)
-
-    public AIAgent Agent { get; }
-    public AgentSession? Session { get; }
-    public IReadOnlyCollection<ChatMessage> RequestMessages { get; }
-    public AgentRunOptions? RunOptions { get; }
-}
-```
-
-Key design decisions:
-
-- **All properties are read-only**: While some of the sub-properties on the provided properties (like `AgentRunOptions.AllowBackgroundResponses`) may be mutable, the `AgentRunContext` itself is immutable and we want to discourage anyone modifying the values in the context.  Modifying the context is unlikely to result in the desired behavior, as the values will typically already have been used by the time any custom code accesses them.
-
-### Benefits
-
-1. **Ambient Access**: Any code executing during the run can access context via `AIAgent.CurrentRunContext` without needing explicit parameters
-2. **Async Flow**: `AsyncLocal<T>` automatically flows across async/await boundaries
-3. **Modifiability**: Components can modify or replace session, messages, or options as needed
-4. **Testability**: The explicit parameter to `RunCoreAsync` makes unit testing straightforward

docs/decisions/0016-structured-output.md

@@ -1,658 +0,0 @@
----
-status: proposed
-contact: sergeymenshykh
-date: 2026-01-22
-deciders: rbarreto, westey-m, stephentoub
-informed: {}
----
-
-# Structured Output
-
-Structured output is a valuable aspect of any agent system, since it forces an agent to produce output in a required format that may include required fields.
-This allows easily turning unstructured data into structured data using a general-purpose language model.
-
-## Context and Problem Statement
-
-Structured output is currently supported only by `ChatClientAgent` and can be configured in two ways:
-
-**Approach 1: ResponseFormat + Deserialize**
-
-Specify the SO type schema via the `ChatClientAgent{Run}Options.ChatOptions.ResponseFormat` property at agent creation or invocation time, then use `JsonSerializer.Deserialize<T>` to extract the structured data from the response text.
-
-	```csharp
-	// SO type can be provided at agent creation time
-	ChatClientAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions()
-	{
-		Name = "...",
-		ChatOptions = new() { ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>() }
-	});
-
-	AgentResponse response = await agent.RunAsync("...");
-
-	PersonInfo personInfo = response.Deserialize<PersonInfo>(JsonSerializerOptions.Web);
-
-	Console.WriteLine($"Name: {personInfo.Name}");
-	Console.WriteLine($"Age: {personInfo.Age}");
-	Console.WriteLine($"Occupation: {personInfo.Occupation}");
-
-	// Alternatively, SO type can be provided at agent invocation time
-	response = await agent.RunAsync("...", new ChatClientAgentRunOptions()
-	{
-		ChatOptions = new() { ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>() }
-	});
-
-	personInfo = response.Deserialize<PersonInfo>(JsonSerializerOptions.Web);
-
-	Console.WriteLine($"Name: {personInfo.Name}");
-	Console.WriteLine($"Age: {personInfo.Age}");
-	Console.WriteLine($"Occupation: {personInfo.Occupation}");
-	```
-
-**Approach 2: Generic RunAsync<T>**
-
-Supply the SO type as a generic parameter to `RunAsync<T>` and access the parsed result directly via the `Result` property.
-
-	```csharp
-	ChatClientAgent agent = ...;
-	
-	AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("...");
-
-	Console.WriteLine($"Name: {response.Result.Name}");
-	Console.WriteLine($"Age: {response.Result.Age}");
-	Console.WriteLine($"Occupation: {response.Result.Occupation}");
-	```
-	Note: `RunAsync<T>` is an instance method of `ChatClientAgent` and not part of the `AIAgent` base class since not all agents support structured output.
-
-Approach 1 is perceived as cumbersome by the community, as it requires additional effort when using primitive or collection types - the SO schema may need to be wrapped in an artificial JSON object. Otherwise, the caller will encounter an error like _Invalid schema for response_format 'Movie': schema must be a JSON Schema of 'type: "object"', got 'type: "array"'_. 
-This occurs because OpenAI and compatible APIs require a JSON object as the root schema.
-
-Approach 1 is also necessary in scenarios where (a) agents can only be configured with SO at creation time (such as with `AIProjectClient`), (b) the SO type is not known at compile time, or (c) the JSON schema is represented as text (for declarative agents) or as a `JsonElement`.
-
-Approach 2 is more convenient and works seamlessly with primitives and collections. However, it requires the SO type to be known at compile time, making it less flexible.
-
-Additionally, since the `RunAsync<T>` methods are instance methods of `ChatClientAgent` and are not part of the `AIAgent` base class, applying decorators like `OpenTelemetryAgent` on top of `ChatClientAgent` prevents users from accessing `RunAsync<T>`, meaning structured output is not available with decorated agents.
-
-Given the different scenarios above in which structured output can be used, there is no one-size-fits-all solution. Each approach has its own advantages and limitations,
-and the two can complement each other to provide a comprehensive structured output experience across various use cases.
-
-## Approaches Overview
-
-1. SO usage via `ResponseFormat` property
-2. SO usage via `RunAsync<T>` generic method
-
-## 1. SO usage via `ResponseFormat` property
-
-This approach should be used in the following scenarios:
- - 1.1 SO result as text is sufficient as is, and deserialization is not required
- - 1.2 SO for inter-agent collaboration
- - 1.3 SO can only be configured at agent creation time (such as with `AIProjectClient`)
- - 1.4 SO type is not known at compile time and represented by System.Type
- - 1.5 SO is represented by JSON schema and there's no corresponding .NET type either at compile time or at runtime
- - 1.6 SO in streaming scenarios, where the SO response is produced in parts
-
-**Note: Primitives and arrays are not supported by this approach.**
-
-When a caller provides a schema via `ResponseFormat`, they are explicitly telling the framework what schema to use. The framework passes that schema through as-is and
-is not responsible for transforming it. Because the framework does not own the schema, it cannot wrap primitives or arrays into a JSON object to satisfy API requirements,
-nor can it unwrap the response afterward - the caller controls the schema and is responsible for ensuring it is compatible with the underlying API.
-
-This is in contrast to the `RunAsync<T>` approach (section 2), where the caller provides a type `T` and says "make it work." In that case, the caller does not
-dictate the schema - the framework infers the schema from `T`, owns the end-to-end pipeline (schema generation, API invocation, and deserialization), and can
-therefore wrap and unwrap primitives and arrays transparently.
-
-Additionally, in streaming scenarios (1.6), the framework cannot reliably unwrap a response it did not wrap, since it has no way of knowing whether the caller wrapped the schema.Wrapping and unwrapping can only be done safely when the framework owns the entire lifecycle - from schema creation through deserialization — which is only the case with `RunAsync<T>`.
-
-If a caller needs to work with primitives or arrays via the `ResponseFormat` approach, they can easily create a wrapper type around them:
-
-```csharp
-public class MovieListWrapper
-{
-    public List<string> Movies { get; set; }
-}
-```
-
-### 1.1 SO result as text is sufficient as is, and deserialization is not required
-
-In this scenario, the caller only needs the raw JSON text returned by the model and does not need to deserialize it into a .NET type.
-The SO schema is specified via `ResponseFormat` at agent creation or invocation time, and the response text is consumed directly from the `AgentResponse`.
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent();
-
-AgentRunOptions runOptions = new()
-{
-        ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>()
-};
-
-AgentResponse response = await agent.RunAsync("...", options: runOptions);
-
-Console.WriteLine(response.Text);
-```
-
-### 1.2 SO for inter-agent collaboration
-
-This scenario assumes a multi-agent setup where agents collaborate by passing messages to each other.
-One agent produces structured output as text that is then passed directly as input to the next agent, without intermediate deserialization.
-
-```csharp
-// First agent extracts structured data from unstructured input
-AIAgent extractionAgent = chatClient.AsAIAgent(new ChatClientAgentOptions()
-{
-    Name = "ExtractionAgent",
-    ChatOptions = new() 
-    { 
-        Instructions = "Extract person information from the provided text.",
-        ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>() 
-    }
-});
-
-AgentResponse extractionResponse = await extractionAgent.RunAsync("John Smith is a 35-year-old software engineer.");
-
-// Pass the message with structured output text directly to the next agent
-ChatMessage soMessage = extractionResponse.Messages.Last();
-
-AIAgent summaryAgent = chatClient.AsAIAgent(new ChatClientAgentOptions()
-{
-    Name = "SummaryAgent",
-    ChatOptions = new() { Instructions = "Given the following structured person data, write a short professional bio." }
-});
-
-AgentResponse summaryResponse = await summaryAgent.RunAsync(soMessage);
-
-Console.WriteLine(summaryResponse);
-```
-
-### 1.3 SO configured at agent creation time
-
-In this scenario, the SO schema can only be configured at agent creation time (such as with `AIProjectClient`) and cannot be changed on a per-run basis.
-The caller specifies the `ResponseFormat` when creating the agent, and all subsequent invocations use the same schema.
-
-```csharp
-AIProjectClient client = ...;
-
-AIAgent agent = await client.CreateAIAgentAsync(model: "<model>", new ChatClientAgentOptions()
-{
-    Name = "...",
-    ChatOptions = new() { ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>() }
-});
-
-AgentResponse response = await agent.RunAsync("Please provide information about John Smith.");
-
-PersonInfo personInfo = JsonSerializer.Deserialize<PersonInfo>(response.Text, JsonSerializerOptions.Web)!;
-
-Console.WriteLine($"Name: {personInfo.Name}");
-Console.WriteLine($"Age: {personInfo.Age}");
-Console.WriteLine($"Occupation: {personInfo.Occupation}");
-```
-
-### 1.4 SO type not known at compile time and represented by System.Type
-
-In this scenario, the SO type is not known at compile time and is provided as a `System.Type` at runtime. This is useful for dynamic scenarios where the schema is determined programmatically, 
-such as when building tooling or frameworks that work with user-defined types.
-
-```csharp
-Type soType = GetStructuredOutputTypeFromConfiguration(); // e.g., typeof(PersonInfo)
-
-ChatResponseFormat responseFormat = ChatResponseFormat.ForJsonSchema(soType);
-
-AgentResponse response = await agent.RunAsync("...", new ChatClientAgentRunOptions()
-{
-    ChatOptions = new() { ResponseFormat = responseFormat }
-});
-
-PersonInfo personInfo = (PersonInfo)JsonSerializer.Deserialize(response.Text, soType, JsonSerializerOptions.Web)!;
-```
-
-### 1.5 SO represented by JSON schema with no corresponding .NET type
-
-In this scenario, the SO schema is represented as raw JSON schema text or a `JsonElement`, and there is no corresponding .NET type available at compile time or runtime.
-This is typical for declarative agents or scenarios where schemas are loaded from external configuration.
-
-```csharp
-// JSON schema provided as a string, e.g., loaded from a configuration file
-string jsonSchema = """
-{
-    "type": "object",
-    "properties": {
-        "name": { "type": "string" },
-        "age": { "type": "integer" },
-        "occupation": { "type": "string" }
-    },
-    "required": ["name", "age", "occupation"]
-}
-""";
-
-ChatResponseFormat responseFormat = ChatResponseFormat.ForJsonSchema(
-    jsonSchemaName: "PersonInfo",
-    jsonSchema: BinaryData.FromString(jsonSchema));
-
-AgentResponse response = await agent.RunAsync("...", new ChatClientAgentRunOptions()
-{
-    ChatOptions = new() { ResponseFormat = responseFormat }
-});
-
-// Consume the SO result as text since there's no .NET type to deserialize into
-Console.WriteLine(response.Text);
-```
-
-### 1.6 SO in streaming scenarios
-
-In this scenario, the SO response is produced incrementally in parts via streaming. The caller specifies the `ResponseFormat` and consumes the response chunks as they arrive.
-Deserialization is performed after all chunks have been received.
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions()
-{
-    Name = "HelpfulAssistant",
-    ChatOptions = new()
-    {
-        Instructions = "You are a helpful assistant.",
-        ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>()
-    }
-});
-
-IAsyncEnumerable<AgentResponseUpdate> updates = agent.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
-
-AgentResponse response = await updates.ToAgentResponseAsync();
-
-// Deserialize the complete SO result after streaming is finished
-PersonInfo personInfo = JsonSerializer.Deserialize<PersonInfo>(response.Text)!;
-```
-
-## 2. SO usage via `RunAsync<T>` generic method
-
-This approach provides a convenient way to work with structured output on a per-run basis when the target type is known at compile time and a typed instance of the result
-is required.
-
-### Decision Drivers
-
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-### Considered Options
-
-1. `RunAsync<T>` as an instance method of `AIAgent` class delegating to virtual `RunCoreAsync<T>`
-2. `RunAsync<T>` as an extension method using feature collection
-3. `RunAsync<T>` as a method of the new `ITypedAIAgent` interface
-4. `RunAsync<T>` as an instance method of `AIAgent` class working via the new `AgentRunOptions.ResponseFormat` property
-
-### 1. `RunAsync<T>` as an instance method of `AIAgent` class delegating to virtual `RunCoreAsync<T>`
-
-This option adds the `RunAsync<T>` method directly to the `AIAgent` base class.
-
-```csharp
-public abstract class AIAgent
-{
-	public Task<AgentResponse<T>> RunAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-		=> this.RunCoreAsync<T>(messages, session, serializerOptions, options, cancellationToken);
-
-    protected virtual Task<AgentResponse<T>> RunCoreAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        throw new NotSupportedException($"The agent of type '{this.GetType().FullName}' does not support typed responses.");
-    }
-}
-```
-
-Agents with native SO support override the `RunCoreAsync<T>` method to provide their implementation. If not overridden, the method throws a `NotSupportedException`.
-
-Users will call the generic `RunAsync<T>` method directly on the agent:
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
-
-AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
-```
-
-Decision drivers satisfied:
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-Pros:
-- The `AIAgent.RunAsync<T>` method is easily discoverable.
-- Both the SO decorator and `ChatClientAgent` have compile-time access to the type `T`, allowing them to use the native `IChatClient.GetResponseAsync<T>` API, which handles primitives and collections seamlessly.
-
-Cons:
-- Agents without native SO support will still expose `RunAsync<T>`, which may be misleading.
-- `ChatClientAgent` exposing `RunAsync<T>` may be misleading when the underlying chat client does not support SO.
-- All `AIAgent` decorators must override `RunCoreAsync<T>` to properly handle `RunAsync<T>` calls.
-
-### 2. `RunAsync<T>` as an extension method using feature collection
-
-This option uses the Agent Framework feature collection (implemented via `AgentRunOptions.AdditionalProperties`) to pass a `StructuredOutputFeature` to agents, signaling that SO is requested.
-
-Agents with native SO support check for this feature. If present, they read the target type, build the schema, invoke the underlying API, and store the response back in the feature.
-```csharp
-public class StructuredOutputFeature
-{
-    public StructuredOutputFeature(Type outputType)
-    {
-        this.OutputType = outputType;
-    }
-
-    [JsonIgnore]
-    public Type OutputType { get; set; }
-
-    public JsonSerializerOptions? SerializerOptions { get; set; }
-
-    public AgentResponse? Response { get; set; }
-}
-```
-
-The `RunAsync<T>` extension method for `AIAgent` adds this feature to the collection.
-```csharp
-public static async Task<AgentResponse<T>> RunAsync<T>(
-    this AIAgent agent,
-    IEnumerable<ChatMessage> messages,
-    AgentSession? session = null,
-    JsonSerializerOptions? serializerOptions = null,
-    AgentRunOptions? options = null,
-    CancellationToken cancellationToken = default)
-{
-    // Create the structured output feature.
-    StructuredOutputFeature structuredOutputFeature = new(typeof(T))
-    {
-        SerializerOptions = serializerOptions,
-    };
-
-    // Register it in the feature collection.
-    ((options ??= new AgentRunOptions()).AdditionalProperties ??= []).Add(typeof(StructuredOutputFeature).FullName!, structuredOutputFeature);
-
-    var response = await agent.RunAsync(messages, session, options, cancellationToken).ConfigureAwait(false);
-
-    if (structuredOutputFeature.Response is not null)
-    {
-        return new StructuredOutputResponse<T>(structuredOutputFeature.Response, response, serializerOptions);
-    }
-
-    throw new InvalidOperationException("No structured output response was generated by the agent.");
-}
-```
-
-Users will call the `RunAsync<T>` extension method directly on the agent:
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
-
-AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
-```
-
-Decision drivers satisfied:
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-Pros:
-- The `RunAsync<T>` extension method is easily discoverable.
-- The `AIAgent` public API surface remains unchanged.
-- No changes required to `AIAgent` decorators.
-
-Cons:
-- Agents without native SO support will still expose `RunAsync<T>`, which may be misleading.
-- `ChatClientAgent` exposing `RunAsync<T>` may be misleading when the underlying chat client does not support SO.
-
-### 3. `RunAsync<T>` as a method of the new `ITypedAIAgent` interface
-
-This option defines a new `ITypedAIAgent` interface that agents with SO support implement. Agents without SO support do not implement it, allowing users to check for SO capability via interface detection.
-
-The interface:
-```csharp
-public interface ITypedAIAgent
-{
-    Task<AgentResponse<T>> RunAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default);
-
-    ...
-}
-```
-
-Agents with SO support implement this interface:
-```csharp
-public sealed partial class ChatClientAgent : AIAgent, ITypedAIAgent
-{
-    public async Task<AgentResponse<T>> RunAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        ...
-    }
-}
-```
-
-However, `ChatClientAgent` presents a challenge: it can work with chat clients that either support or do not support SO. Implementing the interface does not guarantee 
-the underlying chat client supports SO, which undermines the core idea of using interface detection to determine SO capability.
-
-Additionally, to allow users to access interface methods on decorated agents, all decorators must implement `ITypedAIAgent`. This makes it difficult for users to 
-determine whether the underlying agent actually supports SO, further weakening the purpose of this approach.
-
-Furthermore, users would have to probe the agent type to check if it implements the `ITypedAIAgent` interface and cast it accordingly to access the `RunAsync<T>` methods.
-This adds friction to the user experience. A `RunAsync<T>` extension method for `AIAgent` could be provided to alleviate that.
-
-Given these drawbacks, this option is more complex to implement than the others without providing clear benefits.
-
-Decision drivers satisfied:
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-Pros:
-- Both the SO decorator and `ChatClientAgent` have compile-time access to the type `T`, allowing them to use the native `IChatClient.GetResponseAsync<T>` API, which handles primitives and collections seamlessly.
-
-Cons:
-- `ChatClientAgent` implementing `ITypedAIAgent` may be misleading when the underlying chat client does not support SO.
-- All `AIAgent` decorators must implement `ITypedAIAgent` to handle `RunAsync<T>` calls.
-- Decorators implementing the interface may mislead users into thinking the underlying agent natively supports SO.
-- Agents must implement all members of `ITypedAIAgent`, not just a core method.
-- Users must check the agent type and cast to `ITypedAIAgent` to access `RunAsync<T>`.
-
-### 4. `RunAsync<T>` as an instance method of `AIAgent` class working via the new `AgentRunOptions.ResponseFormat` property
-
-This option adds a `ResponseFormat` property of type `ChatResponseFormat` to `AgentRunOptions`. Agents that support SO check for the presence of 
-this property in the options passed to `RunAsync` to determine whether structured output is requested. If present, they use the schema from `ResponseFormat` 
-to invoke the underlying API and obtain the SO response.
-
-```csharp
-public class AgentRunOptions
-{
-    public ChatResponseFormat? ResponseFormat { get; set; }
-}
-```
-
-Additionally, a generic `RunAsync<T>` method is added to `AIAgent` that initializes the `ResponseFormat` based on the type `T` and delegates to the non-generic `RunAsync`.
-
-```csharp
-public abstract class AIAgent
-{
-	public async Task<AgentResponse<T>> RunAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        serializerOptions ??= AgentAbstractionsJsonUtilities.DefaultOptions;
-
-        var responseFormat = ChatResponseFormat.ForJsonSchema<T>(serializerOptions);
-
-        options = options?.Clone() ?? new AgentRunOptions();
-        options.ResponseFormat = responseFormat;
-
-        AgentResponse response = await this.RunAsync(messages, session, options, cancellationToken).ConfigureAwait(false);
-
-        return new AgentResponse<T>(response, serializerOptions);
-    }
-}
-```
-
-Users call the generic `RunAsync<T>` method directly on the agent:
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
-
-AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
-```
-
-Decision drivers satisfied:
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-Pros:
-- The `AIAgent.RunAsync<T>` method is easily discoverable.
-- No changes required to `AIAgent` decorators
-
-Cons:
-- Agents without native SO support will still expose `RunAsync<T>`, which may be misleading.
-- `ChatClientAgent` exposing `RunAsync<T>` may be misleading when the underlying chat client does not support SO.
-
-### Decision Table
-
-|  | Option 1: Instance method + RunCoreAsync<T> | Option 2: Extension method + feature collection | Option 3: ITypedAIAgent Interface | Option 4: Instance method + AgentRunOptions.ResponseFormat |
-|---|---|---|---|---|
-| Discoverability | ✅ `RunAsync<T>` easily discoverable | ✅ `RunAsync<T>` easily discoverable | ❌ Requires type check and cast | ✅ `RunAsync<T>` easily discoverable |
-| Decorator changes | ❌ All decorators must override `RunCoreAsync<T>` | ✅ No changes required | ❌ All decorators must implement `ITypedAIAgent` | ✅ No changes required to decorators |
-| Primitives/collections handling | ✅ Native support via `IChatClient.GetResponseAsync<T>` | ❌ Must wrap/unwrap internally | ✅ Native support via `IChatClient.GetResponseAsync<T>` | ❌ Must wrap/unwrap internally |
-| Misleading API exposure | ❌ Agents without SO still expose `RunAsync<T>` | ❌ Agents without SO still expose `RunAsync<T>` | ❌ Interface on `ChatClientAgent` may be misleading | ❌ Agents without SO still expose `RunAsync<T>` |
-| Implementation burden | ❌ Decorators must override method | ❌ Must handle schema wrapping | ❌ Agents must implement all interface members | ✅ Delegates to existing `RunAsync` via `ResponseFormat` |
-
-## Cross-Cutting Aspects
-
-1. **The `useJsonSchemaResponseFormat` parameter**: The `ChatClientAgent.RunAsync<T>` method has this parameter to enable structured output on LLMs that do not natively support it.
-  It works by adding a user message like "Respond with a JSON value conforming to the following schema:" along with the JSON schema. However, this approach has not been reliable historically. The recommendation is not to carry this parameter forward, regardless of which option is chosen.
-
-2. **Primitives and array types handling**: There are a few options for how primitive and array types can be handled in the Agent Framework:
-
-   1. **Never wrap**, regardless of whether the schema is provided via `ResponseFormat` or `RunAsync<T>`.
-       - Pro: No changes needed; user has full control.
-       - Pro: No issues with unwrapping in streaming scenarios.
-       - Con: User must wrap manually.
-
-   2. **Always wrap**, regardless of whether the schema is provided via `ResponseFormat` or `RunAsync<T>`.
-       - Pro: Consistent wrapping behavior; no manual wrapping needed.
-       - Con: Inconsistent unwrapping behavior; it may be unexpected to have SO result wrapped when schema is provided via `ResponseFormat`.
-       - Con: Impossible to know if SO result is wrapped to unwrap it in streaming scenarios.
-
-   3. **Wrap only for `RunAsync<T>`** and do not wrap the schema provided via `ResponseFormat`.
-       - Pro: No unexpectedly wrapped result when schema is provided via `ResponseFormat`.
-       - Pro: Solves the problem with unwrapping in streaming scenarios.
-
-   4. **User decides** whether to wrap schema provided via `ResponseFormat` using a new `wrapPrimitivesAndArrays` property of `ChatResponseFormatJson`. For SO provided via `RunAsync<T>`, AF always wraps.
-       - Pro: No manual wrapping needed; just flip a switch.
-       - Pro: Solves the problem with unwrapping in streaming scenarios.
-       - Con: Extends the public API surface.
-
-3. **Structured output for agents without native SO support**: Some AI agents in AF do not support structured output natively. This is either because it is not part of the protocol (e.g., A2A agent) or because the agents use LLMs without structured output capabilities.
-   To address this gap, AF can provide the `StructuredOutputAgent` decorator. This decorator wraps any `AIAgent` and adds structured output support by obtaining the text response from the decorated agent and delegating it to a configured chat client for JSON transformation.
-   
-   ```csharp
-   public class StructuredOutputAgent : DelegatingAIAgent
-   {
-        private readonly IChatClient _chatClient;
-
-        public StructuredOutputAgent(AIAgent innerAgent, IChatClient chatClient)
-            : base(innerAgent)
-        {
-            this._chatClient = Throw.IfNull(chatClient);
-        }
-
-        protected override async Task<AgentResponse<T>> RunCoreAsync<T>(
-            IEnumerable<ChatMessage> messages,
-            AgentSession? session = null,
-            JsonSerializerOptions? serializerOptions = null,
-            AgentRunOptions? options = null,
-            CancellationToken cancellationToken = default)
-        {
-            // Run the inner agent first, to get back the text response we want to convert.
-            var textResponse = await this.InnerAgent.RunAsync(messages, session, options, cancellationToken).ConfigureAwait(false);
-
-            // Invoke the chat client to transform the text output into structured data.
-            ChatResponse<T> soResponse = await this._chatClient.GetResponseAsync<T>(
-                messages:
-                [
-                    new ChatMessage(ChatRole.System, "You are a json expert and when provided with any text, will convert it to the requested json format."),
-                    new ChatMessage(ChatRole.User, textResponse.Text)
-                ],
-                serializerOptions: serializerOptions ?? AgentJsonUtilities.DefaultOptions,
-                cancellationToken: cancellationToken).ConfigureAwait(false);
-
-            return new StructuredOutputAgentResponse(soResponse, textResponse);
-        }
-   }
-   ```
-
-   The decorator preserves the original response from the decorated agent and surfaces it via the `OriginalResponse` property on the returned `StructuredOutputAgentResponse`.
-   This allows users to access both the original unstructured response and the new structured response when using this decorator.
-   ```csharp
-   public class StructuredOutputAgentResponse : AgentResponse
-   {
-       internal StructuredOutputAgentResponse(ChatResponse chatResponse, AgentResponse agentResponse) : base(chatResponse)
-       {
-           this.OriginalResponse = agentResponse;
-       }
-       
-       public AgentResponse OriginalResponse { get; }
-    }
-   ```
-   
-   The decorator can be registered during the agent configuration step using the `UseStructuredOutput` extension method on `AIAgentBuilder`.
-
-   ```csharp
-   IChatClient meaiChatClient = chatClient.AsIChatClient();
-
-   AIAgent baseAgent = meaiChatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
-
-   // Register the StructuredOutputAgent decorator during agent building
-   AIAgent agent = baseAgent
-       .AsBuilder()
-       .UseStructuredOutput(meaiChatClient)
-       .Build();
-
-   AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
-
-   Console.WriteLine($"Name: {response.Result.Name}");
-   Console.WriteLine($"Age: {response.Result.Age}");
-   Console.WriteLine($"Occupation: {response.Result.Occupation}");
-   
-   var originalResponse = ((StructuredOutputAgentResponse)response.RawRepresentation!).OriginalResponse;
-   Console.WriteLine($"Original unstructured response: {originalResponse.Text}");
-
-   ```
-
-## Decision Outcome
-
-It was decided to keep both approaches for structured output - via `ResponseFormat` and via `RunAsync<T>` since they serve different scenarios and use cases.
-
-For the `RunAsync<T>` approach, option 4 was selected, which adds a generic `RunAsync<T>` method to `AIAgent` that works via the new `AgentRunOptions.ResponseFormat` property.
-This was chosen for its simplicity and because no changes are required to existing `AIAgent` decorators.
-
-For cross-cutting aspects, the `useJsonSchemaResponseFormat` parameter will not be carried forward due to reliability issues.
-
-For handling primitives and array types, option 3 was selected: wrap only for `RunAsync<T>` and do not wrap the schema provided via `ResponseFormat`.
-This avoids the issues described in the Approach 1 section note.
-
-Finally, it was decided not to include the `StructuredOutputAgent` decorator in the framework, since the reliability of producing structured output via an additional
-LLM call may not be sufficient for all scenarios. Instead, this pattern is provided as a sample to demonstrate how structured output can be achieved for agents without native support,
-giving users a reference implementation they can adapt to their own requirements.

docs/decisions/0017-agent-additional-properties.md

@@ -1,211 +0,0 @@
----
-status: accepted
-contact: westey-m
-date: 2026-02-24
-deciders: sergeymenshykh, markwallace, rbarreto, dmytrostruk, westey-m, eavanvalkenburg, stephentoub, lokitoth, alliscode, taochenosu, moonbox3
-consulted:
-informed:
----
-
-# AdditionalProperties for AIAgent and AgentSession
-
-## Context and Problem Statement
-
-The `AIAgent` base class currently exposes `Id`, `Name`, and `Description` as its core metadata properties, and `AgentSession` exposes only a `StateBag` property.
-Neither type has a mechanism for attaching arbitrary metadata, such as protocol-specific descriptors (e.g., A2A agent cards), hosting attributes, session-level tags, or custom user-defined metadata for discovery and routing.
-
-Other types in the framework already carry `AdditionalProperties` — notably `AgentRunOptions`, `AgentResponse`, and `AgentResponseUpdate` — all using `AdditionalPropertiesDictionary` from `Microsoft.Extensions.AI`.
-Adding a similar property to `AIAgent` and `AgentSession` would give both types a consistent, extensible metadata surface.
-
-Related: [Work Item #2133](https://github.com/microsoft/agent-framework/issues/2133)
-
-## Decision Drivers
-
-- **Consistency**: Other core types (`AgentRunOptions`, `AgentResponse`, `AgentResponseUpdate`) already expose `AdditionalProperties`. `AIAgent` and `AgentSession` are the major abstractions that lack this.
-- **Extensibility**: Hosting libraries, protocol adapters (A2A, AG-UI), and discovery mechanisms need a place to attach agent-level and session-level metadata without subclassing.
-- **Simplicity**: The solution should be easy to understand and use; avoid over-engineering.
-- **Minimal breaking change**: The addition should not require changes to existing agent implementations.
-- **Clear semantics**: Users should understand what `AdditionalProperties` on an agent or session means and how it differs from `AdditionalProperties` on `AgentRunOptions`.
-
-## Considered Options
-
-### Surface Area
-
-- **Option A**: Public get-only property, auto-initialized (`AdditionalPropertiesDictionary AdditionalProperties { get; } = new()`) on both `AIAgent` and `AgentSession`
-- **Option B**: Public get/set nullable property (`AdditionalPropertiesDictionary? AdditionalProperties { get; set; }`) on both `AIAgent` and `AgentSession`
-- **Option C**: Constructor-injected dictionary with public get-only accessor on both `AIAgent` and `AgentSession`
-- **Option D**: External container/wrapper object — metadata lives outside `AIAgent` and `AgentSession`; no changes to the base classes
-
-### Semantics
-
-- **Option 1**: Metadata only — describes the agent or session; not propagated when calling `IChatClient`
-- **Option 2**: Passed down the stack — merged into `ChatOptions.AdditionalProperties` during `ChatClientAgent` runs
-
-## Decision Outcome
-
-The chosen option is **Option D + Option 1**: an external container/wrapper object, used purely as metadata.
-
-### Consequences
-
-- Good, because `AIAgent` and `AgentSession` remain unchanged, avoiding any increase to the core framework surface area while still enabling extensible metadata.
-- Good, because an external wrapper (owned by hosting/protocol libraries or user code, not the `AIAgent` / `AgentSession` base classes) can internally use `AdditionalPropertiesDictionary` to stay consistent with existing patterns on `AgentRunOptions`, `AgentResponse`, and `AgentResponseUpdate`.
-- Good, because metadata-only semantics keep a clean separation from per-run extensibility (`AgentRunOptions.AdditionalProperties`) and avoid unexpected side effects during agent execution.
-- Good, because no additional allocation occurs on `AIAgent` or `AgentSession` when no metadata is needed; external wrappers can be created only when metadata is required.
-- Bad, because callers and libraries must manage and pass around both the agent/session instance and its associated metadata wrapper, keeping them correctly associated.
-- Bad, because different hosting or protocol layers may define their own wrapper types, which can fragment the ecosystem unless conventions are agreed upon.
-
-## Pros and Cons of the Options
-
-### Option A — Public get-only property, auto-initialized
-
-The property is always non-null and ready to use. Users add metadata after construction.
-
-```csharp
-public abstract partial class AIAgent
-{
-    public AdditionalPropertiesDictionary AdditionalProperties { get; } = new();
-}
-
-public abstract partial class AgentSession
-{
-    public AdditionalPropertiesDictionary AdditionalProperties { get; } = new();
-}
-
-// Usage
-agent.AdditionalProperties["protocol"] = "A2A";
-agent.AdditionalProperties.Add<MyAgentCardInfo>(cardInfo);
-session.AdditionalProperties["tenant"] = tenantId;
-```
-
-- Good, because users never encounter `null` — no defensive null checks needed.
-- Good, because the dictionary reference cannot be replaced, preventing accidental data loss.
-- Good, because it is the simplest API surface to use.
-- Neutral, because it always allocates, even when no metadata is needed. The allocation cost is negligible.
-- Bad, because it cannot be set at construction time as a single object (users must populate it post-construction).
-
-### Option B — Public get/set nullable property
-
-Matches the existing pattern on `AgentRunOptions`, `AgentResponse`, and `AgentResponseUpdate`.
-
-```csharp
-public abstract partial class AIAgent
-{
-    public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
-}
-
-public abstract partial class AgentSession
-{
-    public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
-}
-
-// Usage
-agent.AdditionalProperties ??= new();
-agent.AdditionalProperties["protocol"] = "A2A";
-session.AdditionalProperties ??= new();
-session.AdditionalProperties["tenant"] = tenantId;
-```
-
-- Good, because it is consistent with the existing `AdditionalProperties` pattern on `AgentRunOptions` and `AgentResponse`.
-- Good, because it avoids allocation when no metadata is needed.
-- Bad, because every consumer must null-check before reading or writing.
-- Bad, because the entire dictionary can be replaced, risking accidental loss of metadata set by other components (e.g., a hosting library sets metadata, then user code replaces the dictionary).
-
-### Option C — Constructor-injected with public get
-
-The dictionary is provided at construction time and exposed as get-only.
-
-```csharp
-public abstract partial class AIAgent
-{
-    public AdditionalPropertiesDictionary AdditionalProperties { get; }
-
-    protected AIAgent(AdditionalPropertiesDictionary? additionalProperties = null)
-    {
-        this.AdditionalProperties = additionalProperties ?? new();
-    }
-}
-
-public abstract partial class AgentSession
-{
-    public AdditionalPropertiesDictionary AdditionalProperties { get; }
-
-    protected AgentSession(AdditionalPropertiesDictionary? additionalProperties = null)
-    {
-        this.AdditionalProperties = additionalProperties ?? new();
-    }
-}
-```
-
-- Good, because an agent's metadata can be established before any code runs against it.
-- Bad, because `AdditionalPropertiesDictionary` has no read-only variant, so the constructor-injection pattern gives a false sense of immutability — callers can still mutate the dictionary contents after construction.
-- Bad, because it requires adding a constructor parameter to the abstract base classes, which is a source-breaking change for all existing `AIAgent` and `AgentSession` subclasses (even with a default value, it changes the constructor signature that derived classes chain to).
-- Bad, because it is more complex with little practical benefit over Option A, since post-construction mutation is equally possible.
-
-### Option D — External container/wrapper object
-
-Rather than adding `AdditionalProperties` to `AIAgent` or `AgentSession`, users wrap the agent or session in a container object that carries both the instance and any associated metadata. No changes to the base classes are required.
-
-```csharp
-public class AgentWithMetadata
-{
-    public required AIAgent Agent { get; init; }
-    public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
-}
-
-public class SessionWithMetadata
-{
-    public required AgentSession Session { get; init; }
-    public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
-}
-
-// Usage
-var wrapper = new AgentWithMetadata
-{
-    Agent = myAgent,
-    AdditionalProperties = new() { ["protocol"] = "A2A" }
-};
-```
-
-- Good, because it requires no changes to `AIAgent` or `AgentSession`, avoiding any risk of breaking existing implementations.
-- Good, because metadata is clearly external to the agent and session, eliminating any ambiguity about whether it might be passed down the execution stack.
-- Good, because the container pattern gives the user full control over the metadata lifecycle and serialization.
-- Bad, because it is not discoverable — users must know about the container convention; there is no built-in API surface guiding them.
-
-### Option 1 — Metadata only
-
-`AdditionalProperties` on `AIAgent` and `AgentSession` is descriptive metadata. It is **not** automatically propagated when the agent calls downstream services such as `IChatClient`.
-
-- Good, because it keeps a clean separation of concerns: agent/session-level metadata vs. per-run options.
-- Good, because it avoids unintended side effects — metadata added for discovery or hosting won't leak into LLM requests.
-- Good, because per-run extensibility is already served by `AgentRunOptions.AdditionalProperties` (see [ADR 0014](0014-feature-collections.md)), so there is no gap.
-- Neutral, because users who want to pass agent metadata to the chat client can still do so manually via `AgentRunOptions`.
-
-### Option 2 — Passed down the stack
-
-`AdditionalProperties` on `AIAgent` and `AgentSession` are automatically merged into `ChatOptions.AdditionalProperties` (or similar) when `ChatClientAgent` invokes the underlying `IChatClient`.
-
-- Good, because it provides an automatic way to send agent-level configuration to the LLM provider.
-- Bad, because it conflates metadata (describing the agent) with operational parameters (controlling LLM behavior), leading to potential confusion.
-- Bad, because it risks leaking unrelated metadata into LLM calls (e.g., hosting tags, discovery URLs).
-- Bad, because it would be `ChatClientAgent`-specific behavior on a base-class property, creating inconsistency for non-`ChatClientAgent` implementations.
-- Bad, because it duplicates the purpose of `AgentRunOptions.AdditionalProperties`, which already serves as the per-run extensibility point for passing data down the stack.
-
-## Serialization Considerations
-
-`AIAgent` instances are not typically serialized, so `AdditionalProperties` on `AIAgent` does not raise serialization concerns.
-
-`AgentSession` instances, however, are routinely serialized and deserialized — for example, to persist conversation state across application restarts. Adding `AdditionalProperties` to `AgentSession` introduces a serialization challenge: `AdditionalPropertiesDictionary` is a `Dictionary<string, object?>`, and `object?` values do not carry enough type information for the JSON deserializer to reconstruct the original CLR types.
-
-### Default behavior — JsonElement round-tripping
-
-By default, when an `AgentSession` with `AdditionalProperties` is serialized and later deserialized, any complex objects stored as values in the dictionary will be deserialized as `JsonElement` rather than their original types. This is the same behavior exhibited by `ChatMessage.AdditionalProperties` and other `AdditionalPropertiesDictionary` usages in `Microsoft.Extensions.AI`, and is the approach we will follow.
-
-### Custom serialization via JsonSerializerOptions
-
-`AIAgent.SerializeSessionAsync` and `AIAgent.DeserializeSessionAsync` already accept an optional `JsonSerializerOptions` parameter. Users who need strongly-typed round-tripping of `AdditionalProperties` values can supply custom options with appropriate converters or type info resolvers. This is non-trivial to implement but provides full control over deserialization behavior when needed.
-
-## More Information
-
-- [ADR 0014 — Feature Collections](0014-feature-collections.md) established that `AdditionalProperties` on `AgentRunOptions` serves as the per-run extensibility mechanism. The proposed agent-level and session-level properties serve a complementary, distinct purpose: static metadata describing the agent or session itself.
-- `AdditionalPropertiesDictionary` is defined in `Microsoft.Extensions.AI` and is already a dependency of `Microsoft.Agents.AI.Abstractions`. No new package references are needed.
-- Type-safe access is available via the existing `AdditionalPropertiesExtensions` helper methods (`Add<T>`, `TryGetValue<T>`, `Contains<T>`, `Remove<T>`), which use `typeof(T).FullName` as the dictionary key.

docs/decisions/0018-agentthread-serialization.md

@@ -1,163 +0,0 @@
----
-# These are optional elements. Feel free to remove any of them.
-status: accepted
-contact: westey-m
-date: 2026-02-25
-deciders: sergeymenshykh, markwallace, rbarreto, dmytrostruk, westey-m, eavanvalkenburg, stephentoub
-consulted: 
-informed:
----
-
-# AgentSession serialization
-
-## Context and Problem Statement
-
-Serializing AgentSessions is done today by calling SerializeSession on the AIAgent instance and deserialization
-is done via the DeserializeSession method on the AIAgent instance.
-
-This approach has some drawbacks:
-
-1. It requires each AgentSession implementation to implement its own serialization logic. This can lead to inconsistencies and errors if not done correctly.
-1. It means that only one serialization format can be supported at a time. If we want to support multiple formats (e.g., JSON, XML, binary), we would need to implement separate serialization logic for each format.
-1. It is not possible to serialize and deserialize lists of AgentSessions, since each need to be handled individually.
-1. Users may not realise that they need to call these specific methods to serialize/deserialize AgentSessions.
-
-The reason why this approach was chosen initially is that AgentSessions may have behaviors that are attached to them and only the agent knows what behaviors to attach.
-These behaviors also have their own state that are attached to the AgentSession.
-The behaviors may have references to SDKs or other resources that cannot be created via standard deserialization mechanisms.
-E.g. an AgentSession may have a custom ChatMessageStore that knows how to store chat history in a specific storage backend and has a reference to the SDK client for that backend.
-When deserializing the AgentSession, we need to make sure that the ChatMessageStore is created with the correct SDK client.
-
-## Decision Drivers
-
-- A. Ability to continue to support custom behaviors (AIContextProviders / ChatHistoryProviders).
-- B. Ability to serialize and deserialize AgentSessions via standard serialization mechanisms, e.g. JsonSerializer.Serialize and JsonSerializer.Deserialize.
-- C. Ability for the caller to access custom providers.
-
-## Considered Options
-
-- Option 1: Separate state from behavior, serialize state only and re-attach behavior on first usage
-- Option 2: Separate state from behavior, and only have state on AgentSession
-- Option 3: Keep the current approach of custom Serialize/Deserialize methods
-
-### Option 1: Separate state from behavior, serialize state only and re-attach behavior on first usage
-
-Decision Drivers satisfied: A, B and C (C only partially)
-
-Have separate properties on the AgentSession for state and behavior and mark the behavior property with [JsonIgnore].
-After deserializing the AgentSession, the behavior is null and when the AgentSession is first used by the Agent, the behavior is created and attached to the AgentSession.
-
-This requires polymorphic deserialization to be supported, so that the correct AgentSession subclass and the correct behavior state is created during deserialization.
-Since the implementations for AgentSessions and their behaviors are not all known at compile time, we need a way to register custom AgentSession types and their corresponding behavior types for serialization with System.Text.Json on our JsonUtilities helpers.
-
-A drawback of this approach is that the AgentSession is in an incomplete state after deserialization until it is first used,
-so if a user was to call `GetService<MyBehavior>()` on the AgentSession before it is used by the Agent, it would return null.
-
-Behaviors like ChatMessageStore and AIContextProviders would need to change to support taking state as input and exposing state publicly.
-
-```csharp
-public class ChatClientAgentSession
-{
-    ...
-    public ChatMessageStoreState ChatMessageStoreState { get; }
-    public ChatMessageStore? ChatMessageStore { get; }
-    ...
-}
-
-[JsonPolymorphic(TypeDiscriminatorPropertyName = "$type")]
-[JsonDerivedType(typeof(InMemoryChatMessageStoreState), nameof(InMemoryChatMessageStoreState))]
-public abstract class ChatMessageStoreState
-{
-}
-public class InMemoryChatMessageStoreState : ChatMessageStoreState
-{
-    public IList<ChatMessage> Messages { get; set; } = [];
-}
-
-public abstract class ChatMessageStore<TState>
-    where TState : ChatMessageStoreState
-{
-    ...
-    public abstract TState State { get; }
-    ...
-}
-
-public sealed class InMemoryChatMessageStore : ChatMessageStore<InMemoryChatMessageStoreState>, IList<ChatMessage>
-{
-    private readonly InMemoryChatMessageStoreState _state;
-
-    public InMemoryChatMessageStore(InMemoryChatMessageStoreState? state)
-    {
-        this._state = state ?? new InMemoryChatMessageStoreState();
-    }
-
-    public override InMemoryChatMessageStoreState State => this._state;
-
-    ...
-}
-```
-
-ChatClientAgent factories would need to change to support creating behaviors based on state:
-
-```csharp
-    public Func<ChatMessageStoreFactoryContext, ChatMessageStore>? ChatMessageStoreFactory { get; set; }
-
-    public class ChatMessageStoreFactoryContext
-    {
-        public ChatMessageStoreState? State { get; set; }
-    }
-```
-
-The run behavior of the ChatClientAgent would be as follows:
-
-1. If an AgentSession is provided, check if the ChatMessageStore property is null.
-1. If it is, check if the ChatMessageStoreState property is null.
-    1. If ChatMessageStoreState is null, check if there is a provided ChatMessageStoreFactory.
-        1. If there is, call it with a ChatMessageStoreFactoryContext containing null State to create a default ChatMessageStore behavior, and update the AgentSession with the created behavior and its state.
-        2. If there is not, create a default InMemoryChatMessageStore behavior, and update the AgentSession with the created behavior and its state.
-    1. If ChatMessageStoreState is not null, check if there is a provided ChatMessageStoreFactory.
-        1. If there is, call it with a ChatMessageStoreFactoryContext containing the State to create a ChatMessageStore behavior based on the state.
-        2. If there is not, create an InMemoryChatMessageStore behavior based on the State.
-
-### Option 2: Separate state from behavior, and only have state on AgentSession
-
-Decision Drivers satisfied: A, B and C.
-
-This is similar to Option 1 but instead of having a behavior property on the AgentSession, we only have a StateBag property on the AgentSession.
-Behaviors really make more sense to live with the agent rather than the Session, but state should live on the session.
-When the AgentSession is used by the Agent, the Agent runs the behaviors against the Session, and the behavior stores it's state on the Session StateBag.
-
-This means that users are unable to access the behavior from the AgentSession, e.g. via `AgentSession.GetService<TBehavior>()`.
-
-However, the behaviors can be public properties on the Agent or can be retrieved from the agent via `AIAgent.GetService<MyAIContextProvider>()`.
-
-```csharp
-public class AgentSession
-{
-    ...
-    public AgentSessionStateBag StateBag { get; protected set; } = new();
-    ...
-}
-```
-
-### Option 3: Keep the current approach of custom Serialize/Deserialize methods
-
-Decision Drivers satisfied: A and C
-
-This option keeps the current approach of having custom Serialize/Deserialize methods on the AgentSession and AIAgent.
-
-## Decision Outcome
-
-Chosen option:
-
-**Option 2** — separate state from behavior, with only state on the AgentSession — because it satisfies all decision drivers and provides the cleanest separation of concerns. Since not all AgentSession implementations have yet been cleanly separated from their behaviors, AIAgent.SerializeSession and AIAgent.DeserializeSession is kept for the time being, but most session types can be serialized and deserialized directly using JsonSerializer.
-
-### Consequences
-
-- Good, because providers are fully stateless — the same provider instance works correctly across any number of concurrent sessions without risk of state leakage.
-- Good, because `AgentSession` can be serialized and deserialized with standard `System.Text.Json` mechanisms, satisfying decision driver B.
-- Good, because the generic `StateBag` is extensible — new providers can store arbitrary state without requiring changes to the session class.
-- Good, because users can access providers via the agent (e.g. `agent.GetService<InMemoryChatHistoryProvider>()`) satisfying decision driver C.
-- Good, because sessions are always in a complete and valid state after deserialization — there is no "incomplete until first use" problem as in Option 1.
-- Neutral, because providers cannot be accessed directly from the session; callers must go through the agent. This is a minor usability trade-off but keeps the session focused on state only.
-- Bad, because each provider must be disciplined about using `ProviderSessionState<T>` and not storing session-specific data in instance fields. This is a correctness concern for custom provider implementers.

docs/decisions/0020-foundry-agent-type-naming.md

@@ -1,125 +0,0 @@
----
-status: accepted
-contact: rogerbarreto
-date: 2026-03-06
-deciders: rogerbarreto, alliscode
-consulted: ""
-informed: ""
----
-
-# Foundry agent surface stays centered on `ChatClientAgent`
-
-## Context
-
-The Microsoft Foundry integration exposes two distinct usage patterns:
-
-1. Direct Responses usage, where callers provide model, instructions, and tools at runtime.
-2. Server-side versioned agents, where callers create and manage `AgentVersion` resources through `AIProjectClient.Agents`.
-
-We briefly explored adding public wrapper types such as `FoundryAgent`, `FoundryVersionedAgent`, and `FoundryResponsesChatClient` to make those paths feel more specialized. That direction created extra public types, duplicated existing `ChatClientAgent` behavior, and pushed samples toward compatibility helpers instead of the native Azure SDK flow.
-
-## Decision
-
-Keep the public surface centered on `ChatClientAgent`.
-
-- Direct Responses scenarios use `AIProjectClient.AsAIAgent(...)`.
-- Server-side versioned scenarios use native `AIProjectClient.Agents` APIs to create or retrieve agent resources, then wrap `AgentRecord` or `AgentVersion` with `AIProjectClient.AsAIAgent(...)`.
-- Compatibility helpers such as `AIProjectClient.CreateAIAgentAsync(...)` and `AIProjectClient.GetAIAgentAsync(...)` remain only as obsolete migration shims.
-- Public wrapper types `FoundryAgent`, `FoundryVersionedAgent`, `FoundryResponsesChatClient`, and `FoundryResponsesChatClientAgent` are not part of the chosen direction.
-
-## Why
-
-- `ChatClientAgent` is already the framework abstraction used everywhere else.
-- `AIProjectClient` is the native Azure SDK entry point for versioned agent lifecycle operations.
-- A single agent abstraction avoids parallel type hierarchies for the same backend.
-- Samples become clearer when they show either:
-  - direct Responses construction via `AIProjectClient.AsAIAgent(...)`, or
-  - native Foundry resource management via `AIProjectClient.Agents`.
-
-## Consequences
-
-### Direct Responses path
-
-Use the convenience overloads on `AIProjectClient`:
-
-```csharp
-AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
-
-ChatClientAgent agent = aiProjectClient.AsAIAgent(
-    model: deploymentName,
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-```
-
-Or use composed `ChatClientAgent`
-
-```csharp
-ProjectResponsesClient projectResponsesClient = new(new Uri(endpoint), new DefaultAzureCredential(), new AgentReference($"model:{deploymentName}"));
-
-ChatClientAgent agent = new(
-    chatClient: projectResponsesClient.AsIChatClient(),
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-```
-
-This path is code-first and does not create a persistent server-side agent.
-
-### Versioned agent path
-
-Use the convenience overloads on `AIProjectClient`:
-
-```csharp
-AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
-
-AgentVersion version = await aiProjectClient.Agents.CreateAgentVersionAsync(
-    "JokerAgent",
-    new AgentVersionCreationOptions(
-        new PromptAgentDefinition(deploymentName)
-        {
-            Instructions = "You are good at telling jokes."
-        }));
-
-ChatClientAgent agent = aiProjectClient.AsAIAgent(version);
-```
-
-Or use composed `ChatClientAgent`
-
-```csharp
-AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
-
-AgentVersion version = await aiProjectClient.Agents.CreateAgentVersionAsync(
-    "JokerAgent",
-    new AgentVersionCreationOptions(
-        new PromptAgentDefinition(deploymentName)
-        {
-            Instructions = "You are good at telling jokes."
-        }));
-
-ProjectResponsesClient projectResponsesClient = aiProjectClient
-    .GetProjectOpenAIClient()
-    .GetProjectResponsesClientForAgent(new AgentReference(version.Name, version.Version));
-
-ChatClientAgent agent = new(
-    chatClient: projectResponsesClient.AsIChatClient(),
-    name: "JokerAgent");
-```
-
-### Samples
-
-- `FoundryAgents/` samples show the direct Responses path with `AIProjectClient.AsAIAgent(...)`.
-- `FoundryVersionedAgents/` samples should show native `AIProjectClient.Agents` create/get/delete flows plus `AsAIAgent(...)`.
-
-### Compatibility APIs
-
-Obsolete helper extensions remain only to ease migration of existing code. New samples and new guidance should not be written against them.
-
-## Rejected direction
-
-Do not introduce or preserve separate public wrapper types whose main purpose is to forward to `ChatClientAgent` while carrying Foundry-specific naming.
-
-That approach:
-
-- duplicates lifecycle concepts already present on `AIProjectClient`,
-- fragments the public API,
-- complicates samples and docs,
-- and makes migration harder by encouraging wrapper-specific affordances.

docs/decisions/0021-agent-skills-design.md

@@ -1,960 +0,0 @@
-status: proposed
-date: 2026-03-23
-contact: sergeymenshykh
-deciders: rbarreto, westey-m, eavanvalkenburg
----
-
-# Agent Skills: Multi-Source Architecture
-
-## Context and Problem Statement
-
-The Agent Framework needs a skills system that lets agents discover and use domain-specific knowledge, reference documents, and executable scripts. Skills can originate from different sources — filesystem directories (SKILL.md files), inline C# code, or reusable class libraries — and the framework must support all three uniformly while allowing extensibility, composition, and filtering.
-
-## Decision Drivers
-
-- Skills must be definable from multiple sources: filesystem, inline code, reusable classes, etc
-- Common abstractions are needed so the provider and builder work uniformly regardless of skill origin
-- File-based scripts must support user-defined executors, enabling custom runtimes and languages; code/class-based scripts execute in-process as C# delegates
-- Skills must be filterable so consumers can include or exclude specific skills based on defined criteria
-- Multiple skill sources must be composable into a single provider
-- It must be possible to add custom skill sources (e.g., databases, REST APIs, package registries) by implementing a common abstraction
-
-## Architecture
-
-### Model-Facing Tools
-
-Skills are presented to the model as up to three tools that progressively disclose skill content. The system prompt lists available skill names and descriptions; the model then calls these tools on demand:
-
-- **`load_skill(skillName)`** — returns the full skill body (instructions, listed resources, listed scripts)
-- **`read_skill_resource(skillName, resourceName)`** — reads a supplementary resource (file-based or code-defined) associated with a skill
-- **`run_skill_script(skillName, scriptName, arguments?)`** — executes a script associated with a skill; only registered when at least one skill contains scripts
-
-Each tool delegates to the corresponding method on the resolved `AgentSkill` — calling `Resource.ReadAsync()` or `Script.RunAsync()` respectively.
-
-If skills have no scripts defined, the `run_skill_script` tool is **not advertised** to the model and instructions related to script execution are **not included** in the default skills instructions.
-
-### Abstract Base Types
-
-The architecture defines four abstract base types that all skill variants implement:
-
-```csharp
-public abstract class AgentSkill
-{
-    public abstract AgentSkillFrontmatter Frontmatter { get; }
-    public abstract string Content { get; }
-    public abstract IReadOnlyList<AgentSkillResource>? Resources { get; }
-    public abstract IReadOnlyList<AgentSkillScript>? Scripts { get; }
-}
-
-public abstract class AgentSkillResource
-{
-    public string Name { get; }
-    public string? Description { get; }
-    public abstract Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default);
-}
-
-public abstract class AgentSkillScript
-{
-    public string Name { get; }
-    public string? Description { get; }
-    public abstract Task<object?> RunAsync(AgentSkill skill, AIFunctionArguments arguments, CancellationToken cancellationToken = default);
-}
-
-public abstract class AgentSkillsSource
-{
-    public abstract Task<IList<AgentSkill>> GetSkillsAsync(CancellationToken cancellationToken = default);
-}
-```
-
-Skill metadata is captured via `AgentSkillFrontmatter`:
-
-```csharp
-public sealed class AgentSkillFrontmatter
-{
-    public AgentSkillFrontmatter(string name, string description) { ... }
-
-    public string Name { get; }
-    public string Description { get; }
-    public string? License { get; set; }
-    public string? Compatibility { get; set; }
-    public string? AllowedTools { get; set; }
-    public AdditionalPropertiesDictionary? Metadata { get; set; }
-}
-```
-
-The type hierarchy at a glance:
-
-```
-AgentSkill (abstract)                   AgentSkillsSource (abstract)
-├── AgentFileSkill                      ├── AgentFileSkillsSource          (public)
-└── [Programmatic]                      ├── AgentInMemorySkillsSource      (public)
-    ├── AgentInlineSkill                ├── AggregatingAgentSkillsSource     (public)
-    └── AgentClassSkill (abstract)      └── DelegatingAgentSkillsSource    (abstract, public)
-                                            ├── FilteringAgentSkillsSource  (public)
-AgentSkillResource (abstract)               ├── CachingAgentSkillsSource    (public)
-├── AgentFileSkillResource                  └── DeduplicatingAgentSkillsSource (public)
-└── AgentInlineSkillResource
-                                        AgentSkillScript (abstract)
-                                        ├── AgentFileSkillScript
-                                        └── AgentInlineSkillScript
-```
-
-There are two top-level categories of skills:
-
-1. **File-Based Skills** — discovered from `SKILL.md` files on the filesystem. Resources and scripts are files in subdirectories.
-2. **Programmatic Skills** — defined in C# code. These are further divided into:
-   - **Inline Skills** — built at runtime via the `AgentInlineSkill` class and its fluent API. Ideal for quick, agent-specific skill definitions.
-   - **Class-Based Skills** — defined as reusable C# classes that subclass `AgentClassSkill`. Ideal for packaging skills as shared libraries or NuGet packages.
-
-Both programmatic skill types use `AgentInlineSkillResource` and `AgentInlineSkillScript` for their resources and scripts. They are typically served by `AgentInMemorySkillsSource`, which accepts any `AgentSkill` and is not limited to programmatic skills.
-
-### File-Based Skills
-
-File-based skills are authored as `SKILL.md` files on disk. Resources and scripts are discovered from corresponding subfolders within the skill directory.
-
-**`AgentFileSkill`** — A filesystem-based skill discovered from a directory containing a `SKILL.md` file. Parsed from YAML frontmatter; content is the raw markdown body. Resources and scripts are discovered from files in corresponding subfolders:
-
-```csharp
-public sealed class AgentFileSkill : AgentSkill
-{
-    internal AgentFileSkill(
-        AgentSkillFrontmatter frontmatter, string content, string path,
-        IReadOnlyList<AgentSkillResource>? resources = null,
-        IReadOnlyList<AgentSkillScript>? scripts = null) { ... }
-}
-```
-
-**`AgentFileSkillResource`** — A file-based skill resource. Reads content from a file on disk relative to the skill directory:
-
-```csharp
-internal sealed class AgentFileSkillResource : AgentSkillResource
-{
-    public AgentFileSkillResource(string name, string fullPath) { ... }
-
-    public string FullPath { get; }
-
-    public override Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default)
-    {
-        return File.ReadAllTextAsync(FullPath, Encoding.UTF8, cancellationToken);
-    }
-}
-```
-
-**`AgentFileSkillScript`** — A file-based skill script that represents a script file on disk. Delegates execution to an external `AgentFileSkillScriptRunner` callback (e.g., runs Python/shell via `Process.Start`). Throws `NotSupportedException` if no executor is configured:
-
-```csharp
-public delegate Task<object?> AgentFileSkillScriptRunner(
-    AgentFileSkill skill, AgentFileSkillScript script,
-    AIFunctionArguments arguments, CancellationToken cancellationToken);
-
-public sealed class AgentFileSkillScript : AgentSkillScript
-{
-    private readonly AgentFileSkillScriptRunner _executor;
-
-    internal AgentFileSkillScript(string name, string fullPath, AgentFileSkillScriptRunner executor)
-        : base(name) { ... }
-
-    public override async Task<object?> RunAsync(AgentSkill skill, AIFunctionArguments arguments, ...)
-    {
-
-        return await _executor(fileSkill, this, arguments, cancellationToken);
-    }
-}
-```
-
-The executor can be provided at the **provider level** via `AgentSkillsProviderBuilder.UseFileScriptRunner(executor)` and optionally overridden for a **particular file skill** or for a **set of skills** at the file skill source level, giving fine-grained control over how different scripts are executed.
-
-**`AgentFileSkillsSource`** — A skill source that discovers skills from filesystem directories containing `SKILL.md` files. Recursively scans directories (max 2 levels), validates frontmatter, and enforces path traversal and symlink security checks:
-
-```csharp
-public sealed partial class AgentFileSkillsSource : AgentSkillsSource
-{
-    public AgentFileSkillsSource(
-        IEnumerable<string> skillPaths,
-        AgentFileSkillScriptRunner scriptRunner,
-        AgentFileSkillsSourceOptions? options = null,
-        ILoggerFactory? loggerFactory = null) { ... }
-}
-```
-
-**`AgentFileSkillsSourceOptions`** — Configuration options for `AgentFileSkillsSource`. Allows customizing the allowed file extensions for resources and scripts without adding constructor parameters:
-
-```csharp
-public sealed class AgentFileSkillsSourceOptions
-{
-    public IEnumerable<string>? AllowedResourceExtensions { get; set; }
-    public IEnumerable<string>? AllowedScriptExtensions { get; set; }
-}
-```
-
-**Example** — A file-based skill on disk and how it is added to a source:
-
-```
-skills/
-└── unit-converter/
-    ├── SKILL.md               # frontmatter + instructions
-    ├── resources/
-    │   └── conversion-table.csv   # discovered as a resource
-    └── scripts/
-        └── convert.py             # discovered as a script
-```
-
-```csharp
-var source = new AgentFileSkillsSource(skillPaths: ["./skills"], scriptRunner: SubprocessScriptRunner.RunAsync);
-
-var provider = new AgentSkillsProvider(source);
-
-AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
-{
-    AIContextProviders = [provider],
-});
-```
-
-### Programmatic Skills
-
-Programmatic skills are defined in C# code rather than discovered from the filesystem. There are two kinds: **inline** and **class-based**. Both use `AgentInlineSkillResource` and `AgentInlineSkillScript` for resources and scripts, and are held by a single `AgentInMemorySkillsSource`.
-
-**`AgentInMemorySkillsSource`** — A general-purpose skill source that holds any `AgentSkill` instances in memory. Although commonly used for programmatic skills (`AgentInlineSkill` and `AgentClassSkill`), it accepts any `AgentSkill` subclass and is not restricted to code-defined skills:
-
-```csharp
-public sealed class AgentInMemorySkillsSource : AgentSkillsSource
-{
-    public AgentInMemorySkillsSource(
-        IEnumerable<AgentSkill> skills,
-        ILoggerFactory? loggerFactory = null) { ... }
-}
-```
-
-#### Inline Skills
-
-Inline skills are built at runtime via the `AgentInlineSkill` class and its fluent API. They are ideal for quick, agent-specific skill definitions where a full class hierarchy would be overkill.
-
-**`AgentInlineSkill`** — A skill defined entirely in code. Resources can be static values or functions; scripts are always functions. Constructed with name, description, and instructions, then extended with resources and scripts:
-
-```csharp
-public sealed class AgentInlineSkill : AgentSkill
-{
-    public AgentInlineSkill(string name, string description, string instructions, string? license = null, string? compatibility = null, ...) { ... }
-    public AgentInlineSkill(AgentSkillFrontmatter frontmatter, string instructions) { ... }
-
-    public AgentInlineSkill AddResource(object value, string name, string? description = null);
-    public AgentInlineSkill AddResource(Delegate handler, string name, string? description = null);
-    public AgentInlineSkill AddScript(Delegate handler, string name, string? description = null);
-}
-```
-
-**`AgentInlineSkillResource`** — A skill resource that wraps a static value:
-
-```csharp
-public sealed class AgentInlineSkillResource : AgentSkillResource
-{
-    public AgentInlineSkillResource(object value, string name, string? description = null)
-        : base(name, description)
-    {
-        _value = value;
-    }
-
-    public override Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default)
-    {
-        return Task.FromResult<object?>(_value);
-    }
-}
-```
-
-**`AgentInlineSkillResource`** — A skill resource backed by a delegate. The delegate is invoked via an `AIFunction` each time `ReadAsync` is called, producing a dynamic (computed) value:
-
-```csharp
-public sealed class AgentInlineSkillResource : AgentSkillResource
-{
-    public AgentInlineSkillResource(Delegate handler, string name, string? description = null)
-        : base(name, description)
-    {
-        _function = AIFunctionFactory.Create(handler, name: name);
-    }
-
-    public override async Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default)
-    {
-        return await _function.InvokeAsync(new AIFunctionArguments() { Services = serviceProvider }, cancellationToken);
-    }
-}
-```
-
-**`AgentInlineSkillScript`** — A skill script backed by a delegate via an `AIFunction`:
-
-```csharp
-public sealed class AgentInlineSkillScript : AgentSkillScript
-{
-    private readonly AIFunction _function;
-
-    public AgentInlineSkillScript(Delegate handler, string name, string? description = null)
-        : base(name, description)
-    {
-        _function = AIFunctionFactory.Create(handler, name: name);
-    }
-
-    public JsonElement? ParametersSchema => _function.JsonSchema;
-
-    public override async Task<object?> RunAsync(AgentSkill skill, AIFunctionArguments arguments, ...)
-    {
-        return await _function.InvokeAsync(arguments, cancellationToken);
-    }
-}
-```
-
-**Example** — Creating an inline skill with a resource and script, then adding it to a source:
-
-```csharp
-var skill = new AgentInlineSkill(
-        name: "unit-converter",
-        description: "Converts between measurement units.",
-        instructions: """
-            Use this skill to convert values between metric and imperial units.
-            Refer to the conversion-table resource for supported unit pairs.
-            Run the convert script to perform conversions.
-            """
-    )
-    .AddResource("kg=2.205lb, m=3.281ft, L=0.264gal", "conversion-table", "Supported unit pairs")
-    .AddScript(Convert, "convert", "Converts a value between units");
-
-var source = new AgentInMemorySkillsSource([skill]);
-
-var provider = new AgentSkillsProvider(source);
-
-AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
-{
-    AIContextProviders = [provider],
-});
-
-static string Convert(double value, double factor)
-    => JsonSerializer.Serialize(new { result = Math.Round(value * factor, 4) });
-```
-
-#### Class-Based Skills
-
-Class-based skills are designed for packaging skills as reusable libraries. Users subclass `AgentClassSkill` and override properties. Unlike inline skills, class-based skills are self-contained, can live in shared libraries or NuGet packages, and are well-suited for dependency injection.
-
-**`AgentClassSkill`** — An abstract base class for defining skills as reusable C# classes that bundle all skill components (frontmatter, instructions, resources, scripts) together. Designed for packaging skills as distributable libraries:
-
-```csharp
-public abstract class AgentClassSkill : AgentSkill
-{
-    public abstract string Instructions { get; }
-
-    // Content is auto-synthesized from Frontmatter + Instructions + Resources + Scripts
-    public override string Content =>
-        SkillContentBuilder.BuildContent(Frontmatter.Name, Frontmatter.Description,
-            SkillContentBuilder.BuildBody(Instructions, Resources, Scripts));
-}
-```
-
-**Example** — Defining a class-based skill and adding it to a source:
-
-```csharp
-public class UnitConverterSkill : AgentClassSkill
-{
-    public override AgentSkillFrontmatter Frontmatter { get; } =
-        new("unit-converter", "Converts between measurement units.");
-
-    public override string Instructions => """
-        Use this skill to convert values between metric and imperial units.
-        Refer to the conversion-table resource for supported unit pairs.
-        Run the convert script to perform conversions.
-        """;
-
-    public override IReadOnlyList<AgentSkillResource>? Resources { get; } =
-    [
-        new AgentInlineSkillResource("kg=2.205lb, m=3.281ft", "conversion-table"),
-    ];
-
-    public override IReadOnlyList<AgentSkillScript>? Scripts { get; } =
-    [
-        new AgentInlineSkillScript(Convert, "convert"),
-    ];
-
-    private static string Convert(double value, double factor)
-        => JsonSerializer.Serialize(new { result = Math.Round(value * factor, 4) });
-}
-
-var source = new AgentInMemorySkillsSource([new UnitConverterSkill()]);
-
-var provider = new AgentSkillsProvider(source);
-
-AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
-{
-    AIContextProviders = [provider],
-});
-```
-
-## Filtering, Caching, and Deduplication
-
-The following subsections present alternative approaches for handling filtering, caching, and deduplication of skills across multiple sources.
-
-### Via Composition
-
-In this approach, the `AgentSkillsProvider` accepts a **single** `AgentSkillsSource`. Multiple sources are composed externally via an aggregate source, and cross-cutting concerns like filtering, caching, and deduplication are implemented as **source decorators** — subclasses of `DelegatingAgentSkillsSource` that intercept `GetSkillsAsync()`.
-
-**`FilteringAgentSkillsSource`** — A decorator that applies filter logic before returning results. The decorator pattern keeps filtering orthogonal to source implementations and allows composing multiple filters:
-
-```csharp
-public sealed class FilteringAgentSkillsSource : DelegatingAgentSkillsSource
-{
-    private readonly Func<AgentSkill, bool> _predicate;
-
-    public FilteringAgentSkillsSource(AgentSkillsSource innerSource, Func<AgentSkill, bool> predicate)
-        : base(innerSource)
-    {
-        _predicate = predicate;
-    }
-
-    public override async Task<IList<AgentSkill>> GetSkillsAsync(CancellationToken cancellationToken = default)
-    {
-        var skills = await this.InnerSource.GetSkillsAsync(cancellationToken);
-        return skills.Where(_predicate).ToList();
-    }
-}
-```
-
-**`CachingAgentSkillsSource`** — A decorator that caches skills after the first load, keeping the provider stateless and giving consumers control over caching granularity per source. For example, file-based skills (expensive to discover) can be cached while code-defined skills remain uncached:
-
-```csharp
-public sealed class CachingAgentSkillsSource : DelegatingAgentSkillsSource
-{
-    private IList<AgentSkill>? _cached;
-
-    public CachingAgentSkillsSource(AgentSkillsSource innerSource)
-        : base(innerSource)
-    {
-    }
-
-    public override async Task<IList<AgentSkill>> GetSkillsAsync(CancellationToken cancellationToken = default)
-    {
-        return _cached ??= await this.InnerSource.GetSkillsAsync(cancellationToken);
-    }
-}
-```
-
-**Deduplication** is similarly implemented as a decorator (`DeduplicatingAgentSkillsSource`) that deduplicates by name (case-insensitive, first-one-wins) and logs a warning for skipped duplicates.
-
-**Example** — Combining file-based and code-defined sources with filtering and caching:
-
-```csharp
-var fileSource = new CachingAgentSkillsSource(new AgentFileSkillsSource(["./skills"]));
-var codeSource = new AgentInMemorySkillsSource([myCodeSkill]);
-
-var compositeSource = new FilteringAgentSkillsSource(
-    new AggregatingAgentSkillsSource([fileSource, codeSource]),
-    filter: s => s.Frontmatter.Name != "internal");
-
-var provider = new AgentSkillsProvider(compositeSource);
-
-AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
-{
-    AIContextProviders = [provider],
-});
-```
-
-**Pros:**
-- Clean single-responsibility: the provider serves skills, sources provide them.
-- Caching, filtering, and deduplication are composable as source decorators — each concern is a separate, testable wrapper.
-
-**Cons:**
-- DI is less flexible: multiple `AgentSkillsSource` implementations registered in the container cannot be auto-injected into the provider. The consumer must manually compose them via an aggregate source.
-- Increased public API surface: requires additional public classes (aggregate source, caching decorators, filtering decorators) that consumers need to learn and use.
-
-### Via AgentSkillsProvider
-
-In this approach, the `AgentSkillsProvider` accepts **`IEnumerable<AgentSkillsSource>`** and handles aggregation, filtering, caching, and deduplication internally.
-
-The provider aggregates skills from all registered sources, deduplicates by name (case-insensitive, first-one-wins), caches the result after the first load, and optionally applies filtering via a predicate on `AgentSkillsProviderOptions`. Duplicate skill names are logged as warnings.
-
-**Example** — Registering multiple sources directly with the provider:
-
-```csharp
-// Conceptual example — in practice, use AgentSkillsProviderBuilder
-var fileSource = new AgentFileSkillsSource(["./skills"]);
-var codeSource = new AgentInMemorySkillsSource([myCodeSkill]);
-
-var provider = new AgentSkillsProvider(
-    sources: [fileSource, codeSource],
-    options: new AgentSkillsProviderOptions
-    {
-        Filter = s => s.Frontmatter.Name != "internal",
-    });
-
-AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
-{
-    AIContextProviders = [provider],
-});
-```
-
-**Pros:**
-- DI-friendly: register multiple `AgentSkillsSource` implementations in the container, and they are all auto-injected into `AgentSkillsProvider` via `IEnumerable<AgentSkillsSource>`.
-- Smaller public API surface: no need for aggregate source, caching decorators, or filtering decorator classes — these concerns are handled internally by the provider.
-
-**Cons:**
-- The provider takes on multiple responsibilities — aggregation, caching, deduplication, and filtering.
-- Less granular caching control: caching is all-or-nothing across sources rather than per-source as with decorators.
-- Less extensible: new behaviors (e.g., ordering, TTL expiration) require modifying the provider rather than adding a decorator.
-
-### Builder Pattern
-
-**`AgentSkillsProviderBuilder`** provides a fluent API for composing skills from multiple sources. The builder centralizes configuration — script executors, approval callbacks, prompt templates, and filtering — so consumers don't need to know the underlying source types.
-
-The builder internally decides how to wire up the object graph: it creates the appropriate source instances, applies caching and filtering, and returns a fully configured `AgentSkillsProvider`. This keeps the setup code concise while still allowing fine-grained control when needed.
-
-**Example** — Using the builder to combine multiple source types with configuration:
-
-```csharp
-var provider = new AgentSkillsProviderBuilder()
-    .UseFileSkill("./skills")                           // file-based source
-    .UseInlineSkills(codeSkill)                            // code-defined source
-    .UseClassSkills(new ClassSkill())                    // class-based source
-    .UseFileScriptRunner(SubprocessScriptRunner.RunAsync) // script runner
-    .UseScriptApproval()                                // optional human-in-the-loop
-    .UsePromptTemplate(customTemplate)                  // optional prompt customization
-    .UseFilter(s => s.Frontmatter.Name != "internal")   // optional skill filtering
-    .Build();
-
-AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
-{
-    AIContextProviders = [provider],
-});
-```
-
-## Adding a Custom Skill Type
-
-The skills framework is designed for extensibility. While file-based and inline skills cover common
-scenarios, you can introduce entirely new skill types by subclassing the four base classes:
-
-| Base class            | Purpose                                             |
-|-----------------------|-----------------------------------------------------|
-| `AgentSkillsSource`   | Discovers and loads skills from a particular origin  |
-| `AgentSkill`          | Holds metadata, content, resources, and scripts      |
-| `AgentSkillResource`  | Provides supplementary content to a skill            |
-| `AgentSkillScript`    | Represents an executable action within a skill       |
-
-The example below implements a **cloud-based skill type** where skills, resources, and scripts are
-all stored in and executed through a remote cloud service (e.g., Azure Blob Storage + Azure Functions).
-
-### Step 1 — Define a custom resource
-
-A `CloudSkillResource` reads resource content from a cloud storage endpoint instead of the local
-filesystem:
-
-```csharp
-/// <summary>
-/// A skill resource backed by a cloud storage endpoint.
-/// </summary>
-public sealed class CloudSkillResource : AgentSkillResource
-{
-    private readonly HttpClient _httpClient;
-
-    public CloudSkillResource(string name, Uri blobUri, HttpClient httpClient, string? description = null)
-        : base(name, description)
-    {
-        BlobUri = blobUri ?? throw new ArgumentNullException(nameof(blobUri));
-        _httpClient = httpClient ?? throw new ArgumentNullException(nameof(httpClient));
-    }
-
-    /// <summary>
-    /// Gets the URI of the cloud blob that holds this resource's content.
-    /// </summary>
-    public Uri BlobUri { get; }
-
-    /// <inheritdoc/>
-    public override async Task<object?> ReadAsync(
-        IServiceProvider? serviceProvider = null,
-        CancellationToken cancellationToken = default)
-    {
-        return await _httpClient.GetStringAsync(BlobUri, cancellationToken).ConfigureAwait(false);
-    }
-}
-```
-
-### Step 2 — Define a custom script
-
-A `CloudSkillScript` executes a script by calling a cloud function endpoint, passing arguments as
-the request body:
-
-```csharp
-/// <summary>
-/// A skill script executed via a cloud function endpoint.
-/// </summary>
-public sealed class CloudSkillScript : AgentSkillScript
-{
-    private readonly HttpClient _httpClient;
-
-    public CloudSkillScript(string name, Uri functionUri, HttpClient httpClient, string? description = null)
-        : base(name, description)
-    {
-        FunctionUri = functionUri ?? throw new ArgumentNullException(nameof(functionUri));
-        _httpClient = httpClient ?? throw new ArgumentNullException(nameof(httpClient));
-    }
-
-    /// <summary>
-    /// Gets the URI of the cloud function that runs this script.
-    /// </summary>
-    public Uri FunctionUri { get; }
-
-    /// <inheritdoc/>
-    public override async Task<object?> RunAsync(
-        AgentSkill skill,
-        AIFunctionArguments arguments,
-        CancellationToken cancellationToken = default)
-    {
-        var json = JsonSerializer.Serialize(arguments);
-        using var content = new StringContent(json, Encoding.UTF8, "application/json");
-        var response = await _httpClient.PostAsync(FunctionUri, content, cancellationToken)
-            .ConfigureAwait(false);
-        response.EnsureSuccessStatusCode();
-        return await response.Content.ReadAsStringAsync(cancellationToken).ConfigureAwait(false);
-    }
-}
-```
-
-### Step 3 — Define a custom skill
-
-A `CloudSkill` bundles cloud-specific metadata (e.g., the base endpoint) with the standard skill
-shape:
-
-```csharp
-/// <summary>
-/// An <see cref="AgentSkill"/> whose content, resources, and scripts are stored in a cloud service.
-/// </summary>
-public sealed class CloudSkill : AgentSkill
-{
-    public CloudSkill(
-        AgentSkillFrontmatter frontmatter,
-        string content,
-        Uri endpoint,
-        IReadOnlyList<AgentSkillResource>? resources = null,
-        IReadOnlyList<AgentSkillScript>? scripts = null)
-    {
-        Frontmatter = frontmatter ?? throw new ArgumentNullException(nameof(frontmatter));
-        Content = content ?? throw new ArgumentNullException(nameof(content));
-        Endpoint = endpoint ?? throw new ArgumentNullException(nameof(endpoint));
-        Resources = resources;
-        Scripts = scripts;
-    }
-
-    /// <inheritdoc/>
-    public override AgentSkillFrontmatter Frontmatter { get; }
-
-    /// <inheritdoc/>
-    public override string Content { get; }
-
-    /// <summary>
-    /// Gets the base cloud endpoint for this skill.
-    /// </summary>
-    public Uri Endpoint { get; }
-
-    /// <inheritdoc/>
-    public override IReadOnlyList<AgentSkillResource>? Resources { get; }
-
-    /// <inheritdoc/>
-    public override IReadOnlyList<AgentSkillScript>? Scripts { get; }
-}
-```
-
-### Step 4 — Define a custom source
-
-A `CloudSkillsSource` discovers skills from a cloud catalog API and constructs `CloudSkill`
-instances with their associated resources and scripts:
-
-```csharp
-/// <summary>
-/// A skill source that discovers and loads skills from a cloud catalog API.
-/// </summary>
-public sealed class CloudSkillsSource : AgentSkillsSource
-{
-    private readonly Uri _catalogUri;
-    private readonly HttpClient _httpClient;
-
-    public CloudSkillsSource(Uri catalogUri, HttpClient httpClient)
-    {
-        _catalogUri = catalogUri ?? throw new ArgumentNullException(nameof(catalogUri));
-        _httpClient = httpClient ?? throw new ArgumentNullException(nameof(httpClient));
-    }
-
-    /// <inheritdoc/>
-    public override async Task<IList<AgentSkill>> GetSkillsAsync(
-        CancellationToken cancellationToken = default)
-    {
-        // Fetch the skill catalog from the cloud service.
-        var json = await _httpClient.GetStringAsync(_catalogUri, cancellationToken)
-            .ConfigureAwait(false);
-        var catalog = JsonSerializer.Deserialize<CloudSkillCatalog>(json)!;
-
-        var skills = new List<AgentSkill>();
-
-        foreach (var entry in catalog.Skills)
-        {
-            var frontmatter = new AgentSkillFrontmatter(entry.Name, entry.Description);
-
-            // Build cloud-backed resources.
-            var resources = entry.Resources
-                .Select(r => new CloudSkillResource(r.Name, r.BlobUri, _httpClient, r.Description))
-                .ToList<AgentSkillResource>();
-
-            // Build cloud-backed scripts.
-            var scripts = entry.Scripts
-                .Select(s => new CloudSkillScript(s.Name, s.FunctionUri, _httpClient, s.Description))
-                .ToList<AgentSkillScript>();
-
-            skills.Add(new CloudSkill(frontmatter, entry.Content, entry.Endpoint, resources, scripts));
-        }
-
-        return skills;
-    }
-}
-```
-
-### Step 5 — Register with the builder
-
-Use `UseSource` to wire the custom source into the provider:
-
-```csharp
-var httpClient = new HttpClient();
-
-var provider = new AgentSkillsProviderBuilder()
-    .UseSource(new CloudSkillsSource(
-        new Uri("https://my-service.example.com/skills/catalog"),
-        httpClient))
-    // Mix with other source types if needed:
-    .UseFileSkill("/local/skills", scriptRunner)
-    .UseInlineSkills(someInlineSkill)
-    .Build();
-```
-
-The `AgentSkillsProvider` handles all skill types uniformly — any combination of file-based, inline,
-class-based, and custom skills can coexist in the same provider. Custom skills automatically
-participate in the model-facing tools (`load_skill`, `read_skill_resource`, `run_skill_script`),
-filtering, deduplication, and caching — no additional integration work is required.
-
-## Script Representation: `AgentSkillScript` vs `AIFunction`
-
-Two approaches were considered for representing executable scripts within skills:
-
-### Option A — Custom `AgentSkillScript` abstract base class (original design)
-
-Scripts are modeled as a custom `AgentSkillScript` abstract class with `Name`, `Description`, and
-`RunAsync(AgentSkill, AIFunctionArguments, CancellationToken)`. Concrete implementations:
-`AgentInlineSkillScript` (wraps a delegate/`AIFunction`) and `AgentFileSkillScript` (wraps a file path + executor delegate).
-
-```csharp
-// Base type
-public abstract class AgentSkillScript
-{
-    public string Name { get; }
-    public string? Description { get; }
-    public abstract Task<object?> RunAsync(AgentSkill skill, AIFunctionArguments arguments, CancellationToken cancellationToken = default);
-}
-
-// AgentSkill exposes scripts as:
-public abstract IReadOnlyList<AgentSkillScript>? Scripts { get; }
-
-// Inline script wraps an AIFunction internally
-var script = new AgentInlineSkillScript(ConvertUnits, "convert");
-
-// Pre-built AIFunction must be wrapped
-var script = new AgentInlineSkillScript(myAIFunction);
-
-// Class-based skill declares scripts as:
-public override IReadOnlyList<AgentSkillScript>? Scripts { get; } =
-[
-    new AgentInlineSkillScript(ConvertUnits, "convert"),
-];
-
-// Provider executes scripts by passing the owning skill:
-await script.RunAsync(skill, arguments, cancellationToken);
-```
-
-**Pros:**
-
-- **Explicit skill context at execution time.** `RunAsync` receives the owning `AgentSkill`, so any script can access skill metadata or resources during execution without requiring construction-time wiring.
-- **Self-contained abstraction.** A dedicated type communicates clearly that scripts are a skills-framework concept, separate from general-purpose AI functions.
-- **Easier extensibility for custom script types.** Third-party implementations can subclass `AgentSkillScript` and access the owning skill in `RunAsync` without special setup.
-
-**Cons:**
-
-- **Wrapper overhead.** `AgentInlineSkillScript` is a thin pass-through around `AIFunction` — it adds a class, a constructor, and an indirection layer for no behavioral difference.
-- **Parallel abstraction.** `AgentSkillScript` and `AIFunction` serve overlapping purposes (named callable with arguments), creating two parallel hierarchies for the same concept.
-- **Friction for consumers.** Users who already have `AIFunction` instances must wrap them in `AgentInlineSkillScript` to use them as scripts, adding ceremony.
-
-### Option B — Reuse `AIFunction` directly
-
-Scripts are represented as `AIFunction` (from `Microsoft.Extensions.AI`). `AgentSkill.Scripts` returns
-`IReadOnlyList<AIFunction>?`. `AgentInlineSkillScript` is eliminated entirely — callers use
-`AIFunctionFactory.Create(delegate, name: ...)` or pass `AIFunction` instances directly.
-`AgentFileSkillScript` becomes an `AIFunction` subclass that captures its owning `AgentFileSkill` via
-an internal back-reference set during construction.
-
-```csharp
-// AgentSkill exposes scripts as AIFunction directly:
-public abstract IReadOnlyList<AIFunction>? Scripts { get; }
-
-// Inline scripts use AIFunctionFactory — no wrapper class needed
-var skill = new AgentInlineSkill("my-skill", "desc", "instructions");
-skill.AddScript(ConvertUnits, "convert");           // delegate
-skill.AddScript(myAIFunction);                       // pre-built AIFunction — no wrapping
-
-// Class-based skill declares scripts as:
-public override IReadOnlyList<AIFunction>? Scripts { get; } =
-[
-    AIFunctionFactory.Create(ConvertUnits, name: "convert"),
-];
-
-// Provider executes scripts via standard AIFunction invocation:
-await script.InvokeAsync(arguments, cancellationToken);
-
-// File-based scripts extend AIFunction and capture the owning skill internally:
-public sealed class AgentFileSkillScript : AIFunction
-{
-    internal AgentFileSkill? Skill { get; set; }   // set by AgentFileSkill constructor
-
-    protected override async ValueTask<object?> InvokeCoreAsync(
-        AIFunctionArguments arguments, CancellationToken cancellationToken)
-    {
-        return await _executor(Skill!, this, arguments, cancellationToken);
-    }
-}
-```
-
-**Pros:**
-
-- **Fewer types.** Eliminates `AgentSkillScript` and `AgentInlineSkillScript`, reducing the public API surface by two classes.
-- **Seamless interop.** Any `AIFunction` — whether from `AIFunctionFactory`, a custom subclass, or an external library — can be used as a skill script with zero wrapping.
-- **Consistent with `Microsoft.Extensions.AI` ecosystem.** Scripts share the same type as tool functions used by `IChatClient` and `FunctionInvokingChatClient`, reducing conceptual overhead for developers already familiar with the ecosystem.
-
-**Cons:**
-
-- **No owning-skill context in invocation signature.** `AIFunction.InvokeAsync` does not accept an `AgentSkill` parameter, so `AgentFileSkillScript` must capture its owning skill via an internal setter during construction. This adds a construction-order dependency: the skill must set the back-reference on its scripts.
-- **Custom script types lose automatic skill access.** Third-party `AIFunction` subclasses that need the owning skill must implement their own mechanism (e.g., constructor injection, closure capture) instead of receiving it as a method parameter.
-- **Semantic overloading.** `AIFunction` now means both "a tool the model can call" and "a script within a skill", which could blur the distinction for framework users.
-
-## Resource Representation: `AgentSkillResource` vs `AIFunction`
-
-Two approaches were considered for representing skill resources (supplementary content such as references, assets, or dynamic data):
-
-### Option A — Custom `AgentSkillResource` abstract base class (original design)
-
-Resources are modeled as a custom `AgentSkillResource` abstract class with `Name`, `Description`, and
-`ReadAsync(IServiceProvider?, CancellationToken)`. Concrete implementations:
-`AgentInlineSkillResource` (static value, delegate, or `AIFunction` wrapper) and `AgentFileSkillResource` (reads file content from disk).
-
-```csharp
-// Base type
-public abstract class AgentSkillResource
-{
-    public string Name { get; }
-    public string? Description { get; }
-    public abstract Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default);
-}
-
-// AgentSkill exposes resources as:
-public abstract IReadOnlyList<AgentSkillResource>? Resources { get; }
-
-// Static resource
-var resource = new AgentInlineSkillResource("static content", "my-resource");
-
-// Dynamic resource (delegate)
-var resource = new AgentInlineSkillResource((IServiceProvider sp) => GetData(sp), "my-resource");
-
-// Pre-built AIFunction must be wrapped
-var resource = new AgentInlineSkillResource(myAIFunction);
-
-// Class-based skill declares resources as:
-public override IReadOnlyList<AgentSkillResource>? Resources { get; } =
-[
-    new AgentInlineSkillResource("# Conversion Tables\n...", "conversion-table"),
-];
-
-// Provider reads resources via:
-await resource.ReadAsync(serviceProvider, cancellationToken);
-```
-
-**Pros:**
-
-- **Clear semantic distinction.** A dedicated `AgentSkillResource` type distinguishes resources (data providers) from scripts (executable actions), making the API self-documenting.
-- **Purpose-built API.** `ReadAsync` communicates intent better than `InvokeAsync` for a data-access operation.
-
-**Cons:**
-
-- **Wrapper overhead.** `AgentInlineSkillResource` wraps `AIFunction` internally for delegate/function cases — adding a class and indirection for no behavioral difference.
-- **Parallel abstraction.** `AgentSkillResource` and `AIFunction` serve overlapping purposes (named callable that returns data), creating two parallel hierarchies.
-- **Friction for consumers.** Users who already have `AIFunction` instances must wrap them in `AgentInlineSkillResource`, adding ceremony.
-
-### Option B — Reuse `AIFunction` directly
-
-Resources are represented as `AIFunction`. `AgentSkill.Resources` returns `IReadOnlyList<AIFunction>?`.
-`AgentInlineSkillResource` becomes an `AIFunction` subclass (retained as a convenience for the static-value
-pattern: `new AgentInlineSkillResource("data", "name")`). `AgentFileSkillResource` becomes an `AIFunction`
-subclass that reads file content.
-
-```csharp
-// AgentSkill exposes resources as AIFunction directly:
-public abstract IReadOnlyList<AIFunction>? Resources { get; }
-
-// Static resource — AgentInlineSkillResource is retained as a convenience AIFunction subclass
-var resource = new AgentInlineSkillResource("static content", "my-resource");
-
-// Dynamic resource — AgentInlineSkillResource wraps delegate as AIFunction
-var resource = new AgentInlineSkillResource((IServiceProvider sp) => GetData(sp), "my-resource");
-
-// Pre-built AIFunction can be used directly — no wrapping needed
-skill.AddResource(myAIFunction);
-
-// Class-based skill declares resources as:
-public override IReadOnlyList<AIFunction>? Resources { get; } =
-[
-    new AgentInlineSkillResource("# Conversion Tables\n...", "conversion-table"),
-];
-
-// Provider reads resources via standard AIFunction invocation:
-await resource.InvokeAsync(arguments, cancellationToken);
-
-// File-based resources extend AIFunction directly:
-internal sealed class AgentFileSkillResource : AIFunction
-{
-    public string FullPath { get; }
-
-    protected override async ValueTask<object?> InvokeCoreAsync(
-        AIFunctionArguments arguments, CancellationToken cancellationToken)
-    {
-        return await File.ReadAllTextAsync(FullPath, Encoding.UTF8, cancellationToken);
-    }
-}
-```
-
-**Pros:**
-
-- **Fewer base types.** Eliminates the `AgentSkillResource` abstract class, reducing the public API surface.
-- **Seamless interop.** Any `AIFunction` can be used as a skill resource with zero wrapping.
-
-**Cons:**
-
-- **Loss of semantic distinction.** Resources and scripts are now both `AIFunction`, which could make it less obvious which list a function belongs to when reading code.
-- **Static values require a wrapper.** Unlike the original `ReadAsync` which could return a stored value directly, `AIFunction.InvokeAsync` implies invocation. `AgentInlineSkillResource` is retained as a convenience subclass to handle the static-value case, so this is not eliminated — just moved to a different class.
-
-## Decision Outcome
-
-### 1. Keep `AgentSkillResource` and `AgentSkillScript` (Option A for both sections)
-
-We are staying with the custom `AgentSkillResource` and `AgentSkillScript` model classes instead of reusing `AIFunction`:
-
-- **Resources have no parameters.** If a consumer provides an `AIFunction` with parameters, those parameters will never be advertised to the LLM, and the resulting call will fail.
-- **Approval breaks for `AIFunction`-based representations.** When a resource or script represented by an `AIFunction` is configured with approval, the second approval invocation will not work correctly.
-- **Injecting the owning skill into an `AIFunction`-based script is problematic.** Constructor injection would introduce a circular reference between the skill and the script. An internal property setter is possible but adds coupling.
-
-### 2. Make all agent skill classes internal
-
-All agent-skill-related classes are made `internal` to minimize the public API surface while the feature matures. We can reconsider and promote types to `public` later based on community signal.
-
-This leaves two public entry points:
-
-- **`AgentSkillsProvider`** — use directly when all skills come from a single source and filtering is not needed.
-- **`AgentSkillsProviderBuilder`** — use when mixing skill types or when filtering support is required.
-
-### 3. Caching at provider level
-
-Caching of tools and instructions is implemented inside `AgentSkillsProvider` rather than as an external decorator. Recreating tools and instructions on every provider call is wasteful, and a caching decorator sitting outside the provider would not have the information needed to cache them effectively.

docs/decisions/0021-provider-leading-clients.md

@@ -1,72 +0,0 @@
----
-status: accepted
-contact: eavanvalkenburg
-date: 2026-03-20
-deciders: eavanvalkenburg, sphenry, chetantoshnival
-consulted: taochenosu, moonbox3, dmytrostruk, giles17, alliscode
----
-
-# Provider-Leading Client Design & OpenAI Package Extraction
-
-## Context and Problem Statement
-
-The `agent-framework-core` package currently bundles OpenAI and Azure OpenAI client implementations along with their dependencies (`openai`, `azure-identity`, `azure-ai-projects`, `packaging`). This makes core heavier than necessary for users who don't use OpenAI, and it conflates the core abstractions with a specific provider implementation. Additionally, the current class naming (`OpenAIResponsesClient`, `OpenAIChatClient`) is based on the underlying OpenAI API names rather than what users actually want to do, making discoverability harder for newcomers.
-
-## Decision Drivers
-
-- **Lightweight core**: Core should only contain abstractions, middleware infrastructure, and telemetry — no provider-specific code or dependencies.
-- **Discoverability-first**: Import namespaces should guide users to the right client. `from agent_framework.openai import ...` should surface all OpenAI-related clients; `from agent_framework.azure import ...` should surface Foundry, Azure AI, and other Azure-specific classes.
-- **Provider-leading naming**: The primary client name should reflect the provider, not the underlying API. The Responses API is now the recommended default for OpenAI, so its client should be called `OpenAIChatClient` (not `OpenAIResponsesClient`).
-- **Clean separation of concerns**: Azure-specific deprecated wrappers belong in the azure-ai package, not in the OpenAI package.
-
-## Considered Options
-
-- **Keep OpenAI in core**: Simpler but keeps core heavy; doesn't help discoverability.
-- **Extract OpenAI with Azure wrappers in the OpenAI package**: Keeps Azure OpenAI wrappers alongside OpenAI code, but pollutes the OpenAI package with Azure concerns.
-- **Extract OpenAI, place Azure wrappers in azure-ai**: Clean separation; the OpenAI package has zero Azure dependencies; deprecated Azure wrappers live in a single file in azure-ai for easy future deletion.
-
-## Decision Outcome
-
-Chosen option: "Extract OpenAI, place Azure wrappers in azure-ai", because it achieves the lightest core, cleanest OpenAI package, and the most maintainable deprecation path.
-
-Key changes:
-
-1. **New `agent-framework-openai` package** with dependencies on `agent-framework-core`, `openai`, and `packaging` only.
-2. **Class renames**: `OpenAIResponsesClient` → `OpenAIChatClient` (Responses API), `OpenAIChatClient` → `OpenAIChatCompletionClient` (Chat Completions API). Old names remain as deprecated aliases.
-3. **Deprecated classes**: `OpenAIAssistantsClient`, all `AzureOpenAI*Client` classes, `AzureAIClient`, `AzureAIAgentClient`, and `AzureAIProjectAgentProvider` are marked deprecated.
-4. **New `FoundryChatClient`** in azure-ai for Azure AI Foundry Responses API access, built on `RawFoundryChatClient(RawOpenAIChatClient)`.
-5. **All deprecated `AzureOpenAI*` classes** consolidated into a single file (`_deprecated_azure_openai.py`) in the azure-ai package for clean future deletion.
-6. **Core's `agent_framework.openai` and `agent_framework.azure` namespaces** become lazy-loading gateways, preserving backward-compatible import paths while removing hard dependencies.
-7. **Unified `model` parameter** replaces `model_id` (OpenAI), `deployment_name` (Azure OpenAI), and `model_deployment_name` (Azure AI) across all client constructors. The term `model` is intentionally generic: it naturally maps to an OpenAI model name *and* to an Azure OpenAI deployment name, making it straightforward to use `OpenAIChatClient` with either OpenAI or Azure OpenAI backends (via `AsyncAzureOpenAI`). Environment variables are similarly unified (e.g., `OPENAI_MODEL` instead of separate `OPENAI_RESPONSES_MODEL_ID` / `OPENAI_CHAT_MODEL_ID`).
-8. **`FoundryAgent`** replaces the pattern of `Agent(client=AzureAIClient(...))` for connecting to pre-configured agents in Azure AI Foundry (PromptAgents and HostedAgents). The underlying `RawFoundryAgentChatClient` is an implementation detail — most users interact only with `FoundryAgent`. `AzureAIAgentClient` is separately deprecated as it refers to the V1 Agents Service API. See below for design rationale.
-
-### Foundry Agent Design: `FoundryAgentClient` vs `FoundryAgent`
-
-The existing `AzureAIClient` combines two concerns: CRUD lifecycle management (creating/deleting agents on the service) and runtime communication (sending messages via the Responses API). The new design removes CRUD entirely — users connect to agents that already exist in Foundry.
-
-**Two approaches were considered:**
-
-**Option A — `FoundryAgentClient` only (public ChatClient):**
-Users compose `Agent(client=FoundryAgentClient(...), tools=[...])`. This follows the universal `Agent(client=X)` pattern used by every other provider. However, a "client" that wraps a named remote agent (with `agent_name` as a constructor param) is semantically odd — clients typically wrap a model endpoint, not a specific agent.
-
-**Option B — `FoundryAgent` (Agent subclass) + private `_FoundryAgentChatClient` and public `RawFoundryAgentChatClient`:**
-Users write `FoundryAgent(agent_name="my-agent", ...)` for the common case. Internally, `FoundryAgent` creates a `_FoundryAgentChatClient` and passes it to the standard `Agent` base class. For advanced customization, users pass `client_type=RawFoundryAgentChatClient` (or a custom subclass) to control the client middleware layers. The `Agent(client=RawFoundryAgentChatClient(...))` composition pattern still works for users who prefer it.
-
-**Chosen option: Option B**, because:
-- The common case (`FoundryAgent(...)`) is a single object with no boilerplate.
-- `client_type=` gives full control over client middleware without parameter duplication — the agent forwards connection params to the client internally.
-- `RawFoundryAgent(RawAgent)` and `FoundryAgent(Agent)` mirror the established `RawAgent`/`Agent` pattern.
-- Runtime validation (only `FunctionTool` allowed) lives in `RawFoundryAgentChatClient._prepare_options`, ensuring it applies regardless of how the client is used — through `FoundryAgent`, `Agent(client=...)`, or any custom composition.
-
-**Public classes:**
-- `RawFoundryAgentChatClient(RawOpenAIChatClient)` — Responses API client that injects agent reference and validates tools. Extension point for custom client middleware.
-- `RawFoundryAgent(RawAgent)` — Agent without agent-level middleware/telemetry.
-- `FoundryAgent(AgentTelemetryLayer, AgentMiddlewareLayer, RawFoundryAgent)` — Recommended production agent.
-
-**Internal (private):**
-- `_FoundryAgentChatClient` — Full client with function invocation, chat middleware, and telemetry layers. Created automatically by `FoundryAgent`; users customize via `client_type=RawFoundryAgentChatClient` or a custom subclass.
-
-**Deprecated:**
-- `AzureAIClient` — replaced by `FoundryAgent` (which uses `FoundryAgentClient` internally).
-- `AzureAIAgentClient` — refers to V1 Agents Service API, no direct replacement.
-- `AzureAIProjectAgentProvider` — replaced by `FoundryAgent`.

docs/decisions/0022-chat-history-persistence-consistency.md

@@ -1,121 +0,0 @@
----
-status: accepted
-contact: westey-m
-date: 2026-03-23
-deciders: sergeymenshykh, markwallace, rbarreto, dmytrostruk, westey-m, eavanvalkenburg, stephentoub
-consulted:
-informed:
----
-
-# Chat History Persistence Consistency
-
-## Context and Problem Statement
-
-When using `ChatClientAgent` with tools, the `FunctionInvokingChatClient` (FIC) loops multiple times — service call → tool execution → service call → … — before producing a final response. There are two points of discrepancy between how chat history is stored by the framework's `ChatHistoryProvider` and how the underlying AI service stores chat history (e.g., OpenAI Responses with `store=true`):
-
-1. **Persistence timing**: The AI service persists messages after *each* service call within the FIC loop. The `ChatHistoryProvider` currently persists messages only once, at the *end* of the full agent run (after all FIC loop iterations complete).
-
-2. **Trailing `FunctionResultContent` storage**: When tool calling is terminated mid-loop (e.g., via `FunctionInvokingChatClient` termination filters), the final response from the agent may contain `FunctionResultContent` that was never sent to a subsequent service call. The AI service never stores this trailing `FunctionResultContent`, but the `ChatHistoryProvider` currently stores all response content, including the trailing `FunctionResultContent`.
-
-These discrepancies mean that a `ChatHistoryProvider`-managed conversation and a service-managed conversation can diverge in content and structure, even when processing the same interactions.
-
-### Practical Impact: Resuming After Tool-Call Termination
-
-Today, users of `AIAgent` get different behaviors depending on whether chat history is stored service-side or in a `ChatHistoryProvider`. This creates concrete challenges — for example, when the function call loop is terminated and the user wants to resume the conversation in a subsequent run. With service-stored history, the trailing `FunctionResultContent` is never persisted, so the last stored message is the `FunctionCallContent` from the service. With `ChatHistoryProvider`-stored history, the trailing `FunctionResultContent` *is* persisted. The user cannot know whether the last `FunctionResultContent` is in the chat history or not without inspecting the storage mechanism, making it difficult to write resumption logic that works correctly regardless of the storage backend.
-
-### Relationship Between the Two Discrepancies
-
-The persistence timing and `FunctionResultContent` trimming behaviors are interrelated:
-
-- **Per-service-call persistence**: When messages are persisted after each individual service call, trailing `FunctionResultContent` trimming is unnecessary. If tool calling is terminated, the `FunctionResultContent` from the terminated call was never sent to a subsequent service call, so it is never persisted. The per-service-call approach naturally matches the service's behavior.
-
-- **Per-run persistence**: When messages are batched and persisted at the end of the full run, trailing `FunctionResultContent` trimming becomes necessary to match the service's behavior. Without trimming, the stored history contains `FunctionResultContent` that the service would never have stored.
-
-## Decision Drivers
-
-- **A. Consistency**: The default behavior of `ChatHistoryProvider` should produce stored history that closely matches what the underlying AI service would store, minimizing surprise when switching between framework-managed and service-managed chat history.
-- **B. Atomicity**: A run that fails mid-way through a multi-step tool-calling loop should not leave chat history in a partially-updated state, unless the user explicitly opts into that behavior.
-- **C. Recoverability**: For long-running tool-calling loops, it should be possible to recover intermediate progress if the process is interrupted, rather than losing all work from the current run.
-- **D. Simplicity**: The default behavior should be easy to understand and predict for most users, without requiring knowledge of the FIC loop internals.
-- **E. Flexibility**: Regardless of the chosen default, users should be able to opt into the alternative behavior.
-
-## Considered Options
-
-- Option 1: Per-run persistence with opt-in FRC (FunctionResultContent) trimming
-- Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)
-
-## Pros and Cons of the Options
-
-### Option 1: Per-run persistence with opt-in FRC trimming
-
-Keep the current default behavior of persisting chat history only at the end of the full agent run. Add `FunctionResultContent` trimming as an opt-in behavior to improve consistency with service storage.
-
-- Good, because runs are atomic — chat history is only updated when the full run succeeds, satisfying driver B.
-- Good, because the mental model is simple: one run = one history update, satisfying driver D.
-- Good, because trimming trailing `FunctionResultContent` improves consistency with service storage, partially satisfying driver A.
-- Bad, because the default persistence timing still differs from the service's behavior (per-run vs. per-service-call), only partially satisfying driver A.
-- Bad, because if the process crashes mid-loop, all intermediate progress from the current run is lost, not satisfying driver C.
-- Bad, because this option alone does not provide a way for users to opt into per-service-call persistence, not satisfying driver E.
-
-### Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)
-
-Introduce an optional RequirePerServiceCallChatHistoryPersistence setting to persist chat history after each individual service call within the FIC loop, matching the AI service's behavior. Trailing `FunctionResultContent` trimming is unnecessary with this approach (it is naturally handled).
-
-Settings:
-- `RequirePerServiceCallChatHistoryPersistence` = `true`
-
-- Good, because the stored history matches the service's behavior when opting in for both timing and content, fully satisfying driver A.
-- Good, because intermediate progress is preserved if the process is interrupted, satisfying driver C.
-- Good, because no separate `FunctionResultContent` trimming logic is needed, reducing complexity.
-- Bad, because chat history may be left in an incomplete state if the run fails mid-loop (e.g., `FunctionCallContent` stored without corresponding `FunctionResultContent`), not satisfying driver B. A subsequent run cannot proceed without manually providing the missing `FunctionResultContent`.
-- Bad, because the mental model is more complex: a single run may produce multiple history updates, partially failing driver D.
-- Neutral, because users can opt out to per-run persistence if they prefer atomicity, satisfying driver E.
-
-## Decision Outcome
-
-Chosen option: **Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)**. The existing per-run persistence behavior is retained as-is, requiring no changes from users. Per-service-call persistence is available as an opt-in feature via the `RequirePerServiceCallChatHistoryPersistence` setting. This satisfies drivers B (atomicity) and D (simplicity) for the common case, while fully satisfying driver A (consistency) for users who opt into simulated service-stored behavior. Users who need per-service-call persistence for recoverability (driver C) can enable it explicitly.
-
-### Configuration Matrix
-
-The behavior depends on the combination of `UseProvidedChatClientAsIs` and `RequirePerServiceCallChatHistoryPersistence`:
-
-| `UseProvidedChatClientAsIs` | `RequirePerServiceCallChatHistoryPersistence` | Behavior |
-|---|---|---|
-| `false` (default) | `false` (default) | **Per-run persistence.** Messages are persisted at the end of the full agent run via the `ChatHistoryProvider`. |
-| `false` | `true` | **Per-service-call persistence (simulated).** A `PerServiceCallChatHistoryPersistingChatClient` middleware is automatically injected into the chat client pipeline between `FunctionInvokingChatClient` and the leaf `IChatClient`. Messages are persisted after each service call. A sentinel `ConversationId` causes FIC to treat the conversation as service-managed. |
-| `true` | `false` | **Per-run persistence.** No middleware is injected because the user has provided a custom chat client stack. Messages are persisted at the end of the run. |
-| `true` | `true` | **User responsibility.** The system checks whether the custom chat client stack includes a `PerServiceCallChatHistoryPersistingChatClient`. If not, a warning is emitted — the user is expected to have added their own per-service-call persistence mechanism. End-of-run persistence is skipped. |
-
-### Consequences
-
-- Good, because per-run persistence is atomic by default — chat history is only updated when the full run succeeds, satisfying driver B.
-- Good, because the default mental model is simple: one run = one history update, satisfying driver D.
-- Good, because users who opt into `RequirePerServiceCallChatHistoryPersistence` get stored history that matches the service's behavior for both timing and content, fully satisfying driver A.
-- Good, because per-service-call persistence preserves intermediate progress if the process is interrupted, satisfying driver C when opted in.
-- Good, because no separate `FunctionResultContent` trimming logic is needed when per-service-call persistence is active — it is naturally handled.
-- Good, because conflict detection (configurable via `ThrowOnChatHistoryProviderConflict`, `WarnOnChatHistoryProviderConflict`, `ClearOnChatHistoryProviderConflict`) prevents misconfiguration when a service returns a `ConversationId` alongside a configured `ChatHistoryProvider`.
-- Bad, because per-service-call persistence (when opted in) may leave chat history in an incomplete state if the run fails mid-loop (e.g., `FunctionCallContent` stored without corresponding `FunctionResultContent`), requiring manual recovery in rare cases.
-- Neutral, because users who want per-service-call consistency can opt in via `RequirePerServiceCallChatHistoryPersistence = true`, satisfying driver E.
-- Neutral, because increased write frequency from per-service-call persistence may impact performance for some storage backends; this can be mitigated with a caching decorator.
-
-### Implementation Notes
-
-#### Conversation ID Consistency
-
-When `RequirePerServiceCallChatHistoryPersistence` is enabled, the `PerServiceCallChatHistoryPersistingChatClient`
-decorator also updates `session.ConversationId` after each service call. This handles two scenarios:
-
-1. **Framework-managed chat history** — the decorator sets a sentinel `ConversationId` on the response
-   so that `FunctionInvokingChatClient` treats the conversation as service-managed (clearing accumulated
-   history between iterations and not injecting duplicate `FunctionCallContent` during approval processing).
-
-2. **Service-stored chat history** — when the service returns a real `ConversationId`, the decorator
-   updates `session.ConversationId` immediately after each service call, rather than deferring the update
-   to the end of the run. This ensures intermediate ConversationId changes are captured even if the
-   process is interrupted mid-loop.
-
-For some service-stored scenarios (e.g., the Conversations API with the Responses API), there is only
-one thread with one ID, so every service call returns the same ConversationId and this per-call update
-makes no practical difference. Enabling `RequirePerServiceCallChatHistoryPersistence` ensures consistent
-per-service-call behavior across all service types regardless of how they manage ConversationIds.
-

docs/decisions/0023-foundry-evals-integration.md

@@ -1,815 +0,0 @@
----
-status: accepted
-contact: bentho
-date: 2026-02-27
-deciders: bentho, markwallace-microsoft, westey-m
-consulted: Pratyush Mishra, Shivam Shrivastava, Manni Arora (Centrica eval scenario)
-informed: Agent Framework team, Foundry Evals team
----
-
-# Agent Evaluation Architecture with Azure AI Foundry Integration
-
-## Context and Problem Statement
-
-Azure AI Foundry provides a rich evaluation service for AI agents — built-in evaluators for agent behavior (task adherence, intent resolution), tool usage (tool call accuracy, tool selection), quality (coherence, fluency, relevance), and safety (violence, self-harm, prohibited actions). Results are viewable in the Foundry portal with dashboards and comparison views.
-
-However, using Foundry Evals with an agent-framework agent today requires significant manual effort. Developers must:
-
-1. Transform agent-framework's `Message`/`Content` types into the OpenAI-style agent message schema that Foundry evaluators expect
-2. Map tool definitions from agent-framework's `FunctionTool` format to evaluator-compatible schemas
-3. Manually wire up the correct Foundry data source type (`azure_ai_traces`, `jsonl`, `azure_ai_target_completions`, etc.) depending on their scenario
-4. Handle App Insights trace ID queries, response ID collection, and eval polling
-
-Additionally, evaluation is a concern that extends beyond any single provider. Developers may want to use local evaluators (LLM-as-judge, regex, keyword matching), third-party evaluation libraries, or multiple providers in combination. The architecture must support this without creating a Foundry-specific lock-in at the API level.
-
-### Functional Requirements for Agent Evaluation
-
-- **Single agents and workflows.** Evaluate both individual agent responses and multi-agent workflow results, with per-agent breakdown to pinpoint underperformance.
-- **One-shot and multi-turn conversations.** Capture full conversation trajectories — including tool calls and results — not just final query/response pairs.
-- **Conversation factoring.** Support splitting conversations into query/response in multiple ways (last turn, full trajectory, per-turn) because different factorings measure different things.
-- **Multiple providers, mix and match.** Run Foundry LLM-as-judge evaluators alongside fast local checks and custom evaluators on the same data, without restructuring code.
-- **Third-party extensibility.** Any evaluation library can participate by implementing the `Evaluator` protocol (Python) or `IAgentEvaluator` interface (.NET). No predetermined list of supported libraries — the protocol is intentionally simple (`evaluate(items) → results`) so that wrappers for libraries like DeepEval, RAGAS, or Promptfoo are straightforward to write.
-- **Bring your own evaluator.** Creating a custom evaluator should be as simple as writing a function.
-- **Evaluate without re-running.** Evaluate existing responses from logs or previous runs without invoking the agent again.
-
-## Decision Drivers
-
-- **Zero-friction evaluation**: Developers should go from "I have an agent" to "I have eval results" with minimal code.
-- **Provider-agnostic API**: Core evaluation capabilities must not be tied to any specific provider. Provider configuration should be separate from the evaluation call.
-- **Lowest concept count**: Introduce the fewest possible new types, abstractions, and APIs for developers to learn.
-- **Leverage existing knowledge**: The framework already knows which agents exist, what tools they have, and what conversations occurred. Evals should use this automatically rather than requiring the developer to re-specify it.
-- **Foundry-native results**: When using Foundry, results should be viewable in the Foundry portal with dashboards and comparison views.
-- **Progressive disclosure**: Simple scenarios should be near-zero code. Advanced scenarios should build on the same primitives.
-- **Cross-language parity**: Design must be implementable in both Python and .NET.
-
-## Considered Options
-
-1. **Provider-specific functions** — Build Foundry-specific helper functions (`evaluate_agent()`, etc.) directly in the Azure package. All eval functions take Foundry connection parameters.
-2. **Evaluator protocol with shared orchestration** — Define a provider-agnostic `Evaluator` protocol in the base agent library (`agent_framework` in Python, `Microsoft.Agents.AI` in .NET). Orchestration functions live alongside it. Providers implement the protocol.
-3. **Full eval framework** — Build comprehensive eval infrastructure including custom evaluator definitions, scoring profiles, and reporting inside agent-framework.
-
-## Decision Outcome
-
-Proposed option: "Evaluator protocol with shared orchestration", because it delivers the low-friction developer experience, supports multiple providers without API changes, and keeps the concept count low.
-
-### Usage Examples
-
-#### Evaluate an agent
-
-The agent is invoked once per query by default. For statistically meaningful evaluation, provide multiple diverse queries. For measuring **consistency** (does the same query produce reliable results?), use `num_repetitions` to run each query N times independently:
-
-**Python:**
-
-```python
-evals = FoundryEvals(
-    project_client=client,
-    model_deployment="gpt-4o",
-    evaluators=[FoundryEvals.RELEVANCE, FoundryEvals.COHERENCE],
-)
-
-results = await evaluate_agent(
-    agent=my_agent,
-    queries=[
-        "What's the weather in Seattle?",
-        "Plan a weekend trip to Portland",
-        "What restaurants are near Pike Place?",
-    ],
-    evaluators=evals,
-)
-for r in results:
-    r.assert_passed()
-```
-
-**C#:**
-
-```csharp
-var evals = new FoundryEvals(chatConfiguration, FoundryEvals.Relevance, FoundryEvals.Coherence);
-
-AgentEvaluationResults results = await agent.EvaluateAsync(
-    new[] {
-        "What's the weather in Seattle?",
-        "Plan a weekend trip to Portland",
-        "What restaurants are near Pike Place?",
-    },
-    evals);
-
-results.AssertAllPassed();
-```
-
-`evaluate_agent` returns one `EvalResults` per evaluator. Each result contains per-item scores with the evaluated response for auditing:
-
-```
-# results[0] (FoundryEvals)
-EvalResults(status="completed", passed=3, failed=0, total=3)
-  items[0]: EvalItemResult(
-    query="What's the weather in Seattle?",
-    response="It's currently 72°F and sunny in Seattle.",
-    scores={"relevance": 5, "coherence": 5})
-  items[1]: EvalItemResult(
-    query="Plan a weekend trip to Portland",
-    response="Here's a 2-day Portland itinerary...",
-    scores={"relevance": 4, "coherence": 5})
-  items[2]: EvalItemResult(
-    query="What restaurants are near Pike Place?",
-    response="Top restaurants near Pike Place Market: ...",
-    scores={"relevance": 5, "coherence": 4})
-```
-
-#### Measure consistency with repetitions
-
-Run each query multiple times to detect non-deterministic behavior:
-
-**Python:**
-
-```python
-results = await evaluate_agent(
-    agent=my_agent,
-    queries=["What's the weather in Seattle?"],
-    evaluators=evals,
-    num_repetitions=3,  # each query runs 3 times independently
-)
-# results contain 3 items (1 query × 3 repetitions)
-```
-
-**C#:**
-
-```csharp
-AgentEvaluationResults results = await agent.EvaluateAsync(
-    new[] { "What's the weather in Seattle?" },
-    evals,
-    numRepetitions: 3);  // each query runs 3 times independently
-// results contain 3 items (1 query × 3 repetitions)
-```
-
-#### Evaluate a response you already have
-
-When you already have agent responses, pass them directly to skip re-running the agent. Each query is paired with its corresponding response:
-
-**Python:**
-
-```python
-queries = ["What's the weather?", "What's the capital of France?"]
-responses = [await agent.run([Message("user", [q])]) for q in queries]
-
-results = await evaluate_agent(
-    responses=responses,
-    evaluators=evals,
-)
-```
-
-**C#:**
-
-```csharp
-var queries = new[] { "What's the weather?" };
-var responses = new List<AgentResponse>();
-foreach (var q in queries)
-    responses.Add(await agent.RunAsync(new[] { new ChatMessage(ChatRole.User, q) }));
-
-AgentEvaluationResults results = await agent.EvaluateAsync(
-    responses: responses,
-    evals);
-```
-
-Each `AgentResponse` already contains the conversation (query + response), so the evaluator extracts query/response from the conversation. When you pass `responses` without `queries`, the conversation is the source of truth.
-
-#### Evaluate with conversation split strategies
-
-By default, evaluators see only the last turn (final user message → final assistant response). For multi-turn conversations, you can control how the conversation is factored for evaluation:
-
-**Python:**
-
-```python
-results = await evaluate_agent(
-    agent=agent,
-    queries=["Plan a 3-day trip to Paris"],
-    evaluators=evals,
-    conversation_split=ConversationSplit.FULL,      # evaluate entire trajectory
-)
-
-# Or per-turn: each user→assistant exchange scored independently
-results = await evaluate_agent(
-    agent=agent,
-    queries=["Plan a 3-day trip to Paris"],
-    evaluators=evals,
-    conversation_split=ConversationSplit.PER_TURN,
-)
-```
-
-**C#:**
-
-```csharp
-// Full conversation as context
-AgentEvaluationResults results = await agent.EvaluateAsync(
-    new[] { "Plan a 3-day trip to Paris" },
-    evals,
-    splitter: ConversationSplitters.Full);
-
-// Per-turn splitting
-var items = EvalItem.PerTurnItems(conversation);  // one EvalItem per user turn
-var results = await evals.EvaluateAsync(items);
-```
-
-With `PER_TURN`, a 3-turn conversation produces 3 scored items:
-
-```
-EvalResults(status="completed", passed=3, failed=0, total=3)
-  items[0]: query="Plan a 3-day trip to Paris"    scores={"relevance": 5}
-  items[1]: query="What about restaurants?"        scores={"relevance": 4}
-  items[2]: query="Make it budget-friendly"        scores={"relevance": 5}
-```
-
-#### Evaluate a multi-agent workflow
-
-**Python:**
-
-```python
-result = await workflow.run("Plan a trip to Paris")
-eval_results = await evaluate_workflow(
-    workflow=workflow,
-    workflow_result=result,
-    evaluators=evals,
-)
-
-for r in eval_results:
-    print(f"  overall: {r.passed}/{r.total}")
-    for name, sub in r.sub_results.items():
-        print(f"    {name}: {sub.passed}/{sub.total}")
-```
-
-**C#:**
-
-```csharp
-WorkflowRunResult result = await workflow.RunAsync("Plan a trip to Paris");
-
-IReadOnlyList<AgentEvaluationResults> evalResults = await result.EvaluateAsync(evals);
-
-foreach (var r in evalResults)
-{
-    Console.WriteLine($"  overall: {r.Passed}/{r.Total}");
-    foreach (var (name, sub) in r.SubResults)
-        Console.WriteLine($"    {name}: {sub.Passed}/{sub.Total}");
-}
-```
-
-Workflows return one result per evaluator, with sub-results per agent in the workflow:
-
-```
-EvalResults(status="completed", passed=2, failed=0, total=2)
-  sub_results:
-    "planner":  EvalResults(passed=1, total=1)
-    "researcher": EvalResults(passed=1, total=1)
-```
-
-#### Mix multiple providers
-
-**Python:**
-
-```python
-@evaluator
-def is_helpful(response: str) -> bool:
-    return len(response.split()) > 10
-
-foundry = FoundryEvals(
-    project_client=client,
-    model_deployment="gpt-4o",
-    evaluators=[FoundryEvals.RELEVANCE, FoundryEvals.COHERENCE],
-)
-
-results = await evaluate_agent(
-    agent=agent,
-    queries=queries,
-    evaluators=[is_helpful, keyword_check("weather"), foundry],
-)
-```
-
-**C#:**
-
-```csharp
-IReadOnlyList<AgentEvaluationResults> results = await agent.EvaluateAsync(
-    queries,
-    evaluators: new IAgentEvaluator[]
-    {
-        new LocalEvaluator(
-            EvalChecks.KeywordCheck("weather"),
-            FunctionEvaluator.Create("is_helpful", (string r) => r.Split(' ').Length > 10)),
-        new FoundryEvals(chatConfiguration, FoundryEvals.Relevance, FoundryEvals.Coherence),
-    });
-```
-
-Multiple evaluators return one result each — `results[0]` is the local evaluator, `results[1]` is Foundry.
-
-#### Custom function evaluators
-
-**Python:**
-
-```python
-@evaluator
-def mentions_city(response: str, expected_output: str) -> bool:
-    return expected_output.lower() in response.lower()
-
-@evaluator
-def used_tools(conversation: list, tools: list) -> float:
-    # ... scoring logic
-    return score
-
-local = LocalEvaluator(mentions_city, used_tools)
-```
-
-`@evaluator` uses **parameter name injection** — the function's parameter names determine what data it receives from the `EvalItem`. Supported names: `query`, `response`, `expected`, `expected_tool_calls`, `conversation`, `tools`, `context`. Any combination is valid.
-
-**C#:**
-
-```csharp
-var local = new LocalEvaluator(
-    FunctionEvaluator.Create("mentions_city",
-        (EvalItem item) => item.ExpectedOutput != null
-            && item.Response.Contains(item.ExpectedOutput, StringComparison.OrdinalIgnoreCase)),
-    FunctionEvaluator.Create("is_concise",
-        (string response) => response.Split(' ').Length < 500));
-```
-
-## What To Build
-
-### Core: Evaluator Protocol
-
-A runtime-checkable protocol that any evaluation provider implements:
-
-```python
-@runtime_checkable
-class Evaluator(Protocol):
-    name: str
-
-    async def evaluate(
-        self, items: Sequence[EvalItem], *, eval_name: str = "Agent Framework Eval"
-    ) -> EvalResults: ...
-```
-
-The protocol is minimal — just `name` and `evaluate()`.
-
-### Core: EvalItem
-
-Provider-agnostic data format for items to evaluate:
-
-```python
-@dataclass
-class ExpectedToolCall:
-    name: str                                    # Tool/function name
-    arguments: dict[str, Any] | None = None      # None = don't check args
-
-@dataclass
-class EvalItem:
-    conversation: list[Message]               # Single source of truth
-    tools: list[FunctionTool] | None = None   # Agent's available tools
-    context: str | None = None
-    expected_output: str | None = None          # Ground-truth for comparison
-    expected_tool_calls: list[ExpectedToolCall] | None = None
-    split_strategy: ConversationSplitter | None = None
-
-    query: str       # property — derived from conversation split
-    response: str    # property — derived from conversation split
-```
-
-`conversation` is the single source of truth. `query` and `response` are derived properties — splitting the conversation at the last user message (default) and extracting text from each side. Changing the `split_strategy` consistently changes all derived values.
-
-`tools` provides typed `FunctionTool` objects — including MCP tools, which are automatically extracted after agent runs.
-
-### Internal: AgentEvalConverter
-
-Internal class that converts agent-framework types to `EvalItem`. Used by `evaluate_agent()` and `evaluate_workflow()` — not part of the public API:
-
-| Agent Framework | Eval Format |
-|---|---|
-| `Content.function_call` | `tool_call` in OpenAI chat format |
-| `Content.function_result` | `tool_result` in OpenAI chat format |
-| `FunctionTool` | `{name, description, parameters}` schema |
-| `Message` history | `conversation` list + `query`/`response` extraction |
-
-### Core: EvalResults
-
-Rich result type with convenience properties for CI integration:
-
-```python
-results.all_passed          # bool: no failures or errors (recursive for workflow)
-results.passed              # int: passing count
-results.failed              # int: failure count
-results.total               # int: total = passed + failed + errored
-results.items               # list[EvalItemResult]: per-item detail with query, response, and scores
-results.error               # str | None: error details on failure
-results.sub_results         # dict: per-agent breakdown (workflow evals)
-results.report_url          # str | None: portal link (Foundry)
-results.assert_passed()     # raises AssertionError with details
-```
-
-### Core: Orchestration Functions
-
-Provider-agnostic functions that extract data and delegate to evaluators:
-
-| Function | What it does |
-|---|---|
-| `evaluate_agent()` | Runs agent against test queries (or evaluates pre-existing `responses=`), converts to `EvalItem`s, passes to evaluator. Accepts optional `expected_output=` for ground-truth comparison, `expected_tool_calls=` for tool-correctness evaluation, and `num_repetitions=` for consistency measurement |
-| `evaluate_workflow()` | Extracts per-agent data from `WorkflowRunResult`, evaluates each agent and overall output. Per-agent breakdown in `sub_results`. Also accepts `num_repetitions=` |
-
-### Core: Conversation Split Strategies
-
-Multi-turn conversations must be split into query (input) and response (output) halves for evaluation. How you split determines *what you're evaluating*:
-
-**Last-turn split** — split at the last user message. Everything up to and including it is the query context; the agent's subsequent actions are the response:
-
-```
-conversation: user1 → assistant1 → user2 → assistant2(tool) → tool_result → assistant3
-query_messages:    [user1, assistant1, user2]
-response_messages: [assistant2(tool), tool_result, assistant3]
-```
-
-This evaluates: "Given all the context so far, did the agent answer the latest question well?" Best for response quality at a specific point in the conversation.
-
-**Full-conversation split** — the first user message is the query; everything after is the response:
-
-```
-query_messages:    [user1]
-response_messages: [assistant1, user2, assistant2(tool), tool_result, assistant3]
-```
-
-This evaluates: "Given the original request, did the entire conversation trajectory serve the user?" Best for task completion and overall conversation quality.
-
-**Per-turn split** — produces N eval items from an N-turn conversation. Each turn is evaluated with its cumulative context:
-
-```
-item 1: query = [user1],                        response = [assistant1]
-item 2: query = [user1, assistant1, user2],      response = [assistant2(tool), tool_result, assistant3]
-```
-
-This evaluates each response independently. Best for fine-grained analysis and pinpointing where a conversation goes wrong.
-
-These factorings produce different scores for the same conversation. The framework ships all three as built-in strategies, defaulting to last-turn. Developers can also provide a custom splitter — a function (Python) or `IConversationSplitter` implementation (.NET) — and override the strategy at the call site or per evaluator.
-
-### Azure AI: FoundryEvals
-
-`Evaluator` implementation backed by Azure AI Foundry:
-
-```python
-class FoundryEvals:
-    def __init__(self, *, project_client=None, openai_client=None,
-                 model_deployment: str, evaluators=None, ...)
-    async def evaluate(self, items, *, eval_name) -> EvalResults
-```
-
-**Smart auto-detection in `evaluate()`:**
-- Default evaluators: relevance, coherence, task_adherence
-- Auto-adds `tool_call_accuracy` when items have tools/`tool_definitions`
-- Filters out tool evaluators for items without tools
-
-### Azure AI: FoundryEvals Constants
-
-```python
-from agent_framework.foundry import FoundryEvals
-
-evaluators = [FoundryEvals.RELEVANCE, FoundryEvals.TOOL_CALL_ACCURACY]
-```
-
-Categories: Agent behavior, Tool usage, Quality, Safety.
-
-### Azure AI: Foundry-Specific Functions
-
-| Function | What it does |
-|---|---|
-| `evaluate_traces()` | Evaluate from stored response IDs or OTel traces |
-| `evaluate_foundry_target()` | Evaluate a Foundry-registered agent or deployment |
-
-### Core: LocalEvaluator and Function Evaluators
-
-`LocalEvaluator` implements the `Evaluator` protocol for fast, API-free evaluation. It runs check functions locally — useful for inner-loop development, CI smoke tests, and combining with cloud-based evaluators.
-
-Built-in checks:
-- `keyword_check(*keywords)` — response must contain specified keywords
-- `tool_called_check(*tool_names)` — agent must have called specified tools
-- `tool_calls_present` — all `expected_tool_calls` names appear in conversation (unordered, extras OK)
-- `tool_call_args_match` — expected tool calls match on name + arguments (subset match on args)
-
-Custom function evaluators use `@evaluator` to wrap plain Python functions. The function's **parameter names** determine what data it receives from the `EvalItem`:
-
-```python
-from agent_framework import evaluator, LocalEvaluator
-
-# Tier 1: Simple check — just query + response
-@evaluator
-def is_concise(response: str) -> bool:
-    return len(response.split()) < 500
-
-# Tier 2: Ground truth — compare against expected output
-@evaluator
-def mentions_city(response: str, expected_output: str) -> bool:
-    return expected_output.lower() in response.lower()
-
-# Tier 3: Full context — inspect conversation and tools
-@evaluator
-def used_tools(conversation: list, tools: list) -> float:
-    # ... scoring logic
-    return score
-
-local = LocalEvaluator(is_concise, mentions_city, used_tools)
-```
-
-Supported parameters: `query`, `response`, `expected`, `expected_tool_calls`, `conversation`, `tools`, `context`.
-Return types: `bool`, `float` (≥0.5 = pass), `dict` with `score` or `passed` key, or `CheckResult`.
-
-Async functions are handled automatically — `@evaluator` detects `async def` and produces the right wrapper.
-
-### Example: GAIA Benchmark
-
-[GAIA](https://huggingface.co/gaia-benchmark) tests real-world multi-step tasks with known expected answers. Each task has a question and a ground-truth answer, with optional file attachments. The framework accommodates GAIA's knobs (difficulty levels, file inputs, multi-step tool use) through the existing `EvalItem` fields:
-
-```python
-from datasets import load_dataset
-from agent_framework import evaluate_agent, evaluator, LocalEvaluator
-
-gaia = load_dataset("gaia-benchmark/GAIA", "2023_level1", split="test")
-
-@evaluator
-def exact_match(response: str, expected_output: str) -> bool:
-    return expected_output.strip().lower() in response.strip().lower()
-
-# Simple path — evaluate_agent handles running + expected_output stamping
-results = await evaluate_agent(
-    agent=agent,
-    queries=[task["Question"] for task in gaia],
-    expected_output=[task["Final answer"] for task in gaia],
-    evaluators=LocalEvaluator(exact_match),
-)
-```
-
-### Package Location
-
-- Core types and orchestration: `agent_framework._eval`, `agent_framework._local_eval` (Python), `Microsoft.Agents.AI` (.NET)
-- Foundry provider: `agent_framework_azure_ai._foundry_evals` (Python), `Microsoft.Agents.AI.AzureAI` (.NET)
-- Azure-AI re-exports core types for convenience (Python)
-
-## Known Limitations
-
-1. **Tool evaluators require query + agent**: Tool evaluators need tool definition schemas. When using these evaluators with `evaluate_agent(responses=...)`, provide `queries=` and pass an agent with tool definitions.
-2. **`model_deployment` always required**: Could potentially be inferred from the Foundry project configuration.
-
-## Open Questions
-
-1. **Red teaming non-registered agents**: Requires Foundry API support for callback-based flows.
-2. **Datasets with expected outputs**: A dataset abstraction for pre-populating `expected_output` values across eval runs is a natural next step but not yet designed.
-3. **Multi-modal evaluation**: The `conversation` field on `EvalItem` already stores full `Message`/`Content` (Python) and `ChatMessage` (.NET) objects, which can represent multi-modal content (images, audio, structured data). Evaluators that accept the full `EvalItem` or `conversation` parameter can access this content today. However, the convenience shortcuts — `query`/`response` string projections and the `FunctionEvaluator` string overloads — are text-only. Multi-modal-aware evaluators should use the full-item path (`Func<EvalItem, CheckResult>` in .NET, `conversation: list` parameter in Python).
-
-## .NET Implementation Design
-
-### Key Difference: MEAI Ecosystem
-
-Unlike Python, the .NET ecosystem already has `Microsoft.Extensions.AI.Evaluation` (v10.3.0) providing:
-
-- `IEvaluator` — per-item evaluation of `(messages, chatResponse) → EvaluationResult`
-- `CompositeEvaluator` — combines multiple evaluators
-- Quality evaluators — `RelevanceEvaluator`, `CoherenceEvaluator`, `GroundednessEvaluator`
-- Safety evaluators — `ContentHarmEvaluator`, `ProtectedMaterialEvaluator`
-- Metric types — `NumericMetric`, `BooleanMetric`, `StringMetric`
-
-The .NET integration uses MEAI's `IEvaluator` directly — no new evaluator interface. Our contribution is the **orchestration layer**: extension methods that run agents, extract data, call `IEvaluator` per item, and aggregate results.
-
-### Architecture
-
-```
-┌──────────────────────────────────────────────────────────────┐
-│  Developer Code                                              │
-│  agent.EvaluateAsync(queries, evaluator)                     │
-│  run.EvaluateAsync(evaluator)                                │
-└────────────────┬─────────────────────────────────────────────┘
-                 │
-┌────────────────▼─────────────────────────────────────────────┐
-│  Orchestration Layer (Microsoft.Agents.AI)                   │
-│  AgentEvaluationExtensions — runs agents, extracts data,     │
-│  calls IEvaluator per item, aggregates into                  │
-│  AgentEvaluationResults                                      │
-└────────────────┬─────────────────────────────────────────────┘
-                 │ IEvaluator (MEAI)
-                 │
-     ┌───────────┼────────────┐
-     │           │            │
- ┌───▼───-┐  ┌───▼────┐  ┌────▼──────────┐
- │ MEAI   │  │ Local  │  │ Foundry       │
- │ Quality│  │ Checks │  │ (cloud batch) │
- │ Safety │  │ Lambdas│  │               │
- └────────┘  └────────┘  └───────────────┘
-```
-
-All evaluators implement MEAI's `IEvaluator`. The orchestration layer doesn't need to know which kind — it calls `EvaluateAsync(messages, chatResponse)` per item on all of them. `FoundryEvals` handles batching internally (buffers items, submits once, returns per-item results).
-
-### .NET Core Types
-
-**No new evaluator interface.** Use MEAI's `IEvaluator` directly.
-
-**`AgentEvaluationResults`** — The only new type. Aggregates per-item MEAI `EvaluationResult`s across a batch of queries:
-
-```csharp
-public class AgentEvaluationResults
-{
-    public string Provider { get; init; }
-    public string? ReportUrl { get; init; }
-
-    // Per-item — standard MEAI EvaluationResult, unchanged
-    public IReadOnlyList<EvaluationResult> Items { get; init; }
-
-    // Aggregate pass/fail derived from metric interpretations
-    public int Passed { get; }
-    public int Failed { get; }
-    public int Total { get; }
-    public bool AllPassed { get; }
-
-    // Workflow: per-agent breakdown
-    public IReadOnlyDictionary<string, AgentEvaluationResults>? SubResults { get; init; }
-
-    public void AssertAllPassed(string? message = null);
-}
-```
-
-### .NET Evaluator Implementations
-
-All implement MEAI's `IEvaluator`:
-
-**`LocalEvaluator`** — Runs lambda checks locally, returns `BooleanMetric` per check:
-
-```csharp
-var local = new LocalEvaluator(
-    FunctionEvaluator.Create("is_concise",
-        (string response) => response.Split().Length < 500),
-    EvalChecks.KeywordCheck("weather"),
-    EvalChecks.ToolCalledCheck("get_weather"));
-```
-
-**MEAI evaluators** — Used directly, no adapter needed:
-
-```csharp
-var quality = new CompositeEvaluator(
-    new RelevanceEvaluator(),
-    new CoherenceEvaluator());
-```
-
-**`FoundryEvals`** — Implements `IEvaluator` but batches internally. On first call, buffers the item. On the last item (or when explicitly flushed), submits the batch to Foundry and distributes per-item results:
-
-```csharp
-var foundry = new FoundryEvals(projectClient, "gpt-4o");
-```
-
-### .NET Orchestration: Extension Methods
-
-```csharp
-public static class AgentEvaluationExtensions
-{
-    // Evaluate an agent against test queries
-    public static Task<AgentEvaluationResults> EvaluateAsync(
-        this AIAgent agent,
-        IEnumerable<string> queries,
-        IEvaluator evaluator,
-        ChatConfiguration? chatConfiguration = null,
-        IEnumerable<string>? expectedOutput = null,
-        CancellationToken cancellationToken = default);
-
-    // Evaluate pre-existing responses (without re-running the agent)
-    public static Task<AgentEvaluationResults> EvaluateAsync(
-        this AIAgent agent,
-        AgentResponse responses,
-        IEvaluator evaluator,
-        IEnumerable<string>? queries = null,
-        ChatConfiguration? chatConfiguration = null,
-        IEnumerable<string>? expectedOutput = null,
-        CancellationToken cancellationToken = default);
-
-    // Evaluate with multiple evaluators (one result per evaluator)
-    public static Task<IReadOnlyList<AgentEvaluationResults>> EvaluateAsync(
-        this AIAgent agent,
-        IEnumerable<string> queries,
-        IEnumerable<IEvaluator> evaluators,
-        ChatConfiguration? chatConfiguration = null,
-        IEnumerable<string>? expectedOutput = null,
-        CancellationToken cancellationToken = default);
-
-    // Evaluate a workflow run with per-agent breakdown
-    public static Task<AgentEvaluationResults> EvaluateAsync(
-        this Run run,
-        IEvaluator evaluator,
-        ChatConfiguration? chatConfiguration = null,
-        bool includeOverall = true,
-        bool includePerAgent = true,
-        CancellationToken cancellationToken = default);
-}
-```
-
-**Usage:**
-
-```csharp
-// MEAI evaluators — just works
-var results = await agent.EvaluateAsync(
-    queries: ["What's the weather?"],
-    evaluator: new RelevanceEvaluator(),
-    chatConfiguration: new ChatConfiguration(evalClient));
-
-// Local checks
-var results = await agent.EvaluateAsync(
-    queries: ["What's the weather?"],
-    evaluator: new LocalEvaluator(
-        EvalChecks.KeywordCheck("weather")));
-
-// Foundry cloud
-var results = await agent.EvaluateAsync(
-    queries: ["What's the weather?"],
-    evaluator: new FoundryEvals(projectClient, "gpt-4o"));
-
-// Evaluate existing response (without re-running the agent)
-var response = await agent.RunAsync("What's the weather?");
-var results = await agent.EvaluateAsync(
-    responses: response,
-    queries: ["What's the weather?"],
-    evaluator: new FoundryEvals(projectClient, "gpt-4o"));
-
-// Mixed — one result per evaluator
-var results = await agent.EvaluateAsync(
-    queries: ["What's the weather?"],
-    evaluators: [
-        new LocalEvaluator(EvalChecks.KeywordCheck("weather")),
-        new RelevanceEvaluator(),
-        new FoundryEvals(projectClient, "gpt-4o")
-    ],
-    chatConfiguration: new ChatConfiguration(evalClient));
-
-// Workflow with per-agent breakdown
-Run run = await workflowRunner.RunAsync(workflow, "Plan a trip");
-var results = await run.EvaluateAsync(
-    evaluator: new FoundryEvals(projectClient, "gpt-4o"));
-```
-
-### .NET Function Evaluators
-
-Typed factory overloads (C# equivalent of Python's `@evaluator`):
-
-```csharp
-public static class FunctionEvaluator
-{
-    public static EvalCheck Create(string name, Func<string, bool> check);           // response only
-    public static EvalCheck Create(string name, Func<string, string?, bool> check);  // expectedOutput
-    public static EvalCheck Create(string name, Func<EvalItem, bool> check);         // full item
-    public static EvalCheck Create(string name, Func<EvalItem, CheckResult> check);  // full control
-    public static EvalCheck Create(string name, Func<string, Task<bool>> check);     // async
-}
-```
-
-`EvalItem` is a lightweight record used only by `FunctionEvaluator` and `LocalEvaluator` to pass context to check functions. It is not part of the `IEvaluator` interface:
-
-```csharp
-public record ExpectedToolCall(string Name, IReadOnlyDictionary<string, object>? Arguments = null);
-
-public sealed class EvalItem
-{
-    public EvalItem(string query, string response, IReadOnlyList<ChatMessage> conversation);
-
-    public string Query { get; }
-    public string Response { get; }
-    public IReadOnlyList<ChatMessage> Conversation { get; }
-    public IReadOnlyList<AITool>? Tools { get; set; }
-    public string? ExpectedOutput { get; set; }
-    public IReadOnlyList<ExpectedToolCall>? ExpectedToolCalls { get; set; }
-    public string? Context { get; set; }
-    public IConversationSplitter? Splitter { get; set; }
-}
-```
-
-### Workflow Data Extraction (.NET)
-
-`run.EvaluateAsync()` walks `Run.OutgoingEvents` via LINQ:
-
-1. Pair `ExecutorInvokedEvent` / `ExecutorCompletedEvent` by `ExecutorId`
-2. Extract `AgentResponseEvent` for per-agent `ChatResponse`
-3. Call `evaluator.EvaluateAsync()` per invocation
-4. Group by `ExecutorId` for per-agent `SubResults`
-5. Use final workflow output for overall eval
-
-### .NET Package Structure
-
-| Package | Contents |
-|---------|----------|
-| `Microsoft.Agents.AI` | `IAgentEvaluator`, `AgentEvaluationResults`, `LocalEvaluator`, `FunctionEvaluator`, `EvalChecks`, `EvalItem`, `ExpectedToolCall`, `AgentEvaluationExtensions` |
-| `Microsoft.Agents.AI.AzureAI` | `FoundryEvals` (provider + constants) |
-
-### Python ↔ .NET Mapping
-
-| Python | .NET |
-|--------|------|
-| `Evaluator` protocol | `IAgentEvaluator` (our interface; MEAI provides `IEvaluator` for per-item scoring) |
-| `EvalItem` dataclass | `EvalItem` class |
-| `EvalResults` | `AgentEvaluationResults` |
-| `EvalItemResult` / `EvalScoreResult` | MEAI `EvaluationResult` / `EvaluationMetric` (reused) |
-| `LocalEvaluator` | `LocalEvaluator` (implements `IAgentEvaluator`) |
-| `@evaluator` | `FunctionEvaluator.Create()` overloads |
-| `keyword_check()` / `tool_called_check()` | `EvalChecks.KeywordCheck()` / `EvalChecks.ToolCalledCheck()` |
-| `tool_calls_present` / `tool_call_args_match` | (custom `FunctionEvaluator` — same pattern) |
-| `ExpectedToolCall` dataclass | `ExpectedToolCall` record |
-| `FoundryEvals` | `FoundryEvals` (implements `IAgentEvaluator`, includes evaluator name constants) |
-| `evaluate_agent()` | `agent.EvaluateAsync(queries, evaluator)` extension method |
-| `evaluate_agent(responses=)` | `agent.EvaluateAsync(responses, evaluator)` extension method |
-| `evaluate_workflow()` | `run.EvaluateAsync()` extension method |
-
-## More Information
-
-- [Foundry Evals documentation](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-approach-gen-ai) — Azure AI Foundry evaluation overview

docs/features/durable-agents/AGENTS.md

@@ -1,48 +0,0 @@
-# AGENTS.md
-
-Instructions for AI coding agents working on durable agents documentation.
-
-## Scope
-
-This directory contains feature documentation for the durable agents integration. The source code and samples live elsewhere:
-
-- .NET implementation: `dotnet/src/Microsoft.Agents.AI.DurableTask/` and `dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions/`
-- Python implementation: `python/packages/durabletask/` and `python/packages/azurefunctions/` (package `agent-framework-azurefunctions`)
-- .NET samples: `dotnet/samples/04-hosting/DurableAgents/`
-- Python samples: `python/samples/04-hosting/durabletask/`
-- Official docs (Microsoft Learn): <https://learn.microsoft.com/agent-framework/integrations/azure-functions>
-
-## Document structure
-
-| File | Purpose |
-| --- | --- |
-| `README.md` | Main technical overview: architecture, hosting models, orchestration patterns, and links to samples. |
-| `durable-agents-ttl.md` | Deep-dive on session Time-To-Live (TTL) configuration and behavior. |
-
-Add new sibling documents when a topic is too detailed for the README (e.g., a new feature like reliable streaming or MCP tool exposure). Keep the README focused on orientation and link out to siblings for depth.
-
-## Writing guidelines
-
-- **Audience**: Developers already familiar with the Microsoft Agent Framework who want to understand what durability adds and how to use it.
-- **Host-agnostic first**: Durable agents work in console apps, Azure Functions, and any Durable Task–compatible host. Show host-agnostic patterns (plain orchestration functions, `IServiceCollection` registration) before Azure Functions–specific patterns. Avoid giving the impression that Azure Functions is the only hosting option.
-- **Both languages**: Always include C# and Python examples side by side. Keep them equivalent in functionality.
-- **Callout syntax**: Use GitHub-flavored callouts (`> [!NOTE]`, `> [!IMPORTANT]`, `> [!WARNING]`) rather than bold-text callouts (`> **Note:** ...`).
-- **Line length**: Do not wrap long lines. Rely on text viewers / renderers for line wrapping.
-- **Tables**: Use spaces around pipes in separator rows (`| --- |` not `|---|`).
-- **Code snippets**: Keep them minimal and self-contained. Omit boilerplate (using statements, environment variable reads) unless the snippet is specifically about setup.
-- **Cross-references**: Link to Microsoft Learn for conceptual background (Durable Entities, Durable Task Scheduler, Azure Functions). Link to sibling docs within this directory for feature deep-dives.
-
-## Linting
-
-Run markdownlint on all documents before committing, with line-length checks disabled:
-
-```bash
-markdownlint docs/features/durable-agents/ --disable MD013
-```
-
-## When to update these docs
-
-- A new durable agent feature is added (e.g., a new orchestration pattern, hosting model, or configuration option).
-- The public API surface changes in a way that affects how developers use durable agents.
-- New sample directories are added — update the sample links in README.md.
-- The official Microsoft Learn documentation is restructured — update external links.

docs/features/durable-agents/README.md

@@ -1,239 +0,0 @@
-# Durable agents
-
-## Overview
-
-Durable agents extend the standard Microsoft Agent Framework with **durable state management** powered by the Durable Task framework. An ordinary Agent Framework agent runs in-process: its conversation history lives in memory and is lost when the process ends. A durable agent persists conversation history and execution state in external storage so that sessions survive process restarts, failures, and scale-out events.
-
-| Capability | Ordinary agent | Durable agent |
-| --- | --- | --- |
-| Conversation history | In-memory only | Durably persisted |
-| Failure recovery | State lost on crash | Automatically resumed |
-| Multi-instance scale-out | Not supported | Any worker can resume a session |
-| Multi-agent orchestrations | Manual coordination | Deterministic, checkpointed workflows |
-| Human-in-the-loop | Must keep process alive | Can wait days/weeks with zero compute |
-| Hosting | Any process | Console app, Azure Functions, or any Durable Task–compatible host |
-
-> [!NOTE]
-> For a step-by-step tutorial and deployment guidance, see [Azure Functions (Durable)](https://learn.microsoft.com/agent-framework/integrations/azure-functions) on Microsoft Learn.
-
-## How durable agents work
-
-Durable agents are implemented on top of [Durable Entities](https://learn.microsoft.com/azure/azure-functions/durable/durable-functions-entities) (also called "virtual actors"). Each **agent session** maps to one entity instance whose state contains the full conversation history. When you send a message to a durable agent, the following happens:
-
-1. The message is dispatched to the entity identified by an `AgentSessionId` (a composite of the agent name and a unique session key).
-2. The entity loads its persisted `DurableAgentState`, which includes the complete conversation history.
-3. The entity invokes the underlying `AIAgent` with the full conversation history, collects the response, and appends both the request and the response to the state.
-4. The updated state is persisted back to durable storage automatically.
-
-Because the entity framework serializes access to each entity instance, concurrent messages to the same session are processed one at a time, eliminating race conditions.
-
-### Agent session identity
-
-Every durable agent session is identified by an `AgentSessionId`, which has two components:
-
-- **Name** – the registered name of the agent (case-insensitive).
-- **Key** – a unique session key (case-sensitive), typically a GUID.
-
-The session ID is mapped to an underlying Durable Task entity ID with a `dafx-` prefix (e.g., `dafx-joker`). This naming convention is consistent across both .NET and Python implementations.
-
-## Architecture
-
-### .NET
-
-The .NET implementation consists of two NuGet packages:
-
-| Package | Purpose |
-| --- | --- |
-| `Microsoft.Agents.AI.DurableTask` | Core durable agent types: `DurableAIAgent`, `AgentEntity`, `DurableAgentSession`, `AgentSessionId`, `DurableAgentsOptions`, and the state model. |
-| `Microsoft.Agents.AI.Hosting.AzureFunctions` | Azure Functions hosting integration: auto-generated HTTP endpoints, MCP tool triggers, entity function triggers, and the `ConfigureDurableAgents` extension method on `FunctionsApplicationBuilder`. |
-
-Key types:
-
-- **`DurableAIAgent`** – A subclass of `AIAgent` used *inside orchestrations*. Obtained via `context.GetAgent("agentName")`, it routes `RunAsync` calls through the orchestration's entity APIs so that each call is checkpointed.
-- **`DurableAIAgentProxy`** – A subclass of `AIAgent` used *outside orchestrations* (e.g., from HTTP triggers or console apps). It signals the entity via `DurableTaskClient` and polls for the response.
-- **`AgentEntity`** – The `TaskEntity<DurableAgentState>` that hosts the real agent. It loads the registered `AIAgent` by name, wraps it in an `EntityAgentWrapper`, feeds it the full conversation history, and persists the result.
-- **`DurableAgentSession`** – An `AgentSession` subclass that carries the `AgentSessionId`.
-- **`DurableAgentsOptions`** – Builder for registering agents and configuring TTL.
-
-### Python
-
-The core Python implementation is in the `agent-framework-durabletask` package (`python/packages/durabletask`). Azure Functions hosting (including `AgentFunctionApp`) is in the separate `agent-framework-azurefunctions` package (`python/packages/azurefunctions`).
-
-Key types:
-
-- **`DurableAIAgent`** – A generic proxy (`DurableAIAgent[TaskT]`) implementing `SupportsAgentRun`. Returns a `TaskT` from `run()` — either an `AgentResponse` (client context) or a `DurableAgentTask` (orchestration context, must be `yield`ed).
-- **`DurableAIAgentWorker`** – Wraps a `TaskHubGrpcWorker` and registers agents as durable entities via `add_agent()`.
-- **`DurableAIAgentClient`** – Wraps a `TaskHubGrpcClient` for external callers. `get_agent()` returns a `DurableAIAgent[AgentResponse]`.
-- **`DurableAIAgentOrchestrationContext`** – Wraps an `OrchestrationContext` for use inside orchestrations. `get_agent()` returns a `DurableAIAgent[DurableAgentTask]`.
-- **`AgentEntity`** – Platform-agnostic agent execution logic that manages state, invokes the agent, handles streaming, and calls response callbacks.
-
-## Hosting models
-
-### Azure Functions
-
-The recommended production hosting model. A single call to `ConfigureDurableAgents` (C#) or `AgentFunctionApp` (Python) automatically:
-
-- Registers agent entities with the Durable Task worker.
-- Generates HTTP endpoints at `/api/agents/{agentName}/run` for each registered agent.
-- Supports `thread_id` query parameter / JSON field and the `x-ms-thread-id` response header for session continuity.
-- Supports fire-and-forget via the `x-ms-wait-for-response: false` header (returns HTTP 202).
-- Optionally exposes agents as MCP tools.
-
-**C# example:**
-
-```csharp
-using IHost app = FunctionsApplication
-    .CreateBuilder(args)
-    .ConfigureFunctionsWebApplication()
-    .ConfigureDurableAgents(options => options.AddAIAgent(agent))
-    .Build();
-app.Run();
-```
-
-**Python example:**
-
-```python
-app = AgentFunctionApp(agents=[agent])
-```
-
-### Console apps / generic hosts
-
-For self-hosted or non-serverless scenarios, register durable agents via `IServiceCollection.ConfigureDurableAgents` (.NET) or `DurableAIAgentWorker` (Python) with explicit Durable Task worker and client configuration.
-
-**C# example:**
-
-```csharp
-IHost host = Host.CreateDefaultBuilder(args)
-    .ConfigureServices(services =>
-    {
-        services.ConfigureDurableAgents(
-            options => options.AddAIAgent(agent),
-            workerBuilder: b => b.UseDurableTaskScheduler(connectionString),
-            clientBuilder: b => b.UseDurableTaskScheduler(connectionString));
-    })
-    .Build();
-```
-
-**Python example:**
-
-```python
-worker = DurableAIAgentWorker(TaskHubGrpcWorker(host_address="localhost:4001"))
-worker.add_agent(agent)
-worker.start()
-```
-
-## Deterministic multi-agent orchestrations
-
-Durable agents can be composed into deterministic, checkpointed workflows using Durable Task orchestrations. The orchestration framework replays orchestrator code on failure, so completed agent calls are not re-executed.
-
-### Patterns
-
-| Pattern | Description |
-| --- | --- |
-| **Sequential (chaining)** | Call agents one after another, passing outputs forward. |
-| **Parallel (fan-out/fan-in)** | Run multiple agents concurrently and aggregate results. |
-| **Conditional** | Branch orchestration logic based on structured agent output. |
-| **Human-in-the-loop** | Pause for external events (approvals, feedback) with optional timeouts. |
-
-### Using agents in orchestrations
-
-Inside an orchestration function, obtain a `DurableAIAgent` via the orchestration context. Each agent gets its own session (created with `CreateSessionAsync` / `create_session`), and you can call the same agent multiple times on the same session to maintain conversation context across sequential invocations.
-
-**C#:**
-
-```csharp
-static async Task<string> WritingOrchestration(TaskOrchestrationContext context)
-{
-    // Get a durable agent reference — works in any host (console app, Azure Functions, etc.)
-    DurableAIAgent writer = context.GetAgent("WriterAgent");
-
-    // Create a session to maintain conversation context across multiple calls
-    AgentSession session = await writer.CreateSessionAsync();
-
-    // First call: generate an initial draft
-    AgentResponse<TextResponse> draft = await writer.RunAsync<TextResponse>(
-        message: "Write a concise inspirational sentence about learning.",
-        session: session);
-
-    // Second call: refine the draft — the agent sees the full conversation history
-    AgentResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
-        message: $"Improve this further while keeping it under 25 words: {draft.Result.Text}",
-        session: session);
-
-    return refined.Result.Text;
-}
-```
-
-**Python:**
-
-```python
-def writing_orchestration(context, _):
-    agent_ctx = DurableAIAgentOrchestrationContext(context)
-
-    # Get a durable agent reference — works in any host (standalone worker, Azure Functions, etc.)
-    writer = agent_ctx.get_agent("WriterAgent")
-
-    # Create a session to maintain conversation context across multiple calls
-    session = writer.create_session()
-
-    # First call: generate an initial draft
-    draft = yield writer.run(
-        messages="Write a concise inspirational sentence about learning.",
-        session=session,
-    )
-
-    # Second call: refine the draft — the agent sees the full conversation history
-    refined = yield writer.run(
-        messages=f"Improve this further while keeping it under 25 words: {draft.text}",
-        session=session,
-    )
-
-    return refined.text
-```
-
-> [!IMPORTANT]
-> In .NET, `DurableAIAgent.RunAsync<T>` deliberately avoids `ConfigureAwait(false)` because the Durable Task Framework uses a custom synchronization context — all continuations must run on the orchestration thread.
-
-## Streaming and response callbacks
-
-Durable agents do not support true end-to-end streaming because entity operations are request/response. However, **reliable streaming** is supported via response callbacks:
-
-- **`IAgentResponseHandler`** (.NET) or **`AgentResponseCallbackProtocol`** (Python) – Implement this interface to receive streaming updates as the underlying agent generates them (e.g., push tokens to a Redis Stream for client consumption).
-- The entity still returns the complete `AgentResponse` after the stream is fully consumed.
-- Clients can reconnect and resume reading from a cursor-based stream (e.g., Redis Streams) without losing messages.
-
-See the **Reliable Streaming** samples for a complete implementation using Redis Streams.
-
-## Session TTL (Time-To-Live)
-
-Durable agent sessions support automatic cleanup via configurable TTL. See [Session TTL](durable-agents-ttl.md) for details on configuration, behavior, and best practices.
-
-## Observability
-
-When using the [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-functions/durable/durable-task-scheduler/durable-task-scheduler) as the durable backend, you get built-in observability through its dashboard:
-
-- **Conversation history** – View complete chat history for each agent session.
-- **Orchestration visualization** – See multi-agent execution flows, including parallel branches and conditional logic.
-- **Performance metrics** – Monitor agent response times, token usage, and orchestration duration.
-- **Debugging** – Trace tool invocations and external event handling.
-
-## Samples
-
-- **.NET** – [Console app samples](../../../dotnet/samples/04-hosting/DurableAgents/ConsoleApps/) and [Azure Functions samples](../../../dotnet/samples/04-hosting/DurableAgents/AzureFunctions/) covering single-agent, chaining, concurrency, conditionals, human-in-the-loop, long-running tools, MCP tool exposure, and reliable streaming.
-- **Python** – [Durable Task samples](../../../python/samples/04-hosting/durabletask/) covering single-agent, multi-agent, streaming, chaining, concurrency, conditionals, and human-in-the-loop.
-
-## Packages
-
-| Language | Package | Source |
-| --- | --- | --- |
-| .NET | `Microsoft.Agents.AI.DurableTask` | [`dotnet/src/Microsoft.Agents.AI.DurableTask`](../../../dotnet/src/Microsoft.Agents.AI.DurableTask) |
-| .NET | `Microsoft.Agents.AI.Hosting.AzureFunctions` | [`dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions`](../../../dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions) |
-| Python | `agent-framework-durabletask` | [`python/packages/durabletask`](../../../python/packages/durabletask) |
-| Python | `agent-framework-azurefunctions` | [`python/packages/azurefunctions`](../../../python/packages/azurefunctions) |
-
-## Further reading
-
-- [Azure Functions (Durable) — Microsoft Learn](https://learn.microsoft.com/agent-framework/integrations/azure-functions)
-- [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-functions/durable/durable-task-scheduler/durable-task-scheduler)
-- [Durable Entities](https://learn.microsoft.com/azure/azure-functions/durable/durable-functions-entities)
-- [Session TTL](durable-agents-ttl.md)

docs/features/vector-stores-and-embeddings/README.md

@@ -1,390 +0,0 @@
-# Vector Stores and Embeddings
-
-## Overview
-
-This feature ports the vector store abstractions, embedding generator abstractions, and their implementations from Semantic Kernel into Agent Framework. The ported code follows AF's coding standards, feels native to AF, and is structured to allow data models/schemas to be reusable across both frameworks. The embedding abstraction combines the best of SK's `EmbeddingGeneratorBase` and MEAI's `IEmbeddingGenerator<TInput, TEmbedding>`.
-
-| Capability | Description |
-| --- | --- |
-| Embedding generation | Generic embedding client abstraction supporting text, image, and audio inputs |
-| Vector store collections | CRUD operations on vector store collections (upsert, get, delete) |
-| Vector search | Unified search interface with `search_type` parameter (`"vector"`, `"keyword_hybrid"`) |
-| Data model decorator | `@vectorstoremodel` decorator for defining vector store data models (supports Pydantic, dataclasses, plain classes, dicts) |
-| Agent tools | `create_search_tool`, `create_upsert_tool`, `create_get_tool`, `create_delete_tool` for agent-usable vector store operations |
-| In-memory store | Zero-dependency vector store for testing and development |
-| 13+ connectors | Azure AI Search, Qdrant, Redis, PostgreSQL, MongoDB, Cosmos DB, Pinecone, Chroma, Weaviate, Oracle, SQL Server, FAISS |
-
-## Key Design Decisions
-
-### Embedding Abstractions (combining SK + MEAI)
-- **Both Protocol and Base class** (matching AF's `SupportsChatGetResponse` + `BaseChatClient` pattern):
-  - `SupportsGetEmbeddings` — Protocol for duck-typing
-  - `BaseEmbeddingClient` — ABC base class for implementations (similar to `BaseChatClient`)
-- **Generic input type** (`EmbeddingInputT`, default `str`) from MEAI — allows image/audio embeddings in the future
-- **Generic output type** (`EmbeddingT`, default `list[float]`) from MEAI — supports `list[float]`, `list[int]`, `bytes`, etc.
-- **Generic order**: `[EmbeddingInputT, EmbeddingT, EmbeddingOptionsT]` — options last, matching MEAI's `IEmbeddingGenerator<TInput, TEmbedding>` with options appended
-- **TypeVar naming convention**: Use `SuffixT` per AF standard (e.g., `EmbeddingInputT`, `EmbeddingT`, `ModelT`, `KeyT`)
-- `EmbeddingGenerationOptions` TypedDict (inspired by MEAI, matching AF's `ChatOptions` pattern) — `total=False`, includes `dimensions`, `model_id`. No `additional_properties` since each implementation extends with its own fields.
-- Protocol and base class are generic over input, output, and options: `SupportsGetEmbeddings[EmbeddingInputT, EmbeddingT, OptionsContraT]`, `BaseEmbeddingClient[EmbeddingInputT, EmbeddingT, OptionsCoT]`
-- **`Embedding[EmbeddingT]` type** in `_types.py` — a lightweight generic class (not Pydantic) with `vector: EmbeddingT`, `model_id: str | None`, `dimensions: int | None` (explicit or computed from vector), `created_at: datetime | None`, `additional_properties: dict[str, Any]`
-- **`GeneratedEmbeddings[EmbeddingT, EmbeddingOptionsT]` type** — a list-like container of `Embedding[EmbeddingT]` objects with `options: EmbeddingOptionsT | None` (stores the options used to generate), `usage: dict[str, Any] | None`, `additional_properties: dict[str, Any]`
-- **No numpy dependency** — return `list[float]` by default; users cast as needed
-
-### Vector Store Abstractions
-- **Port core abstractions without Pydantic for internal classes** — use plain classes
-- **Both Protocol and Base class** for vector store operations (matching AF pattern):
-  - `SupportsVectorUpsert` / `SupportsVectorSearch` — Protocols for duck-typing (follows `Supports<Capability>` naming convention)
-  - `BaseVectorCollection` / `BaseVectorSearch` — ABC base classes for implementations
-  - `BaseVectorStore` — ABC base class for store operations (factory for collections, no protocol needed)
-- **TypeVar naming convention**: `ModelT`, `KeyT`, `FilterT` (suffix T, per AF standard)
-- **Support Pydantic for user-facing data models** — the `@vectorstoremodel` decorator and `VectorStoreCollectionDefinition` should work with Pydantic models, dataclasses, plain classes, and dicts
-- **Remove SK-specific dependencies** — no `KernelBaseModel`, `KernelFunction`, `KernelParameterMetadata`, `kernel_function`, `PromptExecutionSettings`
-- **Embedding types in `_types.py`**, embedding protocol/base class in `_clients.py`
-- **All vector store specific types, enums, protocols, base classes** in `_vectors.py`
-- **Error handling** uses AF's exception hierarchy (e.g., `IntegrationException` variants)
-
-### Package Structure
-- **Embedding types** (`Embedding`, `GeneratedEmbeddings`, `EmbeddingGenerationOptions`) in `agent_framework/_types.py`
-- **Embedding protocol + base class** (`SupportsGetEmbeddings`, `BaseEmbeddingClient`) in `agent_framework/_clients.py`
-- **All vector store specific code** in a new `agent_framework/_vectors.py` module — this includes:
-  - Enums: `FieldTypes`, `IndexKind`, `DistanceFunction`
-  - `VectorStoreField`, `VectorStoreCollectionDefinition`
-  - `SearchOptions`, `SearchResponse`, `RecordFilterOptions`
-  - `@vectorstoremodel` decorator
-  - Serialization/deserialization protocols
-  - `VectorStoreRecordHandler`, `BaseVectorCollection`, `BaseVectorStore`, `BaseVectorSearch`
-  - `SupportsVectorUpsert`, `SupportsVectorSearch` protocols
-- **OpenAI embeddings** in `agent_framework/openai/` (built into core, like OpenAI chat)
-- **Azure OpenAI embeddings** in `agent_framework/azure/` (built into core, follows `AzureOpenAIChatClient` pattern)
-- **Each vector store connector** in its own AF package under `packages/`
-- **In-memory store** in core (no external deps)
-- **TextSearch and its implementations** (Brave, Google) — last phase, separate work
-
-## Naming: SK → AF
-
-### Names that change
-
-| SK Name | AF Name | Rationale |
-|---------|---------|-----------|
-| `VectorStoreCollection` | `BaseVectorCollection` | Drop redundant `Store`, add `Base` prefix per AF pattern |
-| `VectorStore` | `BaseVectorStore` | Add `Base` prefix per AF pattern |
-| `VectorSearch` | `BaseVectorSearch` | Add `Base` prefix per AF pattern |
-| `VectorSearchOptions` | `SearchOptions` | Shorter — context is already vector search |
-| `VectorSearchResult` | `SearchResponse` | Align with `ChatResponse`/`AgentResponse` |
-| `GetFilteredRecordOptions` | `RecordFilterOptions` | Shorter, more natural |
-| `EmbeddingGeneratorBase` | `BaseEmbeddingClient` | Matches AF `BaseChatClient` pattern |
-| `VectorStoreCollectionProtocol` | `SupportsVectorUpsert` | AF `Supports*` naming convention |
-| `VectorSearchProtocol` | `SupportsVectorSearch` | AF `Supports*` naming convention |
-| `__kernel_vectorstoremodel__` | `__vectorstoremodel__` | Drop SK `kernel` prefix |
-| `__kernel_vectorstoremodel_definition__` | `__vectorstoremodel_definition__` | Drop SK `kernel` prefix |
-| `search()` + `hybrid_search()` | `search(search_type=...)` | Single method with `Literal` parameter |
-| `SearchType` enum | `Literal["vector", "keyword_hybrid"]` | No enum, just a literal |
-| `KernelSearchResults` | `SearchResults` | Drop SK `Kernel` prefix (plural — container of `SearchResponse` items) |
-
-### Names that stay the same
-
-| Name | Location |
-|------|----------|
-| `@vectorstoremodel` | `_vectors.py` |
-| `VectorStoreField` | `_vectors.py` |
-| `VectorStoreCollectionDefinition` | `_vectors.py` |
-| `VectorStoreRecordHandler` | `_vectors.py` |
-| `FieldTypes` | `_vectors.py` |
-| `IndexKind` | `_vectors.py` |
-| `DistanceFunction` | `_vectors.py` |
-| `DISTANCE_FUNCTION_DIRECTION_HELPER` | `_vectors.py` |
-| `Embedding` | `_types.py` |
-| `GeneratedEmbeddings` | `_types.py` |
-| `EmbeddingGenerationOptions` | `_types.py` |
-| `SupportsGetEmbeddings` | `_clients.py` |
-
-### New AF-only names (no SK equivalent)
-
-| Name | Location | Purpose |
-|------|----------|---------|
-| `BaseEmbeddingClient` | `_clients.py` | ABC base for embedding implementations |
-| `EmbeddingInputT` | `_types.py` | TypeVar for generic embedding input (default `str`) |
-| `EmbeddingTelemetryLayer` | `observability.py` | MRO-based OTel tracing for embeddings |
-| `SupportsVectorUpsert` | `_vectors.py` | Protocol for collection CRUD |
-| `SupportsVectorSearch` | `_vectors.py` | Protocol for vector search |
-| `create_search_tool` | `_vectors.py` | Creates AF `FunctionTool` from vector search |
-
-## Source Files Reference (SK → AF mapping)
-
-### SK Source Files
-| SK File | Lines | Content |
-|---------|-------|---------|
-| `data/vector.py` | 2369 | All vector store abstractions, enums, decorator, search |
-| `data/_shared.py` | 184 | SearchOptions, KernelSearchResults, shared search types |
-| `data/text_search.py` | 349 | TextSearch base, TextSearchResult |
-| `connectors/ai/embedding_generator_base.py` | 50 | EmbeddingGeneratorBase ABC |
-| `connectors/in_memory.py` | 520 | InMemoryCollection, InMemoryStore |
-| `connectors/azure_ai_search.py` | 793 | Azure AI Search collection + store |
-| `connectors/azure_cosmos_db.py` | 1104 | Cosmos DB (Mongo + NoSQL) |
-| `connectors/redis.py` | 845 | Redis (Hashset + JSON) |
-| `connectors/qdrant.py` | 653 | Qdrant collection + store |
-| `connectors/postgres.py` | 987 | PostgreSQL collection + store |
-| `connectors/mongodb.py` | 633 | MongoDB Atlas collection + store |
-| `connectors/pinecone.py` | 691 | Pinecone collection + store |
-| `connectors/chroma.py` | 484 | Chroma collection + store |
-| `connectors/faiss.py` | 278 | FAISS (extends InMemory) |
-| `connectors/weaviate.py` | 804 | Weaviate collection + store |
-| `connectors/oracle.py` | 1267 | Oracle collection + store |
-| `connectors/sql_server.py` | 1132 | SQL Server collection + store |
-| `connectors/ai/open_ai/services/open_ai_text_embedding.py` | 91 | OpenAI embedding impl |
-| `connectors/ai/open_ai/services/open_ai_text_embedding_base.py` | 78 | OpenAI embedding base |
-| `connectors/brave.py` | ~200 | Brave TextSearch impl |
-| `connectors/google_search.py` | ~200 | Google TextSearch impl |
-
----
-
-## Implementation Phases
-
-### Phase 1: Core Embedding Abstractions & OpenAI Implementation ✅ DONE
-**Goal:** Establish the embedding generator abstraction and ship one working implementation.
-**Mergeable:** Yes — adds new types/protocols, no breaking changes.
-**Status:** Merged via PR #4153. Closes sub-issue #4163.
-
-#### 1.1 — Embedding types in `_types.py`
-- `EmbeddingInputT` TypeVar (default `str`) — generic input type for embedding generation
-- `EmbeddingT` TypeVar (default `list[float]`) — generic output embedding vector type
-- `Embedding[EmbeddingT]` generic class: `vector: EmbeddingT`, `model_id: str | None`, `dimensions: int | None` (explicit param or computed from vector length), `created_at: datetime | None`, `additional_properties: dict[str, Any]`
-- `GeneratedEmbeddings[EmbeddingT, EmbeddingOptionsT]` generic class: list-like container of `Embedding[EmbeddingT]` objects with `options: EmbeddingOptionsT | None` (the options used to generate), `usage: dict[str, Any] | None`, `additional_properties: dict[str, Any]`
-- `EmbeddingGenerationOptions` TypedDict (`total=False`): `dimensions: int`, `model_id: str` — follows the same pattern as `ChatOptions`. No `additional_properties` needed since it's a TypedDict and each implementation can extend with its own fields.
-
-#### 1.2 — Embedding generator protocol + base class in `_clients.py`
-- `SupportsGetEmbeddings(Protocol[EmbeddingInputT, EmbeddingT, OptionsContraT])`: generic over input, output, and options (all with defaults), `get_embeddings(values: Sequence[EmbeddingInputT], *, options: OptionsContraT | None = None) -> Awaitable[GeneratedEmbeddings[EmbeddingT]]`
-- `BaseEmbeddingClient(ABC, Generic[EmbeddingInputT, EmbeddingT, OptionsCoT])`: ABC base class mirroring `BaseChatClient` pattern
-  - `__init__` with `additional_properties`, etc.
-  - Abstract `get_embeddings(...)` for subclasses to implement directly (no `_inner_*` indirection — simpler than chat, no middleware needed)
-- `EmbeddingTelemetryLayer` in `observability.py` — MRO-based telemetry (no closure), `gen_ai.operation.name = "embeddings"`
-
-#### 1.3 — OpenAI embedding generator in `agent_framework/openai/` and `agent_framework/azure/`
-- `RawOpenAIEmbeddingClient` — implements `get_embeddings` via `_ensure_client()` factory
-- `OpenAIEmbeddingClient(OpenAIConfigMixin, EmbeddingTelemetryLayer[str, list[float], OptionsT], RawOpenAIEmbeddingClient[OptionsT])` — full client with config + telemetry layers
-- `OpenAIEmbeddingOptions(EmbeddingGenerationOptions)` — extends with `encoding_format`, `user`
-- `AzureOpenAIEmbeddingClient` in `agent_framework/azure/` — follows `AzureOpenAIChatClient` pattern with `AzureOpenAIConfigMixin`, `load_settings`, Entra ID credential support
-- `AzureOpenAISettings` extended with `embedding_deployment_name` (env var: `AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME`)
-
-#### 1.4 — Tests and samples
-- Unit tests for types, protocol, base class, OpenAI client, Azure OpenAI client
-- Integration tests for OpenAI and Azure OpenAI (gated behind credentials check, `@pytest.mark.flaky`)
-- Samples in `samples/02-agents/embeddings/` — `openai_embeddings.py`, `azure_openai_embeddings.py`
-
----
-
-### Phase 2: Embedding Generators for Existing Providers
-**Goal:** Add embedding generators to all existing AF provider packages that have chat clients.
-**Mergeable:** Yes — each is independent, added to existing provider packages.
-
-#### 2.1 — Azure AI Inference embedding (in `packages/azure-ai/`)
-#### 2.2 — Ollama embedding (in `packages/ollama/`)
-#### 2.3 — Anthropic embedding (in `packages/anthropic/`)
-#### 2.4 — Bedrock embedding (in `packages/bedrock/`)
-
----
-
-### Phase 3: Core Vector Store Abstractions
-**Goal:** Establish all vector store types, enums, the decorator, collection definition, and base classes.
-**Mergeable:** Yes — adds new abstractions, no breaking changes.
-
-#### 3.1 — Vector store enums and field types in `_vectors.py`
-- `FieldTypes` enum: `KEY`, `VECTOR`, `DATA`
-- `IndexKind` enum: `HNSW`, `FLAT`, `IVF_FLAT`, `DISK_ANN`, `QUANTIZED_FLAT`, `DYNAMIC`, `DEFAULT`
-- `DistanceFunction` enum: `COSINE_SIMILARITY`, `COSINE_DISTANCE`, `DOT_PROD`, `EUCLIDEAN_DISTANCE`, `EUCLIDEAN_SQUARED_DISTANCE`, `MANHATTAN`, `HAMMING`, `DEFAULT`
-- No `SearchType` enum — use `Literal["vector", "keyword_hybrid"]` instead, per AF convention of avoiding unnecessary imports
-- `VectorStoreField` plain class (not Pydantic)
-- `VectorStoreCollectionDefinition` class (not Pydantic internally, but supports Pydantic models as input)
-- `SearchOptions` plain class — includes `score_threshold: float | None` for filtering results by score (see note below)
-- `SearchResponse` generic class
-- `RecordFilterOptions` plain class
-- `DISTANCE_FUNCTION_DIRECTION_HELPER` dict
-
-#### 3.2 — `@vectorstoremodel` decorator
-- Port from SK, works with dataclasses, Pydantic models, plain classes, and dicts
-- Sets `__vectorstoremodel__` and `__vectorstoremodel_definition__` on the class
-- Remove SK-specific `kernel` prefix (`__kernel_vectorstoremodel__` → `__vectorstoremodel__`)
-
-#### 3.3 — Serialization/deserialization protocols
-- `SerializeMethodProtocol`, `ToDictFunctionProtocol`, `FromDictFunctionProtocol`, etc.
-- Port the record handler logic but without Pydantic base class — use plain class or ABC
-
-#### 3.4 — Vector store base classes in `_vectors.py`
-- `VectorStoreRecordHandler` — internal base class that handles serialization/deserialization between user data models and store-specific formats, plus embedding generation for vector fields. Both `BaseVectorCollection` and `BaseVectorSearch` extend this.
-- `BaseVectorCollection(VectorStoreRecordHandler)` — base for collections
-  - Uses `SupportsGetEmbeddings` instead of `EmbeddingGeneratorBase`
-  - Not a Pydantic model — use `__init__` with explicit params
-  - `upsert`, `get`, `delete`, `ensure_collection_exists`, `collection_exists`, `ensure_collection_deleted`
-  - Async context manager support
-- `BaseVectorStore` — base for stores
-  - `get_collection`, `list_collection_names`, `collection_exists`, `ensure_collection_deleted`
-  - Async context manager support
-
-#### 3.5 — Vector search base class
-- `BaseVectorSearch(VectorStoreRecordHandler)` — base for vector search
-  - Single `search(search_type=...)` method with `search_type: Literal["vector", "keyword_hybrid"]` parameter — no enum, just a literal
-  - `_inner_search` abstract method for implementations
-  - Filter building with lambda parser (AST-based)
-  - Vector generation from values using embedding generator
-
-#### 3.6 — Protocols for type checking
-- `SupportsVectorUpsert` — Protocol for upsert/get/delete operations
-- `SupportsVectorSearch` — Protocol for vector search (single `search()` with `search_type` parameter)
-- No separate `SupportsVectorHybridSearch` — search type is a parameter, not a separate capability
-- No protocol for `VectorStore` — it's a factory for collections, not a capability to duck-type against
-
-#### 3.7 — Exception types
-- Add vector store exceptions under `IntegrationException` or create new branch
-- `VectorStoreException`, `VectorStoreOperationException`, `VectorSearchException`, `VectorStoreModelException`, etc.
-
-#### 3.8 — `create_search_tool` on `BaseVectorSearch`
-- Method on `BaseVectorSearch` that creates an AF `FunctionTool` from the vector search
-- Wraps the single `search()` method, passing `search_type` parameter
-- Accepts: `name`, `description`, `search_type`, `top`, `skip`, `filter`, `string_mapper`
-- The tool takes a query string, vectorizes it, searches, and returns results as strings
-- Can also be a standalone factory function in `_vectors.py`
-
-#### 3.9 — Tests for all vector store abstractions
-- Unit tests for enums, field types, collection definition
-- Unit tests for decorator
-- Unit tests for serialization/deserialization
-- Unit tests for record handler
-
----
-
-### Phase 4: In-Memory Vector Store
-**Goal:** Provide a zero-dependency vector store for testing and development.
-**Mergeable:** Yes — first usable vector store.
-
-#### 4.1 — Port `InMemoryCollection` and `InMemoryStore` into core
-- Place in `agent_framework/_vectors.py` (alongside the abstractions)
-- Supports vector search (cosine similarity, etc.)
-- No external dependencies
-
-#### 4.2 — Port FAISS extension (optional, can be separate package)
-- Extends InMemory with FAISS indexing
-
-#### 4.3 — Tests and sample code
-
----
-
-### Phase 5: Vector Store Connectors — Tier 1 (High Priority)
-**Goal:** Ship the most commonly used vector store connectors.
-**Mergeable:** Yes — each connector is independent.
-
-Each connector follows the AF package structure:
-- New package under `packages/`
-- Own `pyproject.toml`, `tests/`, lazy loading in core
-
-#### 5.1 — Azure AI Search (`packages/azure-ai-search/`)
-- May extend existing package or be new
-- `AzureAISearchCollection`, `AzureAISearchStore`
-
-#### 5.2 — Qdrant (`packages/qdrant/`)
-- New package
-- `QdrantCollection`, `QdrantStore`
-
-#### 5.3 — Redis (`packages/redis/`)
-- May extend existing redis package
-- `RedisCollection` (JSON + Hashset variants), `RedisStore`
-
-#### 5.4 — PostgreSQL/pgvector (`packages/postgres/`)
-- New package
-- `PostgresCollection`, `PostgresStore`
-
----
-
-### Phase 6: Vector Store Connectors — Tier 2
-**Goal:** Ship remaining vector store connectors.
-**Mergeable:** Yes — each connector is independent.
-
-#### 6.1 — MongoDB Atlas (`packages/mongodb/`)
-#### 6.2 — Azure Cosmos DB (`packages/azure-cosmos-db/`)
-- Cosmos Mongo + Cosmos NoSQL
-#### 6.3 — Pinecone (`packages/pinecone/`)
-#### 6.4 — Chroma (`packages/chroma/`)
-#### 6.5 — Weaviate (`packages/weaviate/`)
-
----
-
-### Phase 7: Vector Store Connectors — Tier 3
-**Goal:** Ship niche or less common connectors.
-**Mergeable:** Yes — each connector is independent.
-
-#### 7.1 — Oracle (`packages/oracle/`)
-#### 7.2 — SQL Server (`packages/sql-server/`)
-#### 7.3 — FAISS (`packages/faiss/` or in core extending InMemory)
-
-> **Note:** When implementing any SQL-based connector (PostgreSQL, SQL Server, SQLite, Cosmos DB), review the .NET MEVD changes made by @roji (Shay Rojansky) in SK for design patterns, query building, filter translation, and feature parity: https://github.com/microsoft/semantic-kernel/pulls?q=is%3Apr+author%3Aroji+is%3Aclosed
-
----
-
-### Phase 8: Vector Store CRUD Tools
-**Goal:** Provide a full set of agent-usable tools for CRUD operations on vector store collections.
-**Mergeable:** Yes — adds tools without changing existing APIs.
-
-#### 8.1 — `create_upsert_tool` — tool for upserting records into a collection
-#### 8.2 — `create_get_tool` — tool for retrieving records by key
-- Key-based lookup only (by primary key), not a search tool
-- Documentation must clearly distinguish this from `create_search_tool`: get_tool retrieves specific records by their known key, while search_tool performs similarity/filtered search across the collection
-- Consider if this overlaps with filtered search and document when to use which
-#### 8.3 — `create_delete_tool` — tool for deleting records by key
-#### 8.4 — Tests and samples for CRUD tools
-
----
-
-### Phase 9: Additional Embedding Implementations (New Providers)
-**Goal:** Provide embedding generators for providers that don't yet have AF packages.
-**Mergeable:** Yes — each is independent, new packages.
-
-#### 9.1 — HuggingFace/ONNX embedding (new package or lab)
-#### 9.2 — Mistral AI embedding (new package)
-#### 9.3 — Google AI / Vertex AI embedding (new package)
-#### 9.4 — Nvidia embedding (new package)
-
----
-
-### Phase 10: TextSearch Abstractions & Implementations (Separate Work)
-**Goal:** Port text search (non-vector) abstractions and implementations.
-**Mergeable:** Yes — independent of vector stores.
-
-#### 10.1 — TextSearch base class and types
-- `SearchOptions`, `SearchResponse`, `TextSearchResult`
-- `TextSearch` base class with `search()` method
-- `create_search_function()` for kernel integration (may need AF equivalent)
-
-#### 10.2 — Brave Search implementation
-#### 10.3 — Google Search implementation
-#### 10.4 — Vector store text search bridge (connecting VectorSearch to TextSearch interface)
-
----
-
-## Key Considerations
-
-1. **No Pydantic for internal classes**: All AF internal classes should use plain classes. Pydantic is only used for user-facing input validation (e.g., vector store data models).
-
-2. **Protocol + Base class**: Follow AF's pattern of both a `Protocol` for duck-typing and a `Base` ABC for implementation, matching how `SupportsChatGetResponse` + `BaseChatClient` works.
-
-3. **Exception hierarchy**: Use AF's `IntegrationException` branch for vector store operations, since vector stores are external dependencies.
-
-4. **`from __future__ import annotations`**: Required in all files per AF coding standard.
-
-5. **No `**kwargs` escape hatches in public APIs**: For user-facing interfaces, use explicit named parameters per AF coding standard. Internal implementation details (e.g., cooperative multiple inheritance / MRO patterns) may use `**kwargs` where necessary, as long as they are not exposed in public signatures.
-
-6. **Lazy loading**: Connector packages use `__getattr__` lazy loading in core provider folders.
-
-7. **Reusable data models**: The `@vectorstoremodel` decorator and `VectorStoreCollectionDefinition` should be agnostic enough to work with both SK and AF. The core types (`FieldTypes`, `IndexKind`, `DistanceFunction`, `VectorStoreField`) should be identical or easily mapped.
-
-8. **`create_search_tool`**: The AF-native equivalent of SK's `create_search_function`. Instead of creating a `KernelFunction`, this creates an AF `FunctionTool` (via the `@tool` decorator pattern) from a vector search. This allows agents to use vector search as a tool during conversations. Design:
-   - `create_search_tool(name, description, search_type, ...)` → returns a `FunctionTool` that wraps `VectorSearch.search(search_type=...)`
-   - The tool accepts a query string, performs embedding + vector search, and returns results as strings
-   - Supports configurable string mappers, filter functions, top/skip defaults
-   - Lives in `_vectors.py` as a method on `BaseVectorSearch` and/or as a standalone factory function
-
-9. **CRUD tools**: A full set of create/read/update/delete tools for vector store collections, allowing agents to manage data in vector stores. Design:
-   - `create_upsert_tool(...)` → tool for upserting records
-   - `create_get_tool(...)` → tool for retrieving records by key
-   - `create_delete_tool(...)` → tool for deleting records
-   - These are separate from search and are placed in a later phase
-
-10. **Score threshold filtering**: `SearchOptions` includes `score_threshold: float | None` to filter search results by relevance score (ref: [SK .NET PR #13501](https://github.com/microsoft/semantic-kernel/pull/13501)). The semantics depend on the distance function: for similarity functions (cosine similarity, dot product), results *below* the threshold are filtered out; for distance functions (cosine distance, euclidean), results *above* the threshold are filtered out. Use `DISTANCE_FUNCTION_DIRECTION_HELPER` to determine direction. Connectors should implement this natively where the database supports it, falling back to client-side post-filtering otherwise.

dotnet/.github/skills/build-and-test/SKILL.md

@@ -1,130 +0,0 @@
----
-name: build-and-test
-description: How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
----
-
-- Only **UnitTest** projects need to be run locally; IntegrationTests require external dependencies.
-- See `../project-structure/SKILL.md` for project structure details.
-
-## Build, Test, and Lint Commands
-
-```bash
-# From dotnet/ directory
-dotnet restore --tl:off   # Restore dependencies for all projects
-dotnet build --tl:off     # Build all projects
-dotnet test               # Run all tests
-dotnet format             # Auto-fix formatting for all projects
-
-# Build/test/format a specific project (preferred for isolated/internal changes)
-dotnet build src/Microsoft.Agents.AI.<Package> --tl:off
-dotnet test --project tests/Microsoft.Agents.AI.<Package>.UnitTests
-dotnet format src/Microsoft.Agents.AI.<Package>
-
-# Run a single test
-# Replace the filter values with the appropriate assembly, namespace, class, and method names for the test you want to run and use * as a wildcard elsewhere, e.g. "/*/*/HttpClientTests/GetAsync_ReturnsSuccessStatusCode"
-# Use `--ignore-exit-code 8` to avoid failing the build when no tests are found for some projects
-dotnet test --filter-query "/<assemblyFilter>/<namespaceFilter>/<classFilter>/<methodFilter>" --ignore-exit-code 8
-
-# Run unit tests only
-# Use `--ignore-exit-code 8` to avoid failing the build when no tests are found for integration test projects
-dotnet test --filter-query "/*UnitTests*/*/*/*" --ignore-exit-code 8
-```
-
-Use `--tl:off` when building to avoid flickering when running commands in the agent.
-
-## Speeding Up Builds and Testing
-
-The full solution is large. Use these shortcuts:
-
-| Change type | What to do |
-|-------------|------------|
-| Isolated/Internal logic | Build only the affected project and its `*.UnitTests` project. Fix issues, then build the full solution and run all unit tests. |
-| Public API surface | Build the full solution and run all unit tests immediately. |
-
-Example: Building a single code project for all target frameworks
-
-```bash
-# From dotnet/ directory
-dotnet build ./src/Microsoft.Agents.AI.Abstractions
-```
-
-Example: Building a single code project for just .NET 10.
-
-```bash
-# From dotnet/ directory
-dotnet build ./src/Microsoft.Agents.AI.Abstractions -f net10.0
-```
-
-Example: Running tests for a single project using .NET 10.
-
-```bash
-# From dotnet/ directory
-dotnet test --project ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0
-```
-
-Example: Running a single test in a specific project using .NET 10.
-Provide the full namespace, class name, and method name for the test you want to run:
-
-```bash
-# From dotnet/ directory
-dotnet test --project ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0 --filter-query "/*/Microsoft.Agents.AI.Abstractions.UnitTests/AgentRunOptionsTests/CloningConstructorCopiesProperties"
-```
-
-### Multi-target framework tip
-
-Most projects target multiple .NET frameworks. If the affected code does **not** use `#if` directives for framework-specific logic, pass `-f net10.0` to speed up building and testing.
-
-### Package Restore tip
-
-`dotnet build` will try and restore packages for all projects on each build, which can be slow.
-Unless packages have been changed, or it's the first time building the solution, add `--no-restore` to the build command to skip this step and speed up builds.
-
-Just remember to run `dotnet restore` after pulling changes, making changes to project references, or when building for the first time.
-
-### Testing on Linux tip
-
-Unit tests target both .NET Framework as well as .NET Core. When running on Linux, only the .NET Core tests can be run, as .NET Framework is not supported on Linux.
-
-To run only the .NET Core tests, use the `-f net10.0` option with `dotnet test`.
-
-### Microsoft Testing Platform (MTP)
-
-Tests use the [Microsoft Testing Platform](https://learn.microsoft.com/dotnet/core/testing/unit-testing-platform-intro) via xUnit v3. Key differences from the legacy VSTest runner:
-
-- **`dotnet test` requires `--project`** to specify a test project directly (positional arguments are no longer supported).
-- **Test output** uses the MTP format (e.g., `[✓112/x0/↓0]` progress and `Test run summary: Passed!`).
-- **TRX reports** use `--report-xunit-trx` instead of `--logger trx`.
-- **Code coverage** uses `Microsoft.Testing.Extensions.CodeCoverage` with `--coverage --coverage-output-format cobertura`.
-- **Running a test project directly** is supported via `dotnet run --project <test-project>`. This bypasses the `dotnet test` infrastructure and runs the test executable directly with the MTP command line.
-
-- **Running tests across the solution** with a filter may cause some projects to match zero tests, which MTP treats as a failure (exit code 8). Use `--ignore-exit-code 8` to suppress this:
-
-```bash
-# Run all unit tests across the solution, ignoring projects with no matching tests
-dotnet test --solution ./agent-framework-dotnet.slnx --no-build -f net10.0 --ignore-exit-code 8
-```
-
-- **Running tests with `--solution` for a specific TFM** requires all projects in the solution to support that TFM. Not all projects target every framework (e.g., some are `net10.0`-only). Use `./dotnet/eng/scripts/New-FilteredSolution.ps1` to generate a filtered solution:
-
-```powershell
-# Generate a filtered solution for net472 and run tests
-$filtered = ./dotnet/eng/scripts/New-FilteredSolution.ps1 -Solution dotnet/agent-framework-dotnet.slnx -TargetFramework net472
-dotnet test --solution $filtered --no-build -f net472 --ignore-exit-code 8
-
-# Exclude samples and keep only unit test projects
-./dotnet/eng/scripts/New-FilteredSolution.ps1 -Solution dotnet/agent-framework-dotnet.slnx -TargetFramework net10.0 -ExcludeSamples -TestProjectNameFilter "*UnitTests*" -OutputPath dotnet/filtered-unit.slnx
-```
-
-```bash
-# Run tests via dotnet test (uses MTP under the hood)
-dotnet test --project ./tests/Microsoft.Agents.AI.UnitTests -f net10.0
-
-# Run tests with code coverage (Cobertura format)
-dotnet test --project ./tests/Microsoft.Agents.AI.UnitTests -f net10.0 --coverage --coverage-output-format cobertura --coverage-settings ./tests/coverage.runsettings
-
-# Run tests directly via dotnet run (MTP native command line)
-dotnet run --project ./tests/Microsoft.Agents.AI.UnitTests -f net10.0
-
-# Show MTP command line help
-dotnet run --project ./tests/Microsoft.Agents.AI.UnitTests -f net10.0 -- -?
-```

dotnet/.github/skills/project-structure/SKILL.md

@@ -1,31 +0,0 @@
----
-name: project-structure
-description: Explains the project structure of the agent-framework .NET solution
----
-
-# Agent Framework .NET Project Structure
-
-```
-dotnet/
-├── src/
-│   ├── Microsoft.Agents.AI/                      # Core AI agent implementations
-│   ├── Microsoft.Agents.AI.Abstractions/         # Core AI agent abstractions
-│   ├── Microsoft.Agents.AI.A2A/                  # Agent-to-Agent (A2A) provider
-│   ├── Microsoft.Agents.AI.OpenAI/               # OpenAI provider
-│   ├── Microsoft.Agents.AI.AzureAI/              # Azure AI Foundry Agents (v2) provider
-│   ├── Microsoft.Agents.AI.AzureAI.Persistent/   # Legacy Azure AI Foundry Agents (v1) provider
-│   ├── Microsoft.Agents.AI.Anthropic/            # Anthropic provider
-│   ├── Microsoft.Agents.AI.Workflows/            # Workflow orchestration
-│   └── ...                                       # Other packages
-├── samples/                                      # Sample applications
-└── tests/                                        # Unit and integration tests
-```
-
-## Main Folders
-
-| Folder | Contents |
-|--------|----------|
-| `src/` | Source code projects |
-| `tests/` | Test projects — named `<Source-Code-Project>.UnitTests` or `<Source-Code-Project>.IntegrationTests` |
-| `samples/` | Sample projects |
-| `src/Shared`, `src/LegacySupport` | Shared code files included by multiple source code projects (see README.md files in these folders or their subdirectories for instructions on how to include them in a project) |

dotnet/.github/skills/verify-dotnet-samples/SKILL.md

@@ -1,82 +0,0 @@
----
-name: verify-dotnet-samples
-description: How to build, run and verify the .NET sample projects in the Agent Framework repository. Use this when a user wants to verify that the samples still function as expected.
----
-
-# Verifying .NET Sample Projects
-
-## Sample Pre-requisites
-
-We should only support verifying samples that:
-1. Use environment variables for configuration.
-2. Have no complex setup requirements, e.g., where multiple applications need to be run together, or where we need to launch a browser, etc.
-
-Always report to the user which samples were run and which were not, and why.
-
-## Verifying a sample
-
-Samples should be verified to ensure that they actually work as intended and that their output matches what is expected.
-For each sample that is run, output should be produced that shows the result and explains the reasoning about what output
-was expected, what was produced, and why it didn't match what the sample was expected to produce.
-
-Steps to verify a sample:
-1. Read the code for the sample
-1. Check what environment variables are required for the sample
-1. Check if each environment variable has been set
-1. If there are any missing, give the user a list of missing environment variables to set and terminate
-1. Summarize what the expected output of the sample should be
-1. Run the sample
-1. Show the user any output from the sample run as it gets produced, so that they can see the run progress
-1. Check the output of the run against expectations
-1. After running all requested samples, produce output for each sample that was verified:
-  1. If expectations were matched, output the following:
-     ```text
-     [Sample Name] Succeeded
-     ```
-  1. If expectations were not matched, output the following:
-     ```text
-     [Sample Name] Failed
-     Actual Output:
-     [What the sample produced]
-     Expected Output:
-     [Explanation of what was expected and why the actual output didn't match expectations]
-     ```
-
-## Environment Variables
-
-Most samples use environment variables to configure settings.
-
-```csharp
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-```
-
-To run a sample, the environment variables should be set first.
-Before running a sample, check whether each environment variable in the sample has a value and
-then give the user a list of environment variables to set.
-
-You can provide the user some examples of how to set the variables like this:
-
-```bash
-export AZURE_OPENAI_ENDPOINT="https://my-openai-instance.openai.azure.com/"
-export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-To check if a variable has a value use e.g.:
-
-```bash
-echo $AZURE_OPENAI_ENDPOINT
-```
-
-## How to Run a Sample (General Pattern)
-
-```bash
-cd dotnet/samples/<category>/<sample-dir>
-dotnet run
-```
-
-For multi-targeted projects (e.g., Durable console apps), specify the framework:
-
-```bash
-dotnet run --framework net10.0
-```

dotnet/.vscode/settings.json

@@ -1,6 +1,5 @@
 {
     "dotnet.defaultSolution": "agent-framework-dotnet.slnx",
     "git.openRepositoryInParentFolders": "always",
-    "chat.agent.enabled": true,
-    "dotnet.automaticallySyncWithActiveItem": true
+    "chat.agent.enabled": true
 }

dotnet/AGENTS.md

@@ -1,66 +0,0 @@
-# AGENTS.md
-
-Instructions for AI coding agents working in the .NET codebase.
-
-## Build, Test, and Lint Commands
-
-See `./.github/skills/build-and-test/SKILL.md` for detailed instructions on building, testing, and linting projects.
-
-## Project Structure
-
-See `./.github/skills/project-structure/SKILL.md` for an overview of the project structure.
-
-### Core types
-
-- `AIAgent`: The abstract base class that all agents derive from, providing common methods for interacting with an agent.
-- `AgentSession`: The abstract base class that all agent sessions derive from, representing a conversation with an agent.
-- `ChatClientAgent`: An `AIAgent` implementation that uses an `IChatClient` to send messages to an AI provider and receive responses.
-- `IChatClient`: Interface for sending messages to an AI provider and receiving responses. Used by `ChatClientAgent` and implemented by provider-specific packages.
-- `FunctionInvokingChatClient`: Decorator for `IChatClient` that adds function invocation capabilities.
-- `AITool`: Represents a tool that an agent/AI provider can use, with metadata and an execution delegate.
-- `AIFunction`: A specific type of `AITool` that represents a local function the agent/AI provider can call, with parameters and return types defined.
-- `ChatMessage`: Represents a message in a conversation.
-- `AIContent`: Represents content in a message, which can be text, a function call, tool output and more.
-
-### External Dependencies
-
-The framework integrates with `Microsoft.Extensions.AI` and `Microsoft.Extensions.AI.Abstractions` (external NuGet packages)
-using types like `IChatClient`, `FunctionInvokingChatClient`, `AITool`, `AIFunction`, `ChatMessage`, and `AIContent`.
-
-## Key Conventions
-
-- **Encoding**: All new files must be saved with UTF-8 encoding with BOM (Byte Order Mark). This is required for `dotnet format` to work correctly.
-- **Copyright header**: `// Copyright (c) Microsoft. All rights reserved.` at top of all `.cs` files
-- **XML docs**: Required for all public methods and classes
-- **Async**: Use `Async` suffix for methods returning `Task`/`ValueTask`
-- **Private classes**: Should be `sealed` unless subclassed
-- **Config**: Read from environment variables with `UPPER_SNAKE_CASE` naming
-- **Tests**: Add Arrange/Act/Assert comments; use Moq for mocking
-
-## Key Design Principles
-
-When developing or reviewing code, verify adherence to these key design principles:
-
-- **DRY**: Avoid code duplication by moving common logic into helper methods or helper classes.
-- **Single Responsibility**: Each class should have one clear responsibility.
-- **Encapsulation**: Keep implementation details private and expose only necessary public APIs.
-- **Strong Typing**: Use strong typing to ensure that code is self-documenting and to catch errors at compile time.
-
-## Sample Structure
-
-Samples (in `./samples/` folder) should follow this structure:
-
-1. Copyright header: `// Copyright (c) Microsoft. All rights reserved.`
-2. Description comment explaining what the sample demonstrates
-3. Using statements
-4. Main code logic
-5. Helper methods at bottom
-
-Configuration via environment variables (never hardcode secrets). Keep samples simple and focused.
-
-When adding a new sample:
-
-- Create a standalone project in `samples/` with matching directory and project names
-- Include a README.md explaining what the sample does and how to run it
-- Add the project to the solution file
-- Reference the sample in the parent directory's README.md

dotnet/Directory.Build.props

@@ -17,7 +17,6 @@
 
   <PropertyGroup>
     <IsReleaseCandidate>false</IsReleaseCandidate>
-    <IsGenerallyAvailable>false</IsGenerallyAvailable>
   </PropertyGroup>
   
   <PropertyGroup>

dotnet/Directory.Packages.props

@@ -11,18 +11,19 @@
   </PropertyGroup>
   <ItemGroup>
     <!-- Aspire.* -->
-    <PackageVersion Include="Anthropic" Version="12.8.0" />
-    <PackageVersion Include="Anthropic.Foundry" Version="0.4.2" />
+    <PackageVersion Include="Anthropic" Version="12.3.0" />
+    <PackageVersion Include="Anthropic.Foundry" Version="0.4.1" />
     <PackageVersion Include="Aspire.Azure.AI.OpenAI" Version="13.0.0-preview.1.25560.3" />
     <PackageVersion Include="Aspire.Hosting.AppHost" Version="$(AspireAppHostSdkVersion)" />
     <PackageVersion Include="Aspire.Hosting.Azure.CognitiveServices" Version="$(AspireAppHostSdkVersion)" />
     <PackageVersion Include="Aspire.Microsoft.Azure.Cosmos" Version="$(AspireAppHostSdkVersion)" />
     <PackageVersion Include="CommunityToolkit.Aspire.OllamaSharp" Version="13.0.0" />
     <!-- Azure.* -->
-    <PackageVersion Include="Azure.AI.Projects" Version="2.0.0-beta.2" />
-    <PackageVersion Include="Azure.AI.Agents.Persistent" Version="1.2.0-beta.10" />
-    <PackageVersion Include="Azure.AI.OpenAI" Version="2.9.0-beta.1" />
-    <PackageVersion Include="Azure.Identity" Version="1.19.0" />
+    <PackageVersion Include="Azure.AI.Projects" Version="1.2.0-beta.5" />
+    <PackageVersion Include="Azure.AI.Projects.OpenAI" Version="1.0.0-beta.5" />
+    <PackageVersion Include="Azure.AI.Agents.Persistent" Version="1.2.0-beta.8" />
+    <PackageVersion Include="Azure.AI.OpenAI" Version="2.8.0-beta.1" />
+    <PackageVersion Include="Azure.Identity" Version="1.17.1" />
     <PackageVersion Include="Azure.Monitor.OpenTelemetry.Exporter" Version="1.4.0" />
     <!-- Google Gemini  -->
     <PackageVersion Include="Google.GenAI" Version="0.11.0" />
@@ -32,19 +33,18 @@
     <!-- Newtonsoft.Json -->
     <PackageVersion Include="Newtonsoft.Json" Version="13.0.4" />
     <!-- System.* -->
-    <PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.4" />
+    <PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.2" />
     <PackageVersion Include="Microsoft.Bcl.HashCode" Version="6.0.0" />
-    <PackageVersion Include="Microsoft.Bcl.Memory" Version="10.0.4" />
-    <PackageVersion Include="System.ClientModel" Version="1.9.0" />
+    <PackageVersion Include="System.ClientModel" Version="1.8.1" />
     <PackageVersion Include="System.CodeDom" Version="10.0.0" />
     <PackageVersion Include="System.Collections.Immutable" Version="10.0.1" />
     <PackageVersion Include="System.CommandLine" Version="2.0.0-rc.2.25502.107" />
-    <PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="10.0.4" />
-    <PackageVersion Include="System.Linq.AsyncEnumerable" Version="10.0.4" />
+    <PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="10.0.2" />
+    <PackageVersion Include="System.Linq.AsyncEnumerable" Version="10.0.0" />
     <PackageVersion Include="System.Net.Http.Json" Version="10.0.0" />
-    <PackageVersion Include="System.Net.ServerSentEvents" Version="10.0.4" />
-    <PackageVersion Include="System.Text.Json" Version="10.0.4" />
-    <PackageVersion Include="System.Threading.Channels" Version="10.0.4" />
+    <PackageVersion Include="System.Net.ServerSentEvents" Version="10.0.1" />
+    <PackageVersion Include="System.Text.Json" Version="10.0.2" />
+    <PackageVersion Include="System.Threading.Channels" Version="10.0.2" />
     <PackageVersion Include="System.Threading.Tasks.Extensions" Version="4.6.3" />
     <PackageVersion Include="System.Net.Security" Version="4.3.2" />
     <!-- OpenTelemetry -->
@@ -58,30 +58,24 @@
     <PackageVersion Include="OpenTelemetry.Instrumentation.Http" Version="1.13.0" />
     <PackageVersion Include="OpenTelemetry.Instrumentation.Runtime" Version="1.13.0" />
     <!-- Microsoft.AspNetCore.* -->
-    <PackageVersion Include="Microsoft.AspNetCore.Authentication.JwtBearer" Version="10.0.0" />
-    <PackageVersion Include="Microsoft.AspNetCore.Authentication.OpenIdConnect" Version="10.0.0" />
     <PackageVersion Include="Microsoft.AspNetCore.OpenApi" Version="10.0.0" />
     <PackageVersion Include="Swashbuckle.AspNetCore.SwaggerUI" Version="10.0.0" />
     <!-- Microsoft.Extensions.* -->
-    <PackageVersion Include="Microsoft.Extensions.AI" Version="10.4.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="10.4.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation" Version="10.4.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation.Quality" Version="10.4.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation.Safety" Version="10.3.0-preview.1.26109.11" />
-    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="10.4.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI" Version="10.2.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="10.2.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="10.2.0-preview.1.26063.2" />
     <PackageVersion Include="Microsoft.Extensions.Caching.Memory" Version="10.0.0" />
-    <PackageVersion Include="Microsoft.Extensions.Compliance.Abstractions" Version="10.4.0" />
     <PackageVersion Include="Microsoft.Extensions.Configuration" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Configuration.Binder" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Configuration.EnvironmentVariables" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Configuration.Json" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.DependencyInjection" Version="10.0.0" />
-    <PackageVersion Include="Microsoft.Extensions.DependencyInjection.Abstractions" Version="10.0.4" />
+    <PackageVersion Include="Microsoft.Extensions.DependencyInjection.Abstractions" Version="10.0.2" />
     <PackageVersion Include="Microsoft.Extensions.Hosting" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Http.Resilience" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Logging" Version="10.0.0" />
-    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="10.0.4" />
+    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="10.0.2" />
     <PackageVersion Include="Microsoft.Extensions.Logging.Console" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.ServiceDiscovery" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.7.0" />
@@ -95,39 +89,38 @@
     <PackageVersion Include="Microsoft.SemanticKernel.Agents.AzureAI" Version="1.67.0-preview" />
     <PackageVersion Include="Microsoft.SemanticKernel.Plugins.OpenApi" Version="1.67.0" />
     <!-- Agent SDKs -->
-    <PackageVersion Include="GitHub.Copilot.SDK" Version="0.1.29" />
+    <PackageVersion Include="GitHub.Copilot.SDK" Version="0.1.18" />
     <PackageVersion Include="Microsoft.Agents.CopilotStudio.Client" Version="1.3.171-beta" />
     <!-- M365 Agents SDK -->
     <PackageVersion Include="AdaptiveCards" Version="3.1.0" />
     <PackageVersion Include="Microsoft.Agents.Authentication.Msal" Version="1.3.171-beta" />
     <PackageVersion Include="Microsoft.Agents.Hosting.AspNetCore" Version="1.3.171-beta" />
     <!-- A2A -->
-    <PackageVersion Include="A2A" Version="0.3.4-preview" />
-    <PackageVersion Include="A2A.AspNetCore" Version="0.3.4-preview" />
+    <PackageVersion Include="A2A" Version="0.3.3-preview" />
+    <PackageVersion Include="A2A.AspNetCore" Version="0.3.3-preview" />
     <!-- MCP -->
-    <PackageVersion Include="ModelContextProtocol" Version="1.1.0" />
+    <PackageVersion Include="ModelContextProtocol" Version="0.4.0-preview.3" />
     <!-- Inference SDKs -->
     <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.5.1" />
     <PackageVersion Include="Microsoft.ML.OnnxRuntimeGenAI" Version="0.10.0" />
-    <PackageVersion Include="Microsoft.ML.Tokenizers" Version="2.0.0" />
     <PackageVersion Include="OllamaSharp" Version="5.4.8" />
-    <PackageVersion Include="OpenAI" Version="2.9.1" />
+    <PackageVersion Include="OpenAI" Version="2.8.0" />
     <!-- Identity -->
-    <PackageVersion Include="Microsoft.Identity.Client.Extensions.Msal" Version="4.83.1" />
+    <PackageVersion Include="Microsoft.Identity.Client.Extensions.Msal" Version="4.78.0" />
     <!-- Workflows -->
-    <PackageVersion Include="Microsoft.Agents.ObjectModel" Version="2026.2.4.1" />
-    <PackageVersion Include="Microsoft.Agents.ObjectModel.Json" Version="2026.2.4.1" />
-    <PackageVersion Include="Microsoft.Agents.ObjectModel.PowerFx" Version="2026.2.4.1" />
-    <PackageVersion Include="Microsoft.PowerFx.Interpreter" Version="1.8.1" />
+    <PackageVersion Include="Microsoft.Agents.ObjectModel" Version="2026.1.2.3" />
+    <PackageVersion Include="Microsoft.Agents.ObjectModel.Json" Version="2026.1.2.3" />
+    <PackageVersion Include="Microsoft.Agents.ObjectModel.PowerFx" Version="2026.1.2.3" />
+    <PackageVersion Include="Microsoft.PowerFx.Interpreter" Version="1.5.0-build.20251008-1002" />
     <!-- Durable Task -->
-    <PackageVersion Include="Microsoft.DurableTask.Client" Version="1.18.0" />
-    <PackageVersion Include="Microsoft.DurableTask.Client.AzureManaged" Version="1.18.0" />
-    <PackageVersion Include="Microsoft.DurableTask.Worker" Version="1.18.0" />
-    <PackageVersion Include="Microsoft.DurableTask.Worker.AzureManaged" Version="1.18.0" />
+    <PackageVersion Include="Microsoft.DurableTask.Client" Version="1.19.1" />
+    <PackageVersion Include="Microsoft.DurableTask.Client.AzureManaged" Version="1.19.0" />
+    <PackageVersion Include="Microsoft.DurableTask.Worker" Version="1.19.0" />
+    <PackageVersion Include="Microsoft.DurableTask.Worker.AzureManaged" Version="1.19.0" />
     <!-- Azure Functions -->
     <PackageVersion Include="Microsoft.Azure.Functions.Worker" Version="2.50.0" />
     <PackageVersion Include="Microsoft.Azure.Functions.Worker.ApplicationInsights" Version="2.50.0" />
-    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" Version="1.12.1" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" Version="1.13.1" />
     <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" Version="1.0.1" />
     <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Http" Version="3.3.0" />
     <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" Version="2.1.0" />
@@ -142,14 +135,15 @@
     <PackageVersion Include="Microsoft.AspNetCore.TestHost" Condition="'$(TargetFramework)' == 'net10.0'" Version="10.0.0" />
     <PackageVersion Include="Microsoft.NET.Test.Sdk" Version="18.0.0" />
     <PackageVersion Include="Moq" Version="[4.18.4]" />
-    <PackageVersion Include="xunit.v3.mtp-v2" Version="3.2.2" />
-    <PackageVersion Include="xunit.runner.visualstudio" Version="3.1.5" />
-    <PackageVersion Include="xRetry.v3" Version="1.0.0-rc3" />
-    <PackageVersion Include="Microsoft.Testing.Extensions.CodeCoverage" Version="18.4.1" />
+    <PackageVersion Include="xunit" Version="2.9.3" />
+    <PackageVersion Include="xunit.abstractions" Version="2.0.3" />
+    <PackageVersion Include="xunit.runner.visualstudio" Version="3.1.3" />
+    <PackageVersion Include="Xunit.SkippableFact" Version="1.5.23" />
+    <PackageVersion Include="xretry" Version="1.9.0" />
+    <PackageVersion Include="coverlet.collector" Version="6.0.4" />
     <!-- Symbols -->
     <PackageVersion Include="Microsoft.SourceLink.GitHub" Version="8.0.0" />
     <!-- Toolset -->
-    <PackageVersion Include="ReferenceTrimmer" Version="3.4.5" />
     <PackageVersion Include="Microsoft.CodeAnalysis.Analyzers" Version="3.11.0" />
     <PackageVersion Include="Microsoft.CodeAnalysis.CSharp" Version="4.14.0" />
     <PackageVersion Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100" />
@@ -188,4 +182,4 @@
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
     </PackageReference>
   </ItemGroup>
-</Project>
+</Project>

dotnet/README.md

@@ -1,20 +1,26 @@
 # Get Started with Microsoft Agent Framework for C# Developers
 
+## Samples
+
+- [Getting Started with Agents](./samples/GettingStarted/Agents): basic agent creation and tool usage
+- [Agent Provider Samples](./samples/GettingStarted/AgentProviders): samples showing different agent providers
+- [Workflow Samples](./samples/GettingStarted/Workflows): advanced multi-agent patterns and workflow orchestration
+
 ## Quickstart
 
 ### Basic Agent - .NET
 
 ```c#
+using System;
 using Azure.AI.OpenAI;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!;
 var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME")!;
 
 var agent = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
-    .GetResponsesClient(deploymentName)
+    .GetOpenAIResponseClient(deploymentName)
     .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
 
 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
@@ -22,9 +28,9 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram
 
 ## Examples & Samples
 
-- [Getting Started with Agents](./samples/02-agents/Agents): basic agent creation and tool usage
-- [Agent Provider Samples](./samples/02-agents/AgentProviders): samples showing different agent providers
-- [Workflow Samples](./samples/03-workflows): advanced multi-agent patterns and workflow orchestration
+- [Getting Started with Agents](./samples/GettingStarted/Agents): basic agent creation and tool usage
+- [Agent Provider Samples](./samples/GettingStarted/AgentProviders): samples showing different agent providers
+- [Workflow Samples](./samples/GettingStarted/Workflows): advanced multi-agent patterns and workflow orchestration
 
 ## Agent Framework Documentation
 

dotnet/agent-framework-dotnet.slnx

@@ -1,217 +1,233 @@
-<Solution>
+<Solution>
   <Configurations>
     <BuildType Name="Debug" />
     <BuildType Name="Publish" />
     <BuildType Name="Release" />
   </Configurations>
   <Folder Name="/Samples/">
-    <File Path="samples/AGENTS.md" />
     <File Path="samples/README.md" />
   </Folder>
-  <Folder Name="/Samples/01-get-started/">
-    <Project Path="samples/01-get-started/01_hello_agent/01_hello_agent.csproj" />
-    <Project Path="samples/01-get-started/02_add_tools/02_add_tools.csproj" />
-    <Project Path="samples/01-get-started/03_multi_turn/03_multi_turn.csproj" />
-    <Project Path="samples/01-get-started/04_memory/04_memory.csproj" />
-    <Project Path="samples/01-get-started/05_first_workflow/05_first_workflow.csproj" />
-    <Project Path="samples/01-get-started/06_host_your_agent/06_host_your_agent.csproj" />
+  <Folder Name="/Samples/A2AClientServer/">
+    <File Path="samples/A2AClientServer/README.md" />
+    <Project Path="samples/A2AClientServer/A2AClient/A2AClient.csproj" />
+    <Project Path="samples/A2AClientServer/A2AServer/A2AServer.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/">
-    <File Path="samples/02-agents/README.md" />
+  <Folder Name="/Samples/AgentWebChat/">
+    <Project Path="samples/AgentWebChat/AgentWebChat.AgentHost/AgentWebChat.AgentHost.csproj" />
+    <Project Path="samples/AgentWebChat/AgentWebChat.AppHost/AgentWebChat.AppHost.csproj" />
+    <Project Path="samples/AgentWebChat/AgentWebChat.ServiceDefaults/AgentWebChat.ServiceDefaults.csproj" />
+    <Project Path="samples/AgentWebChat/AgentWebChat.Web/AgentWebChat.Web.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AgentProviders/">
-    <File Path="samples/02-agents/AgentProviders/README.md" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_A2A/Agent_With_A2A.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_Anthropic/Agent_With_Anthropic.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent/Agent_With_AzureAIAgentsPersistent.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureAIProject/Agent_With_AzureAIProject.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureFoundryModel/Agent_With_AzureFoundryModel.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureOpenAIChatCompletion/Agent_With_AzureOpenAIChatCompletion.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureOpenAIResponses/Agent_With_AzureOpenAIResponses.csproj" />
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-    <Project Path="samples/02-agents/AgentProviders/Agent_With_OpenAIResponses/Agent_With_OpenAIResponses.csproj" />
+  <Folder Name="/Samples/AGUIClientServer/">
+    <Project Path="samples/AGUIClientServer/AGUIClient/AGUIClient.csproj" />
+    <Project Path="samples/AGUIClientServer/AGUIDojoServer/AGUIDojoServer.csproj" />
+    <Project Path="samples/AGUIClientServer/AGUIServer/AGUIServer.csproj" />
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-    <Project Path="samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Agent_Step19_InFunctionLoopCheckpointing.csproj" />
+  <Folder Name="/Samples/Durable/" />
+  <Folder Name="/Samples/Durable/Agents/" />
+  <Folder Name="/Samples/Durable/Agents/AzureFunctions/">
+    <File Path="samples/Durable/Agents/AzureFunctions/.editorconfig" />
+    <File Path="samples/Durable/Agents/AzureFunctions/README.md" />
+    <Project Path="samples/Durable/Agents/AzureFunctions/01_SingleAgent/01_SingleAgent.csproj" />
+    <Project Path="samples/Durable/Agents/AzureFunctions/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
+    <Project Path="samples/Durable/Agents/AzureFunctions/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
+    <Project Path="samples/Durable/Agents/AzureFunctions/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
+    <Project Path="samples/Durable/Agents/AzureFunctions/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
+    <Project Path="samples/Durable/Agents/AzureFunctions/06_LongRunningTools/06_LongRunningTools.csproj" />
+    <Project Path="samples/Durable/Agents/AzureFunctions/07_AgentAsMcpTool/07_AgentAsMcpTool.csproj" />
+    <Project Path="samples/Durable/Agents/AzureFunctions/08_ReliableStreaming/08_ReliableStreaming.csproj" />
   </Folder>
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-    <Project Path="samples/02-agents/DeclarativeAgents/ChatClient/DeclarativeChatClientAgents.csproj" />
+  <Folder Name="/Samples/Durable/Agents/ConsoleApps/">
+    <File Path="samples/Durable/Agents/ConsoleApps/README.md" />
+    <Project Path="samples/Durable/Agents/ConsoleApps/01_SingleAgent/01_SingleAgent.csproj" />
+    <Project Path="samples/Durable/Agents/ConsoleApps/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
+    <Project Path="samples/Durable/Agents/ConsoleApps/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
+    <Project Path="samples/Durable/Agents/ConsoleApps/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
+    <Project Path="samples/Durable/Agents/ConsoleApps/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
+    <Project Path="samples/Durable/Agents/ConsoleApps/06_LongRunningTools/06_LongRunningTools.csproj" />
+    <Project Path="samples/Durable/Agents/ConsoleApps/07_ReliableStreaming/07_ReliableStreaming.csproj" />
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-  <Folder Name="/Samples/04-hosting/DurableWorkflows/" />
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-    <Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/05_WorkflowEvents/05_WorkflowEvents.csproj" />
-    <Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/06_WorkflowSharedState/06_WorkflowSharedState.csproj" />
-    <Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/07_SubWorkflows/07_SubWorkflows.csproj" />
-    <Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/08_WorkflowHITL/08_WorkflowHITL.csproj" />
+  <Folder Name="/Samples/Durable/Workflows/">
+    <Project Path="samples/Durable/Workflow/ConsoleApps/01_SequentialWorkflow/01_SequentialWorkflow.csproj" />
+    <Project Path="samples/Durable/Workflow/ConsoleApps/02_ConcurrentWorkflow/02_ConcurrentWorkflow.csproj" />
+    <Project Path="samples/Durable/Workflow/ConsoleApps/03_ConditionalEdges/03_ConditionalEdges.csproj" />
+    <Project Path="samples/Durable/Workflow/ConsoleApps/04_WorkflowAndAgents/04_WorkflowAndAgents.csproj" />
+    <Project Path="samples/Durable/Workflow/ConsoleApps/05_WorkflowEvents/05_WorkflowEvents.csproj" />
+    <Project Path="samples/Durable/Workflow/ConsoleApps/06_WorkflowSharedState/06_WorkflowSharedState.csproj" />
+    <Project Path="samples/Durable/Workflow/ConsoleApps/07_SubWorkflows/07_SubWorkflows.csproj" />
+    <Project Path="samples/Durable/Workflow/ConsoleApps/08_WorkflowLoop/08_WorkflowLoop.csproj" />
   </Folder>
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-    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/04_WorkflowMcpTool/04_WorkflowMcpTool.csproj" />
-    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/05_WorkflowAndAgents/05_WorkflowAndAgents.csproj" />
+  <Folder Name="/Samples/Durable/Workflows/AzureFunctions/">
+    <Project Path="samples/Durable/Workflow/AzureFunctions/01_SequentialWorkflow/01_SequentialWorkflow.csproj" />
+    <Project Path="samples/Durable/Workflow/AzureFunctions/02_ConcurrentWorkflow/02_ConcurrentWorkflow.csproj" />
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   <Folder Name="/Samples/GettingStarted/">
     <File Path="samples/GettingStarted/README.md" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AGUI/">
-    <File Path="samples/02-agents/AGUI/README.md" />
+  <Folder Name="/Samples/GettingStarted/A2A/">
+    <File Path="samples/GettingStarted/A2A/README.md" />
+    <Project Path="samples/GettingStarted/A2A/A2AAgent_AsFunctionTools/A2AAgent_AsFunctionTools.csproj" />
+    <Project Path="samples/GettingStarted/A2A/A2AAgent_PollingForTaskCompletion/A2AAgent_PollingForTaskCompletion.csproj" />
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-  <Folder Name="/Samples/02-agents/AGUI/Step01_GettingStarted/">
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-    <Project Path="samples/02-agents/AGUI/Step01_GettingStarted/Server/Server.csproj" />
+  <Folder Name="/Samples/GettingStarted/AgentProviders/">
+    <File Path="samples/GettingStarted/AgentProviders/README.md" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_A2A/Agent_With_A2A.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_Anthropic/Agent_With_Anthropic.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgentsPersistent/Agent_With_AzureAIAgentsPersistent.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIProject/Agent_With_AzureAIProject.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureFoundryModel/Agent_With_AzureFoundryModel.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureOpenAIChatCompletion/Agent_With_AzureOpenAIChatCompletion.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureOpenAIResponses/Agent_With_AzureOpenAIResponses.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_CustomImplementation/Agent_With_CustomImplementation.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_GitHubCopilot/Agent_With_GitHubCopilot.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_GoogleGemini/Agent_With_GoogleGemini.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_Ollama/Agent_With_Ollama.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_ONNX/Agent_With_ONNX.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_OpenAIAssistants/Agent_With_OpenAIAssistants.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_OpenAIChatCompletion/Agent_With_OpenAIChatCompletion.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_OpenAIResponses/Agent_With_OpenAIResponses.csproj" />
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-    <Project Path="samples/02-agents/AGUI/Step02_BackendTools/Server/Server.csproj" />
+  <Folder Name="/Samples/GettingStarted/Agents/">
+    <File Path="samples/GettingStarted/Agents/README.md" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step01_Running/Agent_Step01_Running.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step02_MultiturnConversation/Agent_Step02_MultiturnConversation.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step03_UsingFunctionTools/Agent_Step03_UsingFunctionTools.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step04_UsingFunctionToolsWithApprovals/Agent_Step04_UsingFunctionToolsWithApprovals.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step05_StructuredOutput/Agent_Step05_StructuredOutput.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step06_PersistedConversations/Agent_Step06_PersistedConversations.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step07_3rdPartyThreadStorage/Agent_Step07_3rdPartyThreadStorage.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step08_Observability/Agent_Step08_Observability.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step09_DependencyInjection/Agent_Step09_DependencyInjection.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step10_AsMcpTool/Agent_Step10_AsMcpTool.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step11_UsingImages/Agent_Step11_UsingImages.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step12_AsFunctionTool/Agent_Step12_AsFunctionTool.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step13_BackgroundResponsesWithToolsAndPersistence/Agent_Step13_BackgroundResponsesWithToolsAndPersistence.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step14_Middleware/Agent_Step14_Middleware.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step15_Plugins/Agent_Step15_Plugins.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step16_ChatReduction/Agent_Step16_ChatReduction.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step17_BackgroundResponses/Agent_Step17_BackgroundResponses.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step18_DeepResearch/Agent_Step18_DeepResearch.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step19_Declarative/Agent_Step19_Declarative.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step20_AdditionalAIContext/Agent_Step20_AdditionalAIContext.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AGUI/Step03_FrontendTools/">
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-    <Project Path="samples/02-agents/AGUI/Step03_FrontendTools/Server/Server.csproj" />
+  <Folder Name="/Samples/GettingStarted/DeclarativeAgents/">
+    <Project Path="samples/GettingStarted/DeclarativeAgents/ChatClient/DeclarativeChatClientAgents.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AGUI/Step04_HumanInLoop/">
-    <Project Path="samples/02-agents/AGUI/Step04_HumanInLoop/Client/Client.csproj" />
-    <Project Path="samples/02-agents/AGUI/Step04_HumanInLoop/Server/Server.csproj" />
+  <Folder Name="/Samples/GettingStarted/AGUI/">
+    <File Path="samples/GettingStarted/AGUI/README.md" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AgentSkills/">
-    <File Path="samples/02-agents/AgentSkills/README.md" />
-    <Project Path="samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Agent_Step01_FileBasedSkills.csproj" />
-    <Project Path="samples/02-agents/AgentSkills/Agent_Step02_CodeDefinedSkills/Agent_Step02_CodeDefinedSkills.csproj" />
+  <Folder Name="/Samples/GettingStarted/AGUI/Step01_GettingStarted/">
+    <Project Path="samples/GettingStarted/AGUI/Step01_GettingStarted/Client/Client.csproj" />
+    <Project Path="samples/GettingStarted/AGUI/Step01_GettingStarted/Server/Server.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AGUI/Step05_StateManagement/">
-    <Project Path="samples/02-agents/AGUI/Step05_StateManagement/Client/Client.csproj" />
-    <Project Path="samples/02-agents/AGUI/Step05_StateManagement/Server/Server.csproj" />
+  <Folder Name="/Samples/GettingStarted/AGUI/Step02_BackendTools/">
+    <Project Path="samples/GettingStarted/AGUI/Step02_BackendTools/Client/Client.csproj" />
+    <Project Path="samples/GettingStarted/AGUI/Step02_BackendTools/Server/Server.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/DevUI/">
-    <File Path="samples/02-agents/DevUI/README.md" />
-    <Project Path="samples/02-agents/DevUI/DevUI_Step01_BasicUsage/DevUI_Step01_BasicUsage.csproj" />
+  <Folder Name="/Samples/GettingStarted/AGUI/Step03_FrontendTools/">
+    <Project Path="samples/GettingStarted/AGUI/Step03_FrontendTools/Client/Client.csproj" />
+    <Project Path="samples/GettingStarted/AGUI/Step03_FrontendTools/Server/Server.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AgentWithAnthropic/">
-    <File Path="samples/02-agents/AgentWithAnthropic/README.md" />
-    <Project Path="samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step01_Running/Agent_Anthropic_Step01_Running.csproj" />
-    <Project Path="samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step02_Reasoning/Agent_Anthropic_Step02_Reasoning.csproj" />
-    <Project Path="samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step03_UsingFunctionTools/Agent_Anthropic_Step03_UsingFunctionTools.csproj" />
-    <Project Path="samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step04_UsingSkills/Agent_Anthropic_Step04_UsingSkills.csproj" />
+  <Folder Name="/Samples/GettingStarted/AGUI/Step04_HumanInLoop/">
+    <Project Path="samples/GettingStarted/AGUI/Step04_HumanInLoop/Client/Client.csproj" />
+    <Project Path="samples/GettingStarted/AGUI/Step04_HumanInLoop/Server/Server.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AgentsWithFoundry/">
-    <File Path="samples/02-agents/AgentsWithFoundry/README.md" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step00_FoundryAgentLifecycle/Agent_Step00_FoundryAgentLifecycle.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step01_Basics/Agent_Step01_Basics.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step02.1_MultiturnConversation/Agent_Step02.1_MultiturnConversation.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step02.2_MultiturnWithServerConversations/Agent_Step02.2_MultiturnWithServerConversations.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step03_UsingFunctionTools/Agent_Step03_UsingFunctionTools.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step04_UsingFunctionToolsWithApprovals/Agent_Step04_UsingFunctionToolsWithApprovals.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step05_StructuredOutput/Agent_Step05_StructuredOutput.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step06_PersistedConversations/Agent_Step06_PersistedConversations.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step07_Observability/Agent_Step07_Observability.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step08_DependencyInjection/Agent_Step08_DependencyInjection.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step09_UsingMcpClientAsTools/Agent_Step09_UsingMcpClientAsTools.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step10_UsingImages/Agent_Step10_UsingImages.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step11_AsFunctionTool/Agent_Step11_AsFunctionTool.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step12_Middleware/Agent_Step12_Middleware.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step13_Plugins/Agent_Step13_Plugins.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step14_CodeInterpreter/Agent_Step14_CodeInterpreter.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step15_ComputerUse/Agent_Step15_ComputerUse.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step16_FileSearch/Agent_Step16_FileSearch.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step17_OpenAPITools/Agent_Step17_OpenAPITools.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step18_BingCustomSearch/Agent_Step18_BingCustomSearch.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step19_SharePoint/Agent_Step19_SharePoint.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step20_MicrosoftFabric/Agent_Step20_MicrosoftFabric.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step21_WebSearch/Agent_Step21_WebSearch.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step22_MemorySearch/Agent_Step22_MemorySearch.csproj" />
-    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step23_LocalMCP/Agent_Step23_LocalMCP.csproj" />
+  <Folder Name="/Samples/GettingStarted/AGUI/Step05_StateManagement/">
+    <Project Path="samples/GettingStarted/AGUI/Step05_StateManagement/Client/Client.csproj" />
+    <Project Path="samples/GettingStarted/AGUI/Step05_StateManagement/Server/Server.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AgentWithMemory/">
-    <File Path="samples/02-agents/AgentWithMemory/README.md" />
-    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step01_ChatHistoryMemory/AgentWithMemory_Step01_ChatHistoryMemory.csproj" />
-    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step02_MemoryUsingMem0/AgentWithMemory_Step02_MemoryUsingMem0.csproj" />
-    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj" />
-    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/AgentWithMemory_Step05_BoundedChatHistory.csproj" />
+  <Folder Name="/Samples/GettingStarted/DevUI/">
+    <File Path="samples/GettingStarted/DevUI/README.md" />
+    <Project Path="samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/DevUI_Step01_BasicUsage.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AgentWithOpenAI/">
-    <File Path="samples/02-agents/AgentWithOpenAI/README.md" />
-    <Project Path="samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step01_Running/Agent_OpenAI_Step01_Running.csproj" />
-    <Project Path="samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step02_Reasoning/Agent_OpenAI_Step02_Reasoning.csproj" />
-    <Project Path="samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step03_CreateFromChatClient/Agent_OpenAI_Step03_CreateFromChatClient.csproj" />
-    <Project Path="samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient.csproj" />
-    <Project Path="samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step05_Conversation/Agent_OpenAI_Step05_Conversation.csproj" />
+  <Folder Name="/Samples/GettingStarted/AgentWithAnthropic/">
+    <File Path="samples/GettingStarted/AgentWithAnthropic/README.md" />
+    <Project Path="samples/GettingStarted/AgentWithAnthropic/Agent_Anthropic_Step01_Running/Agent_Anthropic_Step01_Running.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithAnthropic/Agent_Anthropic_Step02_Reasoning/Agent_Anthropic_Step02_Reasoning.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithAnthropic/Agent_Anthropic_Step03_UsingFunctionTools/Agent_Anthropic_Step03_UsingFunctionTools.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AgentWithRAG/">
-    <File Path="samples/02-agents/AgentWithRAG/README.md" />
-    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/AgentWithRAG_Step01_BasicTextRAG.csproj" />
-    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/AgentWithRAG_Step02_CustomVectorStoreRAG.csproj" />
-    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/AgentWithRAG_Step03_CustomRAGDataSource.csproj" />
-    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj" />
+  <Folder Name="/Samples/GettingStarted/AgentWithMemory/">
+    <File Path="samples/GettingStarted/AgentWithMemory/README.md" />
+    <Project Path="samples/GettingStarted/AgentWithMemory/AgentWithMemory_Step01_ChatHistoryMemory/AgentWithMemory_Step01_ChatHistoryMemory.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithMemory/AgentWithMemory_Step02_MemoryUsingMem0/AgentWithMemory_Step02_MemoryUsingMem0.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithMemory/AgentWithMemory_Step03_CustomMemory/AgentWithMemory_Step03_CustomMemory.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/ModelContextProtocol/">
-    <File Path="samples/02-agents/ModelContextProtocol/README.md" />
-    <Project Path="samples/02-agents/ModelContextProtocol/Agent_MCP_Server/Agent_MCP_Server.csproj" />
-    <Project Path="samples/02-agents/ModelContextProtocol/Agent_MCP_Server_Auth/Agent_MCP_Server_Auth.csproj" />
-    <Project Path="samples/02-agents/ModelContextProtocol/FoundryAgent_Hosted_MCP/FoundryAgent_Hosted_MCP.csproj" />
-    <Project Path="samples/02-agents/ModelContextProtocol/ResponseAgent_Hosted_MCP/ResponseAgent_Hosted_MCP.csproj" />
+  <Folder Name="/Samples/GettingStarted/AgentWithOpenAI/">
+    <File Path="samples/GettingStarted/AgentWithOpenAI/README.md" />
+    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step01_Running/Agent_OpenAI_Step01_Running.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step02_Reasoning/Agent_OpenAI_Step02_Reasoning.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step03_CreateFromChatClient/Agent_OpenAI_Step03_CreateFromChatClient.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step05_Conversation/Agent_OpenAI_Step05_Conversation.csproj" />
   </Folder>
-  <Folder Name="/Samples/02-agents/Observability/">
-    <Project Path="samples/02-agents/AgentOpenTelemetry/AgentOpenTelemetry.csproj" />
+  <Folder Name="/Samples/Purview/" />
+  <Folder Name="/Samples/Purview/AgentWithPurview/">
+    <Project Path="samples/Purview/AgentWithPurview/AgentWithPurview.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/">
-    <File Path="samples/03-workflows/README.md" />
+  <Folder Name="/Samples/GettingStarted/AgentWithRAG/">
+    <File Path="samples/GettingStarted/AgentWithRAG/README.md" />
+    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/AgentWithRAG_Step01_BasicTextRAG.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/AgentWithRAG_Step02_CustomVectorStoreRAG.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/AgentWithRAG_Step03_CustomRAGDataSource.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/Concurrent/">
-    <Project Path="samples/03-workflows/Concurrent/Concurrent/Concurrent.csproj" />
-    <Project Path="samples/03-workflows/Concurrent/MapReduce/MapReduce.csproj" />
+  <Folder Name="/Samples/GettingStarted/FoundryAgents/">
+    <File Path="samples/GettingStarted/FoundryAgents/README.md" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.1_Basics/FoundryAgents_Step01.1_Basics.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.2_Running/FoundryAgents_Step01.2_Running.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step02_MultiturnConversation/FoundryAgents_Step02_MultiturnConversation.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/FoundryAgents_Step03_UsingFunctionTools.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step04_UsingFunctionToolsWithApprovals/FoundryAgents_Step04_UsingFunctionToolsWithApprovals.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step05_StructuredOutput/FoundryAgents_Step05_StructuredOutput.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step06_PersistedConversations/FoundryAgents_Step06_PersistedConversations.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step07_Observability/FoundryAgents_Step07_Observability.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step08_DependencyInjection/FoundryAgents_Step08_DependencyInjection.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step09_UsingMcpClientAsTools/FoundryAgents_Step09_UsingMcpClientAsTools.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/FoundryAgents_Step10_UsingImages.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step11_AsFunctionTool/FoundryAgents_Step11_AsFunctionTool.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step12_Middleware/FoundryAgents_Step12_Middleware.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step13_Plugins/FoundryAgents_Step13_Plugins.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/FoundryAgents_Step14_CodeInterpreter.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/FoundryAgents_Step15_ComputerUse.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/ConditionalEdges/">
-    <Project Path="samples/03-workflows/ConditionalEdges/01_EdgeCondition/01_EdgeCondition.csproj" />
-    <Project Path="samples/03-workflows/ConditionalEdges/02_SwitchCase/02_SwitchCase.csproj" />
-    <Project Path="samples/03-workflows/ConditionalEdges/03_MultiSelection/03_MultiSelection.csproj" />
+  <Folder Name="/Samples/GettingStarted/ModelContextProtocol/">
+    <File Path="samples/GettingStarted/ModelContextProtocol/README.md" />
+    <Project Path="samples/GettingStarted/ModelContextProtocol/Agent_MCP_Server/Agent_MCP_Server.csproj" />
+    <Project Path="samples/GettingStarted/ModelContextProtocol/Agent_MCP_Server_Auth/Agent_MCP_Server_Auth.csproj" />
+    <Project Path="samples/GettingStarted/ModelContextProtocol/FoundryAgent_Hosted_MCP/FoundryAgent_Hosted_MCP.csproj" />
+    <Project Path="samples/GettingStarted/ModelContextProtocol/ResponseAgent_Hosted_MCP/ResponseAgent_Hosted_MCP.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/Declarative/">
-    <File Path="samples/03-workflows/Declarative/README.md" />
-    <Project Path="samples/03-workflows/Declarative/ConfirmInput/ConfirmInput.csproj" />
-    <Project Path="samples/03-workflows/Declarative/CustomerSupport/CustomerSupport.csproj" />
-    <Project Path="samples/03-workflows/Declarative/DeepResearch/DeepResearch.csproj" />
-    <Project Path="samples/03-workflows/Declarative/ExecuteCode/ExecuteCode.csproj" />
-    <Project Path="samples/03-workflows/Declarative/ExecuteWorkflow/ExecuteWorkflow.csproj" />
-    <Project Path="samples/03-workflows/Declarative/FunctionTools/FunctionTools.csproj" />
-    <Project Path="samples/03-workflows/Declarative/GenerateCode/GenerateCode.csproj" />
-    <Project Path="samples/03-workflows/Declarative/HostedWorkflow/HostedWorkflow.csproj" />
-    <Project Path="samples/03-workflows/Declarative/InputArguments/InputArguments.csproj" />
-    <Project Path="samples/03-workflows/Declarative/InvokeFunctionTool/InvokeFunctionTool.csproj" />
-    <Project Path="samples/03-workflows/Declarative/InvokeMcpTool/InvokeMcpTool.csproj" />
-    <Project Path="samples/03-workflows/Declarative/Marketing/Marketing.csproj" />
-    <Project Path="samples/03-workflows/Declarative/StudentTeacher/StudentTeacher.csproj" />
-    <Project Path="samples/03-workflows/Declarative/ToolApproval/ToolApproval.csproj" />
+  <Folder Name="/Samples/GettingStarted/Observability/">
+    <Project Path="samples/GettingStarted/AgentOpenTelemetry/AgentOpenTelemetry.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/Declarative/Examples/">
+  <Folder Name="/Samples/GettingStarted/Workflows/">
+    <File Path="samples/GettingStarted/Workflows/README.md" />
+  </Folder>
+  <Folder Name="/Samples/GettingStarted/Workflows/Concurrent/">
+    <Project Path="samples/GettingStarted/Workflows/Concurrent/Concurrent/Concurrent.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Concurrent/MapReduce/MapReduce.csproj" />
+  </Folder>
+  <Folder Name="/Samples/GettingStarted/Workflows/ConditionalEdges/">
+    <Project Path="samples/GettingStarted/Workflows/ConditionalEdges/01_EdgeCondition/01_EdgeCondition.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/ConditionalEdges/02_SwitchCase/02_SwitchCase.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/ConditionalEdges/03_MultiSelection/03_MultiSelection.csproj" />
+  </Folder>
+  <Folder Name="/Samples/GettingStarted/Workflows/Declarative/">
+    <File Path="samples/GettingStarted/Workflows/Declarative/README.md" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/ConfirmInput/ConfirmInput.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/CustomerSupport/CustomerSupport.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/DeepResearch/DeepResearch.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/ExecuteCode/ExecuteCode.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/ExecuteWorkflow/ExecuteWorkflow.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/FunctionTools/FunctionTools.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/GenerateCode/GenerateCode.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/HostedWorkflow/HostedWorkflow.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/InputArguments/InputArguments.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/Marketing/Marketing.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/StudentTeacher/StudentTeacher.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/ToolApproval/ToolApproval.csproj" />
+  </Folder>
+  <Folder Name="/Samples/GettingStarted/Workflows/Declarative/Examples/">
     <File Path="../workflow-samples/CustomerSupport.yaml" />
     <File Path="../workflow-samples/DeepResearch.yaml" />
     <File Path="../workflow-samples/Marketing.yaml" />
@@ -219,118 +235,60 @@
     <File Path="../workflow-samples/README.md" />
     <File Path="../workflow-samples/wttr.json" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/SharedStates/">
-    <Project Path="samples/03-workflows/SharedStates/SharedStates.csproj" />
+  <Folder Name="/Samples/GettingStarted/Workflows/SharedStates/">
+    <Project Path="samples/GettingStarted/Workflows/SharedStates/SharedStates.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/Loop/">
-    <Project Path="samples/03-workflows/Loop/Loop.csproj" />
+  <Folder Name="/Samples/GettingStarted/Workflows/Loop/">
+    <Project Path="samples/GettingStarted/Workflows/Loop/Loop.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/Agents/">
-    <Project Path="samples/03-workflows/Agents/CustomAgentExecutors/CustomAgentExecutors.csproj" />
-    <Project Path="samples/03-workflows/Agents/FoundryAgent/FoundryAgent.csproj" />
-    <Project Path="samples/03-workflows/Agents/GroupChatToolApproval/GroupChatToolApproval.csproj" />
-    <Project Path="samples/03-workflows/Agents/WorkflowAsAnAgent/WorkflowAsAnAgent.csproj" />
+  <Folder Name="/Samples/GettingStarted/Workflows/Agents/">
+    <Project Path="samples/GettingStarted/Workflows/Agents/CustomAgentExecutors/CustomAgentExecutors.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Agents/FoundryAgent/FoundryAgent.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Agents/GroupChatToolApproval/GroupChatToolApproval.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Agents/WorkflowAsAnAgent/WorkflowAsAnAgent.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/Checkpoint/">
-    <Project Path="samples/03-workflows/Checkpoint/CheckpointAndRehydrate/CheckpointAndRehydrate.csproj" />
-    <Project Path="samples/03-workflows/Checkpoint/CheckpointAndResume/CheckpointAndResume.csproj" />
-    <Project Path="samples/03-workflows/Checkpoint/CheckpointWithHumanInTheLoop/CheckpointWithHumanInTheLoop.csproj" />
+  <Folder Name="/Samples/GettingStarted/Workflows/Checkpoint/">
+    <Project Path="samples/GettingStarted/Workflows/Checkpoint/CheckpointAndRehydrate/CheckpointAndRehydrate.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Checkpoint/CheckpointAndResume/CheckpointAndResume.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Checkpoint/CheckpointWithHumanInTheLoop/CheckpointWithHumanInTheLoop.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/HumanInTheLoop/">
-    <Project Path="samples/03-workflows/HumanInTheLoop/HumanInTheLoopBasic/HumanInTheLoopBasic.csproj" />
+  <Folder Name="/Samples/GettingStarted/Workflows/HumanInTheLoop/">
+    <Project Path="samples/GettingStarted/Workflows/HumanInTheLoop/HumanInTheLoopBasic/HumanInTheLoopBasic.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/Observability/">
-    <Project Path="samples/03-workflows/Observability/ApplicationInsights/ApplicationInsights.csproj" />
-    <Project Path="samples/03-workflows/Observability/AspireDashboard/AspireDashboard.csproj" />
-    <Project Path="samples/03-workflows/Observability/WorkflowAsAnAgent/WorkflowAsAnAgentObservability.csproj" />
+  <Folder Name="/Samples/GettingStarted/Workflows/Observability/">
+    <Project Path="samples/GettingStarted/Workflows/Observability/ApplicationInsights/ApplicationInsights.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Observability/AspireDashboard/AspireDashboard.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Observability/WorkflowAsAnAgent/WorkflowAsAnAgentObservability.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/Visualization/">
-    <Project Path="samples/03-workflows/Visualization/Visualization.csproj" />
+  <Folder Name="/Samples/GettingStarted/Workflows/Visualization/">
+    <Project Path="samples/GettingStarted/Workflows/Visualization/Visualization.csproj" />
   </Folder>
-  <Folder Name="/Samples/03-workflows/_StartHere/">
-    <Project Path="samples/03-workflows/_StartHere/01_Streaming/01_Streaming.csproj" />
-    <Project Path="samples/03-workflows/_StartHere/02_AgentsInWorkflows/02_AgentsInWorkflows.csproj" />
-    <Project Path="samples/03-workflows/_StartHere/03_AgentWorkflowPatterns/03_AgentWorkflowPatterns.csproj" />
-    <Project Path="samples/03-workflows/_StartHere/04_MultiModelService/04_MultiModelService.csproj" />
-    <Project Path="samples/03-workflows/_StartHere/05_SubWorkflows/05_SubWorkflows.csproj" />
-    <Project Path="samples/03-workflows/_StartHere/06_MixedWorkflowAgentsAndExecutors/06_MixedWorkflowAgentsAndExecutors.csproj" />
-    <Project Path="samples/03-workflows/_StartHere/07_WriterCriticWorkflow/07_WriterCriticWorkflow.csproj" />
+  <Folder Name="/Samples/GettingStarted/Workflows/_Foundational/">
+    <Project Path="samples/GettingStarted/Workflows/_Foundational/01_ExecutorsAndEdges/01_ExecutorsAndEdges.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/_Foundational/02_Streaming/02_Streaming.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/_Foundational/03_AgentsInWorkflows/03_AgentsInWorkflows.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/_Foundational/04_AgentWorkflowPatterns/04_AgentWorkflowPatterns.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/_Foundational/05_MultiModelService/05_MultiModelService.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/_Foundational/06_SubWorkflows/06_SubWorkflows.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/_Foundational/07_MixedWorkflowAgentsAndExecutors/07_MixedWorkflowAgentsAndExecutors.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/_Foundational/08_WriterCriticWorkflow/08_WriterCriticWorkflow.csproj" />
   </Folder>
-  <Folder Name="/Samples/04-hosting/" />
-  <Folder Name="/Samples/04-hosting/DurableAgents/" />
-  <Folder Name="/Samples/04-hosting/DurableAgents/AzureFunctions/">
-    <File Path="samples/04-hosting/DurableAgents/AzureFunctions/.editorconfig" />
-    <File Path="samples/04-hosting/DurableAgents/AzureFunctions/README.md" />
-    <Project Path="samples/04-hosting/DurableAgents/AzureFunctions/01_SingleAgent/01_SingleAgent.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/AzureFunctions/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/AzureFunctions/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/AzureFunctions/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/AzureFunctions/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/AzureFunctions/06_LongRunningTools/06_LongRunningTools.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/AzureFunctions/07_AgentAsMcpTool/07_AgentAsMcpTool.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/AzureFunctions/08_ReliableStreaming/08_ReliableStreaming.csproj" />
+  <Folder Name="/Samples/HostedAgents/">
+    <Project Path="samples/HostedAgents/AgentsInWorkflows/AgentsInWorkflows.csproj" />
+    <Project Path="samples/HostedAgents/AgentWithHostedMCP/AgentWithHostedMCP.csproj" />
+    <Project Path="samples/HostedAgents/AgentWithTextSearchRag/AgentWithTextSearchRag.csproj" />
   </Folder>
-  <Folder Name="/Samples/04-hosting/DurableAgents/ConsoleApps/">
-    <File Path="samples/04-hosting/DurableAgents/ConsoleApps/README.md" />
-    <Project Path="samples/04-hosting/DurableAgents/ConsoleApps/01_SingleAgent/01_SingleAgent.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/ConsoleApps/06_LongRunningTools/06_LongRunningTools.csproj" />
-    <Project Path="samples/04-hosting/DurableAgents/ConsoleApps/07_ReliableStreaming/07_ReliableStreaming.csproj" />
-  </Folder>
-  <Folder Name="/Samples/04-hosting/A2A/">
-    <File Path="samples/04-hosting/A2A/README.md" />
-    <Project Path="samples/04-hosting/A2A/A2AAgent_AsFunctionTools/A2AAgent_AsFunctionTools.csproj" />
-    <Project Path="samples/04-hosting/A2A/A2AAgent_PollingForTaskCompletion/A2AAgent_PollingForTaskCompletion.csproj" />
-  </Folder>  
-  <Folder Name="/Samples/05-end-to-end/">
-    <Project Path="samples/05-end-to-end/AgentWithPurview/AgentWithPurview.csproj" />
-    <Project Path="samples/05-end-to-end/M365Agent/M365Agent.csproj" />
-  </Folder>
-  <Folder Name="/Samples/05-end-to-end/A2AClientServer/">
-    <File Path="samples/05-end-to-end/A2AClientServer/README.md" />
-    <Project Path="samples/05-end-to-end/A2AClientServer/A2AClient/A2AClient.csproj" />
-    <Project Path="samples/05-end-to-end/A2AClientServer/A2AServer/A2AServer.csproj" />
-  </Folder>
-  <Folder Name="/Samples/05-end-to-end/AgentWebChat/">
-    <Project Path="samples/05-end-to-end/AgentWebChat/AgentWebChat.AgentHost/AgentWebChat.AgentHost.csproj" />
-    <Project Path="samples/05-end-to-end/AgentWebChat/AgentWebChat.AppHost/AgentWebChat.AppHost.csproj" />
-    <Project Path="samples/05-end-to-end/AgentWebChat/AgentWebChat.ServiceDefaults/AgentWebChat.ServiceDefaults.csproj" />
-    <Project Path="samples/05-end-to-end/AgentWebChat/AgentWebChat.Web/AgentWebChat.Web.csproj" />
-  </Folder>
-  <Folder Name="/Samples/05-end-to-end/AGUIClientServer/">
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-    <Project Path="samples/05-end-to-end/AGUIClientServer/AGUIClient/AGUIClient.csproj" />
-    <Project Path="samples/05-end-to-end/AGUIClientServer/AGUIDojoServer/AGUIDojoServer.csproj" />
-    <Project Path="samples/05-end-to-end/AGUIClientServer/AGUIServer/AGUIServer.csproj" />
-  </Folder>
-  <Folder Name="/Samples/05-end-to-end/HostedAgents/">
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-    <Project Path="samples/05-end-to-end/HostedAgents/AgentThreadAndHITL/AgentThreadAndHITL.csproj" />
-    <Project Path="samples/05-end-to-end/HostedAgents/AgentWithHostedMCP/AgentWithHostedMCP.csproj" />
-    <Project Path="samples/05-end-to-end/HostedAgents/AgentWithLocalTools/AgentWithLocalTools.csproj" />
-    <Project Path="samples/05-end-to-end/HostedAgents/AgentWithTextSearchRag/AgentWithTextSearchRag.csproj" />
-    <Project Path="samples/05-end-to-end/HostedAgents/FoundryMultiAgent/FoundryMultiAgent.csproj" />
-    <Project Path="samples/05-end-to-end/HostedAgents/FoundrySingleAgent/FoundrySingleAgent.csproj" />
-  </Folder>
-  <Folder Name="/Samples/05-end-to-end/AspNetAgentAuthorization/">
-    <File Path="samples/05-end-to-end/AspNetAgentAuthorization/docker-compose.yml" />
-    <File Path="samples/05-end-to-end/AspNetAgentAuthorization/README.md" />
-    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/RazorWebClient.csproj" />
-    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/Service/Service.csproj" />
+  <Folder Name="/Samples/M365Agent/">
+    <Project Path="samples/M365Agent/M365Agent.csproj" />
   </Folder>
   <Folder Name="/Solution Items/">
     <File Path=".editorconfig" />
     <File Path=".gitignore" />
-    <File Path="AGENTS.md" />
     <File Path="Directory.Build.props" />
     <File Path="Directory.Build.targets" />
     <File Path="Directory.Packages.props" />
     <File Path="global.json" />
     <File Path="nuget.config" />
-    <File Path="README.md" />
   </Folder>
   <Folder Name="/Solution Items/.github/" />
   <Folder Name="/Solution Items/.github/upgrades/" />
@@ -339,6 +297,7 @@
   </Folder>
   <Folder Name="/Solution Items/.github/workflows/">
     <File Path="../.github/workflows/dotnet-build-and-test.yml" />
+    <File Path="../.github/workflows/dotnet-check-coverage.ps1" />
     <File Path="../.github/workflows/dotnet-format.yml" />
   </Folder>
   <Folder Name="/Solution Items/demos/">
@@ -361,10 +320,6 @@
     <File Path="../docs/decisions/0012-python-typeddict-options.md" />
     <File Path="../docs/decisions/0013-python-get-response-simplification.md" />
     <File Path="../docs/decisions/0014-feature-collections.md" />
-    <File Path="../docs/decisions/0015-agent-run-context.md" />
-    <File Path="../docs/decisions/0016-python-context-middleware.md" />
-    <File Path="../docs/decisions/0017-agent-additional-properties.md" />
-    <File Path="../docs/decisions/0018-agentthread-serialization.md" />
     <File Path="../docs/decisions/adr-short-template.md" />
     <File Path="../docs/decisions/adr-template.md" />
     <File Path="../docs/decisions/README.md" />
@@ -375,10 +330,6 @@
     <File Path="eng/MSBuild/Shared.props" />
     <File Path="eng/MSBuild/Shared.targets" />
   </Folder>
-  <Folder Name="/Solution Items/eng/scripts/">
-    <File Path="eng/scripts/dotnet-check-coverage.ps1" />
-    <File Path="eng/scripts/New-FilteredSolution.ps1" />
-  </Folder>
   <Folder Name="/Solution Items/nuget/">
     <File Path="nuget/icon.png" />
     <File Path="nuget/nuget-package.props" />
@@ -433,10 +384,6 @@
     <File Path="src/Shared/Demos/README.md" />
     <File Path="src/Shared/Demos/SampleEnvironment.cs" />
   </Folder>
-  <Folder Name="/Solution Items/src/Shared/DiagnosticIds/">
-    <File Path="src/Shared/DiagnosticIds/DiagnosticsIds.cs" />
-    <File Path="src/Shared/DiagnosticIds/README.md" />
-  </Folder>
   <Folder Name="/Solution Items/src/Shared/IntegrationTests/">
     <File Path="src/Shared/IntegrationTests/AnthropicConfiguration.cs" />
     <File Path="src/Shared/IntegrationTests/AzureAIConfiguration.cs" />
@@ -444,10 +391,6 @@
     <File Path="src/Shared/IntegrationTests/OpenAIConfiguration.cs" />
     <File Path="src/Shared/IntegrationTests/README.md" />
   </Folder>
-  <Folder Name="/Solution Items/src/Shared/IntegrationTestsAzureCredentials/">
-    <File Path="src/Shared/IntegrationTestsAzureCredentials/README.md" />
-    <File Path="src/Shared/IntegrationTestsAzureCredentials/TestAzureCliCredentials.cs" />
-  </Folder>
   <Folder Name="/Solution Items/src/Shared/Samples/">
     <File Path="src/Shared/Samples/BaseSample.cs" />
     <File Path="src/Shared/Samples/README.md" />
@@ -455,17 +398,10 @@
     <File Path="src/Shared/Samples/TextOutputHelperExtensions.cs" />
     <File Path="src/Shared/Samples/XunitLogger.cs" />
   </Folder>
-  <Folder Name="/Solution Items/src/Shared/Redaction/">
-    <File Path="src/Shared/Redaction/README.md" />
-    <File Path="src/Shared/Redaction/ReplacingRedactor.cs" />
-  </Folder>
   <Folder Name="/Solution Items/src/Shared/Throw/">
     <File Path="src/Shared/Throw/README.md" />
     <File Path="src/Shared/Throw/Throw.cs" />
   </Folder>
-  <Folder Name="/Solution Items/src/Shared/StructuredOutput/">
-    <File Path="src/Shared/StructuredOutput/StructuredOutputSchemaUtilities.cs" />
-  </Folder>
   <Folder Name="/Solution Items/tests/">
     <File Path="tests/.editorconfig" />
     <File Path="tests/Directory.Build.props" />
@@ -475,6 +411,7 @@
     <Project Path="src/Microsoft.Agents.AI.Abstractions/Microsoft.Agents.AI.Abstractions.csproj" />
     <Project Path="src/Microsoft.Agents.AI.AGUI/Microsoft.Agents.AI.AGUI.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Anthropic/Microsoft.Agents.AI.Anthropic.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.GitHub.Copilot/Microsoft.Agents.AI.GitHub.Copilot.csproj" />
     <Project Path="src/Microsoft.Agents.AI.AzureAI.Persistent/Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
     <Project Path="src/Microsoft.Agents.AI.AzureAI/Microsoft.Agents.AI.AzureAI.csproj" />
     <Project Path="src/Microsoft.Agents.AI.CopilotStudio/Microsoft.Agents.AI.CopilotStudio.csproj" />
@@ -482,8 +419,6 @@
     <Project Path="src/Microsoft.Agents.AI.Declarative/Microsoft.Agents.AI.Declarative.csproj" />
     <Project Path="src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj" />
     <Project Path="src/Microsoft.Agents.AI.DurableTask/Microsoft.Agents.AI.DurableTask.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.FoundryMemory/Microsoft.Agents.AI.FoundryMemory.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.GitHub.Copilot/Microsoft.Agents.AI.GitHub.Copilot.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Hosting.A2A.AspNetCore/Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Hosting.A2A/Microsoft.Agents.AI.Hosting.A2A.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
@@ -494,10 +429,9 @@
     <Project Path="src/Microsoft.Agents.AI.OpenAI/Microsoft.Agents.AI.OpenAI.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Purview/Microsoft.Agents.AI.Purview.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.AzureAI/Microsoft.Agents.AI.Workflows.Declarative.AzureAI.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.Mcp/Microsoft.Agents.AI.Workflows.Declarative.Mcp.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative/Microsoft.Agents.AI.Workflows.Declarative.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows/Microsoft.Agents.AI.Workflows.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj" />
     <Project Path="src/Microsoft.Agents.AI/Microsoft.Agents.AI.csproj" />
   </Folder>
   <Folder Name="/Tests/" />
@@ -507,9 +441,8 @@
     <Project Path="tests/AzureAI.IntegrationTests/AzureAI.IntegrationTests.csproj" />
     <Project Path="tests/AzureAIAgentsPersistent.IntegrationTests/AzureAIAgentsPersistent.IntegrationTests.csproj" />
     <Project Path="tests/CopilotStudio.IntegrationTests/CopilotStudio.IntegrationTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.DurableTask.IntegrationTests/Microsoft.Agents.AI.DurableTask.IntegrationTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.FoundryMemory.IntegrationTests/Microsoft.Agents.AI.FoundryMemory.IntegrationTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.DurableTask.IntegrationTests/Microsoft.Agents.AI.DurableTask.IntegrationTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Mem0.IntegrationTests/Microsoft.Agents.AI.Mem0.IntegrationTests.csproj" />
@@ -523,14 +456,13 @@
     <Project Path="tests/Microsoft.Agents.AI.Abstractions.UnitTests/Microsoft.Agents.AI.Abstractions.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.AGUI.UnitTests/Microsoft.Agents.AI.AGUI.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Anthropic.UnitTests/Microsoft.Agents.AI.Anthropic.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.AzureAI.UnitTests/Microsoft.Agents.AI.AzureAI.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.CosmosNoSql.UnitTests/Microsoft.Agents.AI.CosmosNoSql.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Declarative.UnitTests/Microsoft.Agents.AI.Declarative.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.DevUI.UnitTests/Microsoft.Agents.AI.DevUI.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.DurableTask.UnitTests/Microsoft.Agents.AI.DurableTask.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.FoundryMemory.UnitTests/Microsoft.Agents.AI.FoundryMemory.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Microsoft.Agents.AI.Hosting.A2A.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.UnitTests/Microsoft.Agents.AI.Hosting.AzureFunctions.UnitTests.csproj" />
@@ -540,9 +472,8 @@
     <Project Path="tests/Microsoft.Agents.AI.OpenAI.UnitTests/Microsoft.Agents.AI.OpenAI.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Purview.UnitTests/Microsoft.Agents.AI.Purview.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.UnitTests/Microsoft.Agents.AI.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.Mcp.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.Mcp.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.Generators.UnitTests/Microsoft.Agents.AI.Workflows.Generators.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.UnitTests/Microsoft.Agents.AI.Workflows.UnitTests.csproj" />
   </Folder>
-</Solution>
+</Solution>

dotnet/agent-framework-release.slnf

@@ -14,7 +14,6 @@
       "src\\Microsoft.Agents.AI.Declarative\\Microsoft.Agents.AI.Declarative.csproj",
       "src\\Microsoft.Agents.AI.DevUI\\Microsoft.Agents.AI.DevUI.csproj",
       "src\\Microsoft.Agents.AI.DurableTask\\Microsoft.Agents.AI.DurableTask.csproj",
-      "src\\Microsoft.Agents.AI.FoundryMemory\\Microsoft.Agents.AI.FoundryMemory.csproj",
       "src\\Microsoft.Agents.AI.Hosting.A2A.AspNetCore\\Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj",
       "src\\Microsoft.Agents.AI.Hosting.A2A\\Microsoft.Agents.AI.Hosting.A2A.csproj",
       "src\\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj",
@@ -26,7 +25,6 @@
       "src\\Microsoft.Agents.AI.Purview\\Microsoft.Agents.AI.Purview.csproj",
       "src\\Microsoft.Agents.AI.Workflows.Declarative.AzureAI\\Microsoft.Agents.AI.Workflows.Declarative.AzureAI.csproj",
       "src\\Microsoft.Agents.AI.Workflows.Declarative\\Microsoft.Agents.AI.Workflows.Declarative.csproj",
-      "src\\Microsoft.Agents.AI.Workflows.Generators\\Microsoft.Agents.AI.Workflows.Generators.csproj",
       "src\\Microsoft.Agents.AI.Workflows\\Microsoft.Agents.AI.Workflows.csproj",
       "src\\Microsoft.Agents.AI\\Microsoft.Agents.AI.csproj"
     ]

dotnet/eng/MSBuild/Shared.props

@@ -8,9 +8,6 @@
   <ItemGroup Condition="'$(InjectSharedIntegrationTestCode)' == 'true'">
     <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\IntegrationTests\*.cs" LinkBase="Shared\IntegrationTests" />
   </ItemGroup>
-  <ItemGroup Condition="'$(InjectSharedIntegrationTestAzureCredentialsCode)' == 'true'">
-    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\IntegrationTestsAzureCredentials\*.cs" LinkBase="Shared\IntegrationTestsAzureCredentials" />
-  </ItemGroup>
   <ItemGroup Condition="'$(InjectSharedBuildTestCode)' == 'true'">
     <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\CodeTests\*.cs" LinkBase="Shared\CodeTests" />
   </ItemGroup>
@@ -23,13 +20,4 @@
   <ItemGroup Condition="'$(InjectSharedFoundryAgents)' == 'true'">
     <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Foundry\Agents\*.cs" LinkBase="Shared\Foundry" />
   </ItemGroup>
-  <ItemGroup Condition="'$(InjectSharedStructuredOutput)' == 'true'">
-    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\StructuredOutput\*.cs" LinkBase="Shared\StructuredOutput" />
-  </ItemGroup>
-  <ItemGroup Condition="'$(InjectSharedDiagnosticIds)' == 'true'">
-    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\DiagnosticIds\*.cs" LinkBase="Shared\DiagnosticIds" />
-  </ItemGroup>
-  <ItemGroup Condition="'$(InjectSharedRedaction)' == 'true'">
-    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Redaction\*.cs" LinkBase="Shared\Redaction" />
-  </ItemGroup>
 </Project>

dotnet/eng/scripts/New-FilteredSolution.ps1

@@ -1,145 +0,0 @@
-#!/usr/bin/env pwsh
-# Copyright (c) Microsoft. All rights reserved.
-
-<#
-.SYNOPSIS
-    Generates a filtered .slnx solution file by removing projects that don't match the specified criteria.
-
-.DESCRIPTION
-    Parses a .slnx solution file and applies one or more filters:
-    - Removes projects that don't support the specified target framework (via MSBuild query).
-    - Optionally removes all sample projects (under samples/).
-    - Optionally filters test projects by name pattern (e.g., only *UnitTests*).
-    Writes the filtered solution to the specified output path and prints the path.
-
-.PARAMETER Solution
-    Path to the source .slnx solution file.
-
-.PARAMETER TargetFramework
-    The target framework to filter by (e.g., net10.0, net472).
-
-.PARAMETER Configuration
-    Optional MSBuild configuration used when querying TargetFrameworks. Defaults to Debug.
-
-.PARAMETER TestProjectNameFilter
-    Optional wildcard pattern to filter test project names (e.g., *UnitTests*, *IntegrationTests*).
-    When specified, only test projects whose filename matches this pattern are kept.
-
-.PARAMETER ExcludeSamples
-    When specified, removes all projects under the samples/ directory from the solution.
-
-.PARAMETER OutputPath
-    Optional output path for the filtered .slnx file. If not specified, a temp file is created.
-
-.EXAMPLE
-    # Generate a filtered solution and run tests
-    $filtered = ./dotnet/eng/scripts/New-FilteredSolution.ps1 -Solution dotnet/agent-framework-dotnet.slnx -TargetFramework net472
-    dotnet test --solution $filtered --no-build -f net472
-
-.EXAMPLE
-    # Generate a solution with only unit test projects
-    ./dotnet/eng/scripts/New-FilteredSolution.ps1 -Solution dotnet/agent-framework-dotnet.slnx -TargetFramework net10.0 -TestProjectNameFilter "*UnitTests*" -OutputPath filtered-unit.slnx
-
-.EXAMPLE
-    # Inline usage with dotnet test (PowerShell)
-    dotnet test --solution (./dotnet/eng/scripts/New-FilteredSolution.ps1 -Solution dotnet/agent-framework-dotnet.slnx -TargetFramework net472) --no-build -f net472
-#>
-
-[CmdletBinding()]
-param(
-    [Parameter(Mandatory)]
-    [string]$Solution,
-
-    [Parameter(Mandatory)]
-    [string]$TargetFramework,
-
-    [string]$Configuration = "Debug",
-
-    [string]$TestProjectNameFilter,
-
-    [switch]$ExcludeSamples,
-
-    [string]$OutputPath
-)
-
-$ErrorActionPreference = "Stop"
-
-# Resolve the solution path
-$solutionPath = Resolve-Path $Solution
-$solutionDir = Split-Path $solutionPath -Parent
-
-if (-not $OutputPath) {
-    $OutputPath = [System.IO.Path]::Combine([System.IO.Path]::GetTempPath(), "filtered-$(Split-Path $solutionPath -Leaf)")
-}
-
-# Parse the .slnx XML
-[xml]$slnx = Get-Content $solutionPath -Raw
-
-$removed = @()
-$kept = @()
-
-# Remove sample projects if requested
-if ($ExcludeSamples) {
-    $sampleProjects = $slnx.SelectNodes("//Project[contains(@Path, 'samples/')]")
-    foreach ($proj in $sampleProjects) {
-        $projRelPath = $proj.GetAttribute("Path")
-        Write-Verbose "Removing (sample): $projRelPath"
-        $removed += $projRelPath
-        $proj.ParentNode.RemoveChild($proj) | Out-Null
-    }
-    Write-Host "Removed $($sampleProjects.Count) sample project(s)." -ForegroundColor Yellow
-}
-
-# Filter all remaining projects by target framework
-$allProjects = $slnx.SelectNodes("//Project")
-
-foreach ($proj in $allProjects) {
-    $projRelPath = $proj.GetAttribute("Path")
-    $projFullPath = Join-Path $solutionDir $projRelPath
-    $projFileName = Split-Path $projRelPath -Leaf
-    $isTestProject = $projRelPath -like "*tests/*"
-
-    # Filter test projects by name pattern if specified
-    if ($isTestProject -and $TestProjectNameFilter -and ($projFileName -notlike $TestProjectNameFilter)) {
-        Write-Verbose "Removing (name filter): $projRelPath"
-        $removed += $projRelPath
-        $proj.ParentNode.RemoveChild($proj) | Out-Null
-        continue
-    }
-
-    if (-not (Test-Path $projFullPath)) {
-        Write-Verbose "Project not found, keeping in solution: $projRelPath"
-        $kept += $projRelPath
-        continue
-    }
-
-    # Query the project's target frameworks using MSBuild
-    $targetFrameworks = & dotnet msbuild $projFullPath -getProperty:TargetFrameworks -p:Configuration=$Configuration -nologo 2>$null
-    $targetFrameworks = $targetFrameworks.Trim()
-
-    if ($targetFrameworks -like "*$TargetFramework*") {
-        Write-Verbose "Keeping: $projRelPath (targets: $targetFrameworks)"
-        $kept += $projRelPath
-    }
-    else {
-        Write-Verbose "Removing: $projRelPath (targets: $targetFrameworks, missing: $TargetFramework)"
-        $removed += $projRelPath
-        $proj.ParentNode.RemoveChild($proj) | Out-Null
-    }
-}
-
-# Write the filtered solution
-$slnx.Save($OutputPath)
-
-# Report results to stderr so stdout is clean for piping
-Write-Host "Filtered solution written to: $OutputPath" -ForegroundColor Green
-if ($removed.Count -gt 0) {
-    Write-Host "Removed $($removed.Count) project(s):" -ForegroundColor Yellow
-    foreach ($r in $removed) {
-        Write-Host "  - $r" -ForegroundColor Yellow
-    }
-}
-Write-Host "Kept $($kept.Count) project(s)." -ForegroundColor Green
-
-# Output the path for piping
-Write-Output $OutputPath

dotnet/global.json

@@ -1,10 +1,7 @@
 {
     "sdk": {
-        "version": "10.0.200",
+        "version": "10.0.100",
         "rollForward": "minor",
         "allowPrerelease": false
-    },
-    "test": {
-        "runner": "Microsoft.Testing.Platform"
     }
 }

dotnet/nuget/nuget-package.props

@@ -2,19 +2,15 @@
   <PropertyGroup>
     <!-- Central version prefix - applies to all nuget packages. -->
     <VersionPrefix>1.0.0</VersionPrefix>
-    <RCNumber>5</RCNumber>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260330.1</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260330.1</PackageVersion>
-    <GitTag>1.0.0-rc5</GitTag>
+    <PackageVersion Condition="'$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260128.1</PackageVersion>
+    <PackageVersion Condition="'$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260128.1</PackageVersion>
+    <GitTag>1.0.0-preview.260128.1</GitTag>
 
     <Configurations>Debug;Release;Publish</Configurations>
     <IsPackable>true</IsPackable>
 
     <!-- Package validation. Baseline Version should be the latest version available on NuGet. -->
-    <PackageValidationBaselineVersion>1.0.0-rc4</PackageValidationBaselineVersion>
-    <!-- Enable validation for RC packages and GA packages -->
-    <EnablePackageValidation Condition="'$(IsReleaseCandidate)' == 'true' OR '$(IsGenerallyAvailable)' == 'true'">true</EnablePackageValidation>
+    <PackageValidationBaselineVersion>0.0.1</PackageValidationBaselineVersion>
     <!-- Validate assembly attributes only for Publish builds -->
     <NoWarn Condition="'$(Configuration)' != 'Publish'">$(NoWarn);CP0003</NoWarn>
     <!-- Do not validate reference assemblies -->

dotnet/samples/01-get-started/01_hello_agent/01_hello_agent.csproj

@@ -1,21 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/01-get-started/01_hello_agent/Program.cs

@@ -1,29 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to create and use a simple AI agent with Azure OpenAI as the backend.
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using OpenAI.Chat;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
-
-// Invoke the agent and output the text result.
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
-
-// Invoke the agent with streaming support.
-await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate."))
-{
-    Console.WriteLine(update);
-}

dotnet/samples/01-get-started/02_add_tools/02_add_tools.csproj

@@ -1,21 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/01-get-started/02_add_tools/Program.cs

@@ -1,37 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample demonstrates how to use a ChatClientAgent with function tools.
-// It shows both non-streaming and streaming agent interactions using menu-related tools.
-
-using System.ComponentModel;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using OpenAI.Chat;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-[Description("Get the weather for a given location.")]
-static string GetWeather([Description("The location to get the weather for.")] string location)
-    => $"The weather in {location} is cloudy with a high of 15°C.";
-
-// Create the chat client and agent, and provide the function tool to the agent.
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsAIAgent(instructions: "You are a helpful assistant", tools: [AIFunctionFactory.Create(GetWeather)]);
-
-// Non-streaming agent interaction with function tools.
-Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?"));
-
-// Streaming agent interaction with function tools.
-await foreach (var update in agent.RunStreamingAsync("What is the weather like in Amsterdam?"))
-{
-    Console.WriteLine(update);
-}

dotnet/samples/01-get-started/03_multi_turn/03_multi_turn.csproj

@@ -1,21 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/01-get-started/03_multi_turn/Program.cs

@@ -1,36 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to create and use a simple AI agent with a multi-turn conversation.
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using OpenAI.Chat;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
-
-// Invoke the agent with a multi-turn conversation, where the context is preserved in the session object.
-AgentSession session = await agent.CreateSessionAsync();
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
-Console.WriteLine(await agent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session));
-
-// Invoke the agent with a multi-turn conversation and streaming, where the context is preserved in the session object.
-session = await agent.CreateSessionAsync();
-await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate.", session))
-{
-    Console.WriteLine(update);
-}
-await foreach (var update in agent.RunStreamingAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session))
-{
-    Console.WriteLine(update);
-}

dotnet/samples/01-get-started/04_memory/04_memory.csproj

@@ -1,21 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/01-get-started/04_memory/Program.cs

@@ -1,162 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to add a basic custom memory component to an agent.
-// The memory component subscribes to all messages added to the conversation and
-// extracts the user's name and age if provided.
-// The component adds a prompt to ask for this information if it is not already known
-// and provides it to the model before each invocation if known.
-
-using System.Text;
-using System.Text.Json;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using OpenAI.Chat;
-using SampleApp;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-ChatClient chatClient = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-    .GetChatClient(deploymentName);
-
-// Create the agent and provide a factory to add our custom memory component to
-// all sessions created by the agent. Here each new memory component will have its own
-// user info object, so each session will have its own memory.
-// In real world applications/services, where the user info would be persisted in a database,
-// and preferably shared between multiple sessions used by the same user, ensure that the
-// factory reads the user id from the current context and scopes the memory component
-// and its storage to that user id.
-AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions()
-{
-    ChatOptions = new() { Instructions = "You are a friendly assistant. Always address the user by their name." },
-    AIContextProviders = [new UserInfoMemory(chatClient.AsIChatClient())]
-});
-
-// Create a new session for the conversation.
-AgentSession session = await agent.CreateSessionAsync();
-
-Console.WriteLine(">> Use session with blank memory\n");
-
-// Invoke the agent and output the text result.
-Console.WriteLine(await agent.RunAsync("Hello, what is the square root of 9?", session));
-Console.WriteLine(await agent.RunAsync("My name is Ruaidhrí", session));
-Console.WriteLine(await agent.RunAsync("I am 20 years old", session));
-
-// We can serialize the session. The serialized state will include the state of the memory component.
-JsonElement sesionElement = await agent.SerializeSessionAsync(session);
-
-Console.WriteLine("\n>> Use deserialized session with previously created memories\n");
-
-// Later we can deserialize the session and continue the conversation with the previous memory component state.
-var deserializedSession = await agent.DeserializeSessionAsync(sesionElement);
-Console.WriteLine(await agent.RunAsync("What is my name and age?", deserializedSession));
-
-Console.WriteLine("\n>> Read memories using memory component\n");
-
-// It's possible to access the memory component via the agent's GetService method.
-var userInfo = agent.GetService<UserInfoMemory>()?.GetUserInfo(deserializedSession);
-
-// Output the user info that was captured by the memory component.
-Console.WriteLine($"MEMORY - User Name: {userInfo?.UserName}");
-Console.WriteLine($"MEMORY - User Age: {userInfo?.UserAge}");
-
-Console.WriteLine("\n>> Use new session with previously created memories\n");
-
-// It is also possible to set the memories using a memory component on an individual session.
-// This is useful if we want to start a new session, but have it share the same memories as a previous session.
-var newSession = await agent.CreateSessionAsync();
-if (userInfo is not null && agent.GetService<UserInfoMemory>() is UserInfoMemory newSessionMemory)
-{
-    newSessionMemory.SetUserInfo(newSession, userInfo);
-}
-
-// Invoke the agent and output the text result.
-// This time the agent should remember the user's name and use it in the response.
-Console.WriteLine(await agent.RunAsync("What is my name and age?", newSession));
-
-namespace SampleApp
-{
-    /// <summary>
-    /// Sample memory component that can remember a user's name and age.
-    /// </summary>
-    internal sealed class UserInfoMemory : AIContextProvider
-    {
-        private readonly ProviderSessionState<UserInfo> _sessionState;
-        private IReadOnlyList<string>? _stateKeys;
-        private readonly IChatClient _chatClient;
-
-        public UserInfoMemory(IChatClient chatClient, Func<AgentSession?, UserInfo>? stateInitializer = null)
-        {
-            this._sessionState = new ProviderSessionState<UserInfo>(
-                stateInitializer ?? (_ => new UserInfo()),
-                this.GetType().Name);
-            this._chatClient = chatClient;
-        }
-
-        public override IReadOnlyList<string> StateKeys => this._stateKeys ??= [this._sessionState.StateKey];
-
-        public UserInfo GetUserInfo(AgentSession session)
-            => this._sessionState.GetOrInitializeState(session);
-
-        public void SetUserInfo(AgentSession session, UserInfo userInfo)
-            => this._sessionState.SaveState(session, userInfo);
-
-        protected override async ValueTask StoreAIContextAsync(InvokedContext context, CancellationToken cancellationToken = default)
-        {
-            var userInfo = this._sessionState.GetOrInitializeState(context.Session);
-
-            // Try and extract the user name and age from the message if we don't have it already and it's a user message.
-            if ((userInfo.UserName is null || userInfo.UserAge is null) && context.RequestMessages.Any(x => x.Role == ChatRole.User))
-            {
-                var result = await this._chatClient.GetResponseAsync<UserInfo>(
-                    context.RequestMessages,
-                    new ChatOptions()
-                    {
-                        Instructions = "Extract the user's name and age from the message if present. If not present return nulls."
-                    },
-                    cancellationToken: cancellationToken);
-
-                userInfo.UserName ??= result.Result.UserName;
-                userInfo.UserAge ??= result.Result.UserAge;
-            }
-
-            this._sessionState.SaveState(context.Session, userInfo);
-        }
-
-        protected override ValueTask<AIContext> ProvideAIContextAsync(InvokingContext context, CancellationToken cancellationToken = default)
-        {
-            var userInfo = this._sessionState.GetOrInitializeState(context.Session);
-
-            StringBuilder instructions = new();
-
-            // If we don't already know the user's name and age, add instructions to ask for them, otherwise just provide what we have to the context.
-            instructions
-                .AppendLine(
-                    userInfo.UserName is null ?
-                        "Ask the user for their name and politely decline to answer any questions until they provide it." :
-                        $"The user's name is {userInfo.UserName}.")
-                .AppendLine(
-                    userInfo.UserAge is null ?
-                        "Ask the user for their age and politely decline to answer any questions until they provide it." :
-                        $"The user's age is {userInfo.UserAge}.");
-
-            return new ValueTask<AIContext>(new AIContext
-            {
-                Instructions = instructions.ToString()
-            });
-        }
-    }
-
-    internal sealed class UserInfo
-    {
-        public string? UserName { get; set; }
-        public int? UserAge { get; set; }
-    }
-}

dotnet/samples/01-get-started/05_first_workflow/05_first_workflow.csproj

@@ -1,14 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/01-get-started/06_host_your_agent/06_host_your_agent.csproj

@@ -1,31 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-    <!-- The Functions build tools don't like namespaces that start with a number -->
-    <AssemblyName>HostedAgent</AssemblyName>
-    <RootNamespace>HostedAgent</RootNamespace>
-  </PropertyGroup>
-  <ItemGroup>
-    <FrameworkReference Include="Microsoft.AspNetCore.App" />
-  </ItemGroup>
-  <!-- Azure Functions packages -->
-  <ItemGroup>
-    <PackageReference Include="Microsoft.Azure.Functions.Worker" />
-    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
-    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
-    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
-    <PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
-  </ItemGroup>
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
-    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-</Project>

dotnet/samples/01-get-started/06_host_your_agent/Program.cs

@@ -1,45 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to host an AI agent with Azure Functions (DurableAgents).
-//
-// Prerequisites:
-//   - Azure Functions Core Tools
-//   - Azure OpenAI resource
-//
-// Environment variables:
-//   AZURE_OPENAI_ENDPOINT
-//   AZURE_OPENAI_DEPLOYMENT_NAME (defaults to "gpt-4o-mini")
-//
-// Run with: func start
-// Then call: POST http://localhost:7071/api/agents/HostedAgent/run
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.Hosting.AzureFunctions;
-using Microsoft.Azure.Functions.Worker.Builder;
-using Microsoft.Extensions.Hosting;
-using OpenAI.Chat;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// Set up an AI agent following the standard Microsoft Agent Framework pattern.
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsAIAgent(
-        instructions: "You are a helpful assistant hosted in Azure Functions.",
-        name: "HostedAgent");
-
-// Configure the function app to host the AI agent.
-// This will automatically generate HTTP API endpoints for the agent.
-using IHost app = FunctionsApplication
-    .CreateBuilder(args)
-    .ConfigureFunctionsWebApplication()
-    .ConfigureDurableAgents(options => options.AddAIAgent(agent, timeToLive: TimeSpan.FromHours(1)))
-    .Build();
-app.Run();

dotnet/samples/02-agents/AGUI/Step01_GettingStarted/Client/Program.cs

@@ -1,94 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AGUI;
-using Microsoft.Extensions.AI;
-
-string serverUrl = Environment.GetEnvironmentVariable("AGUI_SERVER_URL") ?? "http://localhost:8888";
-
-Console.WriteLine($"Connecting to AG-UI server at: {serverUrl}\n");
-
-// Create the AG-UI client agent
-using HttpClient httpClient = new()
-{
-    Timeout = TimeSpan.FromSeconds(60)
-};
-
-AGUIChatClient chatClient = new(httpClient, serverUrl);
-
-AIAgent agent = chatClient.AsAIAgent(
-    name: "agui-client",
-    description: "AG-UI Client Agent");
-
-AgentSession session = await agent.CreateSessionAsync();
-List<ChatMessage> messages =
-[
-    new(ChatRole.System, "You are a helpful assistant.")
-];
-
-try
-{
-    while (true)
-    {
-        // Get user input
-        Console.Write("\nUser (:q or quit to exit): ");
-        string? message = Console.ReadLine();
-
-        if (string.IsNullOrWhiteSpace(message))
-        {
-            Console.WriteLine("Request cannot be empty.");
-            continue;
-        }
-
-        if (message is ":q" or "quit")
-        {
-            break;
-        }
-
-        messages.Add(new ChatMessage(ChatRole.User, message));
-
-        // Stream the response
-        bool isFirstUpdate = true;
-        string? sessionId = null;
-
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, session))
-        {
-            ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
-
-            // First update indicates run started
-            if (isFirstUpdate)
-            {
-                sessionId = chatUpdate.ConversationId;
-                Console.ForegroundColor = ConsoleColor.Yellow;
-                Console.WriteLine($"\n[Run Started - Session: {chatUpdate.ConversationId}, Run: {chatUpdate.ResponseId}]");
-                Console.ResetColor();
-                isFirstUpdate = false;
-            }
-
-            // Display streaming text content
-            foreach (AIContent content in update.Contents)
-            {
-                if (content is TextContent textContent)
-                {
-                    Console.ForegroundColor = ConsoleColor.Cyan;
-                    Console.Write(textContent.Text);
-                    Console.ResetColor();
-                }
-                else if (content is ErrorContent errorContent)
-                {
-                    Console.ForegroundColor = ConsoleColor.Red;
-                    Console.WriteLine($"\n[Error: {errorContent.Message}]");
-                    Console.ResetColor();
-                }
-            }
-        }
-
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.WriteLine($"\n[Run Finished - Session: {sessionId}]");
-        Console.ResetColor();
-    }
-}
-catch (Exception ex)
-{
-    Console.WriteLine($"\nAn error occurred: {ex.Message}");
-}

dotnet/samples/02-agents/AGUI/Step01_GettingStarted/Server/Program.cs

@@ -1,36 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
-using OpenAI.Chat;
-
-WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
-builder.Services.AddHttpClient().AddLogging();
-builder.Services.AddAGUI();
-
-WebApplication app = builder.Build();
-
-string endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
-
-// Create the AI agent
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-ChatClient chatClient = new AzureOpenAIClient(
-        new Uri(endpoint),
-        new DefaultAzureCredential())
-    .GetChatClient(deploymentName);
-
-AIAgent agent = chatClient.AsAIAgent(
-    name: "AGUIAssistant",
-    instructions: "You are a helpful assistant.");
-
-// Map the AG-UI agent endpoint
-app.MapAGUI("/", agent);
-
-await app.RunAsync();

dotnet/samples/02-agents/AGUI/Step01_GettingStarted/Server/Server.csproj

@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk.Web">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/02-agents/AGUI/Step02_BackendTools/Client/Program.cs

@@ -1,126 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AGUI;
-using Microsoft.Extensions.AI;
-
-string serverUrl = Environment.GetEnvironmentVariable("AGUI_SERVER_URL") ?? "http://localhost:8888";
-
-Console.WriteLine($"Connecting to AG-UI server at: {serverUrl}\n");
-
-// Create the AG-UI client agent
-using HttpClient httpClient = new()
-{
-    Timeout = TimeSpan.FromSeconds(60)
-};
-
-AGUIChatClient chatClient = new(httpClient, serverUrl);
-
-AIAgent agent = chatClient.AsAIAgent(
-    name: "agui-client",
-    description: "AG-UI Client Agent");
-
-AgentSession session = await agent.CreateSessionAsync();
-List<ChatMessage> messages =
-[
-    new(ChatRole.System, "You are a helpful assistant.")
-];
-
-try
-{
-    while (true)
-    {
-        // Get user input
-        Console.Write("\nUser (:q or quit to exit): ");
-        string? message = Console.ReadLine();
-
-        if (string.IsNullOrWhiteSpace(message))
-        {
-            Console.WriteLine("Request cannot be empty.");
-            continue;
-        }
-
-        if (message is ":q" or "quit")
-        {
-            break;
-        }
-
-        messages.Add(new ChatMessage(ChatRole.User, message));
-
-        // Stream the response
-        bool isFirstUpdate = true;
-        string? sessionId = null;
-
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, session))
-        {
-            ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
-
-            // First update indicates run started
-            if (isFirstUpdate)
-            {
-                sessionId = chatUpdate.ConversationId;
-                Console.ForegroundColor = ConsoleColor.Yellow;
-                Console.WriteLine($"\n[Run Started - Session: {chatUpdate.ConversationId}, Run: {chatUpdate.ResponseId}]");
-                Console.ResetColor();
-                isFirstUpdate = false;
-            }
-
-            // Display streaming content
-            foreach (AIContent content in update.Contents)
-            {
-                switch (content)
-                {
-                    case TextContent textContent:
-                        Console.ForegroundColor = ConsoleColor.Cyan;
-                        Console.Write(textContent.Text);
-                        Console.ResetColor();
-                        break;
-
-                    case FunctionCallContent functionCallContent:
-                        Console.ForegroundColor = ConsoleColor.Green;
-                        Console.WriteLine($"\n[Function Call - Name: {functionCallContent.Name}]");
-
-                        // Display individual parameters
-                        if (functionCallContent.Arguments != null)
-                        {
-                            foreach (var kvp in functionCallContent.Arguments)
-                            {
-                                Console.WriteLine($"  Parameter: {kvp.Key} = {kvp.Value}");
-                            }
-                        }
-                        Console.ResetColor();
-                        break;
-
-                    case FunctionResultContent functionResultContent:
-                        Console.ForegroundColor = ConsoleColor.Magenta;
-                        Console.WriteLine($"\n[Function Result - CallId: {functionResultContent.CallId}]");
-
-                        if (functionResultContent.Exception != null)
-                        {
-                            Console.WriteLine($"  Exception: {functionResultContent.Exception}");
-                        }
-                        else
-                        {
-                            Console.WriteLine($"  Result: {functionResultContent.Result}");
-                        }
-                        Console.ResetColor();
-                        break;
-
-                    case ErrorContent errorContent:
-                        Console.ForegroundColor = ConsoleColor.Red;
-                        Console.WriteLine($"\n[Error: {errorContent.Message}]");
-                        Console.ResetColor();
-                        break;
-                }
-            }
-        }
-
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.WriteLine($"\n[Run Finished - Session: {sessionId}]");
-        Console.ResetColor();
-    }
-}
-catch (Exception ex)
-{
-    Console.WriteLine($"\nAn error occurred: {ex.Message}");
-}

dotnet/samples/02-agents/AGUI/Step02_BackendTools/Server/Program.cs

@@ -1,120 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.ComponentModel;
-using System.Text.Json.Serialization;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
-using Microsoft.Extensions.AI;
-using Microsoft.Extensions.Options;
-using OpenAI.Chat;
-
-WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
-builder.Services.AddHttpClient().AddLogging();
-builder.Services.ConfigureHttpJsonOptions(options =>
-    options.SerializerOptions.TypeInfoResolverChain.Add(SampleJsonSerializerContext.Default));
-builder.Services.AddAGUI();
-
-WebApplication app = builder.Build();
-
-string endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
-
-// Define the function tool
-[Description("Search for restaurants in a location.")]
-static RestaurantSearchResponse SearchRestaurants(
-    [Description("The restaurant search request")] RestaurantSearchRequest request)
-{
-    // Simulated restaurant data
-    string cuisine = request.Cuisine == "any" ? "Italian" : request.Cuisine;
-
-    return new RestaurantSearchResponse
-    {
-        Location = request.Location,
-        Cuisine = request.Cuisine,
-        Results =
-        [
-            new RestaurantInfo
-            {
-                Name = "The Golden Fork",
-                Cuisine = cuisine,
-                Rating = 4.5,
-                Address = $"123 Main St, {request.Location}"
-            },
-            new RestaurantInfo
-            {
-                Name = "Spice Haven",
-                Cuisine = cuisine == "Italian" ? "Indian" : cuisine,
-                Rating = 4.7,
-                Address = $"456 Oak Ave, {request.Location}"
-            },
-            new RestaurantInfo
-            {
-                Name = "Green Leaf",
-                Cuisine = "Vegetarian",
-                Rating = 4.3,
-                Address = $"789 Elm Rd, {request.Location}"
-            }
-        ]
-    };
-}
-
-// Get JsonSerializerOptions from the configured HTTP JSON options
-Microsoft.AspNetCore.Http.Json.JsonOptions jsonOptions = app.Services.GetRequiredService<IOptions<Microsoft.AspNetCore.Http.Json.JsonOptions>>().Value;
-
-// Create tool with serializer options
-AITool[] tools =
-[
-    AIFunctionFactory.Create(
-        SearchRestaurants,
-        serializerOptions: jsonOptions.SerializerOptions)
-];
-
-// Create the AI agent with tools
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-ChatClient chatClient = new AzureOpenAIClient(
-        new Uri(endpoint),
-        new DefaultAzureCredential())
-    .GetChatClient(deploymentName);
-
-ChatClientAgent agent = chatClient.AsAIAgent(
-    name: "AGUIAssistant",
-    instructions: "You are a helpful assistant with access to restaurant information.",
-    tools: tools);
-
-// Map the AG-UI agent endpoint
-app.MapAGUI("/", agent);
-
-await app.RunAsync();
-
-// Define request/response types for the tool
-internal sealed class RestaurantSearchRequest
-{
-    public string Location { get; set; } = string.Empty;
-    public string Cuisine { get; set; } = "any";
-}
-
-internal sealed class RestaurantSearchResponse
-{
-    public string Location { get; set; } = string.Empty;
-    public string Cuisine { get; set; } = string.Empty;
-    public RestaurantInfo[] Results { get; set; } = [];
-}
-
-internal sealed class RestaurantInfo
-{
-    public string Name { get; set; } = string.Empty;
-    public string Cuisine { get; set; } = string.Empty;
-    public double Rating { get; set; }
-    public string Address { get; set; } = string.Empty;
-}
-
-// JSON serialization context for source generation
-[JsonSerializable(typeof(RestaurantSearchRequest))]
-[JsonSerializable(typeof(RestaurantSearchResponse))]
-internal sealed partial class SampleJsonSerializerContext : JsonSerializerContext;

dotnet/samples/02-agents/AGUI/Step02_BackendTools/Server/Server.csproj

@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk.Web">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/02-agents/AGUI/Step03_FrontendTools/Client/Program.cs

@@ -1,119 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.ComponentModel;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AGUI;
-using Microsoft.Extensions.AI;
-
-string serverUrl = Environment.GetEnvironmentVariable("AGUI_SERVER_URL") ?? "http://localhost:8888";
-
-Console.WriteLine($"Connecting to AG-UI server at: {serverUrl}\n");
-
-// Define a frontend function tool
-[Description("Get the user's current location from GPS.")]
-static string GetUserLocation()
-{
-    // Access client-side GPS
-    return "Amsterdam, Netherlands (52.37°N, 4.90°E)";
-}
-
-// Create frontend tools
-AITool[] frontendTools = [AIFunctionFactory.Create(GetUserLocation)];
-
-// Create the AG-UI client agent with tools
-using HttpClient httpClient = new()
-{
-    Timeout = TimeSpan.FromSeconds(60)
-};
-
-AGUIChatClient chatClient = new(httpClient, serverUrl);
-
-AIAgent agent = chatClient.AsAIAgent(
-    name: "agui-client",
-    description: "AG-UI Client Agent",
-    tools: frontendTools);
-
-AgentSession session = await agent.CreateSessionAsync();
-List<ChatMessage> messages =
-[
-    new(ChatRole.System, "You are a helpful assistant.")
-];
-
-try
-{
-    while (true)
-    {
-        // Get user input
-        Console.Write("\nUser (:q or quit to exit): ");
-        string? message = Console.ReadLine();
-
-        if (string.IsNullOrWhiteSpace(message))
-        {
-            Console.WriteLine("Request cannot be empty.");
-            continue;
-        }
-
-        if (message is ":q" or "quit")
-        {
-            break;
-        }
-
-        messages.Add(new ChatMessage(ChatRole.User, message));
-
-        // Stream the response
-        bool isFirstUpdate = true;
-        string? sessionId = null;
-
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, session))
-        {
-            ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
-
-            // First update indicates run started
-            if (isFirstUpdate)
-            {
-                sessionId = chatUpdate.ConversationId;
-                Console.ForegroundColor = ConsoleColor.Yellow;
-                Console.WriteLine($"\n[Run Started - Session: {chatUpdate.ConversationId}, Run: {chatUpdate.ResponseId}]");
-                Console.ResetColor();
-                isFirstUpdate = false;
-            }
-
-            // Display streaming content
-            foreach (AIContent content in update.Contents)
-            {
-                if (content is TextContent textContent)
-                {
-                    Console.ForegroundColor = ConsoleColor.Cyan;
-                    Console.Write(textContent.Text);
-                    Console.ResetColor();
-                }
-                else if (content is FunctionCallContent functionCallContent)
-                {
-                    Console.ForegroundColor = ConsoleColor.Green;
-                    Console.WriteLine($"\n[Client Tool Call - Name: {functionCallContent.Name}]");
-                    Console.ResetColor();
-                }
-                else if (content is FunctionResultContent functionResultContent)
-                {
-                    Console.ForegroundColor = ConsoleColor.Magenta;
-                    Console.WriteLine($"[Client Tool Result: {functionResultContent.Result}]");
-                    Console.ResetColor();
-                }
-                else if (content is ErrorContent errorContent)
-                {
-                    Console.ForegroundColor = ConsoleColor.Red;
-                    Console.WriteLine($"\n[Error: {errorContent.Message}]");
-                    Console.ResetColor();
-                }
-            }
-        }
-
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.WriteLine($"\n[Run Finished - Session: {sessionId}]");
-        Console.ResetColor();
-    }
-}
-catch (Exception ex)
-{
-    Console.WriteLine($"\nAn error occurred: {ex.Message}");
-}

dotnet/samples/02-agents/AGUI/Step03_FrontendTools/Server/Program.cs

@@ -1,36 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
-using OpenAI.Chat;
-
-WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
-builder.Services.AddHttpClient().AddLogging();
-builder.Services.AddAGUI();
-
-WebApplication app = builder.Build();
-
-string endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
-
-// Create the AI agent
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-ChatClient chatClient = new AzureOpenAIClient(
-        new Uri(endpoint),
-        new DefaultAzureCredential())
-    .GetChatClient(deploymentName);
-
-AIAgent agent = chatClient.AsAIAgent(
-    name: "AGUIAssistant",
-    instructions: "You are a helpful assistant.");
-
-// Map the AG-UI agent endpoint
-app.MapAGUI("/", agent);
-
-await app.RunAsync();

dotnet/samples/02-agents/AGUI/Step03_FrontendTools/Server/Server.csproj

@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk.Web">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/02-agents/AGUI/Step04_HumanInLoop/Client/Program.cs

@@ -1,152 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text.Json;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AGUI;
-using Microsoft.Extensions.AI;
-
-string serverUrl = Environment.GetEnvironmentVariable("AGUI_SERVER_URL") ?? "http://localhost:5100";
-
-// Connect to the AG-UI server
-using HttpClient httpClient = new()
-{
-    Timeout = TimeSpan.FromSeconds(60)
-};
-
-AGUIChatClient chatClient = new(httpClient, serverUrl);
-
-// Create agent
-ChatClientAgent baseAgent = chatClient.AsAIAgent(
-    name: "AGUIAssistant",
-    instructions: "You are a helpful assistant.");
-
-// Use default JSON serializer options
-JsonSerializerOptions jsonSerializerOptions = JsonSerializerOptions.Default;
-
-// Wrap the agent with ServerFunctionApprovalClientAgent
-ServerFunctionApprovalClientAgent agent = new(baseAgent, jsonSerializerOptions);
-
-List<ChatMessage> messages = [];
-AgentSession? session = null;
-
-Console.ForegroundColor = ConsoleColor.White;
-Console.WriteLine("Ask a question (or type 'exit' to quit):");
-Console.ResetColor();
-
-string? input;
-while ((input = Console.ReadLine()) != null && !input.Equals("exit", StringComparison.OrdinalIgnoreCase))
-{
-    if (string.IsNullOrWhiteSpace(input))
-    {
-        continue;
-    }
-
-    messages.Add(new ChatMessage(ChatRole.User, input));
-    Console.WriteLine();
-
-#pragma warning disable MEAI001
-    List<AIContent> approvalResponses = [];
-
-    do
-    {
-        approvalResponses.Clear();
-
-        List<AgentResponseUpdate> chatResponseUpdates = [];
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, session, cancellationToken: default))
-        {
-            chatResponseUpdates.Add(update);
-            foreach (AIContent content in update.Contents)
-            {
-                switch (content)
-                {
-                    case ToolApprovalRequestContent approvalRequest when approvalRequest.ToolCall is FunctionCallContent fcc:
-                        DisplayApprovalRequest(approvalRequest, fcc);
-
-                        Console.Write($"\nApprove '{fcc.Name}'? (yes/no): ");
-                        string? userInput = Console.ReadLine();
-                        bool approved = userInput?.ToUpperInvariant() is "YES" or "Y";
-
-                        ToolApprovalResponseContent approvalResponse = approvalRequest.CreateResponse(approved);
-
-                        if (approvalRequest.AdditionalProperties != null)
-                        {
-                            approvalResponse.AdditionalProperties = [];
-                            foreach (var kvp in approvalRequest.AdditionalProperties)
-                            {
-                                approvalResponse.AdditionalProperties[kvp.Key] = kvp.Value;
-                            }
-                        }
-
-                        approvalResponses.Add(approvalResponse);
-                        break;
-
-                    case TextContent textContent:
-                        Console.ForegroundColor = ConsoleColor.Cyan;
-                        Console.Write(textContent.Text);
-                        Console.ResetColor();
-                        break;
-
-                    case FunctionCallContent functionCall:
-                        Console.ForegroundColor = ConsoleColor.Green;
-                        Console.WriteLine($"[Tool Call - Name: {functionCall.Name}]");
-                        if (functionCall.Arguments is { } arguments)
-                        {
-                            Console.WriteLine($"  Parameters: {JsonSerializer.Serialize(arguments)}");
-                        }
-                        Console.ResetColor();
-                        break;
-
-                    case FunctionResultContent functionResult:
-                        Console.ForegroundColor = ConsoleColor.Magenta;
-                        Console.WriteLine($"[Tool Result: {functionResult.Result}]");
-                        Console.ResetColor();
-                        break;
-
-                    case ErrorContent error:
-                        Console.ForegroundColor = ConsoleColor.Red;
-                        Console.WriteLine($"[Error: {error.Message}]");
-                        Console.ResetColor();
-                        break;
-                }
-            }
-        }
-
-        AgentResponse response = chatResponseUpdates.ToAgentResponse();
-        messages.AddRange(response.Messages);
-        foreach (AIContent approvalResponse in approvalResponses)
-        {
-            messages.Add(new ChatMessage(ChatRole.Tool, [approvalResponse]));
-        }
-    }
-    while (approvalResponses.Count > 0);
-#pragma warning restore MEAI001
-
-    Console.WriteLine("\n");
-    Console.ForegroundColor = ConsoleColor.White;
-    Console.WriteLine("Ask another question (or type 'exit' to quit):");
-    Console.ResetColor();
-}
-
-#pragma warning disable MEAI001
-static void DisplayApprovalRequest(ToolApprovalRequestContent approvalRequest, FunctionCallContent fcc)
-{
-    Console.ForegroundColor = ConsoleColor.Yellow;
-    Console.WriteLine();
-    Console.WriteLine("============================================================");
-    Console.WriteLine("APPROVAL REQUIRED");
-    Console.WriteLine("============================================================");
-    Console.WriteLine($"Function: {fcc.Name}");
-
-    if (fcc.Arguments != null)
-    {
-        Console.WriteLine("Arguments:");
-        foreach (var arg in fcc.Arguments)
-        {
-            Console.WriteLine($"  {arg.Key} = {arg.Value}");
-        }
-    }
-
-    Console.WriteLine("============================================================");
-    Console.ResetColor();
-}
-#pragma warning restore MEAI001

dotnet/samples/02-agents/AGUI/Step04_HumanInLoop/Server/Program.cs

@@ -1,72 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.ComponentModel;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
-using Microsoft.AspNetCore.Http.Json;
-using Microsoft.AspNetCore.HttpLogging;
-using Microsoft.Extensions.AI;
-using Microsoft.Extensions.Options;
-using OpenAI.Chat;
-using ServerFunctionApproval;
-
-WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
-
-builder.Services.AddHttpLogging(logging =>
-{
-    logging.LoggingFields = HttpLoggingFields.RequestPropertiesAndHeaders | HttpLoggingFields.RequestBody
-        | HttpLoggingFields.ResponsePropertiesAndHeaders | HttpLoggingFields.ResponseBody;
-    logging.RequestBodyLogLimit = int.MaxValue;
-    logging.ResponseBodyLogLimit = int.MaxValue;
-});
-
-builder.Services.AddHttpClient().AddLogging();
-builder.Services.ConfigureHttpJsonOptions(options =>
-    options.SerializerOptions.TypeInfoResolverChain.Add(ApprovalJsonContext.Default));
-builder.Services.AddAGUI();
-
-WebApplication app = builder.Build();
-
-app.UseHttpLogging();
-
-string endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
-
-// Define approval-required tool
-[Description("Approve the expense report.")]
-static string ApproveExpenseReport(string expenseReportId)
-{
-    return $"Expense report {expenseReportId} approved";
-}
-
-// Get JsonSerializerOptions
-var jsonOptions = app.Services.GetRequiredService<IOptions<JsonOptions>>().Value;
-
-// Create approval-required tool
-#pragma warning disable MEAI001 // Type is for evaluation purposes only
-AITool[] tools = [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(ApproveExpenseReport))];
-#pragma warning restore MEAI001
-
-// Create base agent
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-ChatClient openAIChatClient = new AzureOpenAIClient(
-        new Uri(endpoint),
-        new DefaultAzureCredential())
-    .GetChatClient(deploymentName);
-
-ChatClientAgent baseAgent = openAIChatClient.AsAIAgent(
-    name: "AGUIAssistant",
-    instructions: "You are a helpful assistant in charge of approving expenses",
-    tools: tools);
-
-// Wrap with ServerFunctionApprovalAgent
-var agent = new ServerFunctionApprovalAgent(baseAgent, jsonOptions.SerializerOptions);
-
-app.MapAGUI("/", agent);
-await app.RunAsync();

dotnet/samples/02-agents/AGUI/Step04_HumanInLoop/Server/Server.csproj

@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk.Web">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/02-agents/AGUI/Step05_StateManagement/Client/Program.cs

@@ -1,231 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text.Json;
-using System.Text.Json.Serialization;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AGUI;
-using Microsoft.Extensions.AI;
-using RecipeClient;
-
-string serverUrl = Environment.GetEnvironmentVariable("AGUI_SERVER_URL") ?? "http://localhost:8888";
-
-Console.WriteLine($"Connecting to AG-UI server at: {serverUrl}\n");
-
-// Create the AG-UI client agent
-using HttpClient httpClient = new()
-{
-    Timeout = TimeSpan.FromSeconds(60)
-};
-
-AGUIChatClient chatClient = new(httpClient, serverUrl);
-
-AIAgent baseAgent = chatClient.AsAIAgent(
-    name: "recipe-client",
-    description: "AG-UI Recipe Client Agent");
-
-// Wrap the base agent with state management
-JsonSerializerOptions jsonOptions = new(JsonSerializerDefaults.Web)
-{
-    TypeInfoResolver = RecipeSerializerContext.Default
-};
-StatefulAgent<AgentState> agent = new(baseAgent, jsonOptions, new AgentState());
-
-AgentSession session = await agent.CreateSessionAsync();
-List<ChatMessage> messages =
-[
-    new(ChatRole.System, "You are a helpful recipe assistant.")
-];
-
-try
-{
-    while (true)
-    {
-        // Get user input
-        Console.Write("\nUser (:q to quit, :state to show state): ");
-        string? message = Console.ReadLine();
-
-        if (string.IsNullOrWhiteSpace(message))
-        {
-            Console.WriteLine("Request cannot be empty.");
-            continue;
-        }
-
-        if (message is ":q" or "quit")
-        {
-            break;
-        }
-
-        if (message.Equals(":state", StringComparison.OrdinalIgnoreCase))
-        {
-            DisplayState(agent.State.Recipe);
-            continue;
-        }
-
-        messages.Add(new ChatMessage(ChatRole.User, message));
-
-        // Stream the response
-        bool isFirstUpdate = true;
-        string? sessionId = null;
-        bool stateReceived = false;
-
-        Console.WriteLine();
-
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, session))
-        {
-            ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
-
-            // First update indicates run started
-            if (isFirstUpdate)
-            {
-                sessionId = chatUpdate.ConversationId;
-                Console.ForegroundColor = ConsoleColor.Yellow;
-                Console.WriteLine($"[Run Started - Session: {chatUpdate.ConversationId}, Run: {chatUpdate.ResponseId}]");
-                Console.ResetColor();
-                isFirstUpdate = false;
-            }
-
-            // Display streaming content
-            foreach (AIContent content in update.Contents)
-            {
-                switch (content)
-                {
-                    case TextContent textContent:
-                        Console.ForegroundColor = ConsoleColor.Cyan;
-                        Console.Write(textContent.Text);
-                        Console.ResetColor();
-                        break;
-
-                    case DataContent dataContent when dataContent.MediaType == "application/json":
-                        // This is a state snapshot - the StatefulAgent has already updated the state
-                        stateReceived = true;
-                        Console.ForegroundColor = ConsoleColor.Blue;
-                        Console.WriteLine("\n[State Snapshot Received]");
-                        Console.ResetColor();
-                        break;
-
-                    case ErrorContent errorContent:
-                        Console.ForegroundColor = ConsoleColor.Red;
-                        Console.WriteLine($"\n[Error: {errorContent.Message}]");
-                        Console.ResetColor();
-                        break;
-                }
-            }
-        }
-
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.WriteLine($"\n[Run Finished - Session: {sessionId}]");
-        Console.ResetColor();
-
-        // Display final state if received
-        if (stateReceived)
-        {
-            DisplayState(agent.State.Recipe);
-        }
-    }
-}
-catch (Exception ex)
-{
-    Console.WriteLine($"\nAn error occurred: {ex.Message}");
-}
-
-static void DisplayState(RecipeState? state)
-{
-    if (state == null)
-    {
-        Console.ForegroundColor = ConsoleColor.Gray;
-        Console.WriteLine("\n[No state available]");
-        Console.ResetColor();
-        return;
-    }
-
-    Console.ForegroundColor = ConsoleColor.Blue;
-    Console.WriteLine("\n" + new string('=', 60));
-    Console.WriteLine("CURRENT STATE");
-    Console.WriteLine(new string('=', 60));
-    Console.ResetColor();
-
-    if (!string.IsNullOrEmpty(state.Title))
-    {
-        Console.WriteLine("\nRecipe:");
-        Console.WriteLine($"  Title: {state.Title}");
-        if (!string.IsNullOrEmpty(state.Cuisine))
-        {
-            Console.WriteLine($"  Cuisine: {state.Cuisine}");
-        }
-
-        if (!string.IsNullOrEmpty(state.SkillLevel))
-        {
-            Console.WriteLine($"  Skill Level: {state.SkillLevel}");
-        }
-
-        if (state.PrepTimeMinutes > 0)
-        {
-            Console.WriteLine($"  Prep Time: {state.PrepTimeMinutes} minutes");
-        }
-
-        if (state.CookTimeMinutes > 0)
-        {
-            Console.WriteLine($"  Cook Time: {state.CookTimeMinutes} minutes");
-        }
-
-        if (state.Ingredients.Count > 0)
-        {
-            Console.WriteLine("\n  Ingredients:");
-            foreach (var ingredient in state.Ingredients)
-            {
-                Console.WriteLine($"    - {ingredient}");
-            }
-        }
-
-        if (state.Steps.Count > 0)
-        {
-            Console.WriteLine("\n  Steps:");
-            for (int i = 0; i < state.Steps.Count; i++)
-            {
-                Console.WriteLine($"    {i + 1}. {state.Steps[i]}");
-            }
-        }
-    }
-
-    Console.ForegroundColor = ConsoleColor.Blue;
-    Console.WriteLine("\n" + new string('=', 60));
-    Console.ResetColor();
-}
-
-// State wrapper
-internal sealed class AgentState
-{
-    [JsonPropertyName("recipe")]
-    public RecipeState Recipe { get; set; } = new();
-}
-
-// Recipe state model
-internal sealed class RecipeState
-{
-    [JsonPropertyName("title")]
-    public string Title { get; set; } = string.Empty;
-
-    [JsonPropertyName("cuisine")]
-    public string Cuisine { get; set; } = string.Empty;
-
-    [JsonPropertyName("ingredients")]
-    public List<string> Ingredients { get; set; } = [];
-
-    [JsonPropertyName("steps")]
-    public List<string> Steps { get; set; } = [];
-
-    [JsonPropertyName("prep_time_minutes")]
-    public int PrepTimeMinutes { get; set; }
-
-    [JsonPropertyName("cook_time_minutes")]
-    public int CookTimeMinutes { get; set; }
-
-    [JsonPropertyName("skill_level")]
-    public string SkillLevel { get; set; } = string.Empty;
-}
-
-// JSON serialization context
-[JsonSerializable(typeof(AgentState))]
-[JsonSerializable(typeof(RecipeState))]
-[JsonSerializable(typeof(JsonElement))]
-internal sealed partial class RecipeSerializerContext : JsonSerializerContext;

dotnet/samples/02-agents/AGUI/Step05_StateManagement/Server/Program.cs

@@ -1,61 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
-using Microsoft.Extensions.Options;
-using OpenAI.Chat;
-using RecipeAssistant;
-
-WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
-builder.Services.AddHttpClient().AddLogging();
-builder.Services.ConfigureHttpJsonOptions(options =>
-    options.SerializerOptions.TypeInfoResolverChain.Add(RecipeSerializerContext.Default));
-builder.Services.AddAGUI();
-
-// Configure to listen on port 8888
-builder.WebHost.UseUrls("http://localhost:8888");
-
-WebApplication app = builder.Build();
-
-string endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"]
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
-
-// Get JsonSerializerOptions
-var jsonOptions = app.Services.GetRequiredService<IOptions<Microsoft.AspNetCore.Http.Json.JsonOptions>>().Value;
-
-// Create base agent
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-ChatClient chatClient = new AzureOpenAIClient(
-        new Uri(endpoint),
-        new DefaultAzureCredential())
-    .GetChatClient(deploymentName);
-
-AIAgent baseAgent = chatClient.AsAIAgent(
-    name: "RecipeAgent",
-    instructions: """
-        You are a helpful recipe assistant. When users ask you to create or suggest a recipe,
-        respond with a complete AgentState JSON object that includes:
-        - recipe.title: The recipe name
-        - recipe.cuisine: Type of cuisine (e.g., Italian, Mexican, Japanese)
-        - recipe.ingredients: Array of ingredient strings with quantities
-        - recipe.steps: Array of cooking instruction strings
-        - recipe.prep_time_minutes: Preparation time in minutes
-        - recipe.cook_time_minutes: Cooking time in minutes
-        - recipe.skill_level: One of "beginner", "intermediate", or "advanced"
-
-        Always include all fields in the response. Be creative and helpful.
-        """);
-
-// Wrap with state management middleware
-AIAgent agent = new SharedStateAgent(baseAgent, jsonOptions.SerializerOptions);
-
-// Map the AG-UI agent endpoint
-app.MapAGUI("/", agent);
-
-await app.RunAsync();

dotnet/samples/02-agents/AGUI/Step05_StateManagement/Server/Server.csproj

@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk.Web">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/02-agents/AGUI/Step05_StateManagement/Server/SharedStateAgent.cs

@@ -1,158 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Runtime.CompilerServices;
-using System.Text.Json;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-
-namespace RecipeAssistant;
-
-internal sealed class SharedStateAgent : DelegatingAIAgent
-{
-    private readonly JsonSerializerOptions _jsonSerializerOptions;
-
-    public SharedStateAgent(AIAgent innerAgent, JsonSerializerOptions jsonSerializerOptions)
-        : base(innerAgent)
-    {
-        this._jsonSerializerOptions = jsonSerializerOptions;
-    }
-
-    protected override Task<AgentResponse> RunCoreAsync(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        return this.RunCoreStreamingAsync(messages, session, options, cancellationToken)
-            .ToAgentResponseAsync(cancellationToken);
-    }
-
-    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        AgentRunOptions? options = null,
-        [EnumeratorCancellation] CancellationToken cancellationToken = default)
-    {
-        // Check if the client sent state in the request
-        if (options is not ChatClientAgentRunOptions { ChatOptions.AdditionalProperties: { } properties } chatRunOptions ||
-            !properties.TryGetValue("ag_ui_state", out object? stateObj) ||
-            stateObj is not JsonElement state ||
-            state.ValueKind != JsonValueKind.Object)
-        {
-            // No state management requested, pass through to inner agent
-            await foreach (var update in this.InnerAgent.RunStreamingAsync(messages, session, options, cancellationToken).ConfigureAwait(false))
-            {
-                yield return update;
-            }
-            yield break;
-        }
-
-        // Check if state has properties (not empty {})
-        bool hasProperties = false;
-        foreach (JsonProperty _ in state.EnumerateObject())
-        {
-            hasProperties = true;
-            break;
-        }
-
-        if (!hasProperties)
-        {
-            // Empty state - treat as no state
-            await foreach (var update in this.InnerAgent.RunStreamingAsync(messages, session, options, cancellationToken).ConfigureAwait(false))
-            {
-                yield return update;
-            }
-            yield break;
-        }
-
-        // First run: Generate structured state update
-        var firstRunOptions = new ChatClientAgentRunOptions
-        {
-            ChatOptions = chatRunOptions.ChatOptions.Clone(),
-            AllowBackgroundResponses = chatRunOptions.AllowBackgroundResponses,
-            ContinuationToken = chatRunOptions.ContinuationToken,
-            ChatClientFactory = chatRunOptions.ChatClientFactory,
-        };
-
-        // Configure JSON schema response format for structured state output
-        firstRunOptions.ChatOptions.ResponseFormat = ChatResponseFormat.ForJsonSchema<AgentState>(
-            schemaName: "AgentState",
-            schemaDescription: "A response containing a recipe with title, skill level, cooking time, ingredients, and instructions");
-
-        // Add current state to the conversation - state is already a JsonElement
-        ChatMessage stateUpdateMessage = new(
-            ChatRole.System,
-            [
-                new TextContent("Here is the current state in JSON format:"),
-                new TextContent(JsonSerializer.Serialize(state, this._jsonSerializerOptions.GetTypeInfo(typeof(JsonElement)))),
-                new TextContent("The new state is:")
-            ]);
-
-        var firstRunMessages = messages.Append(stateUpdateMessage);
-
-        // Collect all updates from first run
-        var allUpdates = new List<AgentResponseUpdate>();
-        await foreach (var update in this.InnerAgent.RunStreamingAsync(firstRunMessages, session, firstRunOptions, cancellationToken).ConfigureAwait(false))
-        {
-            allUpdates.Add(update);
-
-            // Yield all non-text updates (tool calls, etc.)
-            bool hasNonTextContent = update.Contents.Any(c => c is not TextContent);
-            if (hasNonTextContent)
-            {
-                yield return update;
-            }
-        }
-
-        var response = allUpdates.ToAgentResponse();
-
-        // Try to deserialize the structured state response
-        if (TryDeserialize(response.Text, this._jsonSerializerOptions, out JsonElement stateSnapshot))
-        {
-            // Serialize and emit as STATE_SNAPSHOT via DataContent
-            byte[] stateBytes = JsonSerializer.SerializeToUtf8Bytes(
-                stateSnapshot,
-                this._jsonSerializerOptions.GetTypeInfo(typeof(JsonElement)));
-            yield return new AgentResponseUpdate
-            {
-                Contents = [new DataContent(stateBytes, "application/json")]
-            };
-        }
-        else
-        {
-            yield break;
-        }
-
-        // Second run: Generate user-friendly summary
-        var secondRunMessages = messages.Concat(response.Messages).Append(
-            new ChatMessage(
-                ChatRole.System,
-                [new TextContent("Please provide a concise summary of the state changes in at most two sentences.")]));
-
-        await foreach (var update in this.InnerAgent.RunStreamingAsync(secondRunMessages, session, options, cancellationToken).ConfigureAwait(false))
-        {
-            yield return update;
-        }
-    }
-
-    private static bool TryDeserialize<T>(string json, JsonSerializerOptions jsonSerializerOptions, out T structuredOutput)
-    {
-        try
-        {
-            T? deserialized = JsonSerializer.Deserialize<T>(json, jsonSerializerOptions);
-            if (deserialized is null)
-            {
-                structuredOutput = default!;
-                return false;
-            }
-
-            structuredOutput = deserialized;
-            return true;
-        }
-        catch
-        {
-            structuredOutput = default!;
-            return false;
-        }
-    }
-}

dotnet/samples/02-agents/AgentOpenTelemetry/Program.cs

@@ -1,232 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.ComponentModel;
-using System.Diagnostics;
-using System.Diagnostics.Metrics;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Azure.Monitor.OpenTelemetry.Exporter;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Logging;
-using OpenTelemetry;
-using OpenTelemetry.Logs;
-using OpenTelemetry.Metrics;
-using OpenTelemetry.Resources;
-using OpenTelemetry.Trace;
-
-#region Setup Telemetry
-
-// Source name for this sample's custom ActivitySource and Meter; other instrumentation uses their own sources/categories.
-const string SourceName = "OpenTelemetryAspire.ConsoleApp";
-const string ServiceName = "AgentOpenTelemetry";
-
-// Configure OpenTelemetry for Aspire dashboard
-var otlpEndpoint = Environment.GetEnvironmentVariable("OTEL_EXPORTER_OTLP_ENDPOINT") ?? "http://localhost:4318";
-
-var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
-
-// Create a resource to identify this service
-var resource = ResourceBuilder.CreateDefault()
-    .AddService(ServiceName, serviceVersion: "1.0.0")
-    .AddAttributes(new Dictionary<string, object>
-    {
-        ["service.instance.id"] = Environment.MachineName,
-        ["deployment.environment"] = "development"
-    })
-    .Build();
-
-// Setup tracing with resource
-var tracerProviderBuilder = Sdk.CreateTracerProviderBuilder()
-    .SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName, serviceVersion: "1.0.0"))
-    .AddSource(SourceName) // Our custom activity source
-    .AddHttpClientInstrumentation() // Capture HTTP calls to OpenAI
-    .AddOtlpExporter(options => options.Endpoint = new Uri(otlpEndpoint));
-
-if (!string.IsNullOrWhiteSpace(applicationInsightsConnectionString))
-{
-    tracerProviderBuilder.AddAzureMonitorTraceExporter(options => options.ConnectionString = applicationInsightsConnectionString);
-}
-
-using var tracerProvider = tracerProviderBuilder.Build();
-
-// Setup metrics with resource and instrument name filtering
-using var meterProvider = Sdk.CreateMeterProviderBuilder()
-    .SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName, serviceVersion: "1.0.0"))
-    .AddMeter(SourceName) // Our custom meter source
-    .AddHttpClientInstrumentation() // HTTP client metrics
-    .AddRuntimeInstrumentation() // .NET runtime metrics
-    .AddOtlpExporter(options => options.Endpoint = new Uri(otlpEndpoint))
-    .Build();
-
-// Setup structured logging with OpenTelemetry
-var serviceCollection = new ServiceCollection();
-serviceCollection.AddLogging(loggingBuilder => loggingBuilder
-    .SetMinimumLevel(LogLevel.Debug)
-    .AddOpenTelemetry(options =>
-    {
-        options.SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName, serviceVersion: "1.0.0"));
-        options.AddOtlpExporter(otlpOptions => otlpOptions.Endpoint = new Uri(otlpEndpoint));
-        if (!string.IsNullOrWhiteSpace(applicationInsightsConnectionString))
-        {
-            options.AddAzureMonitorLogExporter(options => options.ConnectionString = applicationInsightsConnectionString);
-        }
-        options.IncludeScopes = true;
-        options.IncludeFormattedMessage = true;
-    }));
-
-using var activitySource = new ActivitySource(SourceName);
-using var meter = new Meter(SourceName);
-
-// Create custom metrics
-var interactionCounter = meter.CreateCounter<int>("agent_interactions_total", description: "Total number of agent interactions");
-var responseTimeHistogram = meter.CreateHistogram<double>("agent_response_time_seconds", description: "Agent response time in seconds");
-
-#endregion
-
-var serviceProvider = serviceCollection.BuildServiceProvider();
-var loggerFactory = serviceProvider.GetRequiredService<ILoggerFactory>();
-var appLogger = loggerFactory.CreateLogger<Program>();
-
-Console.WriteLine("""
-    === OpenTelemetry Aspire Demo ===
-    This demo shows OpenTelemetry integration with the Agent Framework.
-    You can view the telemetry data in the Aspire Dashboard.
-    Type your message and press Enter. Type 'exit' or empty message to quit.
-    """);
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT environment variable is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// Log application startup
-appLogger.LogInformation("OpenTelemetry Aspire Demo application started");
-
-[Description("Get the weather for a given location.")]
-static async Task<string> GetWeatherAsync([Description("The location to get the weather for.")] string location)
-{
-    await Task.Delay(2000);
-    return $"The weather in {location} is cloudy with a high of 15°C.";
-}
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-using var instrumentedChatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-        .AsIChatClient() // Converts a native OpenAI SDK ChatClient into a Microsoft.Extensions.AI.IChatClient
-        .AsBuilder()
-        .UseFunctionInvocation()
-        .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the chat client level
-        .Build();
-
-appLogger.LogInformation("Creating Agent with OpenTelemetry instrumentation");
-// Create the agent with the instrumented chat client
-var agent = new ChatClientAgent(instrumentedChatClient,
-    name: "OpenTelemetryDemoAgent",
-    instructions: "You are a helpful assistant that provides concise and informative responses.",
-    tools: [AIFunctionFactory.Create(GetWeatherAsync)])
-    .AsBuilder()
-    .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the agent level
-    .Build();
-
-var session = await agent.CreateSessionAsync();
-
-appLogger.LogInformation("Agent created successfully with ID: {AgentId}", agent.Id);
-
-// Create a parent span for the entire agent session
-using var sessionActivity = activitySource.StartActivity("Agent Session");
-Console.WriteLine($"Trace ID: {sessionActivity?.TraceId} ");
-
-var sessionId = Guid.NewGuid().ToString("N");
-sessionActivity?
-    .SetTag("agent.name", "OpenTelemetryDemoAgent")
-    .SetTag("session.id", sessionId)
-    .SetTag("session.start_time", DateTimeOffset.UtcNow.ToString("O"));
-
-appLogger.LogInformation("Starting agent session with ID: {SessionId}", sessionId);
-using (appLogger.BeginScope(new Dictionary<string, object> { ["SessionId"] = sessionId, ["AgentName"] = "OpenTelemetryDemoAgent" }))
-{
-    var interactionCount = 0;
-
-    while (true)
-    {
-        Console.Write("You (or 'exit' to quit): ");
-        var userInput = Console.ReadLine();
-
-        if (string.IsNullOrWhiteSpace(userInput) || userInput.Equals("exit", StringComparison.OrdinalIgnoreCase))
-        {
-            appLogger.LogInformation("User requested to exit the session");
-            break;
-        }
-
-        interactionCount++;
-        appLogger.LogInformation("Processing user interaction #{InteractionNumber}: {UserInput}", interactionCount, userInput);
-
-        // Create a child span for each individual interaction
-        using var activity = activitySource.StartActivity("Agent Interaction");
-        activity?
-            .SetTag("user.input", userInput)
-            .SetTag("agent.name", "OpenTelemetryDemoAgent")
-            .SetTag("interaction.number", interactionCount);
-
-        var stopwatch = Stopwatch.StartNew();
-
-        try
-        {
-            appLogger.LogDebug("Starting agent execution for interaction #{InteractionNumber}", interactionCount);
-            Console.Write("Agent: ");
-
-            // Run the agent (this will create its own internal telemetry spans)
-            await foreach (var update in agent.RunStreamingAsync(userInput, session))
-            {
-                Console.Write(update.Text);
-            }
-
-            Console.WriteLine();
-
-            stopwatch.Stop();
-            var responseTime = stopwatch.Elapsed.TotalSeconds;
-
-            // Record metrics (similar to Python example)
-            interactionCounter.Add(1, new KeyValuePair<string, object?>("status", "success"));
-            responseTimeHistogram.Record(responseTime,
-                new KeyValuePair<string, object?>("status", "success"));
-
-            activity?.SetTag("response.success", true);
-
-            appLogger.LogInformation("Agent interaction #{InteractionNumber} completed successfully in {ResponseTime:F2} seconds",
-                interactionCount, responseTime);
-        }
-        catch (Exception ex)
-        {
-            Console.WriteLine($"Error: {ex.Message}");
-            Console.WriteLine();
-
-            stopwatch.Stop();
-            var responseTime = stopwatch.Elapsed.TotalSeconds;
-
-            // Record error metrics
-            interactionCounter.Add(1, new KeyValuePair<string, object?>("status", "error"));
-            responseTimeHistogram.Record(responseTime,
-                new KeyValuePair<string, object?>("status", "error"));
-
-            activity?
-                .SetTag("response.success", false)
-                .SetTag("error.message", ex.Message)
-                .SetStatus(ActivityStatusCode.Error, ex.Message);
-
-            appLogger.LogError(ex, "Agent interaction #{InteractionNumber} failed after {ResponseTime:F2} seconds: {ErrorMessage}",
-                interactionCount, responseTime, ex.Message);
-        }
-    }
-
-    // Add session summary to the parent span
-    sessionActivity?
-        .SetTag("session.total_interactions", interactionCount)
-        .SetTag("session.end_time", DateTimeOffset.UtcNow.ToString("O"));
-
-    appLogger.LogInformation("Agent session completed. Total interactions: {TotalInteractions}", interactionCount);
-} // End of logging scope
-
-appLogger.LogInformation("OpenTelemetry Aspire Demo application shutting down");

dotnet/samples/02-agents/AgentProviders/Agent_With_Anthropic/Program.cs

@@ -1,32 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to create and use an AI agent with Anthropic as the backend.
-
-using Anthropic;
-using Anthropic.Foundry;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-
-string deploymentName = Environment.GetEnvironmentVariable("ANTHROPIC_CHAT_MODEL_NAME") ?? "claude-haiku-4-5";
-
-// The resource is the subdomain name / first name coming before '.services.ai.azure.com' in the endpoint Uri
-// ie: https://(resource name).services.ai.azure.com/anthropic/v1/chat/completions
-string? resource = Environment.GetEnvironmentVariable("ANTHROPIC_RESOURCE");
-string? apiKey = Environment.GetEnvironmentVariable("ANTHROPIC_API_KEY");
-
-const string JokerInstructions = "You are good at telling jokes.";
-const string JokerName = "JokerAgent";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-using AnthropicClient client = (resource is null)
-    ? new AnthropicClient() { ApiKey = apiKey ?? throw new InvalidOperationException("ANTHROPIC_API_KEY is required when no ANTHROPIC_RESOURCE is provided") }  // If no resource is provided, use Anthropic public API
-    : (apiKey is not null)
-        ? new AnthropicFoundryClient(new AnthropicFoundryApiKeyCredentials(apiKey, resource)) // If an apiKey is provided, use Foundry with ApiKey authentication
-        : new AnthropicFoundryClient(new AnthropicFoundryIdentityTokenCredentials(new DefaultAzureCredential(), resource, ["https://ai.azure.com/.default"])); // Otherwise, use Foundry with Azure TokenCredential authentication
-
-AIAgent agent = client.AsAIAgent(model: deploymentName, instructions: JokerInstructions, name: JokerName);
-
-// Invoke the agent and output the text result.
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));

dotnet/samples/02-agents/AgentProviders/Agent_With_Anthropic/README.md

@@ -1,53 +0,0 @@
-# Creating an AIAgent with Anthropic
-
-This sample demonstrates how to create an AIAgent using Anthropic Claude models as the underlying inference service.
-
-The sample supports three deployment scenarios:
-
-1. **Anthropic Public API** - Direct connection to Anthropic's public API
-2. **Azure Foundry with API Key** - Anthropic models deployed through Azure Foundry using API key authentication
-3. **Azure Foundry with Azure CLI** - Anthropic models deployed through Azure Foundry using Azure CLI credentials
-
-## Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
-- .NET 8.0 SDK or later
-
-### For Anthropic Public API
-
-- Anthropic API key
-
-Set the following environment variables:
-
-```powershell
-$env:ANTHROPIC_API_KEY="your-anthropic-api-key"  # Replace with your Anthropic API key
-$env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"  # Optional, defaults to claude-haiku-4-5
-```
-
-### For Azure Foundry with API Key
-
-- Azure Foundry service endpoint and deployment configured
-- Anthropic API key
-
-Set the following environment variables:
-
-```powershell
-$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
-$env:ANTHROPIC_API_KEY="your-anthropic-api-key"  # Replace with your Anthropic API key
-$env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"  # Optional, defaults to claude-haiku-4-5
-```
-
-### For Azure Foundry with Azure CLI
-
-- Azure Foundry service endpoint and deployment configured
-- Azure CLI installed and authenticated (for Azure credential authentication)
-
-Set the following environment variables:
-
-```powershell
-$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
-$env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"  # Optional, defaults to claude-haiku-4-5
-```
-
-**Note**: When using Azure Foundry with Azure CLI, make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent/Program.cs

@@ -1,44 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-#pragma warning disable CS0618 // Type or member is obsolete - sample uses deprecated PersistentAgentsClientExtensions
-
-// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend.
-
-using Azure.AI.Agents.Persistent;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-const string JokerName = "Joker";
-const string JokerInstructions = "You are good at telling jokes.";
-
-// Get a client to create/retrieve server side agents with.
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-var persistentAgentsClient = new PersistentAgentsClient(endpoint, new DefaultAzureCredential());
-
-// You can create a server side persistent agent with the Azure.AI.Agents.Persistent SDK.
-var agentMetadata = await persistentAgentsClient.Administration.CreateAgentAsync(
-    model: deploymentName,
-    name: JokerName,
-    instructions: JokerInstructions);
-
-// You can retrieve an already created server side persistent agent as an AIAgent.
-AIAgent agent1 = await persistentAgentsClient.GetAIAgentAsync(agentMetadata.Value.Id);
-
-// You can also create a server side persistent agent and return it as an AIAgent directly.
-AIAgent agent2 = await persistentAgentsClient.CreateAIAgentAsync(
-    model: deploymentName,
-    name: JokerName,
-    instructions: JokerInstructions);
-
-// You can then invoke the agent like any other AIAgent.
-AgentSession session = await agent1.CreateSessionAsync();
-Console.WriteLine(await agent1.RunAsync("Tell me a joke about a pirate.", session));
-
-// Cleanup for sample purposes.
-await persistentAgentsClient.Administration.DeleteAgentAsync(agent1.Id);
-await persistentAgentsClient.Administration.DeleteAgentAsync(agent2.Id);

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent/README.md

@@ -1,26 +0,0 @@
-# Classic Foundry Agents 
-
-This sample demonstrates how to create an agent using the classic Foundry Agents experience.
-
-# Classic vs New Foundry Agents
-
-Below is a comparison between the classic and new Foundry Agents approaches:
-
-[Migration Guide](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/migrate?view=foundry)
-
-# Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
-- .NET 10 SDK or later
-- Azure Foundry service endpoint and deployment configured
-- Azure CLI installed and authenticated (for Azure credential authentication)
-
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
-```

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIProject/Program.cs

@@ -1,58 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to create and use a AI agents with Azure Foundry Agents as the backend.
-
-using Azure.AI.Projects;
-using Azure.AI.Projects.Agents;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AzureAI;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-const string JokerName = "JokerAgent";
-
-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());
-
-// Define the agent you want to create. (Prompt Agent in this case)
-var agentVersionCreationOptions = new AgentVersionCreationOptions(new PromptAgentDefinition(model: deploymentName) { Instructions = "You are good at telling jokes." });
-// Azure.AI.Agents SDK creates and manages agent by name and versions.
-// You can create a server side agent version with the Azure.AI.Agents SDK client below.
-var createdAgentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: JokerName, options: agentVersionCreationOptions);
-
-// Note:
-//      agentVersion.Id = "<agentName>:<versionNumber>",
-//      agentVersion.Version = <versionNumber>,
-//      agentVersion.Name = <agentName>
-
-// You can use an AIAgent with an already created server side agent version.
-FoundryAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);
-
-// You can also create another AIAgent version by providing the same name with a different definition.
-AgentVersion newJokerAgentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
-    JokerName,
-    new AgentVersionCreationOptions(new PromptAgentDefinition(model: deploymentName) { Instructions = "You are extremely hilarious at telling jokes." }));
-FoundryAgent newJokerAgent = aiProjectClient.AsAIAgent(newJokerAgentVersion);
-
-// You can also get the AIAgent latest version just providing its name.
-AgentRecord jokerAgentRecord = await aiProjectClient.Agents.GetAgentAsync(JokerName);
-FoundryAgent jokerAgentLatest = aiProjectClient.AsAIAgent(jokerAgentRecord);
-AgentVersion latestAgentVersion = jokerAgentRecord.GetLatestVersion();
-
-// The AIAgent version can be accessed via the GetService method.
-Console.WriteLine($"Latest agent version id: {latestAgentVersion.Id}");
-
-// Once you have the AIAgent, you can invoke it like any other AIAgent.
-AgentSession session = await jokerAgentLatest.CreateSessionAsync();
-Console.WriteLine(await jokerAgentLatest.RunAsync("Tell me a joke about a pirate.", session));
-
-// This will use the same session to continue the conversation.
-Console.WriteLine(await jokerAgentLatest.RunAsync("Now tell me a joke about a cat and a dog using last joke as the anchor.", session));
-
-// Cleanup by agent name removes both agent versions created.
-aiProjectClient.Agents.DeleteAgent(existingJokerAgent.Name);

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIProject/README.md

@@ -1,26 +0,0 @@
-# New Foundry Agents 
-
-This sample demonstrates how to create an agent using the new Foundry Agents experience. 
-
-# Classic vs New Foundry Agents
-
-Below is a comparison between the classic and new Foundry Agents approaches:
-
-[Migration Guide](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/migrate?view=foundry)
-
-# Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
-- .NET 10 SDK or later
-- Azure Foundry service endpoint and deployment configured
-- Azure CLI installed and authenticated (for Azure credential authentication)
-
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
-```

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureFoundryModel/Program.cs

@@ -1,34 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Azure AI Foundry.
-// You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in your Azure AI Foundry resource.
-// Note: Ensure that you pick a model that suits your needs. For example, if you want to use function calling, ensure that the model you pick supports function calling.
-
-using System.ClientModel;
-using System.ClientModel.Primitives;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using OpenAI;
-using OpenAI.Chat;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
-var model = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "Phi-4-mini-instruct";
-
-// Since we are using the OpenAI Client SDK, we need to override the default endpoint to point to Azure Foundry.
-var clientOptions = new OpenAIClientOptions() { Endpoint = new Uri(endpoint) };
-
-// Create the OpenAI client with either an API key or Azure CLI credential.
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-OpenAIClient client = string.IsNullOrWhiteSpace(apiKey)
-    ? new OpenAIClient(new BearerTokenPolicy(new DefaultAzureCredential(), "https://ai.azure.com/.default"), clientOptions)
-    : new OpenAIClient(new ApiKeyCredential(apiKey), clientOptions);
-
-AIAgent agent = client
-    .GetChatClient(model)
-    .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
-
-// Invoke the agent and output the text result.
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureFoundryModel/README.md

@@ -1,34 +0,0 @@
-## Overview
-
-This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Azure AI Foundry.
-
-You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in Azure AI Foundry.
-
-**Note**: Ensure that you pick a model that suits your needs. For example, if you want to use function calling, ensure that the model you pick supports function calling.
-
-## Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
-- .NET 10 SDK or later
-- Azure AI Foundry resource
-- A model deployment in your Azure AI Foundry resource. This example defaults to using the `Phi-4-mini-instruct` model,
-so if you want to use a different model, ensure that you set your `AZURE_AI_MODEL_DEPLOYMENT_NAME` environment
-variable to the name of your deployed model.
-- An API key or role based authentication to access the Azure AI Foundry resource
-
-See [here](https://learn.microsoft.com/en-us/azure/ai-foundry/quickstarts/get-started-code?tabs=csharp) for more info on setting up these prerequisites
-
-Set the following environment variables:
-
-```powershell
-# Replace with your Azure AI Foundry resource endpoint
-# Ensure that you have the "/openai/v1/" path in the URL, since this is required when using the OpenAI SDK to access Azure Foundry models.
-$env:AZURE_OPENAI_ENDPOINT="https://ai-foundry-<myresourcename>.services.ai.azure.com/openai/v1/"
-
-# Optional, defaults to using Azure CLI for authentication if not provided
-$env:AZURE_OPENAI_API_KEY="************"
-
-# Optional, defaults to Phi-4-mini-instruct
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="Phi-4-mini-instruct"
-```

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureOpenAIChatCompletion/Program.cs

@@ -1,23 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to create and use a simple AI agent with Azure OpenAI Chat Completion as the backend.
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using OpenAI.Chat;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-     .GetChatClient(deploymentName)
-     .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
-
-// Invoke the agent and output the text result.
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));