Address comments (#2160)

- Adds a link from the /dotnet/samples/README.md to /dotnet/samples/AzureFunctions
- Make DurableAgentThread deserialization internal for future-proofing
- Update JSON serialization logic to address recently discovered issues with source generator serialization

.devcontainer/dotnet/devcontainer.json

@@ -1,31 +1,15 @@
 {
   "name": "C# (.NET)",
-  "image": "mcr.microsoft.com/devcontainers/dotnet",
+  "image": "mcr.microsoft.com/devcontainers/dotnet:9.0",
   "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"
-    },
-    "ghcr.io/devcontainers/features/powershell:1": {
-      "version": "latest"
-    },
-    "ghcr.io/azure/azure-dev/azd:0": {
-      "version": "latest"
-    },
-    "ghcr.io/devcontainers/features/dotnet:2": {
-      "version": "none",
-      "dotnetRuntimeVersions": "10.0",
-      "aspNetCoreRuntimeVersions": "10.0"
-    },
-    "ghcr.io/devcontainers/features/copilot-cli:1": {}
+    "ghcr.io/devcontainers/features/dotnet:2.4.0": {},
+    "ghcr.io/devcontainers/features/powershell:1.5.1": {},
+    "ghcr.io/devcontainers/features/azure-cli:1.2.8": {}
   },
   "workspaceFolder": "/workspaces/agent-framework/dotnet/",
   "customizations": {
     "vscode": {
       "extensions": [
-        "GitHub.copilot",
-        "GitHub.vscode-github-actions",
         "ms-dotnettools.csdevkit",
         "vscode-icons-team.vscode-icons",
         "ms-windows-ai-studio.windows-ai-studio"

.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/CODEOWNERS

@@ -2,6 +2,3 @@
 # https://docs.github.com/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners
 
 python/packages/azurefunctions/ @microsoft/agentframework-durabletask-developers
-python/packages/durabletask/ @microsoft/agentframework-durabletask-developers
-python/samples/getting_started/azure_functions/ @microsoft/agentframework-durabletask-developers
-python/samples/getting_started/durabletask/ @microsoft/agentframework-durabletask-developers

.github/ISSUE_TEMPLATE/config.yml

@@ -1,8 +0,0 @@
-blank_issues_enabled: true
-contact_links:
-  - name: Documentation
-    url: https://aka.ms/agent-framework
-    about: Check out the official documentation for guides and API reference.
-  - name: Discussions
-    url: https://github.com/microsoft/agent-framework/discussions
-    about: Ask questions about Agent Framework.

.github/ISSUE_TEMPLATE/dotnet-issue.yml

@@ -1,70 +0,0 @@
-name: .NET Bug Report
-description: Report a bug in the Agent Framework .NET SDK
-title: ".NET: [Bug]: "
-labels: ["bug", ".NET"]
-type: bug
-body:
-  - type: textarea
-    id: description
-    attributes:
-      label: Description
-      description: Please provide a clear and detailed description of the bug.
-      placeholder: |
-        - What happened?
-        - What did you expect to happen?
-        - Steps to reproduce the issue
-    validations:
-      required: true
-
-  - type: textarea
-    id: code-sample
-    attributes:
-      label: Code Sample
-      description: If applicable, provide a minimal code sample that demonstrates the issue.
-      placeholder: |
-        ```csharp
-        // Your code here
-        ```
-      render: markdown
-    validations:
-      required: false
-
-  - type: textarea
-    id: error-messages
-    attributes:
-      label: Error Messages / Stack Traces
-      description: Include any error messages or stack traces you received.
-      placeholder: |
-        ```
-        Paste error messages or stack traces here
-        ```
-      render: markdown
-    validations:
-      required: false
-
-  - type: input
-    id: dotnet-packages
-    attributes:
-      label: Package Versions
-      description: List the Microsoft.Agents.* packages and versions you are using
-      placeholder: "e.g., Microsoft.Agents.AI.Abstractions: 1.0.0, Microsoft.Agents.AI.OpenAI: 1.0.0"
-    validations:
-      required: true
-
-  - type: input
-    id: dotnet-version
-    attributes:
-      label: .NET Version
-      description: What version of .NET are you using?
-      placeholder: "e.g., .NET 8.0"
-    validations:
-      required: false
-
-  - type: textarea
-    id: additional-context
-    attributes:
-      label: Additional Context
-      description: Add any other context or screenshots that might be helpful.
-      placeholder: "Any additional information..."
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,51 +0,0 @@
-name: Feature Request
-description: Request a new feature for Microsoft Agent Framework
-title: "[Feature]: "
-type: feature
-body:
-
-  - type: textarea
-    id: description
-    attributes:
-      label: Description
-      description: Please describe the feature you'd like and why it would be useful.
-      placeholder: |
-        Describe the feature you're requesting:
-        - What problem does it solve?
-        - What would the expected behavior be?
-        - Are there any alternatives you've considered?
-    validations:
-      required: true
-
-  - type: textarea
-    id: code-sample
-    attributes:
-      label: Code Sample
-      description: If applicable, provide a code sample showing how you'd like to use this feature.
-      placeholder: |
-        ```python
-        # Your code here
-        ```
-
-        or
-
-        ```csharp
-        // Your code here
-        ```
-      render: markdown
-    validations:
-      required: false
-
-  - type: dropdown
-    id: language
-    attributes:
-      label: Language/SDK
-      description: Which language/SDK does this feature apply to?
-      options:
-        - Both
-        - .NET
-        - Python
-        - Other / Not Applicable
-      default: 0
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/python-issue.yml

@@ -1,70 +0,0 @@
-name: Python Bug Report
-description: Report a bug in the Agent Framework Python SDK
-title: "Python: [Bug]: "
-labels: ["bug", "Python"]
-type: bug
-body:
-  - type: textarea
-    id: description
-    attributes:
-      label: Description
-      description: Please provide a clear and detailed description of the bug.
-      placeholder: |
-        - What happened?
-        - What did you expect to happen?
-        - Steps to reproduce the issue
-    validations:
-      required: true
-
-  - type: textarea
-    id: code-sample
-    attributes:
-      label: Code Sample
-      description: If applicable, provide a minimal code sample that demonstrates the issue.
-      placeholder: |
-        ```python
-        # Your code here
-        ```
-      render: markdown
-    validations:
-      required: false
-
-  - type: textarea
-    id: error-messages
-    attributes:
-      label: Error Messages / Stack Traces
-      description: Include any error messages or stack traces you received.
-      placeholder: |
-        ```
-        Paste error messages or stack traces here
-        ```
-      render: markdown
-    validations:
-      required: false
-
-  - type: input
-    id: python-packages
-    attributes:
-      label: Package Versions
-      description: List the agent-framework-* packages and versions you are using
-      placeholder: "e.g., agent-framework-core: 1.0.0, agent-framework-azure-ai: 1.0.0"
-    validations:
-      required: true
-
-  - type: input
-    id: python-version
-    attributes:
-      label: Python Version
-      description: What version of Python are you using?
-      placeholder: "e.g., Python 3.11"
-    validations:
-      required: false
-
-  - type: textarea
-    id: additional-context
-    attributes:
-      label: Additional Context
-      description: Add any other context or screenshots that might be helpful.
-      placeholder: "Any additional information..."
-    validations:
-      required: false

.github/actions/azure-functions-integration-setup/action.yml

@@ -12,7 +12,7 @@ runs:
             docker rm -f dts-emulator
           fi
           echo "Starting Durable Task Scheduler Emulator"
-          docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 -e DTS_USE_DYNAMIC_TASK_HUBS=true mcr.microsoft.com/dts/dts-emulator:latest
+          docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest
           echo "Waiting for Durable Task Scheduler Emulator to be ready"
           timeout 30 bash -c 'until curl --silent http://localhost:8080/healthz; do sleep 1; done'
           echo "Durable Task Scheduler Emulator is ready"
@@ -28,18 +28,6 @@ runs:
           echo "Waiting for Azurite (Azure Storage emulator) to be ready"
           timeout 30 bash -c 'until curl --silent http://localhost:10000/devstoreaccount1; do sleep 1; done'
           echo "Azurite (Azure Storage emulator) is ready"
-      - name: Start Redis
-        shell: bash
-        run: |
-          if [ "$(docker ps -aq -f name=redis)" ]; then
-            echo "Stopping and removing existing Redis"
-            docker rm -f redis
-          fi
-          echo "Starting Redis"
-          docker run -d --name redis -p 6379:6379 redis:latest
-          echo "Waiting for Redis to be ready"
-          timeout 30 bash -c 'until docker exec redis redis-cli ping | grep -q PONG; do sleep 1; done'
-          echo "Redis is ready"
       - name: Install Azure Functions Core Tools
         shell: bash
         run: |

.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 -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/copilot-instructions.md

@@ -1,19 +1,67 @@
 # 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.
 
-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/dependabot.yml

@@ -11,6 +11,9 @@ updates:
     schedule:
       interval: "cron"
       cronjob: "0 8 * * 4,0" # Every Thursday(4) and Sunday(0) at 8:00 UTC
+    experimental:
+      nuget-native-updater: false
+      enable-cooldown-metrics-collection: false
     ignore:
       # For all System.* and Microsoft.Extensions/Bcl.* packages, ignore all major version updates
       - dependency-name: "System.*"
@@ -25,14 +28,6 @@ updates:
       - "dependencies"
 
   # Maintain dependencies for python
-  - package-ecosystem: "pip"
-    directory: "python/"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    labels:
-      - "python"
-      - "dependencies"
   - package-ecosystem: "uv"
     directory: "python/"
     schedule:

.github/instructions/durabletask-dotnet.instructions.md

@@ -1,17 +0,0 @@
----
-applyTo: "dotnet/src/Microsoft.Agents.AI.DurableTask/**,dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions/**"
----
-
-# Durable Task area code instructions
-
-The following guidelines apply to pull requests that modify files under
-`dotnet/src/Microsoft.Agents.AI.DurableTask/**` or
-`dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions/**`:
-
-## CHANGELOG.md
-
-- Each pull request that modifies code should add just one bulleted entry to the `CHANGELOG.md` file containing a change title (usually the PR title) and a link to the PR itself.
-- New PRs should be added to the top of the `CHANGELOG.md` file under a "## [Unreleased]" heading.
-- If the PR is the first since the last release, the existing "## [Unreleased]" heading should be replaced with a "## v[X.Y.Z]" heading and the PRs since the last release should be added to the new "## [Unreleased]" heading.
-- The style of new `CHANGELOG.md` entries should match the style of the other entries in the file.
-- If the PR introduces a breaking change, the changelog entry should be prefixed with "[BREAKING]".

.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/upgrades/prompts/SemanticKernelToAgentFramework.md

@@ -105,7 +105,7 @@ After completing migration, verify these specific items:
 1. **Compilation**: Execute `dotnet build` on all modified projects - zero errors required
 2. **Namespace Updates**: Confirm all `using Microsoft.SemanticKernel.Agents` statements are replaced
 3. **Method Calls**: Verify all `InvokeAsync` calls are changed to `RunAsync`
-4. **Return Types**: Confirm handling of `AgentResponse` instead of `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>`
+4. **Return Types**: Confirm handling of `AgentRunResponse` instead of `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>`
 5. **Thread Creation**: Validate all thread creation uses `agent.GetNewThread()` pattern
 6. **Tool Registration**: Ensure `[KernelFunction]` attributes are removed and `AIFunctionFactory.Create()` is used
 7. **Options Configuration**: Verify `AgentRunOptions` or `ChatClientAgentRunOptions` replaces `AgentInvokeOptions`
@@ -119,7 +119,7 @@ Agent Framework provides functionality for creating and managing AI agents throu
 Key API differences:
 - Agent creation: Remove Kernel dependency, use direct client-based creation
 - Method names: `InvokeAsync` → `RunAsync`, `InvokeStreamingAsync` → `RunStreamingAsync`
-- Return types: `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` → `AgentResponse`
+- Return types: `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` → `AgentRunResponse`
 - Thread creation: Provider-specific constructors → `agent.GetNewThread()`
 - Tool registration: `KernelPlugin` system → Direct `AIFunction` registration
 - Options: `AgentInvokeOptions` → Provider-specific run options (e.g., `ChatClientAgentRunOptions`)
@@ -142,9 +142,9 @@ Replace these Semantic Kernel agent classes with their Agent Framework equivalen
 |----------------------|----------------------------|-------------------|
 | `IChatCompletionService` | `IChatClient` | Convert to `IChatClient` using `chatService.AsChatClient()` extensions |
 | `ChatCompletionAgent` | `ChatClientAgent` | Remove `Kernel` parameter, add `IChatClient` parameter |
-| `OpenAIAssistantAgent` | `AIAgent` (via extension) | ⚠️ **Deprecated** - Use Responses API instead. <br> **New**: `OpenAIClient.GetAssistantClient().CreateAIAgent()` <br> **Existing**: `OpenAIClient.GetAssistantClient().GetAIAgent(assistantId)` |
+| `OpenAIAssistantAgent` | `AIAgent` (via extension) | **New**: `OpenAIClient.GetAssistantClient().CreateAIAgent()` <br> **Existing**: `OpenAIClient.GetAssistantClient().GetAIAgent(assistantId)` |
 | `AzureAIAgent` | `AIAgent` (via extension) | **New**: `PersistentAgentsClient.CreateAIAgent()` <br> **Existing**: `PersistentAgentsClient.GetAIAgent(agentId)` |
-| `OpenAIResponseAgent` | `AIAgent` (via extension) | Replace with `OpenAIClient.GetOpenAIResponseClient(modelId).CreateAIAgent()` |
+| `OpenAIResponseAgent` | `AIAgent` (via extension) | Replace with `OpenAIClient.GetOpenAIResponseClient().CreateAIAgent()` |
 | `A2AAgent` | `AIAgent` (via extension) | Replace with `A2ACardResolver.GetAIAgentAsync()` |
 | `BedrockAgent` | Not supported | Custom implementation required |
 
@@ -166,8 +166,8 @@ Replace these method calls:
 | `thread.DeleteAsync()` | Provider-specific cleanup | Use provider client directly |
 
 Return type changes:
-- `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` → `AgentResponse`
-- `IAsyncEnumerable<StreamingChatMessageContent>` → `IAsyncEnumerable<AgentResponseUpdate>`
+- `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` → `AgentRunResponse`
+- `IAsyncEnumerable<StreamingChatMessageContent>` → `IAsyncEnumerable<AgentRunResponseUpdate>`
 </api_changes>
 
 <configuration_changes>
@@ -191,8 +191,8 @@ Agent Framework changes these behaviors compared to Semantic Kernel Agents:
 1. **Thread Management**: Agent Framework automatically manages thread state. Semantic Kernel required manual thread updates in some scenarios (e.g., OpenAI Responses).
 
 2. **Return Types**:
-   - Non-streaming: Returns single `AgentResponse` instead of `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>`
-   - Streaming: Returns `IAsyncEnumerable<AgentResponseUpdate>` instead of `IAsyncEnumerable<StreamingChatMessageContent>`
+   - Non-streaming: Returns single `AgentRunResponse` instead of `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>`
+   - Streaming: Returns `IAsyncEnumerable<AgentRunResponseUpdate>` instead of `IAsyncEnumerable<StreamingChatMessageContent>`
 
 3. **Tool Registration**: Agent Framework uses direct function registration without requiring `[KernelFunction]` attributes.
 
@@ -397,7 +397,7 @@ await foreach (AgentResponseItem<ChatMessageContent> item in agent.InvokeAsync(u
 
 **With this Agent Framework non-streaming pattern:**
 ```csharp
-AgentResponse result = await agent.RunAsync(userInput, thread, options);
+AgentRunResponse result = await agent.RunAsync(userInput, thread, options);
 Console.WriteLine(result);
 ```
 
@@ -411,7 +411,7 @@ await foreach (StreamingChatMessageContent update in agent.InvokeStreamingAsync(
 
 **With this Agent Framework streaming pattern:**
 ```csharp
-await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(userInput, thread, options))
+await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(userInput, thread, options))
 {
     Console.Write(update);
 }
@@ -420,8 +420,8 @@ await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(userInput,
 **Required changes:**
 1. Replace `agent.InvokeAsync()` with `agent.RunAsync()`
 2. Replace `agent.InvokeStreamingAsync()` with `agent.RunStreamingAsync()`
-3. Change return type handling from `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` to `AgentResponse`
-4. Change streaming type from `StreamingChatMessageContent` to `AgentResponseUpdate`
+3. Change return type handling from `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` to `AgentRunResponse`
+4. Change streaming type from `StreamingChatMessageContent` to `AgentRunResponseUpdate`
 5. Remove `await foreach` for non-streaming calls
 6. Access message content directly from result object instead of iterating
 </api_changes>
@@ -529,14 +529,14 @@ AIAgent agent = new OpenAIClient(apiKey)
     .CreateAIAgent(instructions: instructions);
 ```
 
-**OpenAI Assistants (New):** ⚠️ *Deprecated - Use Responses API instead*
+**OpenAI Assistants (New):**
 ```csharp
 AIAgent agent = new OpenAIClient(apiKey)
     .GetAssistantClient()
     .CreateAIAgent(modelId, instructions: instructions);
 ```
 
-**OpenAI Assistants (Existing):** ⚠️ *Deprecated - Use Responses API instead*
+**OpenAI Assistants (Existing):**
 ```csharp
 AIAgent agent = new OpenAIClient(apiKey)
     .GetAssistantClient()
@@ -562,20 +562,6 @@ AIAgent agent = await new PersistentAgentsClient(endpoint, credential)
     .GetAIAgentAsync(agentId);
 ```
 
-**OpenAI Responses:** *(Recommended for OpenAI)*
-```csharp
-AIAgent agent = new OpenAIClient(apiKey)
-    .GetOpenAIResponseClient(modelId)
-    .CreateAIAgent(instructions: instructions);
-```
-
-**Azure OpenAI Responses:** *(Recommended for Azure OpenAI)*
-```csharp
-AIAgent agent = new AzureOpenAIClient(endpoint, credential)
-    .GetOpenAIResponseClient(deploymentName)
-    .CreateAIAgent(instructions: instructions);
-```
-
 **A2A:**
 ```csharp
 A2ACardResolver resolver = new(new Uri(agentHost));
@@ -661,7 +647,7 @@ await foreach (var result in agent.InvokeAsync(input, thread, options))
 ```csharp
 ChatClientAgentRunOptions options = new(new ChatOptions { MaxOutputTokens = 1000 });
 
-AgentResponse result = await agent.RunAsync(input, thread, options);
+AgentRunResponse result = await agent.RunAsync(input, thread, options);
 Console.WriteLine(result);
 
 // Access underlying content when needed:
@@ -689,7 +675,7 @@ await foreach (var result in agent.InvokeAsync(input, thread, options))
 
 **With this Agent Framework non-streaming usage pattern:**
 ```csharp
-AgentResponse result = await agent.RunAsync(input, thread, options);
+AgentRunResponse result = await agent.RunAsync(input, thread, options);
 Console.WriteLine($"Tokens: {result.Usage.TotalTokenCount}");
 ```
 
@@ -709,7 +695,7 @@ await foreach (StreamingChatMessageContent response in agent.InvokeStreamingAsyn
 
 **With this Agent Framework streaming usage pattern:**
 ```csharp
-await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(input, thread, options))
+await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(input, thread, options))
 {
     if (update.Contents.OfType<UsageContent>().FirstOrDefault() is { } usageContent)
     {
@@ -776,57 +762,35 @@ await foreach (var content in agent.InvokeAsync(userInput, thread))
 
 **With this Agent Framework CodeInterpreter pattern:**
 ```csharp
-using System.Text;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-
 var result = await agent.RunAsync(userInput, thread);
 Console.WriteLine(result);
 
-// Get the CodeInterpreterToolCallContent (code input)
-CodeInterpreterToolCallContent? toolCallContent = result.Messages
-    .SelectMany(m => m.Contents)
-    .OfType<CodeInterpreterToolCallContent>()
-    .FirstOrDefault();
+// Extract chat response MEAI type via first level breaking glass
+var chatResponse = result.RawRepresentation as ChatResponse;
 
-if (toolCallContent?.Inputs is not null)
+// Extract underlying SDK updates via second level breaking glass
+var underlyingStreamingUpdates = chatResponse?.RawRepresentation as IEnumerable<object?> ?? [];
+
+StringBuilder generatedCode = new();
+foreach (object? underlyingUpdate in underlyingStreamingUpdates ?? [])
 {
-    DataContent? codeInput = toolCallContent.Inputs.OfType<DataContent>().FirstOrDefault();
-    if (codeInput?.HasTopLevelMediaType("text") ?? false)
+    if (underlyingUpdate is RunStepDetailsUpdate stepDetailsUpdate && stepDetailsUpdate.CodeInterpreterInput is not null)
     {
-        Console.WriteLine($"Code Input: {Encoding.UTF8.GetString(codeInput.Data.ToArray())}");
+        generatedCode.Append(stepDetailsUpdate.CodeInterpreterInput);
     }
 }
 
-// Get the CodeInterpreterToolResultContent (code output)
-CodeInterpreterToolResultContent? toolResultContent = result.Messages
-    .SelectMany(m => m.Contents)
-    .OfType<CodeInterpreterToolResultContent>()
-    .FirstOrDefault();
-
-if (toolResultContent?.Outputs is not null)
+if (!string.IsNullOrEmpty(generatedCode.ToString()))
 {
-    TextContent? resultOutput = toolResultContent.Outputs.OfType<TextContent>().FirstOrDefault();
-    if (resultOutput is not null)
-    {
-        Console.WriteLine($"Code Tool Result: {resultOutput.Text}");
-    }
-}
-
-// Getting any annotations generated by the tool
-foreach (AIAnnotation annotation in result.Messages
-    .SelectMany(m => m.Contents)
-    .SelectMany(c => c.Annotations ?? []))
-{
-    Console.WriteLine($"Annotation: {annotation}");
+    Console.WriteLine($"\n# {chatResponse?.Messages[0].Role}:Generated Code:\n{generatedCode}");
 }
 ```
 
 **Functional differences:**
-1. Code interpreter content is now available via MEAI abstractions - no breaking glass required
-2. Use `CodeInterpreterToolCallContent` to access code inputs (the generated code)
-3. Use `CodeInterpreterToolResultContent` to access code outputs (execution results)
-4. Annotations are accessible via `AIAnnotation` on content items
+1. Code interpreter output is separate from text content, not a metadata property
+2. Access code via `RunStepDetailsUpdate.CodeInterpreterInput` instead of metadata
+3. Use breaking glass pattern to access underlying SDK objects
+4. Process text content and code interpreter output independently
 </behavioral_changes>
 
 #### Provider-Specific Options Configuration
@@ -839,7 +803,7 @@ var agentOptions = new ChatClientAgentRunOptions(new ChatOptions
 {
     MaxOutputTokens = 8000,
     // Breaking glass to access provider-specific options
-    RawRepresentationFactory = (_) => new OpenAI.Responses.CreateResponseOptions()
+    RawRepresentationFactory = (_) => new OpenAI.Responses.ResponseCreationOptions()
     {
         ReasoningOptions = new()
         {
@@ -1016,8 +980,6 @@ AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential(
 
 ### 3. OpenAI Assistants Migration
 
-> ⚠️ **DEPRECATION WARNING**: The OpenAI Assistants API has been deprecated. The Agent Framework extension methods for Assistants are marked as `[Obsolete]`. **Please use the Responses API instead** (see Section 6: OpenAI Responses Migration).
-
 <configuration_changes>
 **Remove Semantic Kernel Packages:**
 ```xml
@@ -1329,7 +1291,52 @@ var result = await agent.RunAsync(userInput, thread);
 ```
 </api_changes>
 
-### 8. Unsupported Providers (Require Custom Implementation)
+### 8. A2A Migration
+
+<configuration_changes>
+**Remove Semantic Kernel Packages:**
+```xml
+<PackageReference Include="Microsoft.SemanticKernel.Agents.A2A" />
+```
+
+**Add Agent Framework Packages:**
+```xml
+<PackageReference Include="Microsoft.Agents.AI.A2A" />
+```
+</configuration_changes>
+
+<api_changes>
+**Replace this Semantic Kernel pattern:**
+```csharp
+using A2A;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Agents;
+using Microsoft.SemanticKernel.Agents.A2A;
+
+using var httpClient = CreateHttpClient();
+var client = new A2AClient(agentUrl, httpClient);
+var cardResolver = new A2ACardResolver(url, httpClient);
+var agentCard = await cardResolver.GetAgentCardAsync();
+Console.WriteLine(JsonSerializer.Serialize(agentCard, s_jsonSerializerOptions));
+var agent = new A2AAgent(client, agentCard);
+```
+
+**With this Agent Framework pattern:**
+```csharp
+using System;
+using A2A;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.A2A;
+
+// Initialize an A2ACardResolver to get an A2A agent card.
+A2ACardResolver agentCardResolver = new(new Uri(a2aAgentHost));
+
+// Create an instance of the AIAgent for an existing A2A agent specified by the agent card.
+AIAgent agent = await agentCardResolver.GetAIAgentAsync();
+```
+</api_changes>
+
+### 9. Unsupported Providers (Require Custom Implementation)
 
 <behavioral_changes>
 #### BedrockAgent Migration
@@ -1500,7 +1507,7 @@ Console.WriteLine(result);
 ```
 </behavioral_changes>
 
-### 9. Function Invocation Filtering
+### 10. Function Invocation Filtering
 
 **Invocation Context**
 
@@ -1608,4 +1615,25 @@ var filteredAgent = originalAgent
     .Build();
 ```
 
+### 11. Function Invocation Contexts
 
+**Invocation Context**
+
+Semantic Kernel's `IAutoFunctionInvocationFilter` provides a `AutoFunctionInvocationContext` where Agent Framework provides `FunctionInvocationContext` 
+
+The property mapping guide from a `AutoFunctionInvocationContext` to a `FunctionInvocationContext` is as follows:
+
+| Semantic Kernel | Agent Framework |
+| --- | --- |
+| RequestSequenceIndex | Iteration |
+| FunctionSequenceIndex | FunctionCallIndex |
+| ToolCallId | CallContent.CallId |
+| ChatMessageContent | Messages[0] |
+| ExecutionSettings | Options |
+| ChatHistory | Messages |
+| Function | Function |
+| Kernel | N/A |
+| Result | Use `return` from the delegate |
+| Terminate | Terminate |
+| CancellationToken | provided via argument to middleware delegate |
+| Arguments | Arguments |

.github/workflows/codeql-analysis.yml

@@ -32,7 +32,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           persist-credentials: false
 

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

@@ -18,7 +18,6 @@ on:
 
 env:
   COVERAGE_THRESHOLD: 80
-  COVERAGE_FRAMEWORK: net10.0 # framework target for which we run/report code coverage
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -35,57 +34,51 @@ jobs:
       contents: read
       pull-requests: read
     outputs:
-      dotnetChanges: ${{ steps.filter.outputs.dotnet }}
-      cosmosDbChanges: ${{ steps.filter.outputs.cosmosdb }}
+      dotnetChanges: ${{ steps.filter.outputs.dotnet}}
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
       - uses: dorny/paths-filter@v3
         id: filter
         with:
           filters: |
             dotnet:
               - 'dotnet/**'
-            cosmosdb:
-              - 'dotnet/src/Microsoft.Agents.AI.CosmosNoSql/**'
       # run only if 'dotnet' files were changed
       - name: dotnet tests
         if: steps.filter.outputs.dotnet == 'true'
         run: echo "Dotnet file"
-      - name: dotnet CosmosDB tests
-        if: steps.filter.outputs.cosmosdb == 'true'
-        run: echo "Dotnet CosmosDB changes"
       # run only if not 'dotnet' files were changed
       - name: not dotnet tests
         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: "net9.0", os: "windows-latest", configuration: Debug }
-          - { targetFramework: "net8.0", os: "ubuntu-latest", configuration: Release }
-          - { targetFramework: "net472", os: "windows-latest", configuration: Release }
+          - { targetFramework: "net9.0", os: "ubuntu-latest", configuration: Release, integration-tests: true, environment: "integration" }
+          - { targetFramework: "net9.0", os: "ubuntu-latest", configuration: Debug }
+          - { targetFramework: "net9.0", 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
+      - uses: actions/checkout@v5
         with:
-            persist-credentials: false
-            sparse-checkout: |
-              .
-              .github
-              dotnet
-              python
-              workflow-samples
+          persist-credentials: false
+          sparse-checkout: |
+            .
+            .github
+            dotnet
+            python
+            workflow-samples
 
       - name: Setup dotnet
-        uses: actions/setup-dotnet@v5.2.0
+        uses: actions/setup-dotnet@v5.0.0
         with:
           global-json-file: ${{ github.workspace }}/dotnet/global.json
       - name: Build dotnet solutions
@@ -130,106 +123,25 @@ jobs:
           popd
           rm -rf "$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
+      - name: Run Unit Tests Windows
         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
-        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/"
-            )
-          }
-
-          dotnet test --solution ./filtered-unit.slnx `
-            -f ${{ matrix.targetFramework }} `
-            -c ${{ matrix.configuration }} `
-            --no-build -v Normal `
-            --report-xunit-trx `
-            --ignore-exit-code 8 `
-            @coverageArgs
-        env:
-          # Cosmos DB Emulator connection settings
-          COSMOSDB_ENDPOINT: https://localhost:8081
-          COSMOSDB_KEY: C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw==
-
+          export UT_PROJECTS=$(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')
+            
+            # 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 --collect:"XPlat Code Coverage" --results-directory:"TestResults/Coverage/" -- DataCollectionRunSettings.DataCollectors.DataCollector.Configuration.ExcludeByAttribute=GeneratedCodeAttribute,CompilerGeneratedAttribute,ExcludeFromCodeCoverageAttribute
+            else
+              echo "Skipping $project - does not support target framework ${{ matrix.targetFramework }} (supports: $target_frameworks)"
+            fi
+          done
+      
       - name: Log event name and matrix integration-tests
-        shell: bash
-        run: echo "github.event_name:${{ github.event_name }} matrix.integration-tests:${{ matrix.integration-tests }} github.event.action:${{ github.event.action }} github.event.pull_request.merged:${{ github.event.pull_request.merged }}"
+        shell: bash 
+        run: echo "github.event_name:${{ github.event_name }} matrix.integration-tests:${{ matrix.integration-tests }} github.event.action:${{ github.event.action }} github.event.pull_request.merged:${{ github.event.pull_request.merged }}" 
 
       - name: Azure CLI Login
         if: github.event_name != 'pull_request' && matrix.integration-tests
@@ -248,62 +160,61 @@ 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
+            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@5.4.18
         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@v5
         with:
           name: CoverageReport-${{ matrix.os }}-${{ matrix.targetFramework }}-${{ matrix.configuration }} # Artifact name
           path: ./TestResults/Reports # Directory containing files to upload
 
       - 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

@@ -22,7 +22,7 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - { dotnet: "10.0", configuration: Release, os: ubuntu-latest }
+          - { dotnet: "9.0", configuration: Release, os: ubuntu-latest }
 
     runs-on: ${{ matrix.os }}
     env:
@@ -30,7 +30,7 @@ jobs:
 
     steps:
       - name: Check out code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           fetch-depth: 0
           persist-credentials: false
@@ -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/label-issues.yml

@@ -45,58 +45,19 @@ jobs:
               labels.push("triage")
             }
 
-            // Helper function to extract field value from issue form body
-            // Issue forms format fields as: ### Field Name\n\nValue
-            function getFormFieldValue(body, fieldName) {
-              if (!body) return null
-              const regex = new RegExp(`###\\s*${fieldName}\\s*\\n\\n([^\\n#]+)`, 'i')
-              const match = body.match(regex)
-              return match ? match[1].trim() : null
+            // Check if the body or the title contains the word 'python' (case-insensitive)
+            if ((body != null && body.match(/python/i)) || (title != null && title.match(/python/i))) {
+              // Add the 'python' label to the array
+              labels.push("python")
             }
 
-            // Check for language from issue form dropdown first
-            const languageField = getFormFieldValue(body, 'Language')
-            let languageLabelAdded = false
-
-            if (languageField) {
-              if (languageField === 'Python') {
-                labels.push("python")
-                languageLabelAdded = true
-              } else if (languageField === '.NET') {
-                labels.push(".NET")
-                languageLabelAdded = true
-              }
-              // 'None / Not Applicable' - don't add any language label
-            }
-
-            // Fallback: Check if the body or the title contains the word 'python' (case-insensitive)
-            // Only if language wasn't already determined from the form field
-            if (!languageLabelAdded) {
-              if ((body != null && body.match(/python/i)) || (title != null && title.match(/python/i))) {
-                // Add the 'python' label to the array
-                labels.push("python")
-              }
-
-              // Check if the body or the title contains the words 'dotnet', '.net', 'c#' or 'csharp' (case-insensitive)
-              if ((body != null && body.match(/\.net/i)) || (title != null && title.match(/\.net/i)) ||
-                  (body != null && body.match(/dotnet/i)) || (title != null && title.match(/dotnet/i)) ||
-                  (body != null && body.match(/C#/i)) || (title != null && title.match(/C#/i)) ||
-                  (body != null && body.match(/csharp/i)) || (title != null && title.match(/csharp/i))) {
-                // Add the '.NET' label to the array
-                labels.push(".NET")
-              }
-            }
-
-            // Check for issue type from issue form dropdown
-            const issueTypeField = getFormFieldValue(body, 'Type of Issue')
-            if (issueTypeField) {
-              if (issueTypeField === 'Bug') {
-                labels.push("bug")
-              } else if (issueTypeField === 'Feature Request') {
-                labels.push("enhancement")
-              } else if (issueTypeField === 'Question') {
-                labels.push("question")
-              }
+            // Check if the body or the title contains the words 'dotnet', '.net', 'c#' or 'csharp' (case-insensitive)
+            if ((body != null && body.match(/.net/i)) || (title != null && title.match(/.net/i)) ||
+                (body != null && body.match(/dotnet/i)) || (title != null && title.match(/dotnet/i)) ||
+                (body != null && body.match(/C#/i)) || (title != null && title.match(/C#/i)) ||
+                (body != null && body.match(/csharp/i)) || (title != null && title.match(/csharp/i))) {
+              // Add the '.NET' label to the array
+              labels.push(".NET")
             }
 
             // Add the labels to the issue (only if there are labels to add)

.github/workflows/markdown-link-check.yml

@@ -19,7 +19,7 @@ jobs:
     runs-on: ubuntu-22.04
     # check out the latest version of the code
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
         with:
           persist-credentials: false
 

.github/workflows/merge-gatekeeper.yml

@@ -29,7 +29,3 @@ 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

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

@@ -1,383 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Microsoft. All rights reserved.
-"""Check Python test coverage against threshold for enforced targets.
-
-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.
-
-Usage:
-    python python-check-coverage.py <coverage-xml-path> <threshold>
-
-Example:
-    python python-check-coverage.py python-coverage.xml 85
-"""
-
-import sys
-import xml.etree.ElementTree as ET
-from dataclasses import dataclass
-
-# =============================================================================
-# ENFORCED TARGETS 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.
-#
-# 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")
-# =============================================================================
-ENFORCED_TARGETS: set[str] = {
-    # Packages
-    "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.core.agent_framework.azure",
-    "packages.core.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
-}
-
-
-@dataclass
-class PackageCoverage:
-    """Coverage data for a single package."""
-
-    name: str
-    line_rate: float
-    branch_rate: float
-    lines_valid: int
-    lines_covered: int
-    branches_valid: int
-    branches_covered: int
-
-    @property
-    def line_coverage_percent(self) -> float:
-        """Return line coverage as a percentage."""
-        return self.line_rate * 100
-
-    @property
-    def branch_coverage_percent(self) -> float:
-        """Return branch coverage as a percentage."""
-        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]:
-    """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).
-    """
-    tree = ET.parse(xml_path)
-    root = tree.getroot()
-
-    # Get overall coverage from root element
-    overall_line_rate = float(root.get("line-rate", 0))
-    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")
-
-        line_rate = float(package.get("line-rate", 0))
-        branch_rate = float(package.get("branch-rate", 0))
-
-        # Count lines and branches from classes within this package
-        lines_valid = 0
-        lines_covered = 0
-        branches_valid = 0
-        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("/")
-                            )
-                            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
-
-        # Use full package path as the key (no aggregation)
-        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,
-            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
-
-
-def format_coverage_value(coverage: float, threshold: float, is_enforced: bool) -> str:
-    """Format a coverage value with optional pass/fail indicator.
-
-    Args:
-        coverage: Coverage percentage (0-100).
-        threshold: Minimum required coverage percentage.
-        is_enforced: Whether this target is enforced.
-
-    Returns:
-        Formatted string like "85.5%" or "85.5% ✅" or "75.0% ❌".
-    """
-    formatted = f"{coverage:.1f}%"
-    if is_enforced:
-        icon = "✅" if coverage >= threshold else "❌"
-        formatted = f"{formatted} {icon}"
-    return formatted
-
-
-def print_coverage_table(
-    packages: dict[str, PackageCoverage],
-    files: dict[str, PackageCoverage],
-    threshold: float,
-    overall_line_rate: float,
-    overall_branch_rate: float,
-) -> None:
-    """Print a formatted coverage summary 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).
-    """
-    print("\n" + "=" * 80)
-    print("PYTHON TEST COVERAGE REPORT")
-    print("=" * 80)
-
-    # Overall coverage
-    print(f"\nOverall Line Coverage:   {overall_line_rate * 100:.1f}%")
-    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
-    sorted_packages = sorted(
-        packages.values(),
-        key=lambda p: (p.name not in ENFORCED_TARGETS, p.name),
-    )
-
-    for pkg in sorted_packages:
-        is_enforced = normalize_coverage_path(pkg.name) in enforced_targets
-        enforced_marker = "[ENFORCED] " if is_enforced else ""
-        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}"
-
-        print(f"{package_label:<80} {lines_info:<15} {line_cov:<15}")
-
-    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.
-
-    Args:
-        xml_path: Path to the Cobertura XML coverage report.
-        threshold: Minimum required coverage percentage.
-
-    Returns:
-        True if all enforced targets pass, False otherwise.
-    """
-    packages, files, overall_line_rate, overall_branch_rate = parse_coverage_xml(
-        xml_path
-    )
-
-    print_coverage_table(
-        packages, files, threshold, overall_line_rate, overall_branch_rate
-    )
-
-    # Check enforced targets
-    failed_targets: list[str] = []
-    missing_targets: 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)
-            continue
-
-        if target_coverage.line_coverage_percent < threshold:
-            failed_targets.append(
-                f"{target_name} ({target_coverage.line_coverage_percent:.1f}%)"
-            )
-
-    # Report results
-    if missing_targets:
-        print(
-            f"\n❌ FAILED: Enforced targets not found in coverage report: {', '.join(missing_targets)}"
-        )
-        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.")
-        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 found_enforced:
-            print(
-                f"\n✅ PASSED: All enforced targets meet the {threshold}% coverage threshold."
-            )
-
-    return True
-
-
-def main() -> int:
-    """Main entry point.
-
-    Returns:
-        Exit code: 0 for success, 1 for failure.
-    """
-    if len(sys.argv) != 3:
-        print(f"Usage: {sys.argv[0]} <coverage-xml-path> <threshold>")
-        print(f"Example: {sys.argv[0]} python-coverage.xml 85")
-        return 1
-
-    xml_path = sys.argv[1]
-    try:
-        threshold = float(sys.argv[2])
-    except ValueError:
-        print(f"Error: Invalid threshold value: {sys.argv[2]}")
-        return 1
-
-    try:
-        success = check_coverage(xml_path, threshold)
-        return 0 if success else 1
-    except FileNotFoundError:
-        print(f"Error: Coverage file not found: {xml_path}")
-        return 1
-    except ET.ParseError as e:
-        print(f"Error: Failed to parse coverage XML: {e}")
-        return 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())

.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:
@@ -27,9 +27,7 @@ jobs:
     env:
       UV_PYTHON: ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
+      - uses: actions/checkout@v5
       - name: Set up python and install the project
         id: python-setup
         uses: ./.github/actions/python-setup
@@ -37,105 +35,15 @@ jobs:
           python-version: ${{ matrix.python-version }}
           os: ${{ runner.os }}
         env:
+          # Configure a constant location for the uv cache
           UV_CACHE_DIR: /tmp/.uv-cache
-      - uses: actions/cache@v5
+      - uses: actions/cache@v4
         with:
-          path: ~/.cache/prek
-          key: prek|${{ 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 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-docs.yml

@@ -24,7 +24,7 @@ jobs:
       run:
         working-directory: python
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
       - name: Set up uv
         uses: astral-sh/setup-uv@v7
         with:

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

@@ -1,318 +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_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_EMBEDDINGS_MODEL_ID: ${{ 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/core/tests/openai
-          -m integration
-          -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_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
-      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/core/tests/azure
-          -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: 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
-
-  # 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_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      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
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
-
-  # Azure AI integration tests
-  python-tests-azure-ai:
-    name: Python Integration Tests - Azure AI
-    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 }}
-      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 --directory packages/azure-ai poe integration-tests -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-azure-ai,
-        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

@@ -24,7 +24,7 @@ jobs:
     outputs:
       pythonChanges: ${{ steps.filter.outputs.python}}
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
       - uses: dorny/paths-filter@v3
         id: filter
         with:
@@ -59,7 +59,7 @@ jobs:
       run:
         working-directory: python
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
 
       - name: Set up python and install the project
         id: python-setup
@@ -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:
@@ -31,45 +26,15 @@ 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: actions/checkout@v5
       - uses: dorny/paths-filter@v3
         id: filter
         with:
           filters: |
             python:
               - 'python/**'
-            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/core/tests/openai/**'
-            azure:
-              - 'python/packages/core/agent_framework/azure/**'
-              - 'python/packages/core/tests/azure/**'
-            misc:
-              - 'python/packages/anthropic/**'
-              - 'python/packages/ollama/**'
-              - 'python/packages/core/agent_framework/_mcp.py'
-              - 'python/packages/core/tests/core/test_mcp.py'
-            functions:
-              - 'python/packages/azurefunctions/**'
-              - 'python/packages/durabletask/**'
-            azure-ai:
-              - 'python/packages/azure-ai/**'
-            cosmos:
-              - 'python/packages/azure-cosmos/**'
       # run only if 'python' files were changed
       - name: python tests
         if: steps.filter.outputs.python == 'true'
@@ -78,236 +43,48 @@ 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:
+      UV_PYTHON: ${{ matrix.python-version }}
       OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
       OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_EMBEDDINGS_MODEL_ID: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
       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/core/tests/openai
-          -m integration
-          -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_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
-      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/core/tests/azure
-          -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: 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: 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_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
       AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
       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
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
       - name: Set up python and install the project
         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
@@ -318,15 +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
-          --timeout=120 --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 300 --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()
@@ -336,20 +112,22 @@ jobs:
           summary: true
           display-options: fEX
           fail-on-empty: false
-          title: Functions integration test results
+          title: Test results
 
   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 }}
       LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
@@ -357,13 +135,16 @@ jobs:
       run:
         working-directory: python
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
       - name: Set up python and install the project
         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
@@ -372,8 +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 --directory packages/azure-ai poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
+        timeout-minutes: 10
+        run: uv run poe azure-ai-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
@@ -392,78 +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-azure-ai,
-        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-release.yml

@@ -23,7 +23,7 @@ jobs:
       run:
         working-directory: python
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
       - name: Set up python and install the project
         id: python-setup
         uses: ./.github/actions/python-setup

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

@@ -1,814 +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
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-    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: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .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:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      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 }}
-      # 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: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_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_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_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-openai:
-    name: Validate 02-agents/providers/azure_openai
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ 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: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_openai --save-report --report-name 02-agents-azure-openai
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-azure-openai
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-azure-ai:
-    name: Validate 02-agents/providers/azure_ai
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
-      BING_CONNECTION_ID: ${{ secrets.BING_CONNECTION_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: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME=$AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME=$AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "BING_CONNECTION_ID=$BING_CONNECTION_ID" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_ai --save-report --report-name 02-agents-azure-ai
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-azure-ai
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-azure-ai-agent:
-    name: Validate 02-agents/providers/azure_ai_agent
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ 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: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_ai_agent --save-report --report-name 02-agents-azure-ai-agent
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-azure-ai-agent
-          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-local:
-    name: Validate 02-agents/providers/foundry_local
-    if: false  # Temporarily disabled - requires local Foundry setup
-    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/foundry_local --save-report --report-name 02-agents-foundry-local
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-foundry-local
-          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:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ 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: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .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:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ 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:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_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
-      AZURE_AI_MODEL_DEPLOYMENT_NAME_WORKFLOW: ${{ 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:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      # OpenAI configuration
-      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      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: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
-          echo "OPENAI_API_KEY=$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:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_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 }}
-      # 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: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_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=$COPILOTSTUDIOAGENT__AGENTAPPID" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir semantic-kernel-migration --save-report --report-name semantic-kernel-migration
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-semantic-kernel-migration
-          path: python/samples/sample_validation/reports/
-
-  aggregate-results:
-    name: Aggregate Results
-    runs-on: ubuntu-latest
-    if: always()
-    needs:
-      - validate-01-get-started
-      - validate-02-agents
-      - validate-02-agents-openai
-      - validate-02-agents-azure-openai
-      - validate-02-agents-azure-ai
-      - validate-02-agents-azure-ai-agent
-      - validate-02-agents-anthropic
-      - validate-02-agents-github-copilot
-      - validate-02-agents-amazon
-      - validate-02-agents-ollama
-      - validate-02-agents-foundry-local
-      - validate-02-agents-copilotstudio
-      - validate-02-agents-custom
-      - validate-03-workflows
-      - validate-04-hosting
-      - validate-05-end-to-end
-      - validate-autogen-migration
-      - validate-semantic-kernel-migration
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Download all validation reports
-        uses: actions/download-artifact@v7
-        with:
-          pattern: validation-report-*
-          path: reports/
-          merge-multiple: true
-
-      - name: Restore validation history
-        id: cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: validation-history/
-          key: validation-history-${{ 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

@@ -19,9 +19,9 @@ jobs:
       run:
         working-directory: python
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
       - name: Download coverage report
-        uses: actions/download-artifact@v8
+        uses: actions/download-artifact@v6
         with:
           github-token: ${{ secrets.GH_ACTIONS_PR_WRITE }}
           run-id: ${{ github.event.workflow_run.id }}
@@ -34,19 +34,12 @@ jobs:
         # because the workflow_run event does not have access to the PR number
         # The PR number is needed to post the comment on the PR
         run: |
-          if [ ! -s pr_number ]; then
-            echo "PR number file 'pr_number' is missing or empty"
-            exit 1
-          fi
-          PR_NUMBER=$(head -1 pr_number | tr -dc '0-9')
-          if [ -z "$PR_NUMBER" ]; then
-            echo "PR number file 'pr_number' does not contain a valid PR number"
-            exit 1
-          fi
-          echo "PR_NUMBER=$PR_NUMBER" >> "$GITHUB_ENV"
+          PR_NUMBER=$(cat pr_number)
+          echo "PR number: $PR_NUMBER"
+          echo "PR_NUMBER=$PR_NUMBER" >> $GITHUB_ENV
       - name: Pytest coverage comment
         id: coverageComment
-        uses: MishaKav/pytest-coverage-comment@v1.6.0
+        uses: MishaKav/pytest-coverage-comment@v1.1.59
         with:
           github-token: ${{ secrets.GH_ACTIONS_PR_WRITE }}
           issue-number: ${{ env.PR_NUMBER }}

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

@@ -9,8 +9,6 @@ on:
 env:
   # Configure a constant location for the uv cache
   UV_CACHE_DIR: /tmp/.uv-cache
-  # Coverage threshold percentage for enforced modules
-  COVERAGE_THRESHOLD: 85
 
 jobs:
   python-tests-coverage:
@@ -20,9 +18,9 @@ jobs:
       run:
         working-directory: python
     env:
-      UV_PYTHON: "3.11"
+      UV_PYTHON: "3.10"
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
       # Save the PR number to a file since the workflow_run event
       # in the coverage report workflow does not have access to it
       - name: Save PR number
@@ -32,17 +30,15 @@ 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
-      - name: Check coverage threshold
-        run: python ${{ github.workspace }}/.github/workflows/python-check-coverage.py python-coverage.xml ${{ env.COVERAGE_THRESHOLD }}
+        run: uv run poe all-tests-cov --cov-report=xml:python-coverage.xml -q --junitxml=pytest.xml
       - name: Upload coverage report
-        uses: actions/upload-artifact@v7
+        uses: actions/upload-artifact@v5
         with:
           path: |
             python/python-coverage.xml

.github/workflows/python-tests.yml

@@ -27,20 +27,19 @@ jobs:
       run:
         working-directory: python
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
       - 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 }}
-          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

@@ -199,25 +199,20 @@ 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*
-*/__azurite_db_blob_extent__.json*
-*/__azurite_db_queue__.json*
-*/__azurite_db_queue_extent__.json*
-*/__azurite_db_table__.json*
+*/__azurite_db_blob__.json
+*/__azurite_db_blob_extent__.json
+*/__azurite_db_queue__.json
+*/__azurite_db_queue_extent__.json
+*/__azurite_db_table__.json
 */__blobstorage__/
 */__queuestorage__/
-*/AzuriteConfig
 
 # Azure Functions local settings
 local.settings.json
@@ -229,4 +224,3 @@ local.settings.json
 
 # Database files
 *.db
-python/dotnet-ref

README.md

@@ -48,12 +48,10 @@ dotnet add package Microsoft.Agents.AI
 - **[Migration from Semantic Kernel](https://learn.microsoft.com/en-us/agent-framework/migration-guide/from-semantic-kernel)** - Guide to migrate from Semantic Kernel
 - **[Migration from AutoGen](https://learn.microsoft.com/en-us/agent-framework/migration-guide/from-autogen)** - Guide to migrate from AutoGen
 
-Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-community-office-hours) or ask questions in our [Discord channel](https://discord.gg/b5zjErwbQM) to get help from the team and other users.
-
 ### ✨ **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 +71,11 @@ 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!**
 
@@ -108,7 +106,7 @@ async def main():
         # api_version=os.environ["AZURE_OPENAI_API_VERSION"],
         # api_key=os.environ["AZURE_OPENAI_API_KEY"],  # Optional if using AzureCliCredential
         credential=AzureCliCredential(), # Optional, if using api_key
-    ).as_agent(
+    ).create_agent(
         name="HaikuBot",
         instructions="You are an upbeat assistant that writes beautifully.",
     )
@@ -125,14 +123,13 @@ 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")
-    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+    .GetOpenAIResponseClient("gpt-4o-mini")
+    .CreateAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
 
 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```
@@ -143,18 +140,15 @@ Create a simple Agent, using Azure OpenAI Responses with token based auth, that
 // dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
 // dotnet add package Azure.Identity
 // Use `az login` to authenticate with Azure CLI
-using System.ClientModel.Primitives;
-using Azure.Identity;
-using Microsoft.Agents.AI;
+using System;
 using OpenAI;
-using OpenAI.Responses;
 
 // 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") })
-    .GetResponsesClient("gpt-4o-mini")
-    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+    .GetOpenAIResponseClient("gpt-4o-mini")
+    .CreateAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
 
 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```
@@ -163,15 +157,15 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram
 
 ### Python
 
-- [Getting Started with Agents](./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.)
-- [Getting Started with Workflows](./python/samples/03-workflows): workflow creation and integration with agents
+- [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 with Agents](./dotnet/samples/02-agents/Agents): basic agent creation and tool usage
-- [Agent Provider Samples](./dotnet/samples/02-agents/AgentProviders): samples showing different agent providers
-- [Workflow Samples](./dotnet/samples/03-workflows): advanced multi-agent patterns and workflow orchestration
+- [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
 

TRANSPARENCY_FAQ.md

@@ -42,9 +42,9 @@ Microsoft Agent Framework relies on existing LLMs. Using the framework retains c
 
 **Framework-Specific Limitations**:
 
-- **Platform Requirements**: Python 3.10+ required, specific .NET versions (.NET 8.0, 9.0, 10.0, netstandard2.0, net472)
+- **Platform Requirements**: Python 3.10+ required, specific .NET versions (.NET 8.0, 9.0, netstandard2.0, net472)
 - **API Dependencies**: Requires proper configuration of LLM provider keys and endpoints
-- **Orchestration Features**: Advanced orchestration patterns including GroupChat, Sequential, and Concurrent workflows are now available in both Python and .NET implementations. See the respective language documentation for examples.
+- **Orchestration Features**: Advanced orchestration patterns like GroupChat, Sequential, and Concurrent orchestrations are "coming soon" for Python implementation
 - **Privacy and Data Protection**: The framework allows for human participation in conversations between agents. It is important to ensure that user data and conversations are protected and that developers use appropriate measures to safeguard privacy.
 - **Accountability and Transparency**: The framework involves multiple agents conversing and collaborating, it is important to establish clear accountability and transparency mechanisms. Users should be able to understand and trace the decision-making process of the agents involved in order to ensure accountability and address any potential issues or biases.
 - **Security & unintended consequences**: The use of multi-agent conversations and automation in complex tasks may have unintended consequences. Especially, allowing agents to make changes in external environments through tool calls or function execution could pose significant risks. Developers should carefully consider the potential risks and ensure that appropriate safeguards are in place to prevent harm or negative outcomes, including keeping a human in the loop for decision making.

agent-samples/README.md

@@ -1,3 +0,0 @@
-# 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/).

agent-samples/azure/AzureOpenAI.yaml

@@ -1,25 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response.
-model:
-    id: =Env.AZURE_OPENAI_DEPLOYMENT_NAME
-    provider: AzureOpenAI
-    apiType: Chat
-    options:
-        temperature: 0.9
-        topP: 0.95
-outputSchema:
-    properties:
-        language:
-            kind: string
-            required: true
-            description: The language of the answer.
-        answer:
-            kind: string
-            required: true
-            description: The answer text.
-        type:
-            kind: string
-            required: true
-            description: The type of the response.

agent-samples/azure/AzureOpenAIAssistants.yaml

@@ -1,25 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Assistants as the type in your response.
-model:
-    id: gpt-4o-mini
-    provider: AzureOpenAI
-    apiType: Assistants
-    options:
-        temperature: 0.9
-        topP: 0.95
-outputSchema:
-    properties:
-        language:
-            type: string
-            required: true
-            description: The language of the answer.
-        answer:
-            type: string
-            required: true
-            description: The answer text.
-        type:
-            type: string
-            required: true
-            description: The type of the response.

agent-samples/azure/AzureOpenAIChat.yaml

@@ -1,25 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response.
-model:
-    id: gpt-4o-mini
-    provider: AzureOpenAI
-    apiType: Chat
-    options:
-        temperature: 0.9
-        topP: 0.95
-outputSchema:
-    properties:
-        language:
-            type: string
-            required: true
-            description: The language of the answer.
-        answer:
-            type: string
-            required: true
-            description: The answer text.
-        type:
-            type: string
-            required: true
-            description: The type of the response.

agent-samples/azure/AzureOpenAIResponses.yaml

@@ -1,25 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Responses as the type in your response.
-model:
-    id: gpt-4o-mini
-    provider: AzureOpenAI
-    apiType: Responses
-    options:
-        temperature: 0.9
-        topP: 0.95
-outputSchema:
-    properties:
-        language:
-            type: string
-            required: true
-            description: The language of the answer.
-        answer:
-            type: string
-            required: true
-            description: The answer text.
-        type:
-            type: string
-            required: true
-            description: The type of the response.

agent-samples/chatclient/Assistant.yaml

@@ -1,18 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format.
-model:
-    options:
-        temperature: 0.9
-        topP: 0.95
-outputSchema:
-    properties:
-        language:
-            type: string
-            required: true
-            description: The language of the answer.
-        answer:
-            type: string
-            required: true
-            description: The answer text.

agent-samples/chatclient/GetWeather.yaml

@@ -1,29 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions using the tools provided.
-model:
-    options:
-        temperature: 0.9
-        topP: 0.95
-        allowMultipleToolCalls: true
-        chatToolMode: auto
-tools:
-  - kind: function
-    name: GetWeather
-    description: Get the weather for a given location.
-    bindings:
-      get_weather: get_weather
-    parameters:
-      properties:
-        location:
-          kind: string
-          description: The city and state, e.g. San Francisco, CA
-          required: true
-        unit:
-          kind: string
-          description: The unit of temperature. Possible values are 'celsius' and 'fahrenheit'.
-          required: false
-          enum:
-            - celsius
-            - fahrenheit

agent-samples/foundry/FoundryAgent.yaml

@@ -1,22 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format.
-model:
-    id: gpt-4.1-mini
-    options:
-        temperature: 0.9
-        topP: 0.95
-    connection:
-        kind: Remote
-        endpoint: =Env.AZURE_FOUNDRY_PROJECT_ENDPOINT
-outputSchema:
-    properties:
-        language:
-            type: string
-            required: true
-            description: The language of the answer.
-        answer:
-            type: string
-            required: true
-            description: The answer text.

agent-samples/foundry/MicrosoftLearnAgent.yaml

@@ -1,21 +0,0 @@
-kind: Prompt
-name: MicrosoftLearnAgent
-description: Microsoft Learn Agent
-instructions: You answer questions by searching the Microsoft Learn content only.
-model:
-    id: =Env.AZURE_FOUNDRY_PROJECT_MODEL_ID
-    options:
-        temperature: 0.9
-        topP: 0.95
-    connection:
-        kind: remote
-        endpoint: =Env.AZURE_FOUNDRY_PROJECT_ENDPOINT
-tools:
-  - kind: mcp
-    name: microsoft_learn
-    description: Get information from Microsoft Learn.
-    url: https://learn.microsoft.com/api/mcp
-    approvalMode:
-      kind: never
-    allowedTools:
-      - microsoft_docs_search

agent-samples/foundry/PersistentAgent.yaml

@@ -1,22 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format.
-model:
-    id: =Env.AZURE_FOUNDRY_PROJECT_MODEL_ID
-    options:
-        temperature: 0.9
-        topP: 0.95
-    connection:
-        kind: remote
-        endpoint: =Env.AZURE_FOUNDRY_PROJECT_ENDPOINT
-outputSchema:
-    properties:
-        language:
-            kind: string
-            required: true
-            description: The language of the answer.
-        answer:
-            kind: string
-            required: true
-            description: The answer text.

agent-samples/openai/OpenAI.yaml

@@ -1,28 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response.
-model:
-    id: =Env.OPENAI_MODEL
-    provider: OpenAI
-    apiType: Chat
-    options:
-        temperature: 0.9
-        topP: 0.95
-    connection:
-        kind: key
-        key: =Env.OPENAI_API_KEY
-outputSchema:
-    properties:
-        language:
-            kind: string
-            required: true
-            description: The language of the answer.
-        answer:
-            kind: string
-            required: true
-            description: The answer text.
-        type:
-            kind: string
-            required: true
-            description: The type of the response.

agent-samples/openai/OpenAIAssistants.yaml

@@ -1,28 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Assistants as the type in your response.
-model:
-    id: gpt-4.1-mini
-    provider: OpenAI
-    apiType: Assistants
-    options:
-        temperature: 0.9
-        topP: 0.95
-    connection:
-        kind: ApiKey
-        key: =Env.OPENAI_API_KEY
-outputSchema:
-    properties:
-        language:
-            type: string
-            required: true
-            description: The language of the answer.
-        answer:
-            type: string
-            required: true
-            description: The answer text.
-        type:
-            type: string
-            required: true
-            description: The type of the response.

agent-samples/openai/OpenAIChat.yaml

@@ -1,28 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response.
-model:
-    id: gpt-4.1-mini
-    provider: OpenAI
-    apiType: Chat
-    options:
-        temperature: 0.9
-        topP: 0.95
-    connection:
-        kind: ApiKey
-        key: =Env.OPENAI_API_KEY
-outputSchema:
-    properties:
-        language:
-            type: string
-            required: true
-            description: The language of the answer.
-        answer:
-            type: string
-            required: true
-            description: The answer text.
-        type:
-            type: string
-            required: true
-            description: The type of the response.

agent-samples/openai/OpenAIResponses.yaml

@@ -1,28 +0,0 @@
-kind: Prompt
-name: Assistant
-description: Helpful assistant
-instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Responses as the type in your response.
-model:
-    id: gpt-4.1-mini
-    provider: OpenAI
-    apiType: Responses
-    options:
-        temperature: 0.9
-        topP: 0.95
-    connection:
-        kind: key
-        apiKey: =Env.OPENAI_API_KEY
-outputSchema:
-    properties:
-        language:
-            kind: string
-            required: true
-            description: The language of the answer.
-        answer:
-            kind: string
-            required: true
-            description: The answer text.
-        type:
-            kind: string
-            required: true
-            description: The type of the response.

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/api-reference/agent/#strands.agent.agent_result.AgentResult) (Primary) with messages and a reason for the run's completion. | **Approach 2** [Streams events](https://strandsagents.com/latest/api-reference/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) |
@@ -163,8 +163,8 @@ foreach (var update in response.Messages)
 ### Option 2 Run: Container with Primary and Secondary Properties, RunStreaming: Stream of Primary + Secondary
 
 Run returns a new response type that has separate properties for the Primary Content and the Secondary Updates leading up to it.
-The Primary content is available in the `AgentResponse.Messages` property while Secondary updates are in a new `AgentResponse.Updates` property.
-`AgentResponse.Text` returns the Primary content text.
+The Primary content is available in the `AgentRunResponse.Messages` property while Secondary updates are in a new `AgentRunResponse.Updates` property.
+`AgentRunResponse.Text` returns the Primary content text.
 
 Since streaming would still need to return an `IAsyncEnumerable` of updates, the design would differ from non-streaming.
 With non-streaming Primary and Secondary content is split into separate lists, while with streaming it's combined in one stream.
@@ -232,24 +232,24 @@ await foreach (var update in responses)
 ```csharp
 class Agent
 {
-    public abstract Task<AgentResponse> RunAsync(
+    public abstract Task<AgentRunResponse> RunAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default);
 
-    public abstract IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
+    public abstract IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default);
 }
 
-class AgentResponse : ChatResponse
+class AgentRunResponse : ChatResponse
 {
 }
 
-public class AgentResponseUpdate : ChatResponseUpdate
+public class AgentRunResponseUpdate : ChatResponseUpdate
 {
 }
 ```
@@ -265,20 +265,20 @@ The new types could also exclude properties that make less sense for agents, lik
 ```csharp
 class Agent
 {
-    public abstract Task<AgentResponse> RunAsync(
+    public abstract Task<AgentRunResponse> RunAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default);
 
-    public abstract IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
+    public abstract IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default);
 }
 
-class AgentResponse // Compare with ChatResponse
+class AgentRunResponse // Compare with ChatResponse
 {
     public string Text { get; } // Aggregation of TextContent from messages.
 
@@ -294,12 +294,12 @@ class AgentResponse // Compare with ChatResponse
     public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
 }
 
-// Not Included in AgentResponse compared to ChatResponse
+// Not Included in AgentRunResponse compared to ChatResponse
 public ChatFinishReason? FinishReason { get; set; }
 public string? ConversationId { get; set; }
 public string? ModelId { get; set; }
 
-public class AgentResponseUpdate // Compare with ChatResponseUpdate
+public class AgentRunResponseUpdate // Compare with ChatResponseUpdate
 {
     public string Text { get; } // Aggregation of TextContent from Contents.
 
@@ -317,7 +317,7 @@ public class AgentResponseUpdate // Compare with ChatResponseUpdate
     public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
 }
 
-// Not Included in AgentResponseUpdate compared to ChatResponseUpdate
+// Not Included in AgentRunResponseUpdate compared to ChatResponseUpdate
 public ChatFinishReason? FinishReason { get; set; }
 public string? ConversationId { get; set; }
 public string? ModelId { get; set; }
@@ -360,7 +360,7 @@ public class ChatFinishReason
 ### Option 2: Add another property on responses for AgentRun
 
 ```csharp
-class AgentResponse
+class AgentRunResponse
 {
     ...
     public AgentRun RunReference { get; set; } // Reference to long running process
@@ -368,7 +368,7 @@ class AgentResponse
 }
 
 
-public class AgentResponseUpdate
+public class AgentRunResponseUpdate
 {
     ...
     public AgentRun RunReference { get; set; } // Reference to long running process
@@ -424,7 +424,7 @@ Note that where an agent doesn't support structured output, it may also be possi
 See [Structured Outputs Support](#structured-outputs-support) for a comparison on what other agent frameworks and protocols support.
 
 To support a good user experience for structured outputs, I'm proposing that we follow the pattern used by MEAI.
-We would add a generic version of `AgentResponse<T>`, that allows us to get the agent result already deserialized into our preferred type.
+We would add a generic version of `AgentRunResponse<T>`, that allows us to get the agent result already deserialized into our preferred type.
 This would be coupled with generic overload extension methods for Run that automatically builds a schema from the supplied type and updates
 the run options.
 
@@ -438,14 +438,14 @@ class Movie
     public int ReleaseYear { get; set; }
 }
 
-AgentResponse<Movie[]> response = agent.RunAsync<Movie[]>("What are the top 3 children's movies of the 80s.");
+AgentRunResponse<Movie[]> response = agent.RunAsync<Movie[]>("What are the top 3 children's movies of the 80s.");
 Movie[] movies = response.Result
 ```
 
 If we only support requesting a schema at agent creation time or where an agent has a built in schema, the following would be the preferred approach:
 
 ```csharp
-AgentResponse response = agent.RunAsync("What are the top 3 children's movies of the 80s.");
+AgentRunResponse response = agent.RunAsync("What are the top 3 children's movies of the 80s.");
 Movie[] movies = response.TryParseStructuredOutput<Movie[]>();
 ```
 
@@ -463,7 +463,7 @@ Option 2 chosen so that we can vary Agent responses independently of Chat Client
 ### StructuredOutputs Decision
 
 We will not support structured output per run request, but individual agents are free to allow this on the concrete implementation or at construction time.
-We will however add support for easily extracting a structured output type from the `AgentResponse`.
+We will however add support for easily extracting a structured output type from the `AgentRunResponse`.
 
 ## Addendum 1: AIContext Derived Types for different response types / Gap Analysis (Work in progress)
 
@@ -495,10 +495,10 @@ We need to decide what AIContent types, each agent response type will be mapped
 | SDK | Structured Outputs support |
 |-|-|
 | 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/) |
+| Google ADK | **Approach 1** Both [input and output shemas 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/latest/api-reference/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/api-reference/types/#strands.types.event_loop.StopReason) property on the [AgentResult](https://strandsagents.com/latest/api-reference/agent/#strands.agent.agent_result.AgentResult) 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/0005-python-naming-conventions.md

@@ -54,7 +54,7 @@ The table below represents the majority of the naming changes discussed in issue
 | *Mcp* & *Http* | *MCP* & *HTTP* | accepted | Acronyms should be uppercased in class names, according to PEP 8. | None |
 | `agent.run_streaming` | `agent.run_stream` | accepted | Shorter and more closely aligns with AutoGen and Semantic Kernel names for the same methods. | None |
 | `workflow.run_streaming` | `workflow.run_stream` | accepted | In sync with `agent.run_stream` and shorter and more closely aligns with AutoGen and Semantic Kernel names for the same methods. | None |
-| AgentResponse & AgentResponseUpdate | AgentResponse & AgentResponseUpdate | rejected | Rejected, because it is the response to a run invocation and AgentResponse is too generic. | None |
+| AgentRunResponse & AgentRunResponseUpdate | AgentResponse & AgentResponseUpdate | rejected | Rejected, because it is the response to a run invocation and AgentResponse is too generic. | None |
 | *Content | * | rejected | Rejected other content type renames (removing `Content` suffix) because it would reduce clarity and discoverability. | Item was also considered, but rejected as it is very similar to Content, but would be inconsistent with dotnet. |
 | ChatResponse & ChatResponseUpdate | Response & ResponseUpdate | rejected | Rejected, because Response is too generic. | None |
 

docs/decisions/0006-userapproval.md

@@ -161,11 +161,11 @@ while (response.ApprovalRequests.Count > 0)
     response = await agent.RunAsync(messages, thread);
 }
 
-class AgentResponse
+class AgentRunResponse
 {
     ...
 
-    // A new property on AgentResponse to aggregate the ApprovalRequestContent items from
+    // A new property on AgentRunResponse to aggregate the ApprovalRequestContent items from
     // the response messages (Similar to the Text property).
     public IEnumerable<ApprovalRequestContent> ApprovalRequests { get; set; }
 
@@ -251,11 +251,11 @@ while (response.UserInputRequests.Any())
     response = await agent.RunAsync(messages, thread);
 }
 
-class AgentResponse
+class AgentRunResponse
 {
     ...
 
-    // A new property on AgentResponse to aggregate the UserInputRequestContent items from
+    // A new property on AgentRunResponse to aggregate the UserInputRequestContent items from
     // the response messages (Similar to the Text property).
     public IReadOnlyList<UserInputRequestContent> UserInputRequests { get; set; }
 
@@ -366,11 +366,11 @@ while (response.UserInputRequests.Any())
     response = await agent.RunAsync(messages, thread);
 }
 
-class AgentResponse
+class AgentRunResponse
 {
     ...
 
-    // A new property on AgentResponse to aggregate the UserInputRequestContent items from
+    // A new property on AgentRunResponse to aggregate the UserInputRequestContent items from
     // the response messages (Similar to the Text property).
     public IEnumerable<UserInputRequestContent> UserInputRequests { get; set; }
 

docs/decisions/0007-agent-filtering-middleware.md

@@ -115,7 +115,7 @@ public class AIAgent
         }
     }
 
-    public async Task<AgentResponse> RunAsync(
+    public async Task<AgentRunResponse> RunAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -135,7 +135,7 @@ public class AIAgent
         return context.Response ?? throw new InvalidOperationException("Agent execution did not produce a response");
     }
 
-    protected abstract Task<AgentResponse> ExecuteCoreLogicAsync(
+    protected abstract Task<AgentRunResponse> ExecuteCoreLogicAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread,
         AgentRunOptions? options,
@@ -190,7 +190,7 @@ internal sealed class GuardrailCallbackAgent : DelegatingAIAgent
 
     public GuardrailCallbackAgent(AIAgent innerAgent) : base(innerAgent) { }
 
-    public override async Task<AgentResponse> RunAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    public override async Task<AgentRunResponse> RunAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
     {
         var filteredMessages = this.FilterMessages(messages);
         Console.WriteLine($"Guardrail Middleware - Filtered messages: {new ChatResponse(filteredMessages).Text}");
@@ -202,14 +202,14 @@ internal sealed class GuardrailCallbackAgent : DelegatingAIAgent
         return response;
     }
 
-    public override async IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, [EnumeratorCancellation] CancellationToken cancellationToken = default)
+    public override async IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, [EnumeratorCancellation] CancellationToken cancellationToken = default)
     {
         var filteredMessages = this.FilterMessages(messages);
         await foreach (var update in this.InnerAgent.RunStreamingAsync(filteredMessages, thread, options, cancellationToken))
         {
             if (update.Text != null)
             {
-                yield return new AgentResponseUpdate(update.Role, this.FilterContent(update.Text));
+                yield return new AgentRunResponseUpdate(update.Role, this.FilterContent(update.Text));
             }
             else
             {
@@ -252,7 +252,7 @@ internal sealed class RunningCallbackHandlerAgent : DelegatingAIAgent
         this._func = func;
     }
 
-    public override async Task<AgentResponse> RunAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    public override async Task<AgentRunResponse> RunAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
     {
         var context = new AgentInvokeCallbackContext(this, messages, thread, options, isStreaming: false, cancellationToken);
 
@@ -469,7 +469,7 @@ public sealed class CallbackEnabledAgent : DelegatingAIAgent
         this._callbacksProcessor = callbackMiddlewareProcessor ?? new();
     }
 
-    public override async Task<AgentResponse> RunAsync(
+    public override async Task<AgentRunResponse> RunAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -541,7 +541,7 @@ public abstract class AgentContext
 public class AgentRunContext : AgentContext
 {
     public IList<ChatMessage> Messages { get; set; }
-    public AgentResponse? Response { get; set; }
+    public AgentRunResponse? Response { get; set; }
     public AgentThread? Thread { get; }
 
     public AgentRunContext(AIAgent agent, IList<ChatMessage> messages, AgentThread? thread, AgentRunOptions? options)

docs/decisions/0009-support-long-running-operations.md

@@ -687,7 +687,7 @@ This section considers different options for exposing the `RunId`, `Status`, and
 #### 4.1. As AIContent
 
 The `AsyncRunContent` class will represent a long-running operation initiated and managed by an agent/LLM.
-Items of this content type will be returned in a chat message as part of the `AgentResponse` or `ChatResponse`
+Items of this content type will be returned in a chat message as part of the `AgentRunResponse` or `ChatResponse`
 response to represent the long-running operation.
 
 The `AsyncRunContent` class has two properties: `RunId` and `Status`. The `RunId` identifies the 
@@ -1162,29 +1162,29 @@ For cancellation and deletion of long-running operations, new methods will be ad
 public abstract class AIAgent
 {
     // Existing methods...
-    public Task<AgentResponse> RunAsync(string message, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default) { ... }
-    public IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(string message, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default) { ... }
+    public Task<AgentRunResponse> RunAsync(string message, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default) { ... }
+    public IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(string message, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default) { ... }
 
     // New methods for uncommon operations
-    public virtual Task<AgentResponse?> CancelRunAsync(string id, AgentCancelRunOptions? options = null, CancellationToken cancellationToken = default)
+    public virtual Task<AgentRunResponse?> CancelRunAsync(string id, AgentCancelRunOptions? options = null, CancellationToken cancellationToken = default)
     {
-        return Task.FromResult<AgentResponse?>(null);
+        return Task.FromResult<AgentRunResponse?>(null);
     }
 
-    public virtual Task<AgentResponse?> DeleteRunAsync(string id, AgentDeleteRunOptions? options = null, CancellationToken cancellationToken = default)
+    public virtual Task<AgentRunResponse?> DeleteRunAsync(string id, AgentDeleteRunOptions? options = null, CancellationToken cancellationToken = default)
     {
-        return Task.FromResult<AgentResponse?>(null);
+        return Task.FromResult<AgentRunResponse?>(null);
     }
 }
 
 // Agent that supports update and cancellation
 public class CustomAgent : AIAgent
 {
-    public override async Task<AgentResponse?> CancelRunAsync(string id, AgentCancelRunOptions? options = null, CancellationToken cancellationToken = default)
+    public override async Task<AgentRunResponse?> CancelRunAsync(string id, AgentCancelRunOptions? options = null, CancellationToken cancellationToken = default)
     {
         var response = await this._client.CancelRunAsync(id, options?.Thread?.ConversationId);
 
-        return ConvertToAgentResponse(response); 
+        return ConvertToAgentRunResponse(response); 
     }
 
     // No overload for DeleteRunAsync as it's not supported by the underlying API
@@ -1195,7 +1195,7 @@ AIAgent agent = new CustomAgent();
 
 AgentThread thread = agent.GetNewThread();
 
-AgentResponse response = await agent.RunAsync("What is the capital of France?");
+AgentRunResponse response = await agent.RunAsync("What is the capital of France?");
 
 response = await agent.CancelRunAsync(response.ResponseId, new AgentCancelRunOptions { Thread = thread });
 ```
@@ -1251,10 +1251,10 @@ public class AgentRunOptions
 AIAgent agent = ...; // Get an instance of an AIAgent
 
 // Start a long-running execution for the prompt if supported by the underlying API
-AgentResponse response = await agent.RunAsync("<prompt>", new AgentRunOptions { AllowLongRunningResponses = true });
+AgentRunResponse response = await agent.RunAsync("<prompt>", new AgentRunOptions { AllowLongRunningResponses = true });
 
 // Start a quick prompt
-AgentResponse response = await agent.RunAsync("<prompt>");
+AgentRunResponse response = await agent.RunAsync("<prompt>");
 ```
 
 **Pros:** 
@@ -1279,7 +1279,7 @@ Below are the details of the option selected for chat clients that is also selec
 #### 3.1 Continuation Token of a Custom Type
 
 This option suggests using `ContinuationToken` to encapsulate all properties representing a long-running operation. The continuation token will be returned by agents in the 
-`ContinuationToken` property of the `AgentResponse` and `AgentResponseUpdate` responses to indicate that the response is part of a long-running operation. A null value 
+`ContinuationToken` property of the `AgentRunResponse` and `AgentRunResponseUpdate` responses to indicate that the response is part of a long-running operation. A null value 
 of the property will indicate that the response is not part of a long-running operation or the long-running operation has been completed. Callers will set the token in the
 `ContinuationToken` property of the `AgentRunOptions` class in follow-up calls to the `Run{Streaming}Async` methods to indicate that they want to "continue" the long-running
 operation identified by the token.
@@ -1313,18 +1313,18 @@ public class AgentRunOptions
     public ResponseContinuationToken? ContinuationToken { get; set; }
 }
 
-public class AgentResponse
+public class AgentRunResponse
 {
     public ResponseContinuationToken? ContinuationToken { get; }
 }
  
-public class AgentResponseUpdate
+public class AgentRunResponseUpdate
 {
     public ResponseContinuationToken? ContinuationToken { get; }
 }
 
 // Usage example
-AgentResponse response = await agent.RunAsync("What is the capital of France?");
+AgentRunResponse response = await agent.RunAsync("What is the capital of France?");
 
 AgentRunOptions options = new() { ContinuationToken = response.ContinuationToken };
 

docs/decisions/0010-ag-ui-support.md

@@ -36,7 +36,7 @@ Chosen option: "Current approach with internal event types and framework-native
 
 - Protects consumers from protocol changes by keeping AG-UI events internal
 - Maintains framework abstractions through conversion at boundaries
-- Uses existing framework types (AgentResponseUpdate, ChatMessage) for public API
+- Uses existing framework types (AgentRunResponseUpdate, ChatMessage) for public API
 - Focuses on core text streaming functionality
 - Leverages existing properties (ConversationId, ResponseId, ErrorContent) instead of custom types
 - Provides bidirectional client and server support
@@ -69,7 +69,7 @@ Chosen option: "Current approach with internal event types and framework-native
 
 3. **Agent Factory Pattern** - `MapAGUIAgent` uses factory function `(messages) => AIAgent` to allow request-specific agent configuration supporting multi-tenancy
 
-4. **Bidirectional Conversion Architecture** - Symmetric conversion logic in shared namespace compiled into both packages for server (`AgentResponseUpdate` → AG-UI events) and client (AG-UI events → `AgentResponseUpdate`)
+4. **Bidirectional Conversion Architecture** - Symmetric conversion logic in shared namespace compiled into both packages for server (`AgentRunResponseUpdate` → AG-UI events) and client (AG-UI events → `AgentRunResponseUpdate`)
 
 5. **Thread Management** - `AGUIAgentThread` stores only `ThreadId` with thread ID communicated via `ConversationId`; applications manage persistence for parity with other implementations and to be compliant with the protocol. Future extensions will support having the server manage the conversation.
 

docs/decisions/0011-create-get-agent-api.md

@@ -1,368 +0,0 @@
----
-status: proposed
-contact: dmytrostruk
-date: 2025-12-12
-deciders: dmytrostruk, markwallace-microsoft, eavanvalkenburg, giles17
----
-
-# Create/Get Agent API
-
-## Context and Problem Statement
-
-There is a misalignment between the create/get agent API in the .NET and Python implementations.
-
-In .NET, the `CreateAIAgent` method can create either a local instance of an agent or a remote instance if the backend provider supports it. For remote agents, once the agent is created, you can retrieve an existing remote agent by using the `GetAIAgent` method. If a backend provider doesn't support remote agents, `CreateAIAgent` just initializes a new local agent instance and `GetAIAgent` is not available. There is also a `BuildAIAgent` method, which is an extension for the `ChatClientBuilder` class from `Microsoft.Extensions.AI`. It builds pipelines of `IChatClient` instances with an `IServiceProvider`. This functionality does not exist in Python, so `BuildAIAgent` is out of scope.
-
-In Python, there is only one `create_agent` method, which always creates a local instance of the agent. If the backend provider supports remote agents, the remote agent is created only on the first `agent.run()` invocation.
-
-Below is a short summary of different providers and their APIs in .NET:
-
-| Package | Method | Behavior | Python support |
-|---|---|---|---|
-| Microsoft.Agents.AI | `CreateAIAgent` (based on `IChatClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`create_agent` in `BaseChatClient`). |
-| Microsoft.Agents.AI.Anthropic | `CreateAIAgent` (based on `IBetaService` and `IAnthropicClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`AnthropicClient` inherits `BaseChatClient`, which exposes `create_agent`). |
-| Microsoft.Agents.AI.AzureAI (V2) | `GetAIAgent` (based on `AIProjectClient` with `AgentReference`) | Creates a local instance of `ChatClientAgent`. | Partial (Python uses `create_agent` from `BaseChatClient`). |
-| Microsoft.Agents.AI.AzureAI (V2) | `GetAIAgent`/`GetAIAgentAsync` (with `Name`/`ChatClientAgentOptions`) | Fetches `AgentRecord` via HTTP, then creates a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.AzureAI (V2) | `CreateAIAgent`/`CreateAIAgentAsync` (based on `AIProjectClient`) | Creates a remote agent first, then wraps it into a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.AzureAI.Persistent (V1) | `GetAIAgent` (based on `PersistentAgentsClient` with `PersistentAgent`) | Creates a local instance of `ChatClientAgent`. | Partial (Python uses `create_agent` from `BaseChatClient`). |
-| Microsoft.Agents.AI.AzureAI.Persistent (V1) | `GetAIAgent`/`GetAIAgentAsync` (with `AgentId`) | Fetches `PersistentAgent` via HTTP, then creates a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.AzureAI.Persistent (V1) | `CreateAIAgent`/`CreateAIAgentAsync` | Creates a remote agent first, then wraps it into a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.OpenAI | `GetAIAgent` (based on `AssistantClient` with `Assistant`) | Creates a local instance of `ChatClientAgent`. | Partial (Python uses `create_agent` from `BaseChatClient`). |
-| Microsoft.Agents.AI.OpenAI | `GetAIAgent`/`GetAIAgentAsync` (with `AgentId`) | Fetches `Assistant` via HTTP, then creates a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.OpenAI | `CreateAIAgent`/`CreateAIAgentAsync` (based on `AssistantClient`) | Creates a remote agent first, then wraps it into a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.OpenAI | `CreateAIAgent` (based on `ChatClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`create_agent` in `BaseChatClient`). |
-| Microsoft.Agents.AI.OpenAI | `CreateAIAgent` (based on `OpenAIResponseClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`create_agent` in `BaseChatClient`). |
-
-Another difference between Python and .NET implementation is that in .NET `CreateAIAgent`/`GetAIAgent` methods are implemented as extension methods based on underlying SDK client, like `AIProjectClient` from Azure AI or `AssistantClient` from OpenAI:
-
-```csharp
-// Definition
-public static ChatClientAgent CreateAIAgent(
-    this AIProjectClient aiProjectClient,
-    string name,
-    string model,
-    string instructions,
-    string? description = null,
-    IList<AITool>? tools = null,
-    Func<IChatClient, IChatClient>? clientFactory = null,
-    IServiceProvider? services = null,
-    CancellationToken cancellationToken = default)
-{ }
-
-// Usage
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential()); // Initialization of underlying SDK client
-
-var newAgent = await aiProjectClient.CreateAIAgentAsync(name: AgentName, model: deploymentName, instructions: AgentInstructions, tools: [tool]); // ChatClientAgent creation from underlying SDK client
-
-// Alternative usage (same as extension method, just explicit syntax)
-var newAgent = await AzureAIProjectChatClientExtensions.CreateAIAgentAsync(
-    aiProjectClient,
-    name: AgentName,
-    model: deploymentName,
-    instructions: AgentInstructions,
-    tools: [tool]);
-```
-
-Python doesn't support extension methods. Currently `create_agent` method is defined on `BaseChatClient`, but this method only creates a local instance of `ChatAgent` and it can't create remote agents for providers that support it for a couple of reasons:
-
-- It's defined as non-async.
-- `BaseChatClient` implementation is stateful for providers like Azure AI or OpenAI Assistants. The implementation stores agent/assistant metadata like `AgentId` and `AgentName`, so currently it's not possible to create different instances of `ChatAgent` from a single `BaseChatClient` in case if the implementation is stateful.
-
-## Decision Drivers
-
-- API should be aligned between .NET and Python.
-- API should be intuitive and consistent between backend providers in .NET and Python.
-
-## Considered Options
-
-Add missing implementations on the Python side. This should include the following:
-
-### agent-framework-azure-ai (both V1 and V2)
-
-- Add a `get_agent` method that accepts an underlying SDK agent instance and creates a local instance of `ChatAgent`.
-- Add a `get_agent` method that accepts an agent identifier, performs an additional HTTP request to fetch agent data, and then creates a local instance of `ChatAgent`.
-- Override the `create_agent` method from `BaseChatClient` to create a remote agent instance and wrap it into a local `ChatAgent`.
-
-.NET:
-
-```csharp
-var agent1 = new AIProjectClient(...).GetAIAgent(agentInstanceFromSdkType); // Creates a local ChatClientAgent instance from Azure.AI.Projects.OpenAI.AgentReference 
-var agent2 = new AIProjectClient(...).GetAIAgent(agentName); // Fetches agent data, creates a local ChatClientAgent instance
-var agent3 = new AIProjectClient(...).CreateAIAgent(...); // Creates a remote agent, returns a local ChatClientAgent instance
-```
-
-### agent-framework-core (OpenAI Assistants)
-
-- Add a `get_agent` method that accepts an underlying SDK agent instance and creates a local instance of `ChatAgent`.
-- Add a `get_agent` method that accepts an agent name, performs an additional HTTP request to fetch agent data, and then creates a local instance of `ChatAgent`.
-- Override the `create_agent` method from `BaseChatClient` to create a remote agent instance and wrap it into a local `ChatAgent`.
-
-.NET:
-
-```csharp
-var agent1 = new AssistantClient(...).GetAIAgent(agentInstanceFromSdkType); // Creates a local ChatClientAgent instance from OpenAI.Assistants.Assistant
-var agent2 = new AssistantClient(...).GetAIAgent(agentId); // Fetches agent data, creates a local ChatClientAgent instance
-var agent3 = new AssistantClient(...).CreateAIAgent(...); // Creates a remote agent, returns a local ChatClientAgent instance
-```
-
-### Possible Python implementations
-
-Methods like `create_agent` and `get_agent` should be implemented separately or defined on some stateless component that will allow to create multiple agents from the same instance/place.
-
-Possible options:
-
-#### Option 1: Module-level functions
-
-Implement free functions in the provider package that accept the underlying SDK client as the first argument (similar to .NET extension methods, but expressed in Python).
-
-Example:
-
-```python
-from agent_framework.azure import create_agent, get_agent
-
-ai_project_client = AIProjectClient(...)
-
-# Creates a remote agent first, then returns a local ChatAgent wrapper
-created_agent = await create_agent(
-    ai_project_client,
-    name="",
-    instructions="",
-    tools=[tool],
-)
-
-# Gets an existing remote agent and returns a local ChatAgent wrapper
-first_agent = await get_agent(ai_project_client, agent_id=agent_id)
-
-# Wraps an SDK agent instance (no extra HTTP call)
-second_agent = get_agent(ai_project_client, agent_reference)
-```
-
-Pros:
-
-- Naturally supports async `create_agent` / `get_agent`.
-- Supports multiple agents per SDK client.
-- Closest conceptual match to .NET extension methods while staying Pythonic.
-
-Cons:
-
-- Discoverability is lower (users need to know where the functions live).
-- Verbose when creating multiple agents (client must be passed every time):
-
-  ```python
-  agent1 = await azure_agents.create_agent(client, name="Agent1", ...)
-  agent2 = await azure_agents.create_agent(client, name="Agent2", ...)
-  ```
-
-#### Option 2: Provider object
-
-Introduce a dedicated provider type that is constructed from the underlying SDK client, and exposes async `create_agent` / `get_agent` methods.
-
-Example:
-
-```python
-from agent_framework.azure import AzureAIAgentProvider
-
-ai_project_client = AIProjectClient(...)
-provider = AzureAIAgentProvider(ai_project_client)
-
-agent = await provider.create_agent(
-    name="",
-    instructions="",
-    tools=[tool],
-)
-
-agent = await provider.get_agent(agent_id=agent_id)
-agent = provider.get_agent(agent_reference=agent_reference)
-```
-
-Pros:
-
-- High discoverability and clear grouping of related behavior.
-- Keeps SDK clients unchanged and supports multiple agents per SDK client.
-- Concise when creating multiple agents (client passed once):
-
-  ```python
-  provider = AzureAIAgentProvider(ai_project_client)
-  agent1 = await provider.create_agent(name="Agent1", ...)
-  agent2 = await provider.create_agent(name="Agent2", ...)
-  ```
-
-Cons:
-
-- Adds a new public concept/type for users to learn.
-
-#### Option 3: Inheritance (SDK client subclass)
-
-Create a subclass of the underlying SDK client and add `create_agent` / `get_agent` methods.
-
-Example:
-
-```python
-class ExtendedAIProjectClient(AIProjectClient):
-    async def create_agent(self, *, name: str, model: str, instructions: str, **kwargs) -> ChatAgent:
-        ...
-
-    async def get_agent(self, *, agent_id: str | None = None, sdk_agent=None, **kwargs) -> ChatAgent:
-        ...
-
-client = ExtendedAIProjectClient(...)
-agent = await client.create_agent(name="", instructions="")
-```
-
-Pros:
-
-- Discoverable and ergonomic call sites.
-- Mirrors the .NET “methods on the client” feeling.
-
-Cons:
-
-- Many SDK clients are not designed for inheritance; SDK upgrades can break subclasses.
-- Users must opt into subclass everywhere.
-- Typing/initialization can be tricky if the SDK client has non-trivial constructors.
-
-#### Option 4: Monkey patching
-
-Attach `create_agent` / `get_agent` methods to an SDK client class (or instance) at runtime.
-
-Example:
-
-```python
-def _create_agent(self, *, name: str, model: str, instructions: str, **kwargs) -> ChatAgent:
-    ...
-
-AIProjectClient.create_agent = _create_agent  # monkey patch
-```
-
-Pros:
-
-- Produces “extension method-like” call sites without wrappers or subclasses.
-
-Cons:
-
-- Fragile across SDK updates and difficult to type-check.
-- Surprising behavior (global side effects), potential conflicts across packages.
-- Harder to support/debug, especially in larger apps and test suites.
-
-## Decision Outcome
-
-Implement `create_agent`/`get_agent`/`as_agent` API via **Option 2: Provider object**.
-
-### Rationale
-
-| Aspect | Option 1 (Functions) | Option 2 (Provider) |
-|--------|----------------------|---------------------|
-| Multiple implementations | One package may contain V1, V2, and other agent types. Function names like `create_agent` become ambiguous - which agent type does it create? | Each provider class is explicit: `AzureAIAgentsProvider` vs `AzureAIProjectAgentProvider` |
-| Discoverability | Users must know to import specific functions from the package | IDE autocomplete on provider instance shows all available methods |
-| Client reuse | SDK client must be passed to every function call: `create_agent(client, ...)`, `get_agent(client, ...)` | SDK client passed once at construction: `provider = Provider(client)` |
-
-**Option 1 example:**
-```python
-from agent_framework.azure import create_agent, get_agent
-agent1 = await create_agent(client, name="Agent1", ...)  # Which agent type, V1 or V2?
-agent2 = await create_agent(client, name="Agent2", ...)  # Repetitive client passing
-```
-
-**Option 2 example:**
-```python
-from agent_framework.azure import AzureAIProjectAgentProvider
-provider = AzureAIProjectAgentProvider(client)  # Clear which service, client passed once
-agent1 = await provider.create_agent(name="Agent1", ...)
-agent2 = await provider.create_agent(name="Agent2", ...)
-```
-
-### Method Naming
-
-| Operation | Python | .NET | Async |
-|-----------|--------|------|-------|
-| Create on service | `create_agent()` | `CreateAIAgent()` | Yes |
-| Get from service | `get_agent(id=...)` | `GetAIAgent(agentId)` | Yes |
-| Wrap SDK object | `as_agent(reference)` | `AsAIAgent(agentInstance)` | No |
-
-The method names (`create_agent`, `get_agent`) do not explicitly mention "service" or "remote" because:
-- In Python, the provider class name explicitly identifies the service (`AzureAIAgentsProvider`, `OpenAIAssistantProvider`), making additional qualifiers in method names redundant.
-- In .NET, these are extension methods on `AIProjectClient` or `AssistantClient`, which already imply service operations.
-
-### Provider Class Naming
-
-| Package | Provider Class | SDK Client | Service |
-|---------|---------------|------------|---------|
-| `agent_framework.azure` | `AzureAIProjectAgentProvider` | `AIProjectClient` | Azure AI Agent Service, based on Responses API (V2) |
-| `agent_framework.azure` | `AzureAIAgentsProvider` | `AgentsClient` | Azure AI Agent Service (V1) |
-| `agent_framework.openai` | `OpenAIAssistantProvider` | `AsyncOpenAI` | OpenAI Assistants API |
-
-> **Note:** Azure AI naming is temporary. Final naming will be updated according to Azure AI / Microsoft Foundry renaming decisions.
-
-### Usage Examples
-
-#### Azure AI Agent Service V2 (based on Responses API)
-
-```python
-from agent_framework.azure import AzureAIProjectAgentProvider
-from azure.ai.projects import AIProjectClient
-
-client = AIProjectClient(endpoint, credential)
-provider = AzureAIProjectAgentProvider(client)
-
-# Create new agent on service
-agent = await provider.create_agent(name="MyAgent", model="gpt-4", instructions="...")
-
-# Get existing agent by name
-agent = await provider.get_agent(agent_name="MyAgent")
-
-# Wrap already-fetched SDK object (no HTTP calls)
-agent_ref = await client.agents.get("MyAgent")
-agent = provider.as_agent(agent_ref)
-```
-
-#### Azure AI Persistent Agents V1
-
-```python
-from agent_framework.azure import AzureAIAgentsProvider
-from azure.ai.agents import AgentsClient
-
-client = AgentsClient(endpoint, credential)
-provider = AzureAIAgentsProvider(client)
-
-agent = await provider.create_agent(name="MyAgent", model="gpt-4", instructions="...")
-agent = await provider.get_agent(agent_id="persistent-agent-456")
-agent = provider.as_agent(persistent_agent)
-```
-
-#### OpenAI Assistants
-
-```python
-from agent_framework.openai import OpenAIAssistantProvider
-from openai import OpenAI
-
-client = OpenAI()
-provider = OpenAIAssistantProvider(client)
-
-agent = await provider.create_agent(name="MyAssistant", model="gpt-4", instructions="...")
-agent = await provider.get_agent(assistant_id="asst_123")
-agent = provider.as_agent(assistant)
-```
-
-#### Local-Only Agents (No Provider)
-
-Current method `create_agent` (python) / `CreateAIAgent` (.NET) can be renamed to `as_agent` (python) / `AsAIAgent` (.NET) to emphasize the conversion logic rather than creation/initialization logic and to avoid collision with `create_agent` method for remote calls.
-
-```python
-from agent_framework import ChatAgent
-from agent_framework.openai import OpenAIChatClient
-
-# Convert chat client to ChatAgent (no remote service involved)
-client = OpenAIChatClient(model="gpt-4")
-agent = client.as_agent(name="LocalAgent", instructions="...") # instead of create_agent
-```
-
-### Adding New Agent Types
-
-Python:
-
-1. Create provider class in appropriate package.
-2. Implement `create_agent`, `get_agent`, `as_agent` as applicable.
-
-.NET:
-
-1. Create static class for extension methods.
-2. Implement `CreateAIAgentAsync`, `GetAIAgentAsync`, `AsAIAgent` as applicable.

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

@@ -1,129 +0,0 @@
----
-# These are optional elements. Feel free to remove any of them.
-status: proposed
-contact: eavanvalkenburg
-date: 2026-01-08
-deciders: eavanvalkenburg, markwallace-microsoft,  sphenry, alliscode, johanst, brettcannon
-consulted: taochenosu, moonbox3, dmytrostruk, giles17
----
-
-# Leveraging TypedDict and Generic Options in Python Chat Clients
-
-## Context and Problem Statement
-
-The Agent Framework Python SDK provides multiple chat client implementations for different providers (OpenAI, Anthropic, Azure AI, Bedrock, Ollama, etc.). Each provider has unique configuration options beyond the common parameters defined in `ChatOptions`. Currently, developers using these clients lack type safety and IDE autocompletion for provider-specific options, leading to runtime errors and a poor developer experience.
-
-How can we provide type-safe, discoverable options for each chat client while maintaining a consistent API across all implementations?
-
-## Decision Drivers
-
-- **Type Safety**: Developers should get compile-time/static analysis errors when using invalid options
-- **IDE Support**: Full autocompletion and inline documentation for all available options
-- **Extensibility**: Users should be able to define custom options that extend provider-specific options
-- **Consistency**: All chat clients should follow the same pattern for options handling
-- **Provider Flexibility**: Each provider can expose its unique options without affecting the common interface
-
-## Considered Options
-
-- **Option 1: Status Quo - Class `ChatOptions` with `**kwargs`**
-- **Option 2: TypedDict with Generic Type Parameters**
-
-### Option 1: Status Quo - Class `ChatOptions` with `**kwargs`
-
-The current approach uses a base `ChatOptions` Class with common parameters, and provider-specific options are passed via `**kwargs` or loosely typed dictionaries.
-
-```python
-# Current usage - no type safety for provider-specific options
-response = await client.get_response(
-    messages=messages,
-    temperature=0.7,
-    top_k=40,
-    random=42, # No validation
-)
-```
-
-**Pros:**
-- Simple implementation
-- Maximum flexibility
-
-**Cons:**
-- No type checking for provider-specific options
-- No IDE autocompletion for available options
-- Runtime errors for typos or invalid options
-- Documentation must be consulted for each provider
-
-### Option 2: TypedDict with Generic Type Parameters (Chosen)
-
-Each chat client is parameterized with a TypeVar bound to a provider-specific `TypedDict` that extends `ChatOptions`. This enables full type safety and IDE support.
-
-```python
-# Provider-specific TypedDict
-class AnthropicChatOptions(ChatOptions, total=False):
-    """Anthropic-specific chat options."""
-    top_k: int
-    thinking: ThinkingConfig
-    # ... other Anthropic-specific options
-
-# Generic chat client
-class AnthropicChatClient(ChatClientBase[TAnthropicChatOptions]):
-    ...
-
-client = AnthropicChatClient(...)
-
-# Usage with full type safety
-response = await client.get_response(
-    messages=messages,
-    options={
-        "temperature": 0.7,
-        "top_k": 40,
-        "random": 42,  # fails type checking and IDE would flag this
-    }
-)
-
-# Users can extend for custom options
-class MyAnthropicOptions(AnthropicChatOptions, total=False):
-    custom_field: str
-
-
-client = AnthropicChatClient[MyAnthropicOptions](...)
-
-# Usage of custom options with full type safety
-response = await client.get_response(
-    messages=messages,
-    options={
-        "temperature": 0.7,
-        "top_k": 40,
-        "custom_field": "value",
-    }
-)
-
-```
-
-**Pros:**
-- Full type safety with static analysis
-- IDE autocompletion for all options
-- Compile-time error detection
-- Self-documenting through type hints
-- Users can extend options for their specific needs or advances in models
-
-**Cons:**
-- More complex implementation
-- Some type: ignore comments needed for TypedDict field overrides
-- Minor: Requires TypeVar with default (Python 3.13+ or typing_extensions)
-
-> [NOTE!]
-> In .NET this is already achieved through overloads on the `GetResponseAsync` method for each provider-specific options class, e.g., `AnthropicChatOptions`, `OpenAIChatOptions`, etc. So this does not apply to .NET.
-
-### Implementation Details
-
-1. **Base Protocol**: `ChatClientProtocol[TOptions]` is generic over options type, with default set to `ChatOptions` (the new TypedDict)
-2. **Provider TypedDicts**: Each provider defines its options extending `ChatOptions`
-    They can even override fields with type=None to indicate they are not supported.
-3. **TypeVar Pattern**: `TProviderOptions = TypeVar("TProviderOptions", bound=TypedDict, default=ProviderChatOptions, contravariant=True)`
-4. **Option Translation**: Common options are kept in place,and explicitly documented in the Options class how they are used. (e.g., `user` → `metadata.user_id`) in `_prepare_options` (for Anthropic) to preserve easy use of common options.
-
-## Decision Outcome
-
-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.

docs/decisions/0013-python-get-response-simplification.md

@@ -1,258 +0,0 @@
----
-status: Accepted
-contact: eavanvalkenburg
-date: 2026-01-06
-deciders: markwallace-microsoft, dmytrostruk, taochenosu, alliscode, moonbox3, sphenry
-consulted: sergeymenshykh, rbarreto, dmytrostruk, westey-m
-informed:
----
-
-# Simplify Python Get Response API into a single method
-
-## Context and Problem Statement
-
-Currently chat clients must implement two separate methods to get responses, one for streaming and one for non-streaming. This adds complexity to the client implementations and increases the maintenance burden. This was likely done because the .NET version cannot do proper typing with a single method, in Python this is possible and this for instance is also how the OpenAI python client works, this would then also make it simpler to work with the Python version because there is only one method to learn about instead of two.
-
-## Implications of this change
-
-### Current Architecture Overview
-
-The current design has **two separate methods** at each layer:
-
-| Layer | Non-streaming | Streaming |
-|-------|---------------|-----------|
-| **Protocol** | `get_response()` → `ChatResponse` | `get_streaming_response()` → `AsyncIterable[ChatResponseUpdate]` |
-| **BaseChatClient** | `get_response()` (public) | `get_streaming_response()` (public) |
-| **Implementation** | `_inner_get_response()` (private) | `_inner_get_streaming_response()` (private) |
-
-### Key Usage Areas Identified
-
-#### 1. **ChatAgent** (_agents.py)
-- `run()` → calls `self.chat_client.get_response()`
-- `run_stream()` → calls `self.chat_client.get_streaming_response()`
-
-These are parallel methods on the agent, so consolidating the client methods would **not break** the agent API. You could keep `agent.run()` and `agent.run_stream()` unchanged while internally calling `get_response(stream=True/False)`.
-
-#### 2. **Function Invocation Decorator** (_tools.py)
-This is **the most impacted area**. Currently:
-- `_handle_function_calls_response()` decorates `get_response`
-- `_handle_function_calls_streaming_response()` decorates `get_streaming_response`
-- The `use_function_invocation` class decorator wraps **both methods separately**
-
-**Impact**: The decorator logic is almost identical (~200 lines each) with small differences:
-- Non-streaming collects response, returns it
-- Streaming yields updates, returns async iterable
-
-With a unified method, you'd need **one decorator** that:
-- Checks the `stream` parameter
-- Uses `@overload` to determine return type
-- Handles both paths with conditional logic
-- The new decorator could be applied just on the method, instead of the whole class.
-
-This would **reduce code duplication** but add complexity to a single function.
-
-#### 3. **Observability/Instrumentation** (observability.py)
-Same pattern as function invocation:
-- `_trace_get_response()` wraps `get_response`
-- `_trace_get_streaming_response()` wraps `get_streaming_response`
-- `use_instrumentation` decorator applies both
-
-**Impact**: Would need consolidation into a single tracing wrapper.
-
-#### 4. **Chat Middleware** (_middleware.py)
-The `use_chat_middleware` decorator also wraps both methods separately with similar logic.
-
-#### 5. **AG-UI Client** (_client.py)
-Wraps both methods to unwrap server function calls:
-```python
-original_get_streaming_response = chat_client.get_streaming_response
-original_get_response = chat_client.get_response
-```
-
-#### 6. **Provider Implementations** (all subpackages)
-All subclasses implement both `_inner_*` methods, except:
-- OpenAI Assistants Client (and similar clients, such as Foundry Agents V1) - it implements `_inner_get_response` by calling `_inner_get_streaming_response`
-
-### Implications of Consolidation
-
-| Aspect | Impact |
-|--------|--------|
-| **Type Safety** | Overloads work well: `@overload` with `Literal[True]` → `AsyncIterable`, `Literal[False]` → `ChatResponse`. Runtime return type based on `stream` param. |
-| **Breaking Change** | **Major breaking change** for anyone implementing custom chat clients. They'd need to update from 2 methods to 1 (or 2 inner methods to 1). |
-| **Decorator Complexity** | All 3 decorator systems (function invocation, middleware, observability) would need refactoring to handle both paths in one wrapper. |
-| **Code Reduction** | Significant reduction in _tools.py (~200 lines of near-duplicate code) and other decorators. |
-| **Samples/Tests** | Many samples call `get_streaming_response()` directly - would need updates. |
-| **Protocol Simplification** | `ChatClientProtocol` goes from 2 methods + 1 property to 1 method + 1 property. |
-
-### Recommendation
-
-The consolidation makes sense architecturally, but consider:
-
-1. **The overload pattern with `stream: bool`** works well in Python typing:
-   ```python
-   @overload
-   async def get_response(self, messages, *, stream: Literal[True] = True, ...) -> AsyncIterable[ChatResponseUpdate]: ...
-   @overload
-   async def get_response(self, messages, *, stream: Literal[False] = False, ...) -> ChatResponse: ...
-   ```
-
-2. **The decorator complexity** is the biggest concern. The current approach of separate decorators for separate methods is cleaner than conditional logic inside one wrapper.
-
-## Decision Drivers
-
-- Reduce code needed to implement a Chat Client, simplify the public API for chat clients
-- Reduce code duplication in decorators and middleware
-- Maintain type safety and clarity in method signatures
-
-## Considered Options
-
-1. Status quo: Keep separate methods for streaming and non-streaming
-2. Consolidate into a single `get_response` method with a `stream` parameter
-3. Option 2 plus merging `agent.run` and `agent.run_stream` into a single method with a `stream` parameter as well
-
-## Option 1: Status Quo
-- Good: Clear separation of streaming vs non-streaming logic
-- Good: Aligned with .NET design, although it is already `run` for Python and `RunAsync` for .NET
-- Bad: Code duplication in decorators and middleware
-- Bad: More complex client implementations
-
-## Option 2: Consolidate into Single Method
-- Good: Simplified public API for chat clients
-- Good: Reduced code duplication in decorators
-- Good: Smaller API footprint for users to get familiar with
-- Good: People using OpenAI directly already expect this pattern
-- Bad: Increased complexity in decorators and middleware
-- Bad: Less alignment with .NET design (`get_response(stream=True)` vs `GetStreamingResponseAsync`)
-
-## Option 3: Consolidate + Merge Agent and Workflow Methods
-- Good: Further simplifies agent and workflow implementation
-- Good: Single method for all chat interactions
-- Good: Smaller API footprint for users to get familiar with
-- Good: People using OpenAI directly already expect this pattern
-- Good: Workflows internally already use a single method (_run_workflow_with_tracing), so would eliminate public API duplication as well, with hardly any code changes
-- Bad: More breaking changes for agent users
-- Bad: Increased complexity in agent implementation
-- Bad: More extensive misalignment with .NET design (`run(stream=True)` vs `RunStreamingAsync` in addition to `get_response` change)
-
-## Misc
-
-Smaller questions to consider:
-- Should default be `stream=False` or `stream=True`? (Current is False)
-    - Default to `False` makes it simpler for new users, as non-streaming is easier to handle.
-    - Default to `False` aligns with existing behavior.
-    - Streaming tends to be faster, so defaulting to `True` could improve performance for common use cases.
-    - Should this differ between ChatClient, Agent and Workflows? (e.g., Agent and Workflow defaults to streaming, ChatClient to non-streaming)
-
-## Decision Outcome
-
-Chosen Option: **Option 3: Consolidate + Merge Agent and Workflow Methods**
-
-Since this is the most pythonic option and it reduces the API surface and code duplication the most, we will go with this option.
-We will keep the default of `stream=False` for all methods to maintain backward compatibility and simplicity for new users.
-
-# Appendix
-## Code Samples for Consolidated Method
-
-### Python - Option 3: Direct ChatClient + Agent with Single Method
-
-```python
-# Copyright (c) Microsoft. All rights reserved.
-
-import asyncio
-from random import randint
-from typing import Annotated
-
-from agent_framework import ChatAgent
-from agent_framework.openai import OpenAIChatClient
-from pydantic import Field
-
-
-def get_weather(
-    location: Annotated[str, Field(description="The location to get the weather for.")],
-) -> str:
-    """Get the weather for a given location."""
-    conditions = ["sunny", "cloudy", "rainy", "stormy"]
-    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
-
-
-async def main() -> None:
-    # Example 1: Direct ChatClient usage with single method
-    client = OpenAIChatClient()
-    message = "What's the weather in Amsterdam and in Paris?"
-
-    # Non-streaming usage
-    print(f"User: {message}")
-    response = await client.get_response(message, tools=get_weather)
-    print(f"Assistant: {response.text}")
-
-    # Streaming usage - same method, different parameter
-    print(f"\nUser: {message}")
-    print("Assistant: ", end="")
-    async for chunk in client.get_response(message, tools=get_weather, stream=True):
-        if chunk.text:
-            print(chunk.text, end="")
-    print("")
-
-    # Example 2: Agent usage with single method
-    agent = ChatAgent(
-        chat_client=client,
-        tools=get_weather,
-        name="WeatherAgent",
-        instructions="You are a weather assistant.",
-    )
-    thread = agent.get_new_thread()
-
-    # Non-streaming agent
-    print(f"\nUser: {message}")
-    result = await agent.run(message, thread=thread) # default would be stream=False
-    print(f"{agent.name}: {result.text}")
-
-    # Streaming agent - same method, different parameter
-    print(f"\nUser: {message}")
-    print(f"{agent.name}: ", end="")
-    async for update in agent.run(message, thread=thread, stream=True):
-        if update.text:
-            print(update.text, end="")
-    print("")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-### .NET - Current pattern for comparison
-
-```csharp
-// Copyright (c) Microsoft. All rights reserved.
-
-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";
-
-AIAgent agent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new AzureCliCredential())
-    .GetChatClient(deploymentName)
-    .CreateAIAgent(
-        instructions: "You are good at telling jokes about pirates.",
-        name: "PirateJoker");
-
-// Non-streaming: Returns a string directly
-Console.WriteLine("=== Non-streaming ===");
-string result = await agent.RunAsync("Tell me a joke about a pirate.");
-Console.WriteLine(result);
-
-// Streaming: Returns IAsyncEnumerable<AgentUpdate>
-Console.WriteLine("\n=== Streaming ===");
-await foreach (AgentUpdate update in agent.RunStreamingAsync("Tell me a joke about a pirate."))
-{
-    Console.Write(update);
-}
-Console.WriteLine();
-
-```

docs/decisions/0014-feature-collections.md

@@ -1,423 +0,0 @@
----
-status: accepted
-contact: westey-m
-date: 2025-01-21
-deciders: sergeymenshykh, markwallace, rbarreto, westey-m, stephentoub
-consulted: reubenbond
-informed:
----
-
-# Feature Collections
-
-## Context and Problem Statement
-
-When using agents, we often have cases where we want to pass some arbitrary services or data to an agent or some component in the agent execution stack.
-These services or data are not necessarily known at compile time and can vary by the agent stack that the user has built.
-E.g., there may be an agent decorator or chat client decorator that was added to the stack by the user, and an arbitrary payload needs to be passed to that decorator.
-
-Since these payloads are related to components that are not integral parts of the agent framework, they cannot be added as strongly typed settings to the agent run options.
-However, the payloads could be added to the agent run options as loosely typed 'features', that can be retrieved as needed.
-
-In some cases certain classes of agents may support the same capability, but not all agents do.
-Having the configuration for such a capability on the main abstraction would advertise the functionality to all users, even if their chosen agent does not support it.
-The user may type test for certain agent types, and call overloads on the appropriate agent types, with the strongly typed configuration.
-Having a feature collection though, would be an alternative way of passing such configuration, without needing to type check the agent type.
-All agents that support the functionality would be able to check for the configuration and use it, simplifying the user code.
-If the agent does not support the capability, that configuration would be ignored.
-
-### Sample Scenario 1 - Per Run ChatMessageStore Override for hosting Libraries
-
-We are building an agent hosting library, that can host any agent built using the agent framework.
-Where an agent is not built on a service that uses in-service chat history storage, the hosting library wants to force the agent to use
-the hosting library's chat history storage implementation.
-This chat history storage implementation may be specifically tailored to the type of protocol that the hosting library uses, e.g. conversation id based storage or response id based storage.
-The hosting library does not know what type of agent it is hosting, so it cannot provide a strongly typed parameter on the agent.
-Instead, it adds the chat history storage implementation to a feature collection, and if the agent supports custom chat history storage, it retrieves the implementation from the feature collection and uses it.
-
-```csharp
-// Pseudo-code for an agent hosting library that supports conversation id based hosting.
-public async Task<string> HandleConversationsBasedRequestAsync(AIAgent agent, string conversationId, string userInput)
-{
-    var thread = await this._threadStore.GetOrCreateThread(conversationId);
-
-    // The hosting library can set a per-run chat message store via Features that only applies for that run.
-    // This message store will load and save messages under the conversation id provided.
-    ConversationsChatMessageStore messageStore = new(this._dbClient, conversationId);
-    var response = await agent.RunAsync(
-        userInput,
-        thread,
-        options: new AgentRunOptions()
-        {
-            Features = new AgentFeatureCollection().WithFeature<ChatMessageStore>(messageStore)
-        });
-
-    await this._threadStore.SaveThreadAsync(conversationId, thread);
-    return response.Text;
-}
-
-// Pseudo-code for an agent hosting library that supports response id based hosting.
-public async Task<(string responseMessage, string responseId)> HandleResponseIdBasedRequestAsync(AIAgent agent, string previousResponseId, string userInput)
-{
-    var thread = await this._threadStore.GetOrCreateThreadAsync(previousResponseId);
-
-    // The hosting library can set a per-run chat message store via Features that only applies for that run.
-    // This message store will buffer newly added messages until explicitly saved after the run.
-    ResponsesChatMessageStore messageStore = new(this._dbClient, previousResponseId);
-
-    var response = await agent.RunAsync(
-        userInput,
-        thread,
-        options: new AgentRunOptions()
-        {
-            Features = new AgentFeatureCollection().WithFeature<ChatMessageStore>(messageStore)
-        });
-
-    // Since the message store may not actually have been used at all (if the agent's underlying chat client requires service-based chat history storage),
-    // we may not have anything to save back to the database.
-    // We still want to generate a new response id though, so that we can save the updated thread state under that id.
-    // We should also use the same id to save any buffered messages in the message store if there are any.
-    var newResponseId = this.GenerateResponseId();
-    if (messageStore.HasBufferedMessages)
-    {
-        await messageStore.SaveBufferedMessagesAsync(newResponseId);
-    }
-    
-    // Save the updated thread state under the new response id that was generated by the store.
-    await this._threadStore.SaveThreadAsync(newResponseId, thread);
-    return (response.Text, newResponseId);
-}
-```
-
-### Sample Scenario 2 - Structured output
-
-Currently our base abstraction does not support structured output, since the capability is not supported by all agents.
-For those agents that don't support structured output, we could add an agent decorator that takes the response from the underlying agent, and applies structured output parsing on top of it via an additional LLM call.
-
-If we add structured output configuration as a feature, then any agent that supports structured output could retrieve the configuration from the feature collection and apply it, and where it is not supported, the configuration would simply be ignored.
-
-We could add a simple StructuredOutputAgentFeature that can be added to the list of features and also be used to return the generated structured output.
-
-```csharp
-internal class StructuredOutputAgentFeature
-{
-    public Type? OutputType { get; set; }
-
-    public JsonSerializerOptions? SerializerOptions { get; set; }
-
-    public bool? UseJsonSchemaResponseFormat { get; set; }
-
-    // Contains the result of the structured output parsing request.
-    public ChatResponse? ChatResponse { get; set; }
-}
-```
-
-We can add a simple decorator class that does the chat client invocation.
-
-```csharp
-public class StructuredOutputAgent : DelegatingAIAgent
-{
-    private readonly IChatClient _chatClient;
-    public StructuredOutputAgent(AIAgent innerAgent, IChatClient chatClient)
-        : base(innerAgent)
-    {
-        this._chatClient = Throw.IfNull(chatClient);
-    }
-
-    public override async Task<AgentRunResponse> RunAsync(
-        IEnumerable<ChatMessage> messages,
-        AgentThread? thread = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        // Run the inner agent first, to get back the text response we want to convert.
-        var response = await base.RunAsync(messages, thread, options, cancellationToken).ConfigureAwait(false);
-
-        if (options?.Features?.TryGet<StructuredOutputAgentFeature>(out var responseFormatFeature) is true
-            && responseFormatFeature.OutputType is not null)
-        {
-            // Create the chat options to request structured output.
-            ChatOptions chatOptions = new()
-            {
-                ResponseFormat = ChatResponseFormat.ForJsonSchema(responseFormatFeature.OutputType, responseFormatFeature.SerializerOptions)
-            };
-
-            // Invoke the chat client to transform the text output into structured data.
-            // The feature is updated with the result.
-            // The code can be simplified by adding a non-generic structured output GetResponseAsync
-            // overload that takes Type as input.
-            responseFormatFeature.ChatResponse = await this._chatClient.GetResponseAsync(
-                messages: new[]
-                {
-                    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, response.Text)
-                },
-                options: chatOptions,
-                cancellationToken: cancellationToken).ConfigureAwait(false);
-        }
-
-        return response;
-    }
-}
-```
-
-Finally, we can add an extension method on `AIAgent` that can add the feature to the run options and check the feature for the structured output result and add the deserialized result to the response.
-
-```csharp
-public static async Task<AgentRunResponse<T>> RunAsync<T>(
-    this AIAgent agent,
-    IEnumerable<ChatMessage> messages,
-    AgentThread? thread = null,
-    JsonSerializerOptions? serializerOptions = null,
-    AgentRunOptions? options = null,
-    bool? useJsonSchemaResponseFormat = null,
-    CancellationToken cancellationToken = default)
-{
-    // Create the structured output feature.
-    var structuredOutputFeature = new StructuredOutputAgentFeature();
-    structuredOutputFeature.OutputType = typeof(T);
-    structuredOutputFeature.UseJsonSchemaResponseFormat = useJsonSchemaResponseFormat;
-
-    // Run the agent.
-    options ??= new AgentRunOptions();
-    options.Features ??= new AgentFeatureCollection();
-    options.Features.Set(structuredOutputFeature);
-
-    var response = await agent.RunAsync(messages, thread, options, cancellationToken).ConfigureAwait(false);
-
-    // Deserialize the JSON output.
-    if (structuredOutputFeature.ChatResponse is not null)
-    {
-        var typed = new ChatResponse<T>(structuredOutputFeature.ChatResponse, serializerOptions ?? AgentJsonUtilities.DefaultOptions);
-        return new AgentRunResponse<T>(response, typed.Result);
-    }
-
-    throw new InvalidOperationException("No structured output response was generated by the agent.");
-}
-```
-
-We can then use the extension method with any agent that supports structured output or that has
-been decorated with the `StructuredOutputAgent` decorator.
-
-```csharp
-agent = new StructuredOutputAgent(agent, chatClient);
-
-AgentRunResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>([new ChatMessage(
-    ChatRole.User,
-    "Please provide information about John Smith, who is a 35-year-old software engineer.")]);
-```
-
-## Implementation Options
-
-Three options were considered for implementing feature collections:
-
-- **Option 1**: FeatureCollections similar to ASP.NET Core
-- **Option 2**: AdditionalProperties Dictionary
-- **Option 3**: IServiceProvider
-
-Here are some comparisons about their suitability for our use case:
-
-| Criteria         | Feature Collection | Additional Properties | IServiceProvider |
-|------------------|--------------------|-----------------------|------------------|
-|Ease of use       |✅ Good             |❌ Bad                |✅ Good           |
-|User familiarity  |❌ Bad              |✅ Good               |✅ Good           |
-|Type safety       |✅ Good             |❌ Bad                |✅ Good           |
-|Ability to modify registered options when progressing down the stack|✅ Supported|✅ Supported|❌ Not-Supported (IServiceProvider is read-only)|
-|Already available in MEAI stack|❌ No|✅ Yes|❌ No|
-|Ambiguity with existing AdditionalProperties|❌ Yes|✅ No|❌ Yes|
-
-## IServiceProvider
-
-Service Collections and Service Providers provide a very popular way to register and retrieve services by type and could be used as a way to pass features to agents and chat clients.
-
-However, since IServiceProvider is read-only, it is not possible to modify the registered services when progressing down the execution stack.
-E.g. an agent decorator cannot add additional services to the IServiceProvider passed to it when calling into the inner agent.
-
-IServiceProvider also does not expose a way to list all services contained in it, making it difficult to copy services from one provider to another.
-
-This lack of mutability makes IServiceProvider unsuitable for our use case, since we will not be able to use it to build sample scenario 2.
-
-## AdditionalProperties dictionary
-
-The AdditionalProperties dictionary is already available on various options classes in the agent framework as well as in the MEAI stack and
-allows storing arbitrary key/value pairs, where the key is a string and the value is an object.
-
-While FeatureCollection uses Type as a key, AdditionalProperties uses string keys.
-This means that users need to agree on string keys to use for specific features, however it is also possible to use Type.FullName as a key by convention
-to avoid key collisions, which is an easy convention to follow.
-
-Since the value of AdditionalProperties is of type object, users need to cast the value to the expected type when retrieving it, which is also
-a drawback, but when using the convention of using Type.FullName as a key, there is at least a clear expectation of what type to cast to.
-
-```csharp
-// Setting a feature
-options.AdditionalProperties[typeof(MyFeature).FullName] = new MyFeature();
-
-// Retrieving a feature
-if (options.AdditionalProperties.TryGetValue(typeof(MyFeature).FullName, out var featureObj)
-    && featureObj is MyFeature myFeature)
-{
-    // Use myFeature
-}
-```
-
-It would also be possible to add extension methods to simplify setting and getting features from AdditionalProperties.
-Having a base class for features should help make this more feature rich.
-
-```csharp
-// Setting a feature, this can use Type.FullName as the key.
-options.AdditionalProperties
-    .WithFeature(new MyFeature());
-
-// Retrieving a feature, this can use Type.FullName as the key.
-if (options.AdditionalProperties.TryGetFeature<MyFeature>(out var myFeature))
-{
-    // Use myFeature
-}
-```
-
-It would also be possible to add extension methods for a feature to simplify setting and getting features from AdditionalProperties.
-
-```csharp
-// Setting a feature
-options.AdditionalProperties
-    .WithMyFeature(new MyFeature());
-// Retrieving a feature
-if (options.AdditionalProperties.TryGetMyFeature(out var myFeature))
-{
-    // Use myFeature
-}
-```
-
-## Feature Collection
-
-If we choose the feature collection option, we need to decide on the design of the feature collection itself.
-
-### Feature Collections extension points
-
-We need to decide the set of actions that feature collections would be supported for. Here is the suggested list of actions:
-
-**MAAI.AIAgent:**
-
-1. GetNewThread
-    1. E.g. this would allow passing an already existing storage id for the thread to use, or an initialized custom chat message store to use.
-1. DeserializeThread
-    1. E.g. this would allow passing an already existing storage id for the thread to use, or an initialized custom chat message store to use.
-1. Run / RunStreaming
-    1. E.g. this would allow passing an override chat message store just for that run, or a desired schema for a structured output middleware component.
-
-**MEAI.ChatClient:**
-
-1. GetResponse / GetStreamingResponse
-
-### Reconciling with existing AdditionalProperties
-
-If we decide to add feature collections, separately from the existing AdditionalProperties dictionaries, we need to consider how to explain to users when to use each one.
-One possible approach though is to have the one use the other under the hood.
-AdditionalProperties could be stored as a feature in the feature collection.
-
-Users would be able to retrieve additional properties from the feature collection, in addition to retrieving it via a dedicated AdditionalProperties property.
-E.g. `features.Get<AdditionalPropertiesDictionary>()`
-
-One challenge with this approach is that when setting a value in the AdditionalProperties dictionary, the feature collection would need to be created first if it does not already exist.
-
-```csharp
-public class AgentRunOptions
-{
-    public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
-    public IAgentFeatureCollection? Features { get; set; }
-}
-
-var options = new AgentRunOptions();
-// This would need to create the feature collection first, if it does not already exist.
-options.AdditionalProperties ??= new AdditionalPropertiesDictionary();
-```
-
-Since IAgentFeatureCollection is an interface, AgentRunOptions would need to have a concrete implementation of the interface to create, meaning that the user cannot decide.
-It also means that if the user doesn't realise that AdditionalProperties is implemented using feature collections, they may set a value on AdditionalProperties, and then later overwrite the entire feature collection, losing the AdditionalProperties feature.
-
-Options to avoid these issues:
-
-1. Make `Features` readonly.
-    1. This would prevent the user from overwriting the feature collection after setting AdditionalProperties.
-    1. Since the user cannot set their own implementation of IAgentFeatureCollection, having an interface for it may not be necessary.
-
-### Feature Collection Implementation
-
-We have two options for implementing feature collections:
-
-1. Create our own [IAgentFeatureCollection interface](https://github.com/microsoft/agent-framework/pull/2354/files#diff-9c42f3e60d70a791af9841d9214e038c6de3eebfc10e3997cb4cdffeb2f1246d) and [implementation](https://github.com/microsoft/agent-framework/pull/2354/files#diff-a435cc738baec500b8799f7f58c1538e3bb06c772a208afc2615ff90ada3f4ca).
-2. Reuse the asp.net [IFeatureCollection interface](https://github.com/dotnet/aspnetcore/blob/main/src/Extensions/Features/src/IFeatureCollection.cs) and [implementation](https://github.com/dotnet/aspnetcore/blob/main/src/Extensions/Features/src/FeatureCollection.cs).
-
-#### Roll our own
-
-Advantages:
-
-Creating our own IAgentFeatureCollection interface and implementation has the advantage of being more clearly associated with the agent framework and allows us to
-improve on some of the design decisions made in asp.net core's IFeatureCollection.
-
-Drawbacks:
-
-It would mean a different implementation to maintain and test.
-
-#### Reuse asp.net IFeatureCollection
-
-Advantages:
-
-Reusing the asp.net IFeatureCollection has the advantage of being able to reuse the well-established and tested implementation from asp.net
-core. Users who are using agents in an asp.net core application may be able to pass feature collections from asp.net core to the agent framework directly.
-
-Drawbacks:
-
-While the package name is `Microsoft.Extensions.Features`, the namespaces of the types are `Microsoft.AspNetCore.Http.Features`, which may create confusion for users of agent framework who are not building web applications or services.
-Users may rightly ask: Why do I need to use a class from asp.net core when I'm not building a web application / service?
-
-The current design has some design issues that would be good to avoid.  E.g. it does not distinguish between a feature being "not set" and "null". Get returns both as null and there is no tryget method.
-Since the [default implementation](https://github.com/dotnet/aspnetcore/blob/main/src/Extensions/Features/src/FeatureCollection.cs) also supports value types, it throws for null values of value types.
-A TryGet method would be more appropriate.
-
-## Feature Layering
-
-One possible scenario when adding support for feature collections is to allow layering of features by scope.
-
-The following levels of scope could be supported:
-
-1. Application - Application wide features that apply to all agents / chat clients
-2. Artifact (Agent / ChatClient) - Features that apply to all runs of a specific agent or chat client instance
-3. Action (GetNewThread / Run / GetResponse) - Feature that apply to a single action only
-
-When retrieving a feature from the collection, the search would start from the most specific scope (Action) and progress to the least specific scope (Application), returning the first matching feature found.
-
-Introducing layering adds some challenges:
-
-- There may be multiple feature collections at the same scope level, e.g. an Agent that uses a ChatClient where both have their own feature collections.
-  - Do we layer the agent feature collection over the chat client feature collection (Application -> ChatClient -> Agent -> Run), or only use the agent feature collection in the agent (Application -> Agent -> Run), and the chat client feature collection in the chat client (Application -> ChatClient -> Run)?
-- The appropriate base feature collection may change when progressing down the stack, e.g. when an Agent calls a ChatClient, the action feature collection stays the same, but the artifact feature collection changes.
-- Who creates the feature collection hierarchy?
-  - Since the hierarchy changes as it progresses down the execution stack, and the caller can only pass in the action level feature collection, the callee needs to combine it with its own artifact level feature collection and the application level feature collection. Each action will need to build the appropriate feature collection hierarchy, at the start of its execution.
-- For Artifact level features, it seems odd to pass them in as a bag of untyped features, when we are constructing a known artifact type and therefore can have typed settings.
-  - E.g. today we have a strongly typed setting on ChatClientAgentOptions to configure a ChatMessageStore for the agent.
-- To avoid global statics for application level features, the user would need to pass in the application level feature collection to each artifact that they create.
-  - This would be very odd if the user also already has to strongly typed settings for each feature that they want to set at the artifact level.
-
-### Layering Options
-
-1. No layering - only a single feature collection is supported per action (the caller can still create a layered collection if desired, but the callee does not do any layering automatically).
-    1. Fallback is to any features configured on the artifact via strongly typed settings.
-1. Full layering - support layering at all levels (Application -> Artifact -> Action).
-    1. Only apply applicable artifact level features when calling into that artifact.
-    1. Apply upstream artifact features when calling into downstream artifacts, e.g. Feature hierarchy in ChatClientAgent would be `Application -> Agent -> Run` and in ChatClient would be `Application -> ChatClient -> Agent -> Run` or `Application -> Agent -> ChatClient -> Run`
-    1. The user needs to provide the application level feature collection to each artifact that they create and artifact features are passed via strongly typed settings.
-
-### Accessing application level features Options
-
-We need to consider how application level features would be accessed if supported.
-
-1. The user provides the application level feature collection to each artifact that the user constructs
-    1. Passing the application level feature collection to each artifact is tedious for the user.
-1. There is a static application level feature collection that can be accessed globally.
-    1. Statics create issues with testing and isolation.
-
-## Decisions
-
-- Feature Collections Container: Use AdditionalProperties
-- Feature Layering: No layering - only a single collection/dictionary is supported per action. Application layers can be added later if needed.

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-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_azure_ai 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/durable-agents/durable-agents-ttl.md

@@ -1,147 +0,0 @@
-# Time-To-Live (TTL) for durable agent sessions
-
-## Overview
-
-The durable agents automatically maintain conversation history and state for each session. Without automatic cleanup, this state can accumulate indefinitely, consuming storage resources and increasing costs. The Time-To-Live (TTL) feature provides automatic cleanup of idle agent sessions, ensuring that sessions are automatically deleted after a period of inactivity.
-
-## What is TTL?
-
-Time-To-Live (TTL) is a configurable duration that determines how long an agent session state will be retained after its last interaction. When an agent session is idle (no messages sent to it) for longer than the TTL period, the session state is automatically deleted. Each new interaction with an agent resets the TTL timer, extending the session's lifetime.
-
-## Benefits
-
-- **Automatic cleanup**: No manual intervention required to clean up idle agent sessions
-- **Cost optimization**: Reduces storage costs by automatically removing unused session state
-- **Resource management**: Prevents unbounded growth of agent session state in storage
-- **Configurable**: Set TTL globally or per-agent type to match your application's needs
-
-## Configuration
-
-TTL can be configured at two levels:
-
-1. **Global default TTL**: Applies to all agent sessions unless overridden
-2. **Per-agent type TTL**: Overrides the global default for specific agent types
-
-Additionally, you can configure a **minimum deletion delay** that controls how frequently deletion operations are scheduled. The default value is 5 minutes, and the maximum allowed value is also 5 minutes.
-
-> [!NOTE]
-> Reducing the minimum deletion delay below 5 minutes can be useful for testing or for ensuring rapid cleanup of short-lived agent sessions. However, this can also increase the load on the system and should be used with caution.
-
-### Default values
-
-- **Default TTL**: 14 days
-- **Minimum TTL deletion delay**: 5 minutes (maximum allowed value, subject to change in future releases)
-
-### Configuration examples
-
-#### .NET
-
-```csharp
-// Configure global default TTL and minimum signal delay
-services.ConfigureDurableAgents(
-    options =>
-    {
-        // Set global default TTL to 7 days
-        options.DefaultTimeToLive = TimeSpan.FromDays(7);
-
-        // Add agents (will use global default TTL)
-        options.AddAIAgent(myAgent);
-    });
-
-// Configure per-agent TTL
-services.ConfigureDurableAgents(
-    options =>
-    {
-        options.DefaultTimeToLive = TimeSpan.FromDays(14); // Global default
-        
-        // Agent with custom TTL of 1 day
-        options.AddAIAgent(shortLivedAgent, timeToLive: TimeSpan.FromDays(1));
-        
-        // Agent with custom TTL of 90 days
-        options.AddAIAgent(longLivedAgent, timeToLive: TimeSpan.FromDays(90));
-        
-        // Agent using global default (14 days)
-        options.AddAIAgent(defaultAgent);
-    });
-
-// Disable TTL for specific agents by setting TTL to null
-services.ConfigureDurableAgents(
-    options =>
-    {
-        options.DefaultTimeToLive = TimeSpan.FromDays(14);
-        
-        // Agent with no TTL (never expires)
-        options.AddAIAgent(permanentAgent, timeToLive: null);
-    });
-```
-
-## How TTL works
-
-The following sections describe how TTL works in detail.
-
-### Expiration tracking
-
-Each agent session maintains an expiration timestamp in its internally managed state that is updated whenever the session processes a message:
-
-1. When a message is sent to an agent session, the expiration time is set to `current time + TTL`
-2. The runtime schedules a delete operation for the expiration time (subject to minimum delay constraints)
-3. When the delete operation runs, if the current time is past the expiration time, the session state is deleted. Otherwise, the delete operation is rescheduled for the next expiration time.
-
-### State deletion
-
-When an agent session expires, its entire state is deleted, including:
-
-- Conversation history
-- Any custom state data
-- Expiration timestamps
-
-After deletion, if a message is sent to the same agent session, a new session is created with a fresh conversation history.
-
-## Behavior examples
-
-The following examples illustrate how TTL works in different scenarios.
-
-### Example 1: Agent session expires after TTL
-
-1. Agent configured with 30-day TTL
-2. User sends message at Day 0 → agent session created, expiration set to Day 30
-3. No further messages sent
-4. At Day 30 → Agent session is deleted
-5. User sends message at Day 31 → New agent session created with fresh conversation history
-
-### Example 2: TTL reset on interaction
-
-1. Agent configured with 30-day TTL
-2. User sends message at Day 0 → agent session created, expiration set to Day 30
-3. User sends message at Day 15 → Expiration reset to Day 45
-4. User sends message at Day 40 → Expiration reset to Day 70
-5. Agent session remains active as long as there are regular interactions
-
-## Logging
-
-The TTL feature includes comprehensive logging to track state changes:
-
-- **Expiration time updated**: Logged when TTL expiration time is set or updated
-- **Deletion scheduled**: Logged when a deletion check signal is scheduled
-- **Deletion check**: Logged when a deletion check operation runs
-- **Session expired**: Logged when an agent session is deleted due to expiration
-- **TTL rescheduled**: Logged when a deletion signal is rescheduled
-
-These logs help monitor TTL behavior and troubleshoot any issues.
-
-## Best practices
-
-1. **Choose appropriate TTL values**: Balance between storage costs and user experience. Too short TTLs may delete active sessions, while too long TTLs may accumulate unnecessary state.
-
-2. **Use per-agent TTLs**: Different agents may have different usage patterns. Configure TTLs per-agent based on expected session lifetimes.
-
-3. **Monitor expiration logs**: Review logs to understand TTL behavior and adjust configuration as needed.
-
-4. **Test with short TTLs**: During development, use short TTLs (e.g., minutes) to verify TTL behavior without waiting for long periods.
-
-## Limitations
-
-- TTL is based on wall-clock time, not activity time. The expiration timer starts from the last message timestamp.
-- Deletion checks are durably scheduled operations and may have slight delays depending on system load.
-- Once an agent session is deleted, its conversation history cannot be recovered.
-- TTL deletion requires at least one worker to be available to process the deletion operation message.

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.

docs/specs/001-foundry-sdk-alignment.md

@@ -125,7 +125,7 @@ The proposed solution is to add helper methods which allow developers to either
 - [Foundry SDK] Create a `PersistentAgentsClient`
 - [Foundry SDK] Create a `PersistentAgent` using the `PersistentAgentsClient`
 - [Foundry SDK] Retrieve an `AIAgent` using the `PersistentAgentsClient`
-- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentResponse` 
+- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentRunResponse` 
 - [Foundry SDK] Clean up the agent
 
 
@@ -156,7 +156,7 @@ await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
 
 - [Foundry SDK] Create a `PersistentAgentsClient`
 - [Foundry SDK] Create a `AIAgent` using the `PersistentAgentsClient`
-- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentResponse` 
+- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentRunResponse` 
 - [Foundry SDK] Clean up the agent
 
 ```csharp
@@ -184,7 +184,7 @@ await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
 - [Foundry SDK] Create a `PersistentAgentsClient`
 - [Foundry SDK] Create a `AIAgent` using the `PersistentAgentsClient`
 - [Agent Framework SDK] Optionally create an `AgentThread` for the agent run
-- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentResponse` 
+- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentRunResponse` 
 - [Foundry SDK] Clean up the agent and the agent thread
 
 ```csharp
@@ -227,7 +227,7 @@ await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
 - [Foundry SDK] Create a `PersistentAgentsClient`
 - [Foundry SDK] Create multiple `AIAgent` instances using the `PersistentAgentsClient`
 - [Agent Framework SDK] Create a `SequentialOrchestration` and add all of the agents to it
-- [Agent Framework SDK] Invoke the `SequentialOrchestration` instance and access response from the `AgentResponse` 
+- [Agent Framework SDK] Invoke the `SequentialOrchestration` instance and access response from the `AgentRunResponse` 
 - [Foundry SDK] Clean up the agents
 
 ```csharp
@@ -281,7 +281,7 @@ SequentialOrchestration orchestration =
 // Run the orchestration
 string input = "An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours";
 Console.WriteLine($"\n# INPUT: {input}\n");
-AgentResponse result = await orchestration.RunAsync(input);
+AgentRunResponse result = await orchestration.RunAsync(input);
 Console.WriteLine($"\n# RESULT: {result}");
 
 // Cleanup

dotnet/.editorconfig

@@ -209,7 +209,6 @@ dotnet_diagnostic.CA2000.severity = none # Call System.IDisposable.Dispose on ob
 dotnet_diagnostic.CA2225.severity = none # Operator overloads have named alternates
 dotnet_diagnostic.CA2227.severity = none # Change to be read-only by removing the property setter
 dotnet_diagnostic.CA2249.severity = suggestion # Consider using 'Contains' method instead of 'IndexOf' method
-dotnet_diagnostic.CA2252.severity = none # Requires preview
 dotnet_diagnostic.CA2253.severity = none # Named placeholders in the logging message template should not be comprised of only numeric characters
 dotnet_diagnostic.CA2253.severity = none # Named placeholders in the logging message template should not be comprised of only numeric characters
 dotnet_diagnostic.CA2263.severity = suggestion # Use generic overload

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

@@ -3,14 +3,17 @@
     <!-- Default properties inherited by all projects. Projects can override. -->
     <RunAnalyzersDuringBuild>true</RunAnalyzersDuringBuild>
     <EnableNETAnalyzers>true</EnableNETAnalyzers>
-    <AnalysisLevel>10.0-all</AnalysisLevel>
+    <AnalysisMode>AllEnabledByDefault</AnalysisMode>
+    <AnalysisLevel>latest</AnalysisLevel>
     <GenerateDocumentationFile>true</GenerateDocumentationFile>
-    <LangVersion>latest</LangVersion>
+    <LangVersion>13</LangVersion>
     <Nullable>enable</Nullable>
-    <NoWarn>$(NoWarn);NU5128;CS8002</NoWarn>
+    <NoWarn>$(NoWarn);NU5128</NoWarn>
     <TreatWarningsAsErrors>true</TreatWarningsAsErrors>
-    <TargetFrameworksCore>net10.0;net9.0;net8.0</TargetFrameworksCore>
-    <TargetFrameworks>$(TargetFrameworksCore);netstandard2.0;net472</TargetFrameworks>
+    <ProjectsCoreTargetFrameworks>net9.0;net8.0</ProjectsCoreTargetFrameworks>
+    <ProjectsDebugCoreTargetFrameworks>net9.0</ProjectsDebugCoreTargetFrameworks>
+    <ProjectsTargetFrameworks>net9.0;net8.0;netstandard2.0;net472</ProjectsTargetFrameworks>
+    <ProjectsDebugTargetFrameworks>net9.0;net472</ProjectsDebugTargetFrameworks>
     <IsAotCompatible Condition="$([MSBuild]::IsTargetFrameworkCompatible('$(TargetFramework)', 'net7.0'))">true</IsAotCompatible>
     <Configurations>Debug;Release;Publish</Configurations>
   </PropertyGroup>

dotnet/Directory.Build.targets

@@ -5,7 +5,7 @@
   <Sdk Name="Microsoft.Build.CentralPackageVersions" Version="2.1.3" />
   <!-- Only run 'dotnet format' on dev machines, Release builds. Skip on GitHub Actions -->
   <!-- as this runs in its own Actions job. -->
-  <Target Name="DotnetFormatOnBuild" BeforeTargets="Build" Condition=" '$(Configuration)' == 'Release' AND '$(GITHUB_ACTIONS)' == '' ">
+  <Target Name="DotnetFormatOnBuild" BeforeTargets="Build" Condition=" '$(Configuration)' == 'Release' AND '$(GITHUB_ACTIONS)' == '' AND '$(TargetFramework)' == '$(ProjectsDebugTargetFrameworks)'">
     <Message Text="Running dotnet format" Importance="high" />
     <Exec Command="dotnet format --no-restore -v diag $(ProjectFileName)" />
   </Target>

dotnet/Directory.Packages.props

@@ -7,46 +7,34 @@
   </PropertyGroup>
   <PropertyGroup>
     <!-- Aspire -->
-    <AspireAppHostSdkVersion>13.0.2</AspireAppHostSdkVersion>
+    <AspireAppHostSdkVersion>13.0.0</AspireAppHostSdkVersion>
   </PropertyGroup>
   <ItemGroup>
     <!-- Aspire.* -->
-    <PackageVersion Include="Anthropic" Version="12.8.0" />
-    <PackageVersion Include="Anthropic.Foundry" Version="0.4.2" />
     <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" />
+    <PackageVersion Include="CommunityToolkit.Aspire.OllamaSharp" Version="13.0.0-beta.435" />
     <!-- 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.Agents.Persistent" Version="1.2.0-beta.7" />
+    <PackageVersion Include="Azure.AI.OpenAI" Version="2.5.0-beta.1" />
+    <PackageVersion Include="Azure.Identity" Version="1.17.0" />
     <PackageVersion Include="Azure.Monitor.OpenTelemetry.Exporter" Version="1.4.0" />
-    <!-- Google Gemini  -->
-    <PackageVersion Include="Google.GenAI" Version="0.11.0" />
-    <PackageVersion Include="Mscc.GenerativeAI.Microsoft" Version="2.9.3" />
-    <!-- Microsoft.Azure.* -->
-    <PackageVersion Include="Microsoft.Azure.Cosmos" Version="3.54.0" />
-    <!-- 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="10.0.0" />
     <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="1.8.0" />
     <PackageVersion Include="System.CodeDom" Version="10.0.0" />
-    <PackageVersion Include="System.Collections.Immutable" Version="10.0.1" />
+    <PackageVersion Include="System.Collections.Immutable" Version="10.0.0" />
     <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="10.0.0" />
+    <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="10.0.0" />
+    <PackageVersion Include="System.Text.Json" Version="10.0.0" />
+    <PackageVersion Include="System.Threading.Channels" Version="10.0.0" />
     <PackageVersion Include="System.Threading.Tasks.Extensions" Version="4.6.3" />
-    <PackageVersion Include="System.Net.Security" Version="4.3.2" />
     <!-- OpenTelemetry -->
     <PackageVersion Include="OpenTelemetry" Version="1.13.1" />
     <PackageVersion Include="OpenTelemetry.Api" Version="1.13.1" />
@@ -58,30 +46,25 @@
     <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="Microsoft.AspNetCore.OpenApi" Version="9.0.11" />
     <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="10.0.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="10.0.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.AzureAIInference" Version="10.0.0-preview.1.25559.3" />
+    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="10.0.0-preview.1.25559.3" />
     <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="10.0.0" />
     <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="10.0.0" />
     <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" />
@@ -95,62 +78,56 @@
     <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="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" />
+    <PackageVersion Include="Microsoft.Agents.CopilotStudio.Client" Version="1.2.41" />
     <!-- 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="Anthropic.SDK" Version="5.8.0" />
+    <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.4.6" />
     <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="2.6.0" />
     <!-- 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.Bot.ObjectModel" Version="1.2025.1003.2" />
+    <PackageVersion Include="Microsoft.Bot.ObjectModel.Json" Version="1.2025.1003.2" />
+    <PackageVersion Include="Microsoft.Bot.ObjectModel.PowerFx" Version="1.2025.1003.2" />
+    <PackageVersion Include="Microsoft.PowerFx.Interpreter" Version="1.4.0" />
     <!-- 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="1.16.2" />
+    <PackageVersion Include="Microsoft.DurableTask.Client.AzureManaged" Version="1.16.2-preview.1" />
+    <PackageVersion Include="Microsoft.DurableTask.Worker" Version="1.16.2" />
+    <PackageVersion Include="Microsoft.DurableTask.Worker.AzureManaged" Version="1.16.2-preview.1" />
     <!-- 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.AzureManaged" Version="1.0.1" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker" Version="2.2.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.ApplicationInsights" Version="2.0.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" Version="1.9.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" Version="1.0.0" />
     <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" />
     <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Mcp" Version="1.0.0" />
-    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Sdk" Version="2.0.7" />
-    <!-- Redis -->
-    <PackageVersion Include="StackExchange.Redis" Version="2.10.1" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Sdk" Version="2.0.5" />
+    <!-- Community -->
+    <PackageVersion Include="System.Linq.Async" Version="6.0.3" />
     <!-- Test -->
     <PackageVersion Include="FluentAssertions" Version="8.8.0" />
-    <PackageVersion Include="Microsoft.AspNetCore.TestHost" Condition="'$(TargetFramework)' == 'net8.0'" Version="8.0.22" />
-    <PackageVersion Include="Microsoft.AspNetCore.TestHost" Condition="'$(TargetFramework)' == 'net9.0'" Version="9.0.11" />
-    <PackageVersion Include="Microsoft.AspNetCore.TestHost" Condition="'$(TargetFramework)' == 'net10.0'" Version="10.0.0" />
+    <PackageVersion Include="Microsoft.AspNetCore.TestHost" Version="9.0.11" />
     <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="Microsoft.SemanticKernel.Agents.Abstractions" Version="1.67.0" />
+    <PackageVersion Include="Microsoft.SemanticKernel.Agents.Yaml" Version="1.67.0-beta" />
+    <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="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" />
     <PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers">
@@ -172,20 +149,20 @@
       <PrivateAssets>all</PrivateAssets>
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
     </PackageReference>
-    <PackageVersion Include="Roslynator.Analyzers" Version="4.14.1" />
+    <PackageVersion Include="Roslynator.Analyzers" Version="[4.14.1]" />
     <PackageReference Include="Roslynator.Analyzers">
       <PrivateAssets>all</PrivateAssets>
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
     </PackageReference>
-    <PackageVersion Include="Roslynator.CodeAnalysis.Analyzers" Version="4.14.1" />
+    <PackageVersion Include="Roslynator.CodeAnalysis.Analyzers" Version="[4.14.0]" />
     <PackageReference Include="Roslynator.CodeAnalysis.Analyzers">
       <PrivateAssets>all</PrivateAssets>
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
     </PackageReference>
-    <PackageVersion Include="Roslynator.Formatting.Analyzers" Version="4.14.1" />
+    <PackageVersion Include="Roslynator.Formatting.Analyzers" Version="[4.14.0]" />
     <PackageReference Include="Roslynator.Formatting.Analyzers">
       <PrivateAssets>all</PrivateAssets>
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
     </PackageReference>
   </ItemGroup>
-</Project>
+</Project>

dotnet/README.md

@@ -1,30 +1,36 @@
 # 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)
-    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+    .GetOpenAIResponseClient(deploymentName)
+    .CreateAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
 
 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```
 
 ## 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

@@ -5,329 +5,184 @@
     <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" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_CustomImplementation/Agent_With_CustomImplementation.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_GitHubCopilot/Agent_With_GitHubCopilot.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_GoogleGemini/Agent_With_GoogleGemini.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_Ollama/Agent_With_Ollama.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_ONNX/Agent_With_ONNX.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_OpenAIAssistants/Agent_With_OpenAIAssistants.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_OpenAIChatCompletion/Agent_With_OpenAIChatCompletion.csproj" />
-    <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" />
   </Folder>
-  <Folder Name="/Samples/02-agents/Agents/">
-    <File Path="samples/02-agents/Agents/README.md" />
-    <Project Path="samples/02-agents/Agents/Agent_Step01_UsingFunctionToolsWithApprovals/Agent_Step01_UsingFunctionToolsWithApprovals.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step02_StructuredOutput/Agent_Step02_StructuredOutput.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step03_PersistedConversations/Agent_Step03_PersistedConversations.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step04_3rdPartyChatHistoryStorage/Agent_Step04_3rdPartyChatHistoryStorage.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step05_Observability/Agent_Step05_Observability.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step06_DependencyInjection/Agent_Step06_DependencyInjection.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step07_AsMcpTool/Agent_Step07_AsMcpTool.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step08_UsingImages/Agent_Step08_UsingImages.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step09_AsFunctionTool/Agent_Step09_AsFunctionTool.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step10_BackgroundResponsesWithToolsAndPersistence/Agent_Step10_BackgroundResponsesWithToolsAndPersistence.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step11_Middleware/Agent_Step11_Middleware.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step12_Plugins/Agent_Step12_Plugins.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step13_ChatReduction/Agent_Step13_ChatReduction.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step14_BackgroundResponses/Agent_Step14_BackgroundResponses.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step15_DeepResearch/Agent_Step15_DeepResearch.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step16_Declarative/Agent_Step16_Declarative.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Agent_Step17_AdditionalAIContext.csproj" />
-    <Project Path="samples/02-agents/Agents/Agent_Step18_CompactionPipeline/Agent_Step18_CompactionPipeline.csproj" />
-  </Folder>
-  <Folder Name="/Samples/02-agents/DeclarativeAgents/">
-    <Project Path="samples/02-agents/DeclarativeAgents/ChatClient/DeclarativeChatClientAgents.csproj" />
-  </Folder>
-  <Folder Name="/Samples/04-hosting/DurableWorkflows/" />
-  <Folder Name="/Samples/04-hosting/DurableWorkflows/ConsoleApps/">
-    <Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/01_SequentialWorkflow/01_SequentialWorkflow.csproj" />
-    <Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/02_ConcurrentWorkflow/02_ConcurrentWorkflow.csproj" />
-    <Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/03_ConditionalEdges/03_ConditionalEdges.csproj" />
-    <Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/04_WorkflowAndAgents/04_WorkflowAndAgents.csproj" />
-    <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>
-  <Folder Name="/Samples/04-hosting/DurableWorkflows/AzureFunctions/">
-    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/01_SequentialWorkflow/01_SequentialWorkflow.csproj" />
-    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/02_ConcurrentWorkflow/02_ConcurrentWorkflow.csproj" />
-    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/03_WorkflowHITL/03_WorkflowHITL.csproj" />
+  <Folder Name="/Samples/AzureFunctions/">
+    <File Path="samples/AzureFunctions/.editorconfig" />
+    <File Path="samples/AzureFunctions/README.md" />
+    <Project Path="samples/AzureFunctions/01_SingleAgent/01_SingleAgent.csproj" />
+    <Project Path="samples/AzureFunctions/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
+    <Project Path="samples/AzureFunctions/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
+    <Project Path="samples/AzureFunctions/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
+    <Project Path="samples/AzureFunctions/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
+    <Project Path="samples/AzureFunctions/06_LongRunningTools/06_LongRunningTools.csproj" />
+    <Project Path="samples/AzureFunctions/07_AgentAsMcpTool/07_AgentAsMcpTool.csproj" />
   </Folder>
   <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" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AGUI/Step01_GettingStarted/">
-    <Project Path="samples/02-agents/AGUI/Step01_GettingStarted/Client/Client.csproj" />
-    <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_AzureFoundryAgent/Agent_With_AzureFoundryAgent.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_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" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AGUI/Step02_BackendTools/">
-    <Project Path="samples/02-agents/AGUI/Step02_BackendTools/Client/Client.csproj" />
-    <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" />
   </Folder>
-  <Folder Name="/Samples/02-agents/AGUI/Step03_FrontendTools/">
-    <Project Path="samples/02-agents/AGUI/Step03_FrontendTools/Client/Client.csproj" />
-    <Project Path="samples/02-agents/AGUI/Step03_FrontendTools/Server/Server.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/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/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/AgentSkills/">
-    <File Path="samples/02-agents/AgentSkills/README.md" />
-    <Project Path="samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/Agent_Step01_BasicSkills.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" />
   </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/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" />
   </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/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/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/Observability/">
+    <Project Path="samples/GettingStarted/AgentOpenTelemetry/AgentOpenTelemetry.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/Workflows/">
+    <File Path="samples/GettingStarted/Workflows/README.md" />
   </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/Workflows/Concurrent/">
+    <Project Path="samples/GettingStarted/Workflows/Concurrent/Concurrent/Concurrent.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Concurrent/MapReduce/MapReduce.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" />
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-    <File Path="../workflow-samples/CustomerSupport.yaml" />
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     <File Path="../workflow-samples/DeepResearch.yaml" />
+    <File Path="../workflow-samples/HumanInLoop.yaml" />
     <File Path="../workflow-samples/Marketing.yaml" />
     <File Path="../workflow-samples/MathChat.yaml" />
     <File Path="../workflow-samples/README.md" />
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-    <Project Path="samples/03-workflows/SharedStates/SharedStates.csproj" />
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+    <Project Path="samples/GettingStarted/Workflows/SharedStates/SharedStates.csproj" />
   </Folder>
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-    <Project Path="samples/03-workflows/Loop/Loop.csproj" />
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+    <Project Path="samples/GettingStarted/Workflows/Loop/Loop.csproj" />
   </Folder>
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-    <Project Path="samples/03-workflows/Agents/WorkflowAsAnAgent/WorkflowAsAnAgent.csproj" />
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+    <Project Path="samples/GettingStarted/Workflows/Agents/CustomAgentExecutors/CustomAgentExecutors.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Agents/FoundryAgent/FoundryAgent.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Agents/WorkflowAsAnAgent/WorkflowAsAnAgent.csproj" />
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-    <Project Path="samples/03-workflows/Checkpoint/CheckpointWithHumanInTheLoop/CheckpointWithHumanInTheLoop.csproj" />
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+    <Project Path="samples/GettingStarted/Workflows/Checkpoint/CheckpointAndResume/CheckpointAndResume.csproj" />
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-    <Project Path="samples/03-workflows/HumanInTheLoop/HumanInTheLoopBasic/HumanInTheLoopBasic.csproj" />
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+    <Project Path="samples/GettingStarted/Workflows/HumanInTheLoop/HumanInTheLoopBasic/HumanInTheLoopBasic.csproj" />
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-    <Project Path="samples/03-workflows/Observability/WorkflowAsAnAgent/WorkflowAsAnAgentObservability.csproj" />
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+    <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" />
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-    <Project Path="samples/03-workflows/Visualization/Visualization.csproj" />
+  <Folder Name="/Samples/GettingStarted/Workflows/Visualization/">
+    <Project Path="samples/GettingStarted/Workflows/Visualization/Visualization.csproj" Id="99bf0bc6-2440-428e-b3e7-d880e4b7a5fd" />
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-    <Project Path="samples/03-workflows/_StartHere/07_WriterCriticWorkflow/07_WriterCriticWorkflow.csproj" />
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+    <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" />
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+    <Project Path="samples/GettingStarted/Workflows/_Foundational/08_WriterCriticWorkflow/08_WriterCriticWorkflow.csproj" />
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-    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/RazorWebClient.csproj" />
+  <Folder Name="/Samples/HostedAgents/">
+    <Project Path="samples/HostedAgents/AgentsInWorkflows/AgentsInWorkflows.csproj" />
+    <Project Path="samples/HostedAgents/AgentWithTextSearchRag/AgentWithTextSearchRag.csproj" />
+    <Project Path="samples/HostedAgents/AgentWithHostedMCP/AgentWithHostedMCP.csproj" />
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   <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" />
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@@ -430,21 +273,12 @@
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     <File Path="src/Shared/IntegrationTests/OpenAIConfiguration.cs" />
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   </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" />
@@ -452,17 +286,10 @@
     <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" />
@@ -471,16 +298,10 @@
     <Project Path="src/Microsoft.Agents.AI.A2A/Microsoft.Agents.AI.A2A.csproj" />
     <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.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" />
-    <Project Path="src/Microsoft.Agents.AI.CosmosNoSql/Microsoft.Agents.AI.CosmosNoSql.csproj" />
-    <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" />
@@ -489,24 +310,16 @@
     <Project Path="src/Microsoft.Agents.AI.Hosting/Microsoft.Agents.AI.Hosting.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Mem0/Microsoft.Agents.AI.Mem0.csproj" />
     <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/Microsoft.Agents.AI.csproj" />
   </Folder>
   <Folder Name="/Tests/" />
   <Folder Name="/Tests/IntegrationTests/">
     <Project Path="tests/AgentConformance.IntegrationTests/AgentConformance.IntegrationTests.csproj" />
-    <Project Path="tests/AnthropicChatCompletion.IntegrationTests/AnthropicChatCompletion.IntegrationTests.csproj" />
-    <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.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" />
@@ -519,15 +332,8 @@
     <Project Path="tests/Microsoft.Agents.AI.A2A.UnitTests/Microsoft.Agents.AI.A2A.UnitTests.csproj" />
     <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.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" />
@@ -535,11 +341,8 @@
     <Project Path="tests/Microsoft.Agents.AI.Hosting.UnitTests/Microsoft.Agents.AI.Hosting.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Mem0.UnitTests/Microsoft.Agents.AI.Mem0.UnitTests.csproj" />
     <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>

dotnet/agent-framework-release.slnf

@@ -1,34 +0,0 @@
-{
-  "solution": {
-    "path": "agent-framework-dotnet.slnx",
-    "projects": [
-      "src\\Microsoft.Agents.AI.A2A\\Microsoft.Agents.AI.A2A.csproj",
-      "src\\Microsoft.Agents.AI.Abstractions\\Microsoft.Agents.AI.Abstractions.csproj",
-      "src\\Microsoft.Agents.AI.AGUI\\Microsoft.Agents.AI.AGUI.csproj",
-      "src\\Microsoft.Agents.AI.Anthropic\\Microsoft.Agents.AI.Anthropic.csproj",
-      "src\\Microsoft.Agents.AI.GitHub.Copilot\\Microsoft.Agents.AI.GitHub.Copilot.csproj",
-      "src\\Microsoft.Agents.AI.AzureAI.Persistent\\Microsoft.Agents.AI.AzureAI.Persistent.csproj",
-      "src\\Microsoft.Agents.AI.AzureAI\\Microsoft.Agents.AI.AzureAI.csproj",
-      "src\\Microsoft.Agents.AI.CopilotStudio\\Microsoft.Agents.AI.CopilotStudio.csproj",
-      "src\\Microsoft.Agents.AI.CosmosNoSql\\Microsoft.Agents.AI.CosmosNoSql.csproj",
-      "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",
-      "src\\Microsoft.Agents.AI.Hosting.AzureFunctions\\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj",
-      "src\\Microsoft.Agents.AI.Hosting.OpenAI\\Microsoft.Agents.AI.Hosting.OpenAI.csproj",
-      "src\\Microsoft.Agents.AI.Hosting\\Microsoft.Agents.AI.Hosting.csproj",
-      "src\\Microsoft.Agents.AI.Mem0\\Microsoft.Agents.AI.Mem0.csproj",
-      "src\\Microsoft.Agents.AI.OpenAI\\Microsoft.Agents.AI.OpenAI.csproj",
-      "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

@@ -8,28 +8,7 @@
   <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>
-  <ItemGroup Condition="'$(InjectSharedWorkflowsExecution)' == 'true'">
-    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Workflows\Execution\*.cs" LinkBase="Shared\Workflows" />
-  </ItemGroup>
-  <ItemGroup Condition="'$(InjectSharedWorkflowsSettings)' == 'true'">
-    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Workflows\Settings\*.cs" LinkBase="Shared\Workflows" />
-  </ItemGroup>
-  <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",
-        "rollForward": "minor",
+        "version": "9.0.300",
+        "rollForward": "latestMajor",
         "allowPrerelease": false
-    },
-    "test": {
-        "runner": "Microsoft.Testing.Platform"
     }
 }

dotnet/nuget/nuget-package.props

@@ -2,11 +2,9 @@
   <PropertyGroup>
     <!-- Central version prefix - applies to all nuget packages. -->
     <VersionPrefix>1.0.0</VersionPrefix>
-    <RCNumber>4</RCNumber>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260311.1</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260311.1</PackageVersion>
-    <GitTag>1.0.0-rc4</GitTag>
+    <PackageVersion Condition="'$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).251110.2</PackageVersion>
+    <PackageVersion Condition="'$(VersionSuffix)' == ''">$(VersionPrefix)-preview.251110.2</PackageVersion>
+    <GitTag>1.0.0-preview.251110.2</GitTag>
 
     <Configurations>Debug;Release;Publish</Configurations>
     <IsPackable>true</IsPackable>

dotnet/samples/.editorconfig

@@ -1,7 +1,6 @@
 # Suppressing errors for Sample projects under dotnet/samples folder
 [*.cs]
 dotnet_diagnostic.CA1716.severity = none # Add summary to documentation comment.
-dotnet_diagnostic.CA1873.severity = none # Evaluation of logging arguments may be expensive
 dotnet_diagnostic.CA2000.severity = none # Call System.IDisposable.Dispose on object before all references to it are out of scope
 dotnet_diagnostic.CA2007.severity = none # Do not directly await a Task
 

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);
-}