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1423 changed files with 10871 additions and 58364 deletions
+5 -1
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@@ -1,6 +1,10 @@
{
"name": "C# (.NET)",
"image": "mcr.microsoft.com/devcontainers/dotnet",
//"image": "mcr.microsoft.com/devcontainers/dotnet",
// Workaround for https://github.com/devcontainers/images/issues/1752
"build": {
"dockerfile": "dotnet.Dockerfile"
},
"features": {
"ghcr.io/devcontainers/features/azure-cli:1.2.9": {},
"ghcr.io/devcontainers/features/github-cli:1": {
+5
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@@ -0,0 +1,5 @@
FROM mcr.microsoft.com/devcontainers/universal:latest
# Remove Yarn repository with expired GPG key to prevent apt-get update failures
# Tracking issue: https://github.com/devcontainers/images/issues/1752
RUN rm -f /etc/apt/sources.list.d/yarn.list
-1
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@@ -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/
@@ -1,48 +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@v4
- 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 }}
+1 -1
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@@ -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/**
+68 -116
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@@ -7,6 +7,13 @@ name: dotnet-build-and-test
on:
workflow_dispatch:
workflow_call:
inputs:
checkout-ref:
description: "Git ref to checkout (e.g., a commit SHA from a PR)"
required: false
type: string
default: ""
pull_request:
branches: ["main", "feature*"]
merge_group:
@@ -39,6 +46,8 @@ jobs:
cosmosDbChanges: ${{ steps.filter.outputs.cosmosdb }}
steps:
- uses: actions/checkout@v6
with:
ref: ${{ inputs.checkout-ref }}
- uses: dorny/paths-filter@v3
id: filter
with:
@@ -59,23 +68,24 @@ jobs:
if: steps.filter.outputs.dotnet != 'true'
run: echo "NOT dotnet file"
# Build the full solution (including samples) on all TFMs. No tests.
dotnet-build:
dotnet-build-and-test:
needs: paths-filter
if: needs.paths-filter.outputs.dotnetChanges == 'true'
strategy:
fail-fast: false
matrix:
include:
- { targetFramework: "net10.0", os: "ubuntu-latest", configuration: Release }
- { targetFramework: "net10.0", os: "ubuntu-latest", configuration: Release, integration-tests: true, environment: "integration" }
- { targetFramework: "net9.0", os: "windows-latest", configuration: Debug }
- { targetFramework: "net8.0", os: "ubuntu-latest", configuration: Release }
- { targetFramework: "net472", os: "windows-latest", configuration: Release }
- { targetFramework: "net472", os: "windows-latest", configuration: Release, integration-tests: true, environment: "integration" }
runs-on: ${{ matrix.os }}
environment: ${{ matrix.environment }}
steps:
- uses: actions/checkout@v6
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
sparse-checkout: |
.
@@ -84,6 +94,16 @@ jobs:
python
workflow-samples
# Start Cosmos DB Emulator for all integration tests and only for unit tests when CosmosDB changes happened)
- name: Start Azure Cosmos DB Emulator
if: ${{ runner.os == 'Windows' && (needs.paths-filter.outputs.cosmosDbChanges == 'true' || (github.event_name != 'pull_request' && matrix.integration-tests)) }}
shell: pwsh
run: |
Write-Host "Launching Azure Cosmos DB Emulator"
Import-Module "$env:ProgramFiles\Azure Cosmos DB Emulator\PSModules\Microsoft.Azure.CosmosDB.Emulator"
Start-CosmosDbEmulator -NoUI -Key "C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw=="
echo "COSMOS_EMULATOR_AVAILABLE=true" >> $env:GITHUB_ENV
- name: Setup dotnet
uses: actions/setup-dotnet@v5.1.0
with:
@@ -130,98 +150,25 @@ jobs:
popd
rm -rf "$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@v5.1.0
with:
global-json-file: ${{ github.workspace }}/dotnet/global.json
- name: Generate test solution (no samples)
shell: pwsh
run: |
./dotnet/eng/scripts/New-FilteredSolution.ps1 `
-Solution dotnet/agent-framework-dotnet.slnx `
-TargetFramework ${{ matrix.targetFramework }} `
-Configuration ${{ matrix.configuration }} `
-ExcludeSamples `
-OutputPath dotnet/filtered.slnx `
-Verbose
- name: Build src and tests
shell: bash
run: dotnet build dotnet/filtered.slnx -c ${{ matrix.configuration }} -f ${{ matrix.targetFramework }} --warnaserror
- name: Generate test-type filtered solutions
shell: pwsh
run: |
$commonArgs = @{
Solution = "dotnet/filtered.slnx"
TargetFramework = "${{ matrix.targetFramework }}"
Configuration = "${{ matrix.configuration }}"
Verbose = $true
}
./dotnet/eng/scripts/New-FilteredSolution.ps1 @commonArgs `
-TestProjectNameFilter "*UnitTests*" `
-OutputPath dotnet/filtered-unit.slnx
./dotnet/eng/scripts/New-FilteredSolution.ps1 @commonArgs `
-TestProjectNameFilter "*IntegrationTests*" `
-OutputPath dotnet/filtered-integration.slnx
- name: Run Unit Tests
shell: pwsh
working-directory: dotnet
shell: bash
run: |
$coverageSettings = Join-Path $PWD "tests/coverage.runsettings"
$coverageArgs = @()
if ("${{ matrix.targetFramework }}" -eq "${{ env.COVERAGE_FRAMEWORK }}") {
$coverageArgs = @(
"--coverage",
"--coverage-output-format", "cobertura",
"--coverage-settings", $coverageSettings,
"--results-directory", "../TestResults/Coverage/"
)
}
export UT_PROJECTS=$(find ./dotnet -type f -name "*.UnitTests.csproj" | tr '\n' ' ')
for project in $UT_PROJECTS; do
# Query the project's target frameworks using MSBuild with the current configuration
target_frameworks=$(dotnet msbuild $project -getProperty:TargetFrameworks -p:Configuration=${{ matrix.configuration }} -nologo 2>/dev/null | tr -d '\r')
dotnet test --solution ./filtered-unit.slnx `
-f ${{ matrix.targetFramework }} `
-c ${{ matrix.configuration }} `
--no-build -v Normal `
--report-xunit-trx `
--ignore-exit-code 8 `
@coverageArgs
# Check if the project supports the target framework
if [[ "$target_frameworks" == *"${{ matrix.targetFramework }}"* ]]; then
if [[ "${{ matrix.targetFramework }}" == "${{ env.COVERAGE_FRAMEWORK }}" ]]; then
dotnet test -f ${{ matrix.targetFramework }} -c ${{ matrix.configuration }} $project --no-build -v Normal --logger trx --collect:"XPlat Code Coverage" --results-directory:"TestResults/Coverage/" -- DataCollectionRunSettings.DataCollectors.DataCollector.Configuration.ExcludeByAttribute=GeneratedCodeAttribute,CompilerGeneratedAttribute,ExcludeFromCodeCoverageAttribute
else
dotnet test -f ${{ matrix.targetFramework }} -c ${{ matrix.configuration }} $project --no-build -v Normal --logger trx
fi
else
echo "Skipping $project - does not support target framework ${{ matrix.targetFramework }} (supports: $target_frameworks)"
fi
done
env:
# Cosmos DB Emulator connection settings
COSMOSDB_ENDPOINT: https://localhost:8081
@@ -248,42 +195,47 @@ jobs:
id: azure-functions-setup
- name: Run Integration Tests
shell: pwsh
working-directory: dotnet
shell: bash
if: github.event_name != 'pull_request' && matrix.integration-tests
run: |
dotnet test --solution ./filtered-integration.slnx `
-f ${{ matrix.targetFramework }} `
-c ${{ matrix.configuration }} `
--no-build -v Normal `
--report-xunit-trx `
--ignore-exit-code 8 `
--filter-not-trait "Category=IntegrationDisabled" `
--parallel-algorithm aggressive `
--max-threads 2.0x
export INTEGRATION_TEST_PROJECTS=$(find ./dotnet -type f -name "*IntegrationTests.csproj" | tr '\n' ' ')
for project in $INTEGRATION_TEST_PROJECTS; do
# Query the project's target frameworks using MSBuild with the current configuration
target_frameworks=$(dotnet msbuild $project -getProperty:TargetFrameworks -p:Configuration=${{ matrix.configuration }} -nologo 2>/dev/null | tr -d '\r')
# Check if the project supports the target framework
if [[ "$target_frameworks" == *"${{ matrix.targetFramework }}"* ]]; then
dotnet test -f ${{ matrix.targetFramework }} -c ${{ matrix.configuration }} $project --no-build -v Normal --logger trx --filter "Category!=IntegrationDisabled"
else
echo "Skipping $project - does not support target framework ${{ matrix.targetFramework }} (supports: $target_frameworks)"
fi
done
env:
# Cosmos DB Emulator connection settings
COSMOSDB_ENDPOINT: https://localhost:8081
COSMOSDB_KEY: C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw==
# OpenAI Models
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_CHAT_MODEL_NAME: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
OPENAI_REASONING_MODEL_NAME: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
OpenAI__ApiKey: ${{ secrets.OPENAI__APIKEY }}
OpenAI__ChatModelId: ${{ vars.OPENAI__CHATMODELID }}
OpenAI__ChatReasoningModelId: ${{ vars.OPENAI__CHATREASONINGMODELID }}
# Azure OpenAI Models
AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZURE_OPENAI_ENDPOINT }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
# Azure AI Foundry
AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZURE_AI_MODEL_DEPLOYMENT_NAME }}
AZURE_AI_BING_CONNECTION_ID: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}
AzureAI__Endpoint: ${{ secrets.AZUREAI__ENDPOINT }}
AzureAI__DeploymentName: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
AzureAI__BingConnectionId: ${{ vars.AZUREAI__BINGCONECTIONID }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MEDIA_DEPLOYMENT_NAME: ${{ vars.FOUNDRY_MEDIA_DEPLOYMENT_NAME }}
FOUNDRY_MODEL_DEPLOYMENT_NAME: ${{ vars.FOUNDRY_MODEL_DEPLOYMENT_NAME }}
FOUNDRY_CONNECTION_GROUNDING_TOOL: ${{ vars.FOUNDRY_CONNECTION_GROUNDING_TOOL }}
# Generate test reports and check coverage
- name: Generate test reports
if: matrix.targetFramework == env.COVERAGE_FRAMEWORK
uses: danielpalme/ReportGenerator-GitHub-Action@5.5.1
with:
reports: "./TestResults/Coverage/**/*.cobertura.xml"
reports: "./TestResults/Coverage/**/coverage.cobertura.xml"
targetdir: "./TestResults/Reports"
reporttypes: "HtmlInline;JsonSummary"
@@ -297,13 +249,13 @@ jobs:
- name: Check coverage
if: matrix.targetFramework == env.COVERAGE_FRAMEWORK
shell: pwsh
run: ./dotnet/eng/scripts/dotnet-check-coverage.ps1 -JsonReportPath "TestResults/Reports/Summary.json" -CoverageThreshold $env:COVERAGE_THRESHOLD
run: .github/workflows/dotnet-check-coverage.ps1 -JsonReportPath "TestResults/Reports/Summary.json" -CoverageThreshold $env:COVERAGE_THRESHOLD
# This final job is required to satisfy the merge queue. It must only run (or succeed) if no tests failed
dotnet-build-and-test-check:
if: always()
runs-on: ubuntu-latest
needs: [dotnet-build, dotnet-test]
needs: [dotnet-build-and-test]
steps:
- name: Get Date
shell: bash
@@ -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@v5.1.0
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 }}
+11 -39
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@@ -2,9 +2,8 @@
# 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),
# It reuses the existing dotnet-build-and-test and python-merge-tests workflows,
# 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)
@@ -38,8 +37,6 @@ jobs:
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
@@ -48,6 +45,7 @@ jobs:
PR_NUMBER: ${{ github.event.inputs.pr-number }}
BRANCH: ${{ github.event.inputs.branch }}
REPO: ${{ github.repository }}
REPO_OWNER: ${{ github.repository_owner }}
run: |
if [ -n "$PR_NUMBER" ] && [ -n "$BRANCH" ]; then
echo "::error::Please provide either a PR number or a branch name, not both."
@@ -65,14 +63,20 @@ jobs:
exit 1
fi
PR_DATA=$(gh pr view "$PR_NUMBER" --repo "$REPO" --json state)
PR_DATA=$(gh pr view "$PR_NUMBER" --repo "$REPO" --json state,headRepository,headRepositoryOwner)
PR_STATE=$(echo "$PR_DATA" | jq -r '.state')
HEAD_OWNER=$(echo "$PR_DATA" | jq -r '.headRepositoryOwner.login')
if [ "$PR_STATE" != "OPEN" ]; then
echo "::error::PR #$PR_NUMBER is not open (state: $PR_STATE)"
exit 1
fi
if [ "$HEAD_OWNER" != "$REPO_OWNER" ]; then
echo "::error::PR #$PR_NUMBER is from a fork ($HEAD_OWNER). Running integration tests against fork PRs is not allowed for security reasons."
exit 1
fi
echo "checkout-ref=refs/pull/$PR_NUMBER/head" >> "$GITHUB_OUTPUT"
echo "Running integration tests for PR #$PR_NUMBER"
else
@@ -85,41 +89,10 @@ jobs:
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
uses: ./.github/workflows/dotnet-build-and-test.yml
with:
checkout-ref: ${{ needs.resolve-ref.outputs.checkout-ref }}
secrets: inherit
@@ -127,8 +100,7 @@ jobs:
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
uses: ./.github/workflows/python-merge-tests.yml
with:
checkout-ref: ${{ needs.resolve-ref.outputs.checkout-ref }}
secrets: inherit
+1 -4
View File
@@ -29,7 +29,4 @@ jobs:
token: ${{ secrets.GITHUB_TOKEN }}
timeout: 3600
interval: 30
# "Cleanup artifacts", "Agent", "Prepare", and "Upload results" are check runs
# created by an org-level GitHub App (MSDO), not by any workflow in this repo.
# They are outside our control and their transient failures should not block merges.
ignored: CodeQL,CodeQL analysis (csharp),Cleanup artifacts,Agent,Prepare,Upload results
ignored: CodeQL,CodeQL analysis (csharp)
@@ -1,319 +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 all-tests
-m "not integration"
-n logical --dist worksteal
--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: "3.10"
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!')
+61 -331
View File
@@ -1,12 +1,14 @@
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:
workflow_call:
inputs:
checkout-ref:
description: "Git ref to checkout (e.g., a commit SHA from a PR)"
required: false
type: string
default: ""
pull_request:
branches: ["main"]
merge_group:
@@ -15,13 +17,13 @@ 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 +33,17 @@ 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
with:
ref: ${{ inputs.checkout-ref }}
- 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'
@@ -78,237 +52,50 @@ 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 all-tests
-m "not 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: 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: "3.10"
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
with:
ref: ${{ inputs.checkout-ref }}
- 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
@@ -319,15 +106,13 @@ 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
run: uv run poe all-tests -n logical --dist loadfile --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
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()
@@ -337,20 +122,22 @@ 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 }}
@@ -359,12 +146,17 @@ jobs:
working-directory: python
steps:
- uses: actions/checkout@v6
with:
ref: ${{ inputs.checkout-ref }}
- 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
@@ -374,7 +166,7 @@ jobs:
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
run: uv run --directory packages/azure-ai poe integration-tests -n logical --dist loadfile --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
working-directory: ./python
- name: Test Azure AI samples
timeout-minutes: 10
@@ -393,78 +185,16 @@ 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')
@@ -1,302 +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: 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@v4
if: always()
with:
name: validation-report-01-get-started
path: python/scripts/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 }}
# OpenAI configuration
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
# Observability
ENABLE_INSTRUMENTATION: "true"
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@v6
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
with:
azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
os: ${{ runner.os }}
- name: Run sample validation
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents --save-report --report-name 02-agents
- name: Upload validation report
uses: actions/upload-artifact@v4
if: always()
with:
name: validation-report-02-agents
path: python/scripts/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: 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@v4
if: always()
with:
name: validation-report-03-workflows
path: python/scripts/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@v4
if: always()
with:
name: validation-report-04-hosting
path: python/scripts/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@v4
if: always()
with:
name: validation-report-05-end-to-end
path: python/scripts/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: 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@v4
if: always()
with:
name: validation-report-autogen-migration
path: python/scripts/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: 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@v4
if: always()
with:
name: validation-report-semantic-kernel-migration
path: python/scripts/sample_validation/reports/
+7 -7
View File
@@ -53,7 +53,7 @@ Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-commu
### ✨ **Highlights**
- **Graph-based Workflows**: Connect agents and deterministic functions using data flows with streaming, checkpointing, human-in-the-loop, and time-travel capabilities
- [Python workflows](./python/samples/03-workflows/) | [.NET workflows](./dotnet/samples/03-workflows/)
- [Python workflows](./python/samples/03-workflows/) | [.NET workflows](./dotnet/samples/GettingStarted/Workflows/)
- **AF Labs**: Experimental packages for cutting-edge features including benchmarking, reinforcement learning, and research initiatives
- [Labs directory](./python/packages/lab/)
- **DevUI**: Interactive developer UI for agent development, testing, and debugging workflows
@@ -73,11 +73,11 @@ Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-commu
- **Python and C#/.NET Support**: Full framework support for both Python and C#/.NET implementations with consistent APIs
- [Python packages](./python/packages/) | [.NET source](./dotnet/src/)
- **Observability**: Built-in OpenTelemetry integration for distributed tracing, monitoring, and debugging
- [Python observability](./python/samples/02-agents/observability/) | [.NET telemetry](./dotnet/samples/02-agents/AgentOpenTelemetry/)
- [Python observability](./python/samples/02-agents/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/02-agents/providers/) | [.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/02-agents/middleware/) | [.NET middleware](./dotnet/samples/GettingStarted/Agents/Agent_Step14_Middleware/)
### 💬 **We want your feedback!**
@@ -169,9 +169,9 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram
### .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
+1 -1
View File
@@ -11,7 +11,7 @@ model:
topP: 0.95
connection:
kind: key
apiKey: =Env.OPENAI_API_KEY
apiKey: =Env.OPENAI_APIKEY
outputSchema:
properties:
language:
+1 -1
View File
@@ -498,7 +498,7 @@ We need to decide what AIContent types, each agent response type will be mapped
| 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/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.structured_output) |
| LangGraph | **Approach 1** Supports [configuring an agent](https://langchain-ai.github.io/langgraph/agents/agents/?h=structured#6-configure-structured-output) at agent construction time, and a [structured response](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) can be retrieved as a special property on the agent response |
| Agno | **Approach 1** Supports [configuring an agent](https://docs.agno.com/input-output/structured-output/agent) at agent construction time |
| Agno | **Approach 1** Supports [configuring an agent](https://docs.agno.com/examples/getting-started/structured-output) at agent construction time |
| A2A | **Informal Approach 2** Doesn't formally support schema negotiation, but [hints can be provided via metadata](https://a2a-protocol.org/latest/specification/#97-structured-data-exchange-requesting-and-providing-json) at invocation time |
| Protocol Activity | Supports returning [Complex types](https://github.com/microsoft/Agents/blob/main/specs/activity/protocol-activity.md#complex-types) but no support for requesting a type |
@@ -1114,7 +1114,6 @@ Defaults introduced by this change:
- `RedisContextProvider.DEFAULT_SOURCE_ID = "redis"`
- `RedisHistoryProvider.DEFAULT_SOURCE_ID = "redis_memory"`
- `AzureAISearchContextProvider.DEFAULT_SOURCE_ID = "azure_ai_search"`
- `FoundryMemoryProvider.DEFAULT_SOURCE_ID = "foundry_memory"`
## Comparison to .NET Implementation
@@ -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.
@@ -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.
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@@ -8,7 +8,7 @@ This directory contains feature documentation for the durable agents integration
- .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/`
- .NET samples: `dotnet/samples/Durable/Agents/`
- Python samples: `python/samples/04-hosting/durabletask/`
- Official docs (Microsoft Learn): <https://learn.microsoft.com/agent-framework/integrations/azure-functions>
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@@ -219,7 +219,7 @@ When using the [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-
## 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.
- **.NET** [Console app samples](../../../dotnet/samples/Durable/Agents/ConsoleApps/) and [Azure Functions samples](../../../dotnet/samples/Durable/Agents/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
@@ -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.
+5 -50
View File
@@ -17,17 +17,14 @@ 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 test 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
dotnet test --filter "FullyQualifiedName~Namespace.TestClassName.TestMethodName"
# 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
dotnet test --filter FullyQualifiedName\~UnitTests
```
Use `--tl:off` when building to avoid flickering when running commands in the agent.
@@ -59,7 +56,7 @@ 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
dotnet test ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0
```
Example: Running a single test in a specific project using .NET 10.
@@ -67,7 +64,7 @@ Provide the full namespace, class name, and method name for the test you want to
```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"
dotnet test ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0 --filter "FullyQualifiedName~Microsoft.Agents.AI.Abstractions.UnitTests.AgentRunOptionsTests.CloningConstructorCopiesProperties"
```
### Multi-target framework tip
@@ -86,45 +83,3 @@ Just remember to run `dotnet restore` after pulling changes, making changes to p
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 $filtered --no-build -f net472 --ignore-exit-code 8
# Exclude samples and keep only unit test projects
./dotnet/eng/scripts/New-FilteredSolution.ps1 -Solution dotnet/agent-framework-dotnet.slnx -TargetFramework net10.0 -ExcludeSamples -TestProjectNameFilter "*UnitTests*" -OutputPath dotnet/filtered-unit.slnx
```
```bash
# Run tests via dotnet test (uses MTP under the hood)
dotnet test --project ./tests/Microsoft.Agents.AI.UnitTests -f net10.0
# Run tests with code coverage (Cobertura format)
dotnet test --project ./tests/Microsoft.Agents.AI.UnitTests -f net10.0 --coverage --coverage-output-format cobertura --coverage-settings ./tests/coverage.runsettings
# Run tests directly via dotnet run (MTP native command line)
dotnet run --project ./tests/Microsoft.Agents.AI.UnitTests -f net10.0
# Show MTP command line help
dotnet run --project ./tests/Microsoft.Agents.AI.UnitTests -f net10.0 -- -?
```
+14 -14
View File
@@ -19,8 +19,8 @@
<PackageVersion Include="Aspire.Microsoft.Azure.Cosmos" Version="$(AspireAppHostSdkVersion)" />
<PackageVersion Include="CommunityToolkit.Aspire.OllamaSharp" Version="13.0.0" />
<!-- Azure.* -->
<PackageVersion Include="Azure.AI.Projects" Version="2.0.0-beta.1" />
<PackageVersion Include="Azure.AI.Projects.OpenAI" Version="2.0.0-beta.1" />
<PackageVersion Include="Azure.AI.Projects" Version="1.2.0-beta.5" />
<PackageVersion Include="Azure.AI.Projects.OpenAI" Version="1.0.0-beta.5" />
<PackageVersion Include="Azure.AI.Agents.Persistent" Version="1.2.0-beta.8" />
<PackageVersion Include="Azure.AI.OpenAI" Version="2.8.0-beta.1" />
<PackageVersion Include="Azure.Identity" Version="1.17.1" />
@@ -35,7 +35,7 @@
<!-- System.* -->
<PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.3" />
<PackageVersion Include="Microsoft.Bcl.HashCode" Version="6.0.0" />
<PackageVersion Include="System.ClientModel" Version="1.9.0" />
<PackageVersion Include="System.ClientModel" Version="1.8.1" />
<PackageVersion Include="System.CodeDom" Version="10.0.0" />
<PackageVersion Include="System.Collections.Immutable" Version="10.0.1" />
<PackageVersion Include="System.CommandLine" Version="2.0.0-rc.2.25502.107" />
@@ -58,8 +58,6 @@
<PackageVersion Include="OpenTelemetry.Instrumentation.Http" Version="1.13.0" />
<PackageVersion Include="OpenTelemetry.Instrumentation.Runtime" Version="1.13.0" />
<!-- Microsoft.AspNetCore.* -->
<PackageVersion Include="Microsoft.AspNetCore.Authentication.JwtBearer" Version="10.0.0" />
<PackageVersion Include="Microsoft.AspNetCore.Authentication.OpenIdConnect" Version="10.0.0" />
<PackageVersion Include="Microsoft.AspNetCore.OpenApi" Version="10.0.0" />
<PackageVersion Include="Swashbuckle.AspNetCore.SwaggerUI" Version="10.0.0" />
<!-- Microsoft.Extensions.* -->
@@ -94,7 +92,7 @@
<PackageVersion Include="Microsoft.SemanticKernel.Agents.AzureAI" Version="1.67.0-preview" />
<PackageVersion Include="Microsoft.SemanticKernel.Plugins.OpenApi" Version="1.67.0" />
<!-- Agent SDKs -->
<PackageVersion Include="GitHub.Copilot.SDK" Version="0.1.29" />
<PackageVersion Include="GitHub.Copilot.SDK" Version="0.1.25" />
<PackageVersion Include="Microsoft.Agents.CopilotStudio.Client" Version="1.3.171-beta" />
<!-- M365 Agents SDK -->
<PackageVersion Include="AdaptiveCards" Version="3.1.0" />
@@ -113,9 +111,9 @@
<!-- Identity -->
<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.Agents.ObjectModel" Version="2026.2.3.1" />
<PackageVersion Include="Microsoft.Agents.ObjectModel.Json" Version="2026.2.3.1" />
<PackageVersion Include="Microsoft.Agents.ObjectModel.PowerFx" Version="2026.2.3.1" />
<PackageVersion Include="Microsoft.PowerFx.Interpreter" Version="1.8.1" />
<!-- Durable Task -->
<PackageVersion Include="Microsoft.DurableTask.Client" Version="1.18.0" />
@@ -140,10 +138,12 @@
<PackageVersion Include="Microsoft.AspNetCore.TestHost" Condition="'$(TargetFramework)' == 'net10.0'" Version="10.0.0" />
<PackageVersion Include="Microsoft.NET.Test.Sdk" Version="18.0.0" />
<PackageVersion Include="Moq" Version="[4.18.4]" />
<PackageVersion Include="xunit.v3.mtp-v2" Version="3.2.2" />
<PackageVersion Include="xunit.runner.visualstudio" Version="3.1.5" />
<PackageVersion Include="xRetry.v3" Version="1.0.0-rc3" />
<PackageVersion Include="Microsoft.Testing.Extensions.CodeCoverage" Version="18.4.1" />
<PackageVersion Include="xunit" Version="2.9.3" />
<PackageVersion Include="xunit.abstractions" Version="2.0.3" />
<PackageVersion Include="xunit.runner.visualstudio" Version="3.1.3" />
<PackageVersion Include="Xunit.SkippableFact" Version="1.5.23" />
<PackageVersion Include="xretry" Version="1.9.0" />
<PackageVersion Include="coverlet.collector" Version="6.0.4" />
<!-- Symbols -->
<PackageVersion Include="Microsoft.SourceLink.GitHub" Version="8.0.0" />
<!-- Toolset -->
@@ -185,4 +185,4 @@
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
</ItemGroup>
</Project>
</Project>
+9 -3
View File
@@ -1,5 +1,11 @@
# 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
@@ -22,9 +28,9 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram
## Examples & Samples
- [Getting Started with Agents](./samples/02-agents/Agents): basic agent creation and tool usage
- [Agent Provider Samples](./samples/02-agents/AgentProviders): samples showing different agent providers
- [Workflow Samples](./samples/03-workflows): advanced multi-agent patterns and workflow orchestration
- [Getting Started with Agents](./samples/GettingStarted/Agents): basic agent creation and tool usage
- [Agent Provider Samples](./samples/GettingStarted/AgentProviders): samples showing different agent providers
- [Workflow Samples](./samples/GettingStarted/Workflows): advanced multi-agent patterns and workflow orchestration
## Agent Framework Documentation
+234 -269
View File
@@ -5,189 +5,226 @@
<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" />
<Folder Name="/Samples/Durable/" />
<Folder Name="/Samples/Durable/Agents/" />
<Folder Name="/Samples/Durable/Agents/AzureFunctions/">
<File Path="samples/Durable/Agents/AzureFunctions/.editorconfig" />
<File Path="samples/Durable/Agents/AzureFunctions/README.md" />
<Project Path="samples/Durable/Agents/AzureFunctions/01_SingleAgent/01_SingleAgent.csproj" />
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<Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step20_MicrosoftFabric/FoundryAgents_Step20_MicrosoftFabric.csproj" />
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@@ -195,119 +232,60 @@
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<Project Path="src/Microsoft.Agents.AI.Hosting.A2A.AspNetCore/Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj" />
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@@ -463,11 +429,11 @@
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@@ -481,11 +447,11 @@
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<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" />
-3
View File
@@ -8,9 +8,6 @@
<ItemGroup Condition="'$(InjectSharedIntegrationTestCode)' == 'true'">
<Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\IntegrationTests\*.cs" LinkBase="Shared\IntegrationTests" />
</ItemGroup>
<ItemGroup Condition="'$(InjectSharedIntegrationTestAzureCredentialsCode)' == 'true'">
<Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\IntegrationTestsAzureCredentials\*.cs" LinkBase="Shared\IntegrationTestsAzureCredentials" />
</ItemGroup>
<ItemGroup Condition="'$(InjectSharedBuildTestCode)' == 'true'">
<Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\CodeTests\*.cs" LinkBase="Shared\CodeTests" />
</ItemGroup>
-145
View File
@@ -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 $OutputPath
-3
View File
@@ -3,8 +3,5 @@
"version": "10.0.100",
"rollForward": "minor",
"allowPrerelease": false
},
"test": {
"runner": "Microsoft.Testing.Platform"
}
}
+4 -4
View File
@@ -2,11 +2,11 @@
<PropertyGroup>
<!-- Central version prefix - applies to all nuget packages. -->
<VersionPrefix>1.0.0</VersionPrefix>
<RCNumber>3</RCNumber>
<RCNumber>1</RCNumber>
<PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260304.1</PackageVersion>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260304.1</PackageVersion>
<GitTag>1.0.0-rc3</GitTag>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260219.1</PackageVersion>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260219.1</PackageVersion>
<GitTag>1.0.0-rc1</GitTag>
<Configurations>Debug;Release;Publish</Configurations>
<IsPackable>true</IsPackable>
@@ -1,14 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
</ItemGroup>
</Project>
@@ -1,31 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<AzureFunctionsVersion>v4</AzureFunctionsVersion>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<!-- The Functions build tools don't like namespaces that start with a number -->
<AssemblyName>HostedAgent</AssemblyName>
<RootNamespace>HostedAgent</RootNamespace>
</PropertyGroup>
<ItemGroup>
<FrameworkReference Include="Microsoft.AspNetCore.App" />
</ItemGroup>
<!-- Azure Functions packages -->
<ItemGroup>
<PackageReference Include="Microsoft.Azure.Functions.Worker" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
@@ -1,45 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to host an AI agent with Azure Functions (DurableAgents).
//
// Prerequisites:
// - Azure Functions Core Tools
// - Azure OpenAI resource
//
// Environment variables:
// AZURE_OPENAI_ENDPOINT
// AZURE_OPENAI_DEPLOYMENT_NAME (defaults to "gpt-4o-mini")
//
// Run with: func start
// Then call: POST http://localhost:7071/api/agents/HostedAgent/run
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AzureFunctions;
using Microsoft.Azure.Functions.Worker.Builder;
using Microsoft.Extensions.Hosting;
using OpenAI.Chat;
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// Set up an AI agent following the standard Microsoft Agent Framework pattern.
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
.GetChatClient(deploymentName)
.AsAIAgent(
instructions: "You are a helpful assistant hosted in Azure Functions.",
name: "HostedAgent");
// Configure the function app to host the AI agent.
// This will automatically generate HTTP API endpoints for the agent.
using IHost app = FunctionsApplication
.CreateBuilder(args)
.ConfigureFunctionsWebApplication()
.ConfigureDurableAgents(options => options.AddAIAgent(agent, timeToLive: TimeSpan.FromHours(1)))
.Build();
app.Run();
@@ -1,22 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);CA1812;CS8321</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.Extensions.Logging.Console" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Azure.AI.Projects" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
</ItemGroup>
</Project>
@@ -1,76 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use Bing Custom Search Tool with AI Agents.
using Azure.AI.Projects;
using Azure.AI.Projects.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI.Responses;
string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
string connectionId = Environment.GetEnvironmentVariable("AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID") ?? throw new InvalidOperationException("AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID is not set.");
string instanceName = Environment.GetEnvironmentVariable("AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME") ?? throw new InvalidOperationException("AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME is not set.");
const string AgentInstructions = """
You are a helpful agent that can use Bing Custom Search tools to assist users.
Use the available Bing Custom Search tools to answer questions and perform tasks.
""";
// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
// Bing Custom Search tool parameters shared by both options
BingCustomSearchToolParameters bingCustomSearchToolParameters = new([
new BingCustomSearchConfiguration(connectionId, instanceName)
]);
AIAgent agent = await CreateAgentWithMEAIAsync();
// AIAgent agent = await CreateAgentWithNativeSDKAsync();
Console.WriteLine($"Created agent: {agent.Name}");
// Run the agent with a search query
AgentResponse response = await agent.RunAsync("Search for the latest news about Microsoft AI");
Console.WriteLine("\n=== Agent Response ===");
foreach (var message in response.Messages)
{
Console.WriteLine(message.Text);
}
// Cleanup by deleting the agent
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
Console.WriteLine($"\nDeleted agent: {agent.Name}");
// --- Agent Creation Options ---
// Option 1 - Using AsAITool wrapping for the ResponseTool returned by AgentTool.CreateBingCustomSearchTool (MEAI + AgentFramework)
async Task<AIAgent> CreateAgentWithMEAIAsync()
{
return await aiProjectClient.CreateAIAgentAsync(
model: deploymentName,
name: "BingCustomSearchAgent-MEAI",
instructions: AgentInstructions,
tools: [((ResponseTool)AgentTool.CreateBingCustomSearchTool(bingCustomSearchToolParameters)).AsAITool()]);
}
// Option 2 - Using PromptAgentDefinition with AgentTool.CreateBingCustomSearchTool (Native SDK)
async Task<AIAgent> CreateAgentWithNativeSDKAsync()
{
return await aiProjectClient.CreateAIAgentAsync(
name: "BingCustomSearchAgent-NATIVE",
creationOptions: new AgentVersionCreationOptions(
new PromptAgentDefinition(model: deploymentName)
{
Instructions = AgentInstructions,
Tools = {
(ResponseTool)AgentTool.CreateBingCustomSearchTool(bingCustomSearchToolParameters),
}
})
);
}
@@ -1,63 +0,0 @@
# Using Bing Custom Search with AI Agents
This sample demonstrates how to use the Bing Custom Search tool with AI agents to perform customized web searches.
## What this sample demonstrates
- Creating agents with Bing Custom Search capabilities
- Configuring custom search instances via connection ID and instance name
- Two agent creation approaches: MEAI abstraction (Option 1) and Native SDK (Option 2)
- Running search queries through the agent
- Managing agent lifecycle (creation and deletion)
## Agent creation options
This sample provides two approaches for creating agents with Bing Custom Search:
- **Option 1 - MEAI + AgentFramework**: Uses the Agent Framework `ResponseTool` wrapped with `AsAITool()` to call the `CreateAIAgentAsync` overload that accepts `tools:[]`, while still relying on the same underlying Azure AI Projects SDK types as Option 2.
- **Option 2 - Native SDK**: Uses `PromptAgentDefinition` with `AgentVersionCreationOptions` to create the agent directly with the Azure AI Projects SDK types.
Both options produce the same result. Toggle between them by commenting/uncommenting the corresponding `CreateAgentWith*Async` call in `Program.cs`.
## Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
- A Bing Custom Search resource configured in Azure and connected to your Foundry project
**Note**: This demo uses Azure Default credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource.
Set the following environment variables:
```powershell
$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
$env:BING_CUSTOM_SEARCH_PROJECT_CONNECTION_ID="/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>"
$env:BING_CUSTOM_SEARCH_INSTANCE_NAME="your-configuration-name"
```
### Finding the connection ID and instance name
- **Connection ID**: The full ARM resource path including the `/projects/<name>/connections/<connection-name>` segment. Find the connection name in your Foundry project under **Management center****Connected resources**.
- **Instance Name**: The **configuration name** from the Bing Custom Search resource (Azure portal → your Bing Custom Search resource → **Configurations**). This is _not_ the Azure resource name.
## Run the sample
Navigate to the FoundryAgents sample directory and run:
```powershell
cd dotnet/samples/02-agents/FoundryAgents
dotnet run --project .\FoundryAgents_Step18_BingCustomSearch
```
## Expected behavior
The sample will:
1. Create an agent with Bing Custom Search tool capabilities
2. Run the agent with a search query about Microsoft AI
3. Display the search results returned by the agent
4. Clean up resources by deleting the agent
@@ -1,22 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);CA1812;CS8321</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.Extensions.Logging.Console" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Azure.AI.Projects" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
</ItemGroup>
</Project>
@@ -1,84 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use SharePoint Grounding Tool with AI Agents.
using Azure.AI.Projects;
using Azure.AI.Projects.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI.Responses;
string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
string sharepointConnectionId = Environment.GetEnvironmentVariable("SHAREPOINT_PROJECT_CONNECTION_ID") ?? throw new InvalidOperationException("SHAREPOINT_PROJECT_CONNECTION_ID is not set.");
const string AgentInstructions = """
You are a helpful agent that can use SharePoint tools to assist users.
Use the available SharePoint tools to answer questions and perform tasks.
""";
// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
// Create SharePoint tool options with project connection
var sharepointOptions = new SharePointGroundingToolOptions();
sharepointOptions.ProjectConnections.Add(new ToolProjectConnection(sharepointConnectionId));
AIAgent agent = await CreateAgentWithMEAIAsync();
// AIAgent agent = await CreateAgentWithNativeSDKAsync();
Console.WriteLine($"Created agent: {agent.Name}");
AgentResponse response = await agent.RunAsync("List the documents available in SharePoint");
// Display the response
Console.WriteLine("\n=== Agent Response ===");
Console.WriteLine(response);
// Display grounding annotations if any
foreach (var message in response.Messages)
{
foreach (var content in message.Contents)
{
if (content.Annotations is not null)
{
foreach (var annotation in content.Annotations)
{
Console.WriteLine($"Annotation: {annotation}");
}
}
}
}
// Cleanup by agent name removes the agent version created.
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
Console.WriteLine($"\nDeleted agent: {agent.Name}");
// --- Agent Creation Options ---
// Option 1 - Using AgentTool.CreateSharepointTool + AsAITool() (MEAI + AgentFramework)
async Task<AIAgent> CreateAgentWithMEAIAsync()
{
return await aiProjectClient.CreateAIAgentAsync(
model: deploymentName,
name: "SharePointAgent-MEAI",
instructions: AgentInstructions,
tools: [((ResponseTool)AgentTool.CreateSharepointTool(sharepointOptions)).AsAITool()]);
}
// Option 2 - Using PromptAgentDefinition SDK native type
async Task<AIAgent> CreateAgentWithNativeSDKAsync()
{
return await aiProjectClient.CreateAIAgentAsync(
name: "SharePointAgent-NATIVE",
creationOptions: new AgentVersionCreationOptions(
new PromptAgentDefinition(model: deploymentName)
{
Instructions = AgentInstructions,
Tools = { AgentTool.CreateSharepointTool(sharepointOptions) }
})
);
}
@@ -1,50 +0,0 @@
# Using SharePoint Grounding with AI Agents
This sample demonstrates how to use the SharePoint grounding tool with AI agents. The SharePoint grounding tool enables agents to search and retrieve information from SharePoint sites.
## What this sample demonstrates
- Creating agents with SharePoint grounding capabilities
- Using AgentTool.CreateSharepointTool (MEAI abstraction)
- Using native SDK SharePoint tools (PromptAgentDefinition)
- Managing agent lifecycle (creation and deletion)
## Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Azure authentication configured for `DefaultAzureCredential` (for example, Azure CLI logged in with `az login`, environment variables, managed identity, or IDE sign-in)
- A SharePoint project connection configured in Azure Foundry
**Note**: This demo uses `DefaultAzureCredential` for authentication. This credential will try multiple authentication mechanisms in order (such as environment variables, managed identity, Azure CLI login, and IDE sign-in) and use the first one that works. A common option for local development is to sign in with the Azure CLI using `az login` and ensure you have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively) and the [DefaultAzureCredential documentation](https://learn.microsoft.com/dotnet/api/azure.identity.defaultazurecredential).
Set the following environment variables:
```powershell
$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
$env:SHAREPOINT_PROJECT_CONNECTION_ID="your-sharepoint-connection-id" # Required: SharePoint project connection ID
```
## Run the sample
Navigate to the FoundryAgents sample directory and run:
```powershell
cd dotnet/samples/02-agents/FoundryAgents
dotnet run --project .\FoundryAgents_Step19_SharePoint
```
## Expected behavior
The sample will:
1. Create two agents with SharePoint grounding capabilities:
- Option 1: Using AgentTool.CreateSharepointTool (MEAI abstraction)
- Option 2: Using native SDK SharePoint tools
2. Run the agent with a query: "List the documents available in SharePoint"
3. The agent will use SharePoint grounding to search and retrieve relevant documents
4. Display the response and any grounding annotations
5. Clean up resources by deleting both agents
@@ -1,22 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);CA1812;CS8321</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.Extensions.Logging.Console" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Azure.AI.Projects" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
</ItemGroup>
</Project>
@@ -1,72 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use Microsoft Fabric Tool with AI Agents.
using Azure.AI.Projects;
using Azure.AI.Projects.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI.Responses;
string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
string fabricConnectionId = Environment.GetEnvironmentVariable("FABRIC_PROJECT_CONNECTION_ID") ?? throw new InvalidOperationException("FABRIC_PROJECT_CONNECTION_ID is not set.");
const string AgentInstructions = "You are a helpful assistant with access to Microsoft Fabric data. Answer questions based on data available through your Fabric connection.";
// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
// Configure Microsoft Fabric tool options with project connection
var fabricToolOptions = new FabricDataAgentToolOptions();
fabricToolOptions.ProjectConnections.Add(new ToolProjectConnection(fabricConnectionId));
AIAgent agent = await CreateAgentWithMEAIAsync();
// AIAgent agent = await CreateAgentWithNativeSDKAsync();
Console.WriteLine($"Created agent: {agent.Name}");
// Run the agent with a sample query
AgentResponse response = await agent.RunAsync("What data is available in the connected Fabric workspace?");
Console.WriteLine("\n=== Agent Response ===");
foreach (var message in response.Messages)
{
Console.WriteLine(message.Text);
}
// Cleanup by deleting the agent
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
Console.WriteLine($"\nDeleted agent: {agent.Name}");
// --- Agent Creation Options ---
// Option 1 - Using AsAITool wrapping for the ResponseTool returned by AgentTool.CreateMicrosoftFabricTool (MEAI + AgentFramework)
async Task<AIAgent> CreateAgentWithMEAIAsync()
{
return await aiProjectClient.CreateAIAgentAsync(
model: deploymentName,
name: "FabricAgent-MEAI",
instructions: AgentInstructions,
tools: [((ResponseTool)AgentTool.CreateMicrosoftFabricTool(fabricToolOptions)).AsAITool()]);
}
// Option 2 - Using PromptAgentDefinition with AgentTool.CreateMicrosoftFabricTool (Native SDK)
async Task<AIAgent> CreateAgentWithNativeSDKAsync()
{
return await aiProjectClient.CreateAIAgentAsync(
name: "FabricAgent-NATIVE",
creationOptions: new AgentVersionCreationOptions(
new PromptAgentDefinition(model: deploymentName)
{
Instructions = AgentInstructions,
Tools =
{
AgentTool.CreateMicrosoftFabricTool(fabricToolOptions),
}
})
);
}
@@ -1,57 +0,0 @@
# Using Microsoft Fabric Tool with AI Agents
This sample demonstrates how to use the Microsoft Fabric tool with AI Agents, allowing agents to query and interact with data in Microsoft Fabric workspaces.
## What this sample demonstrates
- Creating agents with Microsoft Fabric data access capabilities
- Using FabricDataAgentToolOptions to configure Fabric connections
- Two agent creation approaches: MEAI abstraction (Option 1) and Native SDK (Option 2)
- Managing agent lifecycle (creation and deletion)
## Agent creation options
This sample provides two approaches for creating agents with Microsoft Fabric:
- **Option 1 - MEAI + AgentFramework**: Uses the Agent Framework `ResponseTool` wrapped with `AsAITool()` to call the `CreateAIAgentAsync` overload that accepts `tools:[]`, while still relying on the same underlying Azure AI Projects SDK types as Option 2.
- **Option 2 - Native SDK**: Uses `PromptAgentDefinition` with `AgentVersionCreationOptions` to create the agent directly with the Azure AI Projects SDK types.
Both options produce the same result. Toggle between them by commenting/uncommenting the corresponding `CreateAgentWith*Async` call in `Program.cs`.
## Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
- A Microsoft Fabric workspace with a configured project connection in Azure Foundry
**Note**: This demo uses Azure Default credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource.
Set the following environment variables:
```powershell
$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
$env:FABRIC_PROJECT_CONNECTION_ID="your-fabric-connection-id" # The Fabric project connection ID from Azure Foundry
```
## Run the sample
Navigate to the FoundryAgents sample directory and run:
```powershell
cd dotnet/samples/02-agents/FoundryAgents
dotnet run --project .\FoundryAgents_Step20_MicrosoftFabric
```
## Expected behavior
The sample will:
1. Create an agent with Microsoft Fabric tool capabilities
2. Configure the agent with a Fabric project connection
3. Run the agent with a query about available Fabric data
4. Display the agent's response
5. Clean up resources by deleting the agent
@@ -1,22 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);CA1812;CS8321</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.Extensions.Logging.Console" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Azure.AI.Projects" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
</ItemGroup>
</Project>
@@ -1,65 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use the Responses API Web Search Tool with AI Agents.
using Azure.AI.Projects;
using Azure.AI.Projects.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI.Responses;
string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
const string AgentInstructions = "You are a helpful assistant that can search the web to find current information and answer questions accurately.";
const string AgentName = "WebSearchAgent";
// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
// Option 1 - Using HostedWebSearchTool (MEAI + AgentFramework)
AIAgent agent = await CreateAgentWithMEAIAsync();
// Option 2 - Using PromptAgentDefinition with the Responses API native type
// AIAgent agent = await CreateAgentWithNativeSDKAsync();
AgentResponse response = await agent.RunAsync("What's the weather today in Seattle?");
// Get the text response
Console.WriteLine($"Response: {response.Text}");
// Getting any annotations/citations generated by the web search tool
foreach (AIAnnotation annotation in response.Messages.SelectMany(m => m.Contents).SelectMany(c => c.Annotations ?? []))
{
Console.WriteLine($"Annotation: {annotation}");
if (annotation.RawRepresentation is UriCitationMessageAnnotation urlCitation)
{
Console.WriteLine($$"""
Title: {{urlCitation.Title}}
URL: {{urlCitation.Uri}}
""");
}
}
// Cleanup by agent name removes the agent version created.
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
// Creates the agent using the HostedWebSearchTool MEAI abstraction that maps to the built-in Responses API web search tool.
async Task<AIAgent> CreateAgentWithMEAIAsync()
=> await aiProjectClient.CreateAIAgentAsync(
name: AgentName,
model: deploymentName,
instructions: AgentInstructions,
tools: [new HostedWebSearchTool()]);
// Creates the agent using the PromptAgentDefinition with the Responses API native ResponseTool.CreateWebSearchTool().
async Task<AIAgent> CreateAgentWithNativeSDKAsync()
=> await aiProjectClient.CreateAIAgentAsync(
AgentName,
new AgentVersionCreationOptions(
new PromptAgentDefinition(model: deploymentName)
{
Instructions = AgentInstructions,
Tools = { ResponseTool.CreateWebSearchTool() }
}));
@@ -1,52 +0,0 @@
# Using Web Search with AI Agents
This sample demonstrates how to use the Responses API web search tool with AI agents. The web search tool allows agents to search the web for current information to answer questions accurately.
## What this sample demonstrates
- Creating agents with web search capabilities
- Using HostedWebSearchTool (MEAI abstraction)
- Using native SDK web search tools (ResponseTool.CreateWebSearchTool)
- Extracting text responses and URL citations from agent responses
- Managing agent lifecycle (creation and deletion)
## Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Azure authentication configured for `DefaultAzureCredential` (for example, Azure CLI logged in with `az login`, environment variables, managed identity, or IDE sign-in)
**Note**: This sample authenticates using `DefaultAzureCredential` from the Azure Identity library, which will try several credential sources (including Azure CLI, environment variables, managed identity, and IDE sign-in). Ensure at least one supported credential source is available. For more information, see the [Azure Identity documentation](https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme).
**Note**: The web search tool uses the built-in web search capability from the OpenAI Responses API.
Set the following environment variables:
```powershell
$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
```
## Run the sample
Navigate to the FoundryAgents sample directory and run:
```powershell
cd dotnet/samples/02-agents/FoundryAgents
dotnet run --project .\FoundryAgents_Step21_WebSearch
```
## Expected behavior
The sample will:
1. Create an agent with web search capabilities using HostedWebSearchTool (MEAI abstraction)
- Alternative: Using native SDK web search tools (commented out in code)
- Alternative: Retrieving an existing agent by name (commented out in code)
2. Run the agent with a query: "What's the weather today in Seattle?"
3. The agent will use the web search tool to find current information
4. Display the text response from the agent
5. Display any URL citations from web search results
6. Clean up resources by deleting the agent
@@ -1,23 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);CA1812</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.Extensions.Logging.Console" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Azure.AI.Projects" />
<PackageReference Include="Azure.AI.Projects.OpenAI" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
</ItemGroup>
</Project>
@@ -1,124 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to use the Memory Search Tool with AI Agents.
// The Memory Search Tool enables agents to recall information from previous conversations,
// supporting user profile persistence and chat summaries across sessions.
using Azure.AI.Projects;
using Azure.AI.Projects.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI.Responses;
string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// Memory store configuration
// NOTE: Memory stores must be created beforehand via Azure Portal or Python SDK.
// The .NET SDK currently only supports using existing memory stores with agents.
string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? throw new InvalidOperationException("AZURE_AI_MEMORY_STORE_ID is not set.");
const string AgentInstructions = """
You are a helpful assistant that remembers past conversations.
Use the memory search tool to recall relevant information from previous interactions.
When a user shares personal details or preferences, remember them for future conversations.
""";
const string AgentNameMEAI = "MemorySearchAgent-MEAI";
const string AgentNameNative = "MemorySearchAgent-NATIVE";
// Scope identifies the user or context for memory isolation.
// Using a unique user identifier ensures memories are private to that user.
string userScope = $"user_{Environment.MachineName}";
// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
// Create the Memory Search tool configuration
MemorySearchPreviewTool memorySearchTool = new(memoryStoreName, userScope)
{
// Optional: Configure how quickly new memories are indexed (in seconds)
UpdateDelay = 1,
// Optional: Configure search behavior
SearchOptions = new MemorySearchToolOptions
{
// Additional search options can be configured here if needed
}
};
// Create agent using Option 1 (MEAI) or Option 2 (Native SDK)
AIAgent agent = await CreateAgentWithMEAI();
// AIAgent agent = await CreateAgentWithNativeSDK();
Console.WriteLine("Agent created with Memory Search tool. Starting conversation...\n");
// Conversation 1: Share some personal information
Console.WriteLine("User: My name is Alice and I love programming in C#.");
AgentResponse response1 = await agent.RunAsync("My name is Alice and I love programming in C#.");
Console.WriteLine($"Agent: {response1.Messages.LastOrDefault()?.Text}\n");
// Allow time for memory to be indexed
await Task.Delay(2000);
// Conversation 2: Test if the agent remembers
Console.WriteLine("User: What's my name and what programming language do I prefer?");
AgentResponse response2 = await agent.RunAsync("What's my name and what programming language do I prefer?");
Console.WriteLine($"Agent: {response2.Messages.LastOrDefault()?.Text}\n");
// Inspect memory search results if available in raw response items
// Note: Memory search tool call results appear as AgentResponseItem types
foreach (var message in response2.Messages)
{
if (message.RawRepresentation is AgentResponseItem agentResponseItem &&
agentResponseItem is MemorySearchToolCallResponseItem memorySearchResult)
{
Console.WriteLine($"Memory Search Status: {memorySearchResult.Status}");
Console.WriteLine($"Memory Search Results Count: {memorySearchResult.Results.Count}");
foreach (var result in memorySearchResult.Results)
{
var memoryItem = result.MemoryItem;
Console.WriteLine($" - Memory ID: {memoryItem.MemoryId}");
Console.WriteLine($" Scope: {memoryItem.Scope}");
Console.WriteLine($" Content: {memoryItem.Content}");
Console.WriteLine($" Updated: {memoryItem.UpdatedAt}");
}
}
}
// Cleanup: Delete the agent (memory store persists and should be cleaned up separately if needed)
Console.WriteLine("\nCleaning up agent...");
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
Console.WriteLine("Agent deleted successfully.");
// NOTE: Memory stores are long-lived resources and are NOT deleted with the agent.
// To delete a memory store, use the Azure Portal or Python SDK:
// await project_client.memory_stores.delete(memory_store.name)
// --- Agent Creation Options ---
#pragma warning disable CS8321 // Local function is declared but never used
// Option 1 - Using MemorySearchTool wrapped as MEAI AITool
async Task<AIAgent> CreateAgentWithMEAI()
{
return await aiProjectClient.CreateAIAgentAsync(
model: deploymentName,
name: AgentNameMEAI,
instructions: AgentInstructions,
tools: [((ResponseTool)memorySearchTool).AsAITool()]);
}
// Option 2 - Using PromptAgentDefinition with MemorySearchTool (Native SDK)
async Task<AIAgent> CreateAgentWithNativeSDK()
{
return await aiProjectClient.CreateAIAgentAsync(
name: AgentNameNative,
creationOptions: new AgentVersionCreationOptions(
new PromptAgentDefinition(model: deploymentName)
{
Instructions = AgentInstructions,
Tools = { memorySearchTool }
})
);
}
@@ -1,92 +0,0 @@
# Using Memory Search with AI Agents
This sample demonstrates how to use the Memory Search tool with AI agents. The Memory Search tool enables agents to recall information from previous conversations, supporting user profile persistence and chat summaries across sessions.
## What this sample demonstrates
- Creating an agent with Memory Search tool capabilities
- Configuring memory scope for user isolation
- Having conversations where the agent remembers past information
- Inspecting memory search results from agent responses
- Managing agent lifecycle (creation and deletion)
## Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
- **A pre-created Memory Store** (see below)
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
### Creating a Memory Store
Memory stores must be created before running this sample. The .NET SDK currently only supports **using** existing memory stores with agents. To create a memory store, use one of these methods:
**Option 1: Azure Portal**
1. Navigate to your Azure AI Foundry project
2. Go to the Memory section
3. Create a new memory store with your desired settings
**Option 2: Python SDK**
```python
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import MemoryStoreDefaultDefinition, MemoryStoreDefaultOptions
from azure.identity import DefaultAzureCredential
project_client = AIProjectClient(
endpoint="https://your-endpoint.openai.azure.com/",
credential=DefaultAzureCredential()
)
memory_store = await project_client.memory_stores.create(
name="my-memory-store",
description="Memory store for Agent Framework conversations",
definition=MemoryStoreDefaultDefinition(
chat_model=os.environ["AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME"],
embedding_model=os.environ["AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME"],
options=MemoryStoreDefaultOptions(
user_profile_enabled=True,
chat_summary_enabled=True
)
)
)
```
## Environment Variables
Set the following environment variables:
```powershell
$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
$env:AZURE_AI_MEMORY_STORE_NAME="your-memory-store-name" # Required - name of pre-created memory store
```
## Run the sample
Navigate to the FoundryAgents sample directory and run:
```powershell
cd dotnet/samples/02-agents/FoundryAgents
dotnet run --project .\FoundryAgents_Step22_MemorySearch
```
## Expected behavior
The sample will:
1. Create an agent with Memory Search tool configured
2. Send a message with personal information ("My name is Alice and I love programming in C#")
3. Wait for memory indexing
4. Ask the agent to recall the previously shared information
5. Display memory search results if available in the response
6. Clean up by deleting the agent (note: memory store persists)
## Important notes
- **Memory Store Lifecycle**: Memory stores are long-lived resources and are NOT deleted when the agent is deleted. Clean them up separately via Azure Portal or Python SDK.
- **Scope**: The `scope` parameter isolates memories per user/context. Use unique identifiers for different users.
- **Update Delay**: The `UpdateDelay` parameter controls how quickly new memories are indexed.
@@ -1,27 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
<!-- Include Workflows source generator when using [MessageHandler] attribute -->
<ProjectReference Include="$(RepoRoot)/dotnet/src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj"
OutputItemType="Analyzer"
ReferenceOutputAssembly="false"
GlobalPropertiesToRemove="TargetFramework" />
</ItemGroup>
</Project>
@@ -1,19 +0,0 @@
{
"profiles": {
"Default": {
"commandName": "Project"
},
"Reboot": {
"commandName": "Project",
"commandLineArgs": "\"My PC keeps rebooting and I can't use it.\""
},
"License": {
"commandName": "Project",
"commandLineArgs": "\"My M365 Office license key isn't activating.\""
},
"Windows": {
"commandName": "Project",
"commandLineArgs": "\"How do I change my mouse speed settings?\""
}
}
}
@@ -1,11 +0,0 @@
{
"profiles": {
"Default": {
"commandName": "Project"
},
"Bus Stop": {
"commandName": "Project",
"commandLineArgs": "\"What is the closest bus-stop that is next to ISHONI YAKINIKU in Seattle?\""
}
}
}
@@ -1,32 +0,0 @@
{
"profiles": {
"Marketing": {
"commandName": "Project",
"commandLineArgs": "\"Marketing.yaml\" \"An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours\""
},
"MathChat": {
"commandName": "Project",
"commandLineArgs": "\"MathChat.yaml\" \"How would you compute the value of PI?\""
},
"Question": {
"commandName": "Project",
"commandLineArgs": "\"Question.yaml\" \"Iko\""
},
"Research": {
"commandName": "Project",
"commandLineArgs": "\"DeepResearch.yaml\" \"What is the closest bus-stop that is next to ISHONI YAKINIKU in Seattle?\""
},
"ResponseObject": {
"commandName": "Project",
"commandLineArgs": "\"ResponseObject.yaml\" \"Can you help me plan a trip somewhere soon?\""
},
"UserInput": {
"commandName": "Project",
"commandLineArgs": "\"UserInput.yaml\" \"Iko\""
},
"ParseValue": {
"commandName": "Project",
"commandLineArgs": "\"Pradeep-ParseValue-Number.yaml\" \"Test this case:\""
}
}
}
@@ -1,11 +0,0 @@
{
"profiles": {
"Default": {
"commandName": "Project"
},
"Soup": {
"commandName": "Project",
"commandLineArgs": "\"What is the soup of the day?\""
}
}
}
@@ -1,28 +0,0 @@
{
"profiles": {
"Marketing": {
"commandName": "Project",
"commandLineArgs": "\"Marketing.yaml\""
},
"MathChat": {
"commandName": "Project",
"commandLineArgs": "\"MathChat.yaml\""
},
"Question": {
"commandName": "Project",
"commandLineArgs": "\"Question.yaml\""
},
"Research": {
"commandName": "Project",
"commandLineArgs": "\"DeepResearch.yaml\""
},
"ResponseObject": {
"commandName": "Project",
"commandLineArgs": "\"ResponseObject.yaml\""
},
"UserInput": {
"commandName": "Project",
"commandLineArgs": "\"UserInput.yaml\""
}
}
}
@@ -1,11 +0,0 @@
{
"profiles": {
"Default": {
"commandName": "Project"
},
"Vacation": {
"commandName": "Project",
"commandLineArgs": "\"I'd like to go on vacation.\""
}
}
}
@@ -1,39 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<PropertyGroup>
<InjectIsExternalInitOnLegacy>true</InjectIsExternalInitOnLegacy>
<InjectSharedFoundryAgents>true</InjectSharedFoundryAgents>
<InjectSharedWorkflowsExecution>true</InjectSharedWorkflowsExecution>
<InjectSharedWorkflowsSettings>true</InjectSharedWorkflowsSettings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.Extensions.Configuration" />
<PackageReference Include="Microsoft.Extensions.Configuration.Binder" />
<PackageReference Include="Microsoft.Extensions.Configuration.EnvironmentVariables" />
<PackageReference Include="Microsoft.Extensions.Configuration.Json" />
<PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" />
<PackageReference Include="Microsoft.Extensions.DependencyInjection" />
<PackageReference Include="Microsoft.Extensions.Logging" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Workflows.Declarative\Microsoft.Agents.AI.Workflows.Declarative.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Workflows.Declarative.AzureAI\Microsoft.Agents.AI.Workflows.Declarative.AzureAI.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Workflows.Declarative.Mcp\Microsoft.Agents.AI.Workflows.Declarative.Mcp.csproj" />
</ItemGroup>
<ItemGroup>
<None Include="InvokeMcpTool.yaml">
<CopyToOutputDirectory>Always</CopyToOutputDirectory>
</None>
</ItemGroup>
</Project>
@@ -1,63 +0,0 @@
#
# This workflow demonstrates invoking MCP tools directly from a declarative workflow.
# Uses the Foundry MCP server to search AI model details.
#
# The workflow:
# 1. Accepts a model search term as input
# 2. Invokes the Foundry MCP tool
# 3. Invokes the Microsoft Learn MCP tool
# 4. Uses an agent to summarize the results
#
# Example input:
# gpt-4.1
#
kind: Workflow
trigger:
kind: OnConversationStart
id: workflow_invoke_mcp_tool
actions:
# Set the search query from user input or use default
- kind: SetVariable
id: set_search_query
variable: Local.SearchQuery
value: =System.LastMessage.Text
# Invoke MCP search tool on Foundry MCP server
- kind: InvokeMcpTool
id: invoke_foundry_search
serverUrl: https://mcp.ai.azure.com
serverLabel: azure_mcp_server
toolName: model_details_get
conversationId: =System.ConversationId
arguments:
modelName: =Local.SearchQuery
output:
autoSend: true
result: Local.FoundrySearchResult
# Invoke MCP search tool on Microsoft Learn server
- kind: InvokeMcpTool
id: invoke_docs_search
serverUrl: https://learn.microsoft.com/api/mcp
serverLabel: microsoft_docs
toolName: microsoft_docs_search
conversationId: =System.ConversationId
arguments:
query: =Local.SearchQuery
output:
autoSend: true
result: Local.DocsSearchResult
# Use the search agent to provide a helpful response based on results
- kind: InvokeAzureAgent
id: summarize_results
agent:
name: McpSearchAgent
conversationId: =System.ConversationId
input:
messages: =UserMessage("Based on the search results for '" & Local.SearchQuery & "', please provide a helpful summary.")
output:
autoSend: true
result: Local.Summary
@@ -1,141 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates using the InvokeMcpTool action to call MCP (Model Context Protocol)
// server tools directly from a declarative workflow. MCP servers expose tools that can be
// invoked to perform specific tasks, like searching documentation or executing operations.
using Azure.AI.Projects;
using Azure.AI.Projects.OpenAI;
using Azure.Core;
using Azure.Identity;
using Microsoft.Agents.AI.Workflows.Declarative.Mcp;
using Microsoft.Extensions.Configuration;
using Shared.Foundry;
using Shared.Workflows;
namespace Demo.Workflows.Declarative.InvokeMcpTool;
/// <summary>
/// Demonstrates a workflow that uses InvokeMcpTool to call MCP server tools
/// directly from the workflow.
/// </summary>
/// <remarks>
/// <para>
/// The InvokeMcpTool action allows workflows to invoke tools on MCP (Model Context Protocol)
/// servers. This enables:
/// </para>
/// <list type="bullet">
/// <item>Searching external data sources like documentation</item>
/// <item>Executing operations on remote servers</item>
/// <item>Integrating with MCP-compatible services</item>
/// </list>
/// <para>
/// This sample uses the Microsoft Learn MCP server to search Azure documentation and the Azure foundry MCP server to get AI model details.
/// When you run the sample, provide an AI model (e.g. gpt-4.1-mini) as input,
/// The workflow will use the MCP tools to find relevant information about the model from Microsoft Learn and foundry, then an agent will summarize the results.
/// </para>
/// <para>
/// See the README.md file in the parent folder (../README.md) for detailed
/// information about the configuration required to run this sample.
/// </para>
/// </remarks>
internal sealed class Program
{
public static async Task Main(string[] args)
{
// Initialize configuration
IConfiguration configuration = Application.InitializeConfig();
Uri foundryEndpoint = new(configuration.GetValue(Application.Settings.FoundryEndpoint));
// Ensure sample agent exists in Foundry
await CreateAgentAsync(foundryEndpoint, configuration);
// Get input from command line or console
string workflowInput = Application.GetInput(args);
// Create the MCP tool handler for invoking MCP server tools.
// The HttpClient callback allows configuring authentication per MCP server.
// Different MCP servers may require different authentication configurations.
// For Production scenarios, consider implementing a more robust HttpClient management strategy to reuse HttpClient instances and manage their lifetimes appropriately.
List<HttpClient> createdHttpClients = [];
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
DefaultAzureCredential credential = new();
DefaultMcpToolHandler mcpToolHandler = new(
httpClientProvider: async (serverUrl, cancellationToken) =>
{
if (serverUrl.StartsWith("https://mcp.ai.azure.com", StringComparison.OrdinalIgnoreCase))
{
// Acquire token for the Azure MCP server
AccessToken token = await credential.GetTokenAsync(
new TokenRequestContext(["https://mcp.ai.azure.com/.default"]),
cancellationToken);
// Create HttpClient with Authorization header
HttpClient httpClient = new();
httpClient.DefaultRequestHeaders.Authorization =
new System.Net.Http.Headers.AuthenticationHeaderValue("Bearer", token.Token);
createdHttpClients.Add(httpClient);
return httpClient;
}
if (serverUrl.StartsWith("https://learn.microsoft.com", StringComparison.OrdinalIgnoreCase))
{
// Microsoft Learn MCP server does not require authentication
HttpClient httpClient = new();
createdHttpClients.Add(httpClient);
return httpClient;
}
// Return null for unknown servers to use the default HttpClient without auth.
return null;
});
try
{
// Create the workflow factory with MCP tool provider
WorkflowFactory workflowFactory = new("InvokeMcpTool.yaml", foundryEndpoint)
{
McpToolHandler = mcpToolHandler
};
// Execute the workflow
WorkflowRunner runner = new() { UseJsonCheckpoints = true };
await runner.ExecuteAsync(workflowFactory.CreateWorkflow, workflowInput);
}
finally
{
// Clean up connections and dispose created HttpClients
await mcpToolHandler.DisposeAsync();
foreach (HttpClient httpClient in createdHttpClients)
{
httpClient.Dispose();
}
}
}
private static async Task CreateAgentAsync(Uri foundryEndpoint, IConfiguration configuration)
{
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
AIProjectClient aiProjectClient = new(foundryEndpoint, new DefaultAzureCredential());
await aiProjectClient.CreateAgentAsync(
agentName: "McpSearchAgent",
agentDefinition: DefineSearchAgent(configuration),
agentDescription: "Provides information based on search results");
}
private static PromptAgentDefinition DefineSearchAgent(IConfiguration configuration)
{
return new PromptAgentDefinition(configuration.GetValue(Application.Settings.FoundryModel))
{
Instructions =
"""
You are a helpful assistant that answers questions based on search results.
Use the information provided in the conversation history to answer questions.
If the information is already available in the conversation, use it directly.
Be concise and helpful in your responses.
"""
};
}
}
@@ -1,11 +0,0 @@
{
"profiles": {
"Default": {
"commandName": "Project"
},
"Water Bottle": {
"commandName": "Project",
"commandLineArgs": "\"An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours.\""
}
}
}
@@ -1,32 +0,0 @@
{
"profiles": {
"Marketing": {
"commandName": "Project",
"commandLineArgs": "\"Marketing.yaml\" \"An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours\""
},
"MathChat": {
"commandName": "Project",
"commandLineArgs": "\"MathChat.yaml\" \"How would you compute the value of PI?\""
},
"Question": {
"commandName": "Project",
"commandLineArgs": "\"Question.yaml\" \"Iko\""
},
"Research": {
"commandName": "Project",
"commandLineArgs": "\"DeepResearch.yaml\" \"What is the closest bus-stop that is next to ISHONI YAKINIKU in Seattle?\""
},
"ResponseObject": {
"commandName": "Project",
"commandLineArgs": "\"ResponseObject.yaml\" \"Can you help me plan a trip somewhere soon?\""
},
"UserInput": {
"commandName": "Project",
"commandLineArgs": "\"UserInput.yaml\" \"Iko\""
},
"ParseValue": {
"commandName": "Project",
"commandLineArgs": "\"Pradeep-ParseValue-Number.yaml\" \"Test this case:\""
}
}
}
@@ -1,32 +0,0 @@
{
"profiles": {
"Marketing": {
"commandName": "Project",
"commandLineArgs": "\"Marketing.yaml\" \"An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours\""
},
"MathChat": {
"commandName": "Project",
"commandLineArgs": "\"MathChat.yaml\" \"How would you compute the value of PI?\""
},
"Question": {
"commandName": "Project",
"commandLineArgs": "\"Question.yaml\" \"Iko\""
},
"Research": {
"commandName": "Project",
"commandLineArgs": "\"DeepResearch.yaml\" \"What is the closest bus-stop that is next to ISHONI YAKINIKU in Seattle?\""
},
"ResponseObject": {
"commandName": "Project",
"commandLineArgs": "\"ResponseObject.yaml\" \"Can you help me plan a trip somewhere soon?\""
},
"UserInput": {
"commandName": "Project",
"commandLineArgs": "\"UserInput.yaml\" \"Iko\""
},
"ParseValue": {
"commandName": "Project",
"commandLineArgs": "\"Pradeep-ParseValue-Number.yaml\" \"Test this case:\""
}
}
}
@@ -1,11 +0,0 @@
{
"profiles": {
"Default": {
"commandName": "Project"
},
"Compute PI": {
"commandName": "Project",
"commandLineArgs": "\"How would you compute the value of PI based on its fundamental definition?\""
}
}
}
@@ -1,11 +0,0 @@
{
"profiles": {
"Default": {
"commandName": "Project"
},
"Graph API": {
"commandName": "Project",
"commandLineArgs": "\"What is Microsoft Graph API used for?\""
}
}
}
@@ -1,16 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
<ProjectReference Include="..\Concurrent\MapReduce\MapReduce.csproj" />
</ItemGroup>
</Project>
@@ -1,15 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
</ItemGroup>
</Project>
@@ -1,22 +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.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
</ItemGroup>
</Project>
@@ -1,22 +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.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
</ItemGroup>
</Project>
@@ -1,22 +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.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
</ItemGroup>
</Project>
@@ -1,10 +0,0 @@
{
"IsEncrypted": false,
"Values": {
"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
"DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
"AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
"AZURE_OPENAI_DEPLOYMENT_NAME": "<AZURE_OPENAI_DEPLOYMENT_NAME>"
}
}
@@ -1,10 +0,0 @@
{
"IsEncrypted": false,
"Values": {
"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
"DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
"AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
"AZURE_OPENAI_DEPLOYMENT_NAME": "<AZURE_OPENAI_DEPLOYMENT_NAME>"
}
}
@@ -1,10 +0,0 @@
{
"IsEncrypted": false,
"Values": {
"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
"DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
"AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
"AZURE_OPENAI_DEPLOYMENT_NAME": "<AZURE_OPENAI_DEPLOYMENT_NAME>"
}
}
@@ -1,22 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<UserSecretsId>b9c3f1e1-2fb4-5g29-0e52-53e2b7g9gf21</UserSecretsId>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AGUI\Microsoft.Agents.AI.AGUI.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
@@ -1,22 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<UserSecretsId>a8b2e9f0-1ea3-4f18-9d41-42d1a6f8fe10</UserSecretsId>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AGUI\Microsoft.Agents.AI.AGUI.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
@@ -1,21 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AGUI\Microsoft.Agents.AI.AGUI.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
@@ -1,18 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<NoWarn>$(NoWarn);CA1812</NoWarn>
</PropertyGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hosting.A2A\Microsoft.Agents.AI.Hosting.A2A.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hosting.OpenAI\Microsoft.Agents.AI.Hosting.OpenAI.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hosting\Microsoft.Agents.AI.Hosting.csproj" />
<ProjectReference Include="..\AgentWebChat.ServiceDefaults\AgentWebChat.ServiceDefaults.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Abstractions\Microsoft.Agents.AI.Abstractions.csproj" />
</ItemGroup>
</Project>
@@ -1,156 +0,0 @@
# Auth Client-Server Sample
This sample demonstrates how to authorize AI agents and their tools using OAuth 2.0 scopes. It shows two levels of access control: an endpoint-level scope (`agent.chat`) that gates access to the agent, and tool-level scopes (`expenses.view`, `expenses.approve`) that control what the agent can do on behalf of each user.
While this sample uses Keycloak to avoid complex setup in order to run the sample, Keycloak can easily be replaced with any OIDC compatible provider, including [Microsoft Entra Id](https://www.microsoft.com/security/business/identity-access/microsoft-entra-id).
## Overview
The sample has three components, all launched with a single `docker compose up`:
| Service | Port | Description |
|---------|------|-------------|
| **WebClient** | `http://localhost:8080` | Razor Pages web app with OIDC login and a chat UI that calls the AgentService |
| **AgentService** | `http://localhost:5001` | ASP.NET Minimal API hosting an expense approval agent with scope-authorized tools |
| **Keycloak** | `http://localhost:5002` | OIDC identity provider, auto-provisioned with realm, clients, scopes, and test users |
```
┌──────────────┐ OIDC login ┌───────────┐
│ WebClient │ ◄──────────────────► │ Keycloak │
│ (Razor app) │ (browser flow) │ (Docker) │
│ :8080 │ │ :5002 │
└──────┬───────┘ └─────┬─────┘
│ REST + Bearer token │
▼ │
┌───────────────┐ JWT validation ──────┘
│ AgentService │ ◄──── (jwks from Keycloak)
│ (Minimal API) │
│ :5001 │
└───────────────┘
```
## Prerequisites
- [Docker](https://docs.docker.com/get-docker/) and Docker Compose
## Configuring Environment Variables
The AgentService requires an OpenAI-compatible endpoint. Set these environment variables before running:
```bash
export OPENAI_API_KEY="<your-openai-api-key>"
export OPENAI_MODEL="gpt-4.1-mini"
```
## Running the Sample
### Option 1: Docker Compose (Recommended)
```bash
cd dotnet/samples/05-end-to-end/AspNetAgentAuthorization
docker compose up
```
This starts Keycloak, the AgentService, and the WebClient. Wait for Keycloak to finish importing the realm (you'll see `Running the server` in the logs).
#### Running in GitHub Codespaces
This sample has been built in such a way that it can be run from GitHub Codespaces.
The Agent Framework repository has a C# specific dev container, named "C# (.NET)", that is configured for Codespaces.
When running in Codespaces, the sample auto-detects the environment via
`CODESPACE_NAME` and `GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN` and configures
Keycloak and the web client accordingly. Just make the required ports public:
```bash
# Make Keycloak and WebClient ports publicly accessible
gh codespace ports visibility 5002:public 8080:public -c $CODESPACE_NAME
# Start the containers (Codespaces is auto-detected)
docker compose up
```
Then open the Codespaces-forwarded URL for port 8080 (shown in the **Ports** tab) in your browser.
### Option 2: Run Locally
1. Start Keycloak:
```bash
docker compose up keycloak
```
2. In a new terminal, start the AgentService:
```bash
cd Service
dotnet run --urls "http://localhost:5001"
```
3. In another terminal, start the WebClient:
```bash
cd RazorWebClient
dotnet run --urls "http://localhost:8080"
```
## Using the Sample
1. Open `http://localhost:8080` in your browser
2. Click **Login** — you'll be redirected to Keycloak
3. Sign in with one of the pre-configured users:
- **`testuser` / `password`** — can chat, view expenses, and approve expenses (up to €1,000)
- **`viewer` / `password`** — can chat and view expenses, but **cannot approve** them
4. Try asking the agent:
- _"Show me the pending expenses"_ — both users can do this
- _"Approve expense #1"_ — only `testuser` can do this; `viewer` will be denied
- _"Approve expense #3"_ — even `testuser` will be denied (€4,500 exceeds the €1,000 limit)
## Pre-Configured Keycloak Realm
The `keycloak/dev-realm.json` file auto-provisions:
| Resource | Details |
|----------|---------|
| **Realm** | `dev` |
| **Client: agent-service** | Confidential client (the API audience) |
| **Client: web-client** | Public client for the Razor app's OIDC login |
| **Scope: agent.chat** | Required to call the `/chat` endpoint |
| **Scope: expenses.view** | Required to list pending expenses |
| **Scope: expenses.approve** | Required to approve expenses |
| **User: testuser** | Has `agent.chat`, `expenses.view`, and `expenses.approve` scopes |
| **User: viewer** | Has `agent.chat` and `expenses.view` scopes (no approval) |
### Pre-Seeded Expenses
The service starts with five demo expenses:
| # | Description | Amount | Status |
|---|-------------|--------|--------|
| 1 | Conference travel — Berlin | €850 | Pending |
| 2 | Team dinner — Q4 celebration | €320 | Pending |
| 3 | Cloud infrastructure — annual renewal | €4,500 | Pending (over limit) |
| 4 | Office supplies — ergonomic keyboards | €675 | Pending |
| 5 | Client gift baskets — holiday season | €980 | Pending |
Keycloak admin console: `http://localhost:5002` (login: `admin` / `admin`).
## API Endpoints
### POST /chat (requires `agent.chat` scope)
```bash
# Get a token for testuser
TOKEN=$(curl -s -X POST http://localhost:5002/realms/dev/protocol/openid-connect/token \
-d "grant_type=password&client_id=web-client&username=testuser&password=password&scope=openid agent.chat expenses.view expenses.approve" \
| jq -r '.access_token')
# Chat with the agent
curl -X POST http://localhost:5001/chat \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-d '{"message": "Show me the pending expenses"}'
```
## Key Concepts Demonstrated
- **Endpoint-Level Authorization** — The `/chat` endpoint requires the `agent.chat` scope, gating access to the agent itself
- **Tool-Level Authorization** — Each agent tool checks its own scope (`expenses.view`, `expenses.approve`) at runtime, so different users have different capabilities within the same chat session
- **Scope-Based Role Mapping** — Keycloak realm roles map to OAuth scopes, allowing administrators to control which users can access which agent capabilities
@@ -1,29 +0,0 @@
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /repo
# Copy solution-level files for restore
COPY Directory.Build.props Directory.Build.targets Directory.Packages.props global.json nuget.config ./
COPY eng/ eng/
COPY src/Shared/ src/Shared/
COPY samples/Directory.Build.props samples/
# Create sentinel file so $(RepoRoot) resolves correctly inside the container.
# RepoRoot is the parent of the dir containing CODE_OF_CONDUCT.md,
# and src projects import $(RepoRoot)/dotnet/nuget/nuget-package.props.
RUN touch /CODE_OF_CONDUCT.md
# Copy project file for restore
COPY samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/RazorWebClient.csproj samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/
RUN dotnet restore samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/RazorWebClient.csproj -p:TargetFramework=net10.0 -p:TreatWarningsAsErrors=false
# Copy everything and build
COPY samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/ samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/
RUN dotnet publish samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/RazorWebClient.csproj -c Release -f net10.0 -o /app -p:TreatWarningsAsErrors=false
FROM mcr.microsoft.com/dotnet/aspnet:10.0
WORKDIR /app
COPY --from=build /app .
ENV ASPNETCORE_URLS=http://+:8080
EXPOSE 8080
ENTRYPOINT ["dotnet", "RazorWebClient.dll"]
@@ -1,35 +0,0 @@
@page
@using Microsoft.AspNetCore.Authorization
@attribute [Authorize]
@model AspNetAgentAuthorization.RazorWebClient.Pages.ChatModel
@{
Layout = "_Layout";
}
<h1>Chat with the Agent</h1>
<form method="post">
<div style="display: flex; gap: 8px; margin-bottom: 16px;">
<input type="text" name="message" value="@Model.Message" placeholder="Type your message..."
style="flex: 1; padding: 10px; border: 1px solid #ddd; border-radius: 4px; font-size: 14px;" />
<button type="submit"
style="padding: 10px 20px; background: #0066cc; color: white; border: none; border-radius: 4px; cursor: pointer; font-size: 14px;">
Send
</button>
</div>
</form>
@if (Model.Error is not null)
{
<div style="background: #fee; border: 1px solid #fcc; border-radius: 4px; padding: 12px; margin-bottom: 12px; color: #c00;">
<strong>Error:</strong> @Model.Error
</div>
}
@if (Model.Reply is not null)
{
<div style="background: #f0f7ff; border: 1px solid #cce0ff; border-radius: 4px; padding: 12px; margin-bottom: 12px;">
<div style="font-size: 12px; color: #666; margin-bottom: 4px;">Agent (responding to @Model.ReplyUser):</div>
<div>@Model.Reply</div>
</div>
}
@@ -1,79 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;
using Microsoft.AspNetCore.Authentication;
using Microsoft.AspNetCore.Mvc;
using Microsoft.AspNetCore.Mvc.RazorPages;
namespace AspNetAgentAuthorization.RazorWebClient.Pages;
public class ChatModel : PageModel
{
private readonly IHttpClientFactory _httpClientFactory;
public ChatModel(IHttpClientFactory httpClientFactory)
{
this._httpClientFactory = httpClientFactory;
}
[BindProperty]
public string? Message { get; set; }
public string? Reply { get; set; }
public string? ReplyUser { get; set; }
public string? Error { get; set; }
public void OnGet()
{
}
public async Task OnPostAsync()
{
if (string.IsNullOrWhiteSpace(this.Message))
{
return;
}
try
{
// Get the access token stored during OIDC login
string? accessToken = await this.HttpContext.GetTokenAsync("access_token");
if (accessToken is null)
{
this.Error = "No access token available. Please log in again.";
return;
}
// Call the AgentService with the Bearer token
var client = this._httpClientFactory.CreateClient("AgentService");
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", accessToken);
var payload = JsonSerializer.Serialize(new { message = this.Message });
var content = new StringContent(payload, Encoding.UTF8, "application/json");
var response = await client.PostAsync(new Uri("/chat", UriKind.Relative), content);
if (response.IsSuccessStatusCode)
{
using var json = await JsonDocument.ParseAsync(await response.Content.ReadAsStreamAsync());
this.Reply = json.RootElement.GetProperty("reply").GetString();
this.ReplyUser = json.RootElement.GetProperty("user").GetString();
}
else
{
this.Error = response.StatusCode switch
{
System.Net.HttpStatusCode.Unauthorized => "Authentication failed (401). Your session may have expired.",
System.Net.HttpStatusCode.Forbidden => "Access denied (403). Your account does not have the required 'agent.chat' scope.",
_ => $"AgentService returned {(int)response.StatusCode} {response.ReasonPhrase}."
};
}
}
catch (Exception ex)
{
this.Error = $"Failed to contact the AgentService: {ex.Message}";
}
}
}
@@ -1,18 +0,0 @@
@page
@model AspNetAgentAuthorization.RazorWebClient.Pages.IndexModel
@{
Layout = "_Layout";
}
<h1>Welcome</h1>
<p>This sample demonstrates securing an AI agent API with OAuth 2.0 / OpenID Connect.</p>
@if (User.Identity?.IsAuthenticated == true)
{
<p>You are logged in as <strong>@User.Identity.Name</strong>.</p>
<p><a href="/Chat">Go to Chat →</a></p>
}
else
{
<p>Please <a href="/Chat">log in</a> to chat with the agent.</p>
}
@@ -1,24 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using Microsoft.AspNetCore.Authentication;
using Microsoft.AspNetCore.Authentication.Cookies;
using Microsoft.AspNetCore.Authentication.OpenIdConnect;
using Microsoft.AspNetCore.Mvc;
using Microsoft.AspNetCore.Mvc.RazorPages;
namespace AspNetAgentAuthorization.RazorWebClient.Pages;
public class IndexModel : PageModel
{
public void OnGet()
{
}
public IActionResult OnGetLogout()
{
return this.SignOut(
new AuthenticationProperties { RedirectUri = "/" },
CookieAuthenticationDefaults.AuthenticationScheme,
OpenIdConnectDefaults.AuthenticationScheme);
}
}
@@ -1,35 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Auth Agent Chat</title>
<style>
body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; background: #f5f5f5; }
nav { display: flex; justify-content: space-between; align-items: center; padding: 10px 0; border-bottom: 1px solid #ddd; margin-bottom: 20px; }
nav a { text-decoration: none; color: #0066cc; margin-left: 10px; }
.user-info { color: #666; }
.container { background: white; border-radius: 8px; padding: 20px; box-shadow: 0 1px 3px rgba(0,0,0,0.1); }
h1 { color: #333; }
</style>
</head>
<body>
<nav>
<strong>🤖 Auth Agent Chat</strong>
<div>
@if (User.Identity?.IsAuthenticated == true)
{
<span class="user-info">@User.Identity.Name</span>
<a href="/Index?handler=Logout">Logout</a>
}
else
{
<a href="/Chat">Login</a>
}
</div>
</nav>
<div class="container">
@RenderBody()
</div>
</body>
</html>
@@ -1,3 +0,0 @@
@using Microsoft.AspNetCore.Authentication
@namespace AspNetAgentAuthorization.RazorWebClient.Pages
@addTagHelper *, Microsoft.AspNetCore.Mvc.TagHelpers
@@ -1,142 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates an OIDC-authenticated Razor Pages web client
// that calls a JWT-secured AI agent REST API.
using Microsoft.AspNetCore.Authentication.Cookies;
using Microsoft.AspNetCore.Authentication.OpenIdConnect;
using Microsoft.AspNetCore.DataProtection;
using Microsoft.IdentityModel.Protocols.OpenIdConnect;
WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
builder.Services.AddRazorPages();
// Persist data protection keys so antiforgery tokens survive container rebuilds
builder.Services.AddDataProtection()
.PersistKeysToFileSystem(new DirectoryInfo("/app/keys"));
// ---------------------------------------------------------------------------
// Authentication: Cookie + OpenID Connect (Keycloak)
// ---------------------------------------------------------------------------
string authority = builder.Configuration["Auth:Authority"]
?? throw new InvalidOperationException("Auth:Authority is not configured.");
// PublicKeycloakUrl is the browser-facing Keycloak base URL. When the
// web-client runs inside Docker, Authority points to the internal hostname
// (e.g. http://keycloak:8080) for backchannel discovery, while
// PublicKeycloakUrl is what the browser can reach (e.g. http://localhost:5002).
// When running outside Docker, Authority already IS the public URL and
// PublicKeycloakUrl is not needed.
string? publicKeycloakUrl = builder.Configuration["Auth:PublicKeycloakUrl"];
// In Codespaces, override the public URLs with the tunnel endpoints.
string? codespaceName = Environment.GetEnvironmentVariable("CODESPACE_NAME");
string? codespaceDomain = Environment.GetEnvironmentVariable("GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN");
bool isCodespaces = !string.IsNullOrEmpty(codespaceName) && !string.IsNullOrEmpty(codespaceDomain);
if (isCodespaces)
{
publicKeycloakUrl = $"https://{codespaceName}-5002.{codespaceDomain}";
}
// Derive the internal base URL from Authority for URL rewriting.
string internalKeycloakBase = new Uri(authority).GetLeftPart(UriPartial.Authority);
builder.Services
.AddAuthentication(options =>
{
options.DefaultScheme = CookieAuthenticationDefaults.AuthenticationScheme;
options.DefaultChallengeScheme = OpenIdConnectDefaults.AuthenticationScheme;
})
.AddCookie()
.AddOpenIdConnect(options =>
{
options.Authority = authority;
options.ClientId = builder.Configuration["Auth:ClientId"]
?? throw new InvalidOperationException("Auth:ClientId is not configured.");
options.ResponseType = OpenIdConnectResponseType.Code;
options.SaveTokens = true;
options.GetClaimsFromUserInfoEndpoint = true;
// Request scopes so the access token includes them
options.Scope.Clear();
options.Scope.Add("openid");
options.Scope.Add("profile");
options.Scope.Add("email");
options.Scope.Add("agent.chat");
options.Scope.Add("expenses.view");
options.Scope.Add("expenses.approve");
// For local development with HTTP-only Keycloak
options.RequireHttpsMetadata = !builder.Environment.IsDevelopment();
// When the web-client is inside Docker, the backchannel Authority uses
// an internal hostname that differs from the browser-facing URL.
// Rewrite the authorization/logout endpoints so the browser is
// redirected to the public Keycloak URL, and disable issuer validation
// because the token issuer (public URL) won't match the discovery
// document issuer (internal URL).
if (publicKeycloakUrl is not null)
{
#pragma warning disable CA5404 // Token issuer validation disabled: backchannel uses internal Docker hostname while tokens are issued via the public URL.
options.TokenValidationParameters.ValidateIssuer = false;
#pragma warning restore CA5404
// The UserInfo endpoint is on the internal URL but the token
// issuer is the public URL — Keycloak rejects the mismatch.
// The ID token already contains all needed claims.
options.GetClaimsFromUserInfoEndpoint = false;
// In Codespaces the tunnel delivers with Host: localhost, so the
// auto-generated redirect_uri is wrong. Override it explicitly.
string? publicWebClientBase = isCodespaces
? $"https://{codespaceName}-8080.{codespaceDomain}"
: null;
options.Events = new OpenIdConnectEvents
{
OnRedirectToIdentityProvider = context =>
{
context.ProtocolMessage.IssuerAddress = context.ProtocolMessage.IssuerAddress
.Replace(internalKeycloakBase, publicKeycloakUrl);
if (publicWebClientBase is not null)
{
context.ProtocolMessage.RedirectUri = $"{publicWebClientBase}/signin-oidc";
}
return Task.CompletedTask;
},
OnRedirectToIdentityProviderForSignOut = context =>
{
context.ProtocolMessage.IssuerAddress = context.ProtocolMessage.IssuerAddress
.Replace(internalKeycloakBase, publicKeycloakUrl);
if (publicWebClientBase is not null)
{
context.ProtocolMessage.PostLogoutRedirectUri = $"{publicWebClientBase}/signout-callback-oidc";
}
return Task.CompletedTask;
},
};
}
});
// ---------------------------------------------------------------------------
// HttpClient for calling the AgentService — attaches Bearer token
// ---------------------------------------------------------------------------
builder.Services.AddHttpClient("AgentService", client =>
{
string baseUrl = builder.Configuration["AgentService:BaseUrl"] ?? "http://localhost:5001";
client.BaseAddress = new Uri(baseUrl);
});
WebApplication app = builder.Build();
app.UseStaticFiles();
app.UseRouting();
app.UseAuthentication();
app.UseAuthorization();
app.MapRazorPages();
await app.RunAsync();
@@ -1,12 +0,0 @@
{
"profiles": {
"RazorWebClient": {
"commandName": "Project",
"launchBrowser": true,
"environmentVariables": {
"ASPNETCORE_ENVIRONMENT": "Development"
},
"applicationUrl": "https://localhost:58080;http://localhost:8080"
}
}
}
@@ -1,15 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<NoWarn>$(NoWarn);CS1591</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.AspNetCore.Authentication.OpenIdConnect" />
</ItemGroup>
</Project>
@@ -1,15 +0,0 @@
{
"Logging": {
"LogLevel": {
"Default": "Information",
"Microsoft.AspNetCore": "Warning"
}
},
"Auth": {
"Authority": "http://localhost:5002/realms/dev",
"ClientId": "web-client"
},
"AgentService": {
"BaseUrl": "http://localhost:5001"
}
}
@@ -1,34 +0,0 @@
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /repo
# Copy solution-level files for restore
COPY Directory.Build.props Directory.Build.targets Directory.Packages.props global.json nuget.config ./
COPY eng/ eng/
COPY nuget/ nuget/
COPY src/Shared/ src/Shared/
COPY samples/Directory.Build.props samples/
# Create sentinel file so $(RepoRoot) resolves correctly inside the container.
# RepoRoot is the parent of the dir containing CODE_OF_CONDUCT.md,
# and src projects import $(RepoRoot)/dotnet/nuget/nuget-package.props.
RUN touch /CODE_OF_CONDUCT.md && mkdir -p /dotnet/nuget && cp /repo/nuget/* /dotnet/nuget/
# Copy project files for restore
COPY src/Microsoft.Agents.AI.Abstractions/Microsoft.Agents.AI.Abstractions.csproj src/Microsoft.Agents.AI.Abstractions/
COPY src/Microsoft.Agents.AI/Microsoft.Agents.AI.csproj src/Microsoft.Agents.AI/
COPY src/Microsoft.Agents.AI.OpenAI/Microsoft.Agents.AI.OpenAI.csproj src/Microsoft.Agents.AI.OpenAI/
COPY samples/05-end-to-end/AspNetAgentAuthorization/Service/Service.csproj samples/05-end-to-end/AspNetAgentAuthorization/Service/
RUN dotnet restore samples/05-end-to-end/AspNetAgentAuthorization/Service/Service.csproj -p:TargetFramework=net10.0 -p:TreatWarningsAsErrors=false
# Copy everything and build
COPY src/ src/
COPY samples/05-end-to-end/AspNetAgentAuthorization/Service/ samples/05-end-to-end/AspNetAgentAuthorization/Service/
RUN dotnet publish samples/05-end-to-end/AspNetAgentAuthorization/Service/Service.csproj -c Release -f net10.0 -o /app -p:TreatWarningsAsErrors=false
FROM mcr.microsoft.com/dotnet/aspnet:10.0
WORKDIR /app
COPY --from=build /app .
ENV ASPNETCORE_URLS=http://+:5001
EXPOSE 5001
ENTRYPOINT ["dotnet", "Service.dll"]
@@ -1,110 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Collections.Concurrent;
using System.ComponentModel;
namespace AspNetAgentAuthorization.Service;
/// <summary>
/// Represents an expense awaiting approval.
/// </summary>
public sealed class Expense
{
public int Id { get; init; }
public string Description { get; init; } = string.Empty;
public decimal Amount { get; init; }
public string Submitter { get; init; } = string.Empty;
public string Status { get; set; } = "Pending";
public string? ApprovedBy { get; set; }
}
/// <summary>
/// Manages expense approvals. Pre-seeded with demo data so there are
/// expenses to review immediately. Uses <see cref="IUserContext"/> to
/// identify the caller and enforce scope-based permissions.
/// </summary>
public sealed class ExpenseService
{
/// <summary>Maximum amount (EUR) that can be approved.</summary>
private const decimal ApprovalLimit = 1000m;
private static readonly ConcurrentDictionary<int, Expense> s_expenses = new(
new Dictionary<int, Expense>
{
[1] = new() { Id = 1, Description = "Conference travel — Berlin", Amount = 850m, Submitter = "Alice" },
[2] = new() { Id = 2, Description = "Team dinner — Q4 celebration", Amount = 320m, Submitter = "Bob" },
[3] = new() { Id = 3, Description = "Cloud infrastructure — annual renewal", Amount = 4500m, Submitter = "Carol" },
[4] = new() { Id = 4, Description = "Office supplies — ergonomic keyboards", Amount = 675m, Submitter = "Dave" },
[5] = new() { Id = 5, Description = "Client gift baskets — holiday season", Amount = 980m, Submitter = "Eve" },
});
private readonly IUserContext _userContext;
public ExpenseService(IUserContext userContext)
{
this._userContext = userContext;
}
/// <summary>
/// Lists all pending expenses awaiting approval.
/// </summary>
[Description("Lists all pending expenses awaiting approval. Requires the expenses.view scope.")]
public string ListPendingExpenses()
{
if (!this._userContext.Scopes.Contains("expenses.view"))
{
return "Access denied. You do not have the expenses.view scope.";
}
var pending = s_expenses.Values
.Where(e => e.Status == "Pending")
.OrderBy(e => e.Id)
.ToList();
if (pending.Count == 0)
{
return "No pending expenses.";
}
return string.Join("\n", pending.Select(e =>
$"#{e.Id}: {e.Description} — €{e.Amount:N2} (submitted by {e.Submitter})"));
}
/// <summary>
/// Approves a pending expense by its ID.
/// </summary>
[Description("Approves a pending expense by its ID. Requires the expenses.approve scope.")]
public string ApproveExpense([Description("The ID of the expense to approve")] int expenseId)
{
if (!this._userContext.Scopes.Contains("expenses.approve"))
{
return "Access denied. You do not have the expenses.approve scope.";
}
if (!s_expenses.TryGetValue(expenseId, out var expense))
{
return $"Expense #{expenseId} not found.";
}
if (expense.Status != "Pending")
{
return $"Expense #{expenseId} has already been approved.";
}
if (expense.Amount > ApprovalLimit)
{
return $"Cannot approve expense #{expenseId} (€{expense.Amount:N2}). " +
$"Amount exceeds the €{ApprovalLimit:N2} approval limit.";
}
expense.Status = "Approved";
expense.ApprovedBy = this._userContext.DisplayName;
return $"Expense #{expenseId} (\"{expense.Description}\", €{expense.Amount:N2}) has been approved.";
}
}
@@ -1,130 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to authorize AI agent tools using OAuth 2.0
// scopes. The /chat endpoint requires the "agent.chat" scope, and each tool
// checks its own scope (expenses.view, expenses.approve) at runtime.
using System.Security.Claims;
using System.Text.Json.Serialization;
using AspNetAgentAuthorization.Service;
using Microsoft.Agents.AI;
using Microsoft.AspNetCore.Authentication.JwtBearer;
using Microsoft.AspNetCore.Authorization;
using Microsoft.Extensions.AI;
using OpenAI;
WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
// ---------------------------------------------------------------------------
// Authentication: JWT Bearer tokens validated against the OIDC provider
// ---------------------------------------------------------------------------
builder.Services
.AddAuthentication(JwtBearerDefaults.AuthenticationScheme)
.AddJwtBearer(options =>
{
options.Authority = builder.Configuration["Auth:Authority"]
?? throw new InvalidOperationException("Auth:Authority is not configured.");
options.Audience = builder.Configuration["Auth:Audience"]
?? throw new InvalidOperationException("Auth:Audience is not configured.");
// For local development with HTTP-only Keycloak
options.RequireHttpsMetadata = !builder.Environment.IsDevelopment();
options.TokenValidationParameters.ValidateAudience = true;
options.TokenValidationParameters.ValidateLifetime = true;
// In Codespaces, tokens are issued with the public tunnel URL as
// issuer (Keycloak sees X-Forwarded-Host from the tunnel) but the
// agent-service discovers Keycloak via the internal Docker hostname.
// Disable issuer validation in development to handle this mismatch.
options.TokenValidationParameters.ValidateIssuer = !builder.Environment.IsDevelopment();
});
// ---------------------------------------------------------------------------
// Authorization: policy requiring the "agent.chat" scope
// ---------------------------------------------------------------------------
builder.Services.AddAuthorizationBuilder()
.AddPolicy("AgentChat", policy =>
policy.RequireAuthenticatedUser()
.RequireAssertion(context =>
{
// Keycloak puts scopes in the "scope" claim (space-delimited)
var scopeClaim = context.User.FindFirstValue("scope");
if (scopeClaim is not null)
{
var scopes = scopeClaim.Split(' ', StringSplitOptions.RemoveEmptyEntries);
if (scopes.Contains("agent.chat", StringComparer.OrdinalIgnoreCase))
{
return true;
}
}
return false;
}));
// ---------------------------------------------------------------------------
// Configure JSON serialization
// ---------------------------------------------------------------------------
builder.Services.ConfigureHttpJsonOptions(options =>
options.SerializerOptions.TypeInfoResolverChain.Add(SampleServiceSerializerContext.Default));
// ---------------------------------------------------------------------------
// Create the AI agent with expense approval tools, registered in DI
// ---------------------------------------------------------------------------
string apiKey = builder.Configuration["OPENAI_API_KEY"]
?? throw new InvalidOperationException("Set the OPENAI_API_KEY environment variable.");
string model = builder.Configuration["OPENAI_MODEL"] ?? "gpt-4.1-mini";
// Here we are using Singleton lifetime, since none of the services, function tools and user context classes in the sample have state that are per request.
// You should evaluate the appropriate lifetime for your own services and tools based on their behavior and dependencies.
// E.g. if any of the service instances or tools maintain state that is specific to a user, and each request may be from a different user,
// you should use Scoped lifetime instead, so that a new instance is created for each request.
// Note that if you use Scoped lifetime for any dependencies, you must also use Scoped lifetime for any class that uses it, including the agent itself.
builder.Services.AddHttpContextAccessor();
builder.Services.AddSingleton<IUserContext, KeycloakUserContext>();
builder.Services.AddSingleton<ExpenseService>();
builder.Services.AddSingleton<AIAgent>(sp =>
{
var expenseService = sp.GetRequiredService<ExpenseService>();
return new OpenAIClient(apiKey)
.GetChatClient(model)
.AsIChatClient()
.AsAIAgent(
name: "ExpenseApprovalAgent",
instructions: "You are an expense approval assistant. You can list pending expenses "
+ "and approve them if the user has the required permissions and approval limit. "
+ "Keep responses concise.",
tools:
[
AIFunctionFactory.Create(expenseService.ListPendingExpenses),
AIFunctionFactory.Create(expenseService.ApproveExpense),
]);
});
WebApplication app = builder.Build();
app.UseAuthentication();
app.UseAuthorization();
// ---------------------------------------------------------------------------
// POST /chat — requires the "agent.chat" scope
// ---------------------------------------------------------------------------
app.MapPost("/chat", [Authorize(Policy = "AgentChat")] async (ChatRequest request, IUserContext userContext, AIAgent agent) =>
{
var response = await agent.RunAsync(request.Message);
return Results.Ok(new ChatResponse(response.Text, userContext.DisplayName));
});
await app.RunAsync();
// ---------------------------------------------------------------------------
// Request / Response models
// ---------------------------------------------------------------------------
internal sealed record ChatRequest(string Message);
internal sealed record ChatResponse(string Reply, string User);
[JsonSerializable(typeof(ChatRequest))]
[JsonSerializable(typeof(ChatResponse))]
internal sealed partial class SampleServiceSerializerContext : JsonSerializerContext;
@@ -1,12 +0,0 @@
{
"profiles": {
"Service": {
"commandName": "Project",
"launchBrowser": true,
"environmentVariables": {
"ASPNETCORE_ENVIRONMENT": "Development"
},
"applicationUrl": "https://localhost:55001;http://localhost:5001"
}
}
}
@@ -1,20 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<NoWarn>$(NoWarn);CS1591</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.AspNetCore.Authentication.JwtBearer" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
@@ -1,99 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Security.Claims;
namespace AspNetAgentAuthorization.Service;
/// <summary>
/// Provides the authenticated user's identity for the current request.
/// </summary>
public interface IUserContext
{
/// <summary>Unique identifier for the current user (e.g. the OIDC "sub" claim).</summary>
string UserId { get; }
/// <summary>Login name for the current user.</summary>
string UserName { get; }
/// <summary>Human-readable display name (e.g. "Test User").</summary>
string DisplayName { get; }
/// <summary>OAuth scopes granted in the current access token.</summary>
IReadOnlySet<string> Scopes { get; }
}
/// <summary>
/// Resolves the current user's identity from Keycloak-specific JWT claims.
/// Keycloak uses <c>sub</c> for the user ID, <c>preferred_username</c>
/// for the login name, <c>given_name</c>/<c>family_name</c> for the
/// display name, and <c>scope</c> (space-delimited) for granted scopes.
/// Registered as a singleton — claims are parsed once per request and
/// cached in <see cref="HttpContext.Items"/>.
/// </summary>
public sealed class KeycloakUserContext : IUserContext
{
private static readonly object s_cacheKey = new();
private readonly IHttpContextAccessor _httpContextAccessor;
public KeycloakUserContext(IHttpContextAccessor httpContextAccessor)
{
this._httpContextAccessor = httpContextAccessor;
}
public string UserId => this.GetOrCreateCachedInfo().UserId;
public string UserName => this.GetOrCreateCachedInfo().UserName;
public string DisplayName => this.GetOrCreateCachedInfo().DisplayName;
public IReadOnlySet<string> Scopes => this.GetOrCreateCachedInfo().Scopes;
private CachedUserInfo GetOrCreateCachedInfo()
{
HttpContext? httpContext = this._httpContextAccessor.HttpContext;
if (httpContext is not null && httpContext.Items.TryGetValue(s_cacheKey, out object? cached) && cached is CachedUserInfo info)
{
return info;
}
info = ParseClaims(httpContext?.User);
if (httpContext is not null)
{
httpContext.Items[s_cacheKey] = info;
}
return info;
}
private static CachedUserInfo ParseClaims(ClaimsPrincipal? user)
{
string userId = user?.FindFirstValue(ClaimTypes.NameIdentifier)
?? user?.FindFirstValue("sub")
?? "anonymous";
string userName = user?.FindFirstValue("preferred_username")
?? user?.FindFirstValue(ClaimTypes.Name)
?? "unknown";
string? givenName = user?.FindFirstValue("given_name") ?? user?.FindFirstValue(ClaimTypes.GivenName);
string? familyName = user?.FindFirstValue("family_name") ?? user?.FindFirstValue(ClaimTypes.Surname);
string displayName = (givenName, familyName) switch
{
(not null, not null) => $"{givenName} {familyName}",
(not null, null) => givenName,
(null, not null) => familyName,
_ => userName,
};
string? scopeClaim = user?.FindFirstValue("scope");
IReadOnlySet<string> scopes = scopeClaim is not null
? new HashSet<string>(scopeClaim.Split(' ', StringSplitOptions.RemoveEmptyEntries), StringComparer.OrdinalIgnoreCase)
: new HashSet<string>(StringComparer.OrdinalIgnoreCase);
return new CachedUserInfo(userId, userName, displayName, scopes);
}
private sealed record CachedUserInfo(string UserId, string UserName, string DisplayName, IReadOnlySet<string> Scopes);
}
@@ -1,12 +0,0 @@
{
"Logging": {
"LogLevel": {
"Default": "Information",
"Microsoft.AspNetCore": "Warning"
}
},
"Auth": {
"Authority": "http://localhost:5002/realms/dev",
"Audience": "agent-service"
}
}
@@ -1,80 +0,0 @@
services:
keycloak:
image: quay.io/keycloak/keycloak:latest
container_name: auth-keycloak
environment:
- KC_BOOTSTRAP_ADMIN_USERNAME=admin
- KC_BOOTSTRAP_ADMIN_PASSWORD=admin
- KC_HOSTNAME_STRICT=false
- KC_PROXY_HEADERS=xforwarded
volumes:
- ./keycloak/dev-realm.json:/opt/keycloak/data/import/dev-realm.json
command: ["start-dev", "--import-realm"]
ports:
- "5002:8080"
healthcheck:
test: ["CMD-SHELL", "exec 3<>/dev/tcp/localhost/8080 && echo -e 'GET /realms/master HTTP/1.1\\r\\nHost: localhost\\r\\nConnection: close\\r\\n\\r\\n' >&3 && cat <&3 | grep -q '200'"]
interval: 10s
timeout: 5s
retries: 30
start_period: 30s
# One-shot init container that registers the Codespaces redirect URI
# with Keycloak after it becomes healthy. Auto-detects Codespaces via
# CODESPACE_NAME and GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN env vars.
keycloak-init:
image: curlimages/curl:latest
container_name: auth-keycloak-init
environment:
- KEYCLOAK_URL=http://keycloak:8080
- CODESPACE_NAME=${CODESPACE_NAME:-}
- GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN=${GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN:-}
volumes:
- ./keycloak/setup-redirect-uris.sh:/setup-redirect-uris.sh:ro
entrypoint: ["sh", "/setup-redirect-uris.sh"]
depends_on:
keycloak:
condition: service_healthy
agent-service:
build:
context: ../../..
dockerfile: samples/05-end-to-end/AspNetAgentAuthorization/Service/Dockerfile
container_name: auth-agent-service
environment:
- ASPNETCORE_ENVIRONMENT=Development
- Auth__Authority=http://keycloak:8080/realms/dev
- Auth__Audience=agent-service
- OPENAI_API_KEY=${OPENAI_API_KEY}
- OPENAI_MODEL=${OPENAI_MODEL:-gpt-4.1-mini}
ports:
- "5001:5001"
depends_on:
keycloak:
condition: service_healthy
web-client:
build:
context: ../../..
dockerfile: samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/Dockerfile
container_name: auth-web-client
environment:
- ASPNETCORE_ENVIRONMENT=Development
- Auth__Authority=http://keycloak:8080/realms/dev
- Auth__PublicKeycloakUrl=http://localhost:5002
- Auth__ClientId=web-client
- AgentService__BaseUrl=http://agent-service:5001
- CODESPACE_NAME=${CODESPACE_NAME:-}
- GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN=${GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN:-}
ports:
- "8080:8080"
volumes:
- web-client-keys:/app/keys
depends_on:
keycloak:
condition: service_healthy
agent-service:
condition: service_started
volumes:
web-client-keys:
@@ -1,232 +0,0 @@
{
"realm": "dev",
"enabled": true,
"sslRequired": "none",
"registrationAllowed": false,
"roles": {
"realm": [
{
"name": "agent-chat-user",
"description": "Grants access to the agent.chat scope"
},
{
"name": "expenses-viewer",
"description": "Grants access to the expenses.view scope"
},
{
"name": "expenses-approver",
"description": "Grants access to the expenses.approve scope"
}
]
},
"scopeMappings": [
{
"clientScope": "agent.chat",
"roles": ["agent-chat-user"]
},
{
"clientScope": "expenses.view",
"roles": ["expenses-viewer"]
},
{
"clientScope": "expenses.approve",
"roles": ["expenses-approver"]
}
],
"clientScopes": [
{
"name": "openid",
"description": "OpenID Connect scope",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true"
},
"protocolMappers": [
{
"name": "sub",
"protocol": "openid-connect",
"protocolMapper": "oidc-sub-mapper",
"config": {
"introspection.token.claim": "true",
"access.token.claim": "true"
}
}
]
},
{
"name": "profile",
"description": "OpenID Connect profile scope",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true"
},
"protocolMappers": [
{
"name": "preferred_username",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-attribute-mapper",
"config": {
"user.attribute": "username",
"claim.name": "preferred_username",
"jsonType.label": "String",
"id.token.claim": "true",
"access.token.claim": "true",
"userinfo.token.claim": "true"
}
},
{
"name": "given_name",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-attribute-mapper",
"config": {
"user.attribute": "firstName",
"claim.name": "given_name",
"jsonType.label": "String",
"id.token.claim": "true",
"access.token.claim": "true",
"userinfo.token.claim": "true"
}
},
{
"name": "family_name",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-attribute-mapper",
"config": {
"user.attribute": "lastName",
"claim.name": "family_name",
"jsonType.label": "String",
"id.token.claim": "true",
"access.token.claim": "true",
"userinfo.token.claim": "true"
}
}
]
},
{
"name": "email",
"description": "OpenID Connect email scope",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true"
}
},
{
"name": "agent.chat",
"description": "Allows chatting with the agent",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true"
}
},
{
"name": "expenses.view",
"description": "Allows viewing pending expenses",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true"
}
},
{
"name": "expenses.approve",
"description": "Allows approving pending expenses",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true"
}
},
{
"name": "agent-service-audience",
"description": "Adds the agent-service audience to access tokens",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "false",
"display.on.consent.screen": "false"
},
"protocolMappers": [
{
"name": "agent-service-audience-mapper",
"protocol": "openid-connect",
"protocolMapper": "oidc-audience-mapper",
"config": {
"included.client.audience": "agent-service",
"id.token.claim": "false",
"access.token.claim": "true"
}
}
]
}
],
"clients": [
{
"clientId": "agent-service",
"enabled": true,
"publicClient": false,
"secret": "agent-service-secret",
"directAccessGrantsEnabled": true,
"serviceAccountsEnabled": false,
"standardFlowEnabled": false,
"protocol": "openid-connect"
},
{
"clientId": "web-client",
"enabled": true,
"publicClient": true,
"directAccessGrantsEnabled": true,
"standardFlowEnabled": true,
"fullScopeAllowed": false,
"protocol": "openid-connect",
"redirectUris": [
"http://localhost:8080/*"
],
"webOrigins": [
"http://localhost:8080"
],
"defaultClientScopes": [
"openid",
"profile",
"email",
"agent-service-audience"
],
"optionalClientScopes": [
"agent.chat",
"expenses.view",
"expenses.approve"
]
}
],
"users": [
{
"username": "testuser",
"enabled": true,
"email": "testuser@example.com",
"firstName": "Test",
"lastName": "User",
"realmRoles": ["agent-chat-user", "expenses-viewer", "expenses-approver"],
"credentials": [
{
"type": "password",
"value": "password",
"temporary": false
}
]
},
{
"username": "viewer",
"enabled": true,
"email": "viewer@example.com",
"firstName": "View",
"lastName": "Only",
"realmRoles": ["agent-chat-user", "expenses-viewer"],
"credentials": [
{
"type": "password",
"value": "password",
"temporary": false
}
]
}
]
}
@@ -1,50 +0,0 @@
#!/bin/bash
# Adds an extra redirect URI to the Keycloak web-client configuration.
# Auto-detects GitHub Codespaces via CODESPACE_NAME and
# GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN environment variables.
set -e
KEYCLOAK_URL="${KEYCLOAK_URL:-http://keycloak:8080}"
# Auto-detect Codespaces
if [ -n "$CODESPACE_NAME" ] && [ -n "$GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN" ]; then
WEBCLIENT_PUBLIC_URL="https://${CODESPACE_NAME}-8080.${GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN}"
fi
if [ -z "$WEBCLIENT_PUBLIC_URL" ]; then
echo "Not running in Codespaces — skipping redirect URI setup."
exit 0
fi
echo "Configuring Keycloak redirect URIs for: $WEBCLIENT_PUBLIC_URL"
# Get admin token
TOKEN=$(curl -sf -X POST "$KEYCLOAK_URL/realms/master/protocol/openid-connect/token" \
-d "grant_type=password&client_id=admin-cli&username=admin&password=admin" \
| sed -n 's/.*"access_token":"\([^"]*\)".*/\1/p')
if [ -z "$TOKEN" ]; then
echo "ERROR: Failed to get admin token" >&2
exit 1
fi
# Get web-client UUID
CLIENT_UUID=$(curl -sf "$KEYCLOAK_URL/admin/realms/dev/clients?clientId=web-client" \
-H "Authorization: Bearer $TOKEN" \
| sed -n 's/.*"id":"\([^"]*\)".*/\1/p')
if [ -z "$CLIENT_UUID" ]; then
echo "ERROR: Failed to find web-client UUID" >&2
exit 1
fi
# Update redirect URIs and web origins
curl -sf -X PUT "$KEYCLOAK_URL/admin/realms/dev/clients/$CLIENT_UUID" \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-d "{
\"redirectUris\": [\"http://localhost:8080/*\", \"${WEBCLIENT_PUBLIC_URL}/*\"],
\"webOrigins\": [\"http://localhost:8080\", \"${WEBCLIENT_PUBLIC_URL}\"]
}"
echo "Keycloak redirect URIs updated successfully."
@@ -1,69 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);MEAI001</NoWarn>
<!--
Disable central package management for this project.
This project requires explicit package references with versions specified inline rather than
inheriting them from Directory.Packages.props. This is necessary because a Docker image will
be created from this project, and the Docker build process only has access to this folder
and cannot access parent folders where Directory.Packages.props resides.
-->
<ManagePackageVersionsCentrally>false</ManagePackageVersionsCentrally>
</PropertyGroup>
<!--
Remove analyzer PackageReference items inherited from Directory.Packages.props.
Note: ManagePackageVersionsCentrally only controls PackageVersion items, not PackageReference items.
Directory.Packages.props contains both PackageVersion and PackageReference entries for analyzers,
and the PackageReference items are always inherited through MSBuild imports regardless of the
ManagePackageVersionsCentrally setting. We must explicitly remove them before adding our own versions.
-->
<ItemGroup>
<PackageReference Remove="Microsoft.CodeAnalysis.NetAnalyzers" />
<PackageReference Remove="Microsoft.VisualStudio.Threading.Analyzers" />
<PackageReference Remove="xunit.analyzers" />
<PackageReference Remove="Moq.Analyzers" />
<PackageReference Remove="Roslynator.Analyzers" />
<PackageReference Remove="Roslynator.CodeAnalysis.Analyzers" />
<PackageReference Remove="Roslynator.Formatting.Analyzers" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.AgentServer.AgentFramework" Version="1.0.0-beta.9" />
<PackageReference Include="Azure.AI.OpenAI" Version="2.8.0-beta.1" />
<PackageReference Include="Azure.Identity" Version="1.17.1" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-rc1" />
</ItemGroup>
<!-- Add analyzers with compatible versions -->
<ItemGroup>
<PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Microsoft.VisualStudio.Threading.Analyzers" Version="17.14.15">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.CodeAnalysis.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.Formatting.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
</ItemGroup>
</Project>
@@ -1,20 +0,0 @@
# Build the application
FROM mcr.microsoft.com/dotnet/sdk:10.0-alpine AS build
WORKDIR /src
# Copy files from the current directory on the host to the working directory in the container
COPY . .
RUN dotnet restore
RUN dotnet build -c Release --no-restore
RUN dotnet publish -c Release --no-build -o /app -f net10.0
# Run the application
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
# Copy everything needed to run the app from the "build" stage.
COPY --from=build /app .
EXPOSE 8088
ENTRYPOINT ["dotnet", "AgentThreadAndHITL.dll"]

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