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Merge branch 'main' into features/3768-devui-aspire-integration

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This commit is contained in:
Tommaso Stocchi
2026-04-03 20:29:53 +02:00
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parent 1f53a8cc5a e94cfc6aef
commit c7dc3e427b
837 changed files with 22534 additions and 32556 deletions
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.github/ISSUE_TEMPLATE/python-issue.yml
+1 -1
View File
@@ -47,7 +47,7 @@ body:
attributes:
label: Package Versions
description: List the agent-framework-* packages and versions you are using
placeholder: "e.g., agent-framework-core: 1.0.0, agent-framework-azure-ai: 1.0.0"
placeholder: "e.g., agent-framework-core: 1.0.0, agent-framework-foundry: 1.0.0"
validations:
required: true
.github/workflows/dotnet-build-and-test.yml
+2 -2
View File
@@ -82,7 +82,7 @@ jobs:
.github
dotnet
python
workflow-samples
declarative-agents
- name: Setup dotnet
uses: actions/setup-dotnet@v5.2.0
@@ -152,7 +152,7 @@ jobs:
.github
dotnet
python
workflow-samples
declarative-agents
# Start Cosmos DB Emulator for all integration tests and only for unit tests when CosmosDB changes happened)
- name: Start Azure Cosmos DB Emulator
.github/workflows/dotnet-integration-tests.yml
+1 -1
View File
@@ -38,7 +38,7 @@ jobs:
.github
dotnet
python
workflow-samples
declarative-agents
- name: Start Azure Cosmos DB Emulator
if: runner.os == 'Windows'
.github/workflows/dotnet-verify-samples.yml
+122
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@@ -0,0 +1,122 @@
#
# Runs the .NET sample verification tool, which builds and executes sample projects
# and verifies their output using deterministic checks and AI-powered verification.
#
# Results are displayed as a GitHub Job Summary and the CSV report is uploaded as an artifact.
#
name: dotnet-verify-samples
on:
workflow_dispatch:
inputs:
category:
description: "Sample category to run (blank for all)"
required: false
type: choice
options:
- ""
- "01-get-started"
- "02-agents"
- "03-workflows"
parallelism:
description: "Max parallel sample runs"
required: false
default: "8"
type: string
schedule:
- cron: "0 6 * * 1-5" # Weekdays at 6:00 UTC
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
permissions:
contents: read
id-token: write
jobs:
verify-samples:
runs-on: ubuntu-latest
environment: 'integration'
timeout-minutes: 90
steps:
- uses: actions/checkout@v6
with:
persist-credentials: false
sparse-checkout: |
.
.github
dotnet
workflow-samples
- name: Setup dotnet
uses: actions/setup-dotnet@v5.2.0
with:
global-json-file: ${{ github.workspace }}/dotnet/global.json
- 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: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
- name: Run verify-samples
id: verify
working-directory: dotnet
shell: bash
run: |
CATEGORY_ARG=""
if [ -n "$CATEGORY_INPUT" ]; then
CATEGORY_ARG="--category $CATEGORY_INPUT"
fi
dotnet run --project eng/verify-samples -- \
$CATEGORY_ARG \
--parallel "$PARALLELISM" \
--md results.md \
--csv results.csv \
--log results.log
env:
CATEGORY_INPUT: ${{ github.event.inputs.category || '' }}
PARALLELISM: ${{ github.event.inputs.parallelism || '8' }}
# 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 }}
# 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 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: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}
- name: Write Job Summary
if: always()
working-directory: dotnet
shell: bash
run: |
if [ -f results.md ]; then
cat results.md >> "$GITHUB_STEP_SUMMARY"
else
echo "⚠️ No results.md generated — verify-samples may have failed to start." >> "$GITHUB_STEP_SUMMARY"
fi
- name: Upload results
if: always()
uses: actions/upload-artifact@v7
with:
name: verify-samples-results
path: |
dotnet/results.csv
dotnet/results.log
if-no-files-found: warn
- name: Fail if samples failed
if: always() && steps.verify.outcome == 'failure'
shell: bash
run: exit 1
.github/workflows/python-check-coverage.py
+5 -5
View File
@@ -34,14 +34,14 @@ from dataclasses import dataclass
# (e.g., "packages/core/agent_framework/observability.py")
# =============================================================================
ENFORCED_TARGETS: set[str] = {
# Packages
"packages.azure-ai.agent_framework_azure_ai",
"packages.core.agent_framework",
"packages.core.agent_framework._workflows",
"packages.purview.agent_framework_purview",
# Packages (sorted alphabetically)
"packages.anthropic.agent_framework_anthropic",
"packages.azure-ai-search.agent_framework_azure_ai_search",
"packages.core.agent_framework",
"packages.core.agent_framework._workflows",
"packages.foundry.agent_framework_foundry",
"packages.openai.agent_framework_openai",
"packages.purview.agent_framework_purview",
# Individual files (if you want to enforce specific files instead of whole packages)
"packages/core/agent_framework/observability.py",
# Add more targets here as coverage improves
.github/workflows/python-integration-tests.yml
+16 -16
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@@ -60,8 +60,8 @@ jobs:
environment: integration
timeout-minutes: 60
env:
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
@@ -95,10 +95,10 @@ jobs:
environment: integration
timeout-minutes: 60
env:
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
AZURE_OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_EMBEDDING_MODEL: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
defaults:
run:
@@ -126,8 +126,6 @@ jobs:
packages/openai/tests/openai/test_openai_chat_completion_client_azure.py
packages/openai/tests/openai/test_openai_chat_client_azure.py
packages/openai/tests/openai/test_openai_embedding_client_azure.py
packages/azure-ai/tests/azure_openai
--ignore=packages/azure-ai/tests/azure_openai/test_azure_responses_client_foundry.py
-m integration
-n logical --dist worksteal
--timeout=120 --session-timeout=900 --timeout_method thread
@@ -141,7 +139,7 @@ jobs:
timeout-minutes: 60
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
ANTHROPIC_CHAT_MODEL_ID: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
ANTHROPIC_CHAT_MODEL: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
defaults:
run:
@@ -203,14 +201,15 @@ jobs:
timeout-minutes: 60
env:
UV_PYTHON: "3.11"
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
FUNCTIONS_WORKER_RUNTIME: "python"
@@ -257,12 +256,14 @@ jobs:
environment: integration
timeout-minutes: 60
env:
AZURE_AI_PROJECT_ENDPOINT: ${{ secrets.AZUREAI__ENDPOINT }}
AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME }}
FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION }}
FOUNDRY_MODELS_ENDPOINT: ${{ vars.FOUNDRY_MODELS_ENDPOINT || '' }}
FOUNDRY_MODELS_API_KEY: ${{ secrets.FOUNDRY_MODELS_API_KEY || '' }}
FOUNDRY_EMBEDDING_MODEL: ${{ vars.FOUNDRY_EMBEDDING_MODEL || '' }}
FOUNDRY_IMAGE_EMBEDDING_MODEL: ${{ vars.FOUNDRY_IMAGE_EMBEDDING_MODEL || '' }}
LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
defaults:
run:
@@ -288,7 +289,6 @@ jobs:
timeout-minutes: 15
run: >
uv run pytest --import-mode=importlib
packages/azure-ai/tests/azure_openai/test_azure_responses_client_foundry.py
packages/foundry/tests
-m integration
-n logical --dist worksteal
.github/workflows/python-merge-tests.yml
+17 -28
View File
@@ -37,7 +37,7 @@ jobs:
azureChanged: ${{ steps.filter.outputs.azure }}
miscChanged: ${{ steps.filter.outputs.misc }}
functionsChanged: ${{ steps.filter.outputs.functions }}
azureAiChanged: ${{ steps.filter.outputs.azure-ai }}
foundryChanged: ${{ steps.filter.outputs.foundry }}
cosmosChanged: ${{ steps.filter.outputs.cosmos }}
steps:
- uses: actions/checkout@v6
@@ -62,9 +62,7 @@ jobs:
azure:
- 'python/packages/openai/**'
- 'python/packages/core/agent_framework/azure/**'
- 'python/packages/azure-ai/agent_framework_azure_ai/_deprecated_azure_openai.py'
- 'python/packages/azure-ai/tests/azure_openai/**'
- 'python/samples/**/providers/azure/openai_chat_completion_client_azure*.py'
- 'python/samples/**/providers/azure/**'
misc:
- 'python/packages/anthropic/**'
- 'python/packages/ollama/**'
@@ -77,10 +75,10 @@ jobs:
functions:
- 'python/packages/azurefunctions/**'
- 'python/packages/durabletask/**'
azure-ai:
- 'python/packages/azure-ai/**'
foundry:
- 'python/packages/foundry/**'
- 'python/samples/**/providers/foundry/**'
- 'python/samples/02-agents/embeddings/foundry_embeddings.py'
cosmos:
- 'python/packages/azure-cosmos/**'
# run only if 'python' files were changed
@@ -141,8 +139,8 @@ jobs:
runs-on: ubuntu-latest
environment: integration
env:
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
@@ -194,10 +192,10 @@ jobs:
runs-on: ubuntu-latest
environment: integration
env:
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
AZURE_OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_EMBEDDING_MODEL: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
defaults:
run:
@@ -223,8 +221,6 @@ jobs:
packages/openai/tests/openai/test_openai_chat_completion_client_azure.py
packages/openai/tests/openai/test_openai_chat_client_azure.py
packages/openai/tests/openai/test_openai_embedding_client_azure.py
packages/azure-ai/tests/azure_openai
--ignore=packages/azure-ai/tests/azure_openai/test_azure_responses_client_foundry.py
-m integration
-n logical --dist worksteal
--timeout=120 --session-timeout=900 --timeout_method thread
@@ -259,7 +255,7 @@ jobs:
environment: integration
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
ANTHROPIC_CHAT_MODEL_ID: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
ANTHROPIC_CHAT_MODEL: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
defaults:
run:
@@ -334,14 +330,15 @@ jobs:
environment: integration
env:
UV_PYTHON: "3.11"
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
FUNCTIONS_WORKER_RUNTIME: "python"
@@ -396,13 +393,11 @@ jobs:
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.foundryChanged == 'true' ||
needs.paths-filter.outputs.coreChanged == 'true')
runs-on: ubuntu-latest
environment: integration
env:
AZURE_AI_PROJECT_ENDPOINT: ${{ secrets.AZUREAI__ENDPOINT }}
AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME }}
@@ -430,18 +425,12 @@ jobs:
timeout-minutes: 15
run: >
uv run pytest --import-mode=importlib
packages/azure-ai/tests/azure_openai/test_azure_responses_client_foundry.py
packages/foundry/tests
-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 AI samples
timeout-minutes: 10
if: env.RUN_SAMPLES_TESTS == 'true'
run: uv run pytest tests/samples/ -m "azure-ai"
working-directory: ./python
- name: Surface failing tests
if: always()
uses: pmeier/pytest-results-action@v0.7.2
.github/workflows/python-sample-validation.yml
+72 -92
View File
@@ -23,10 +23,8 @@ jobs:
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 }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
defaults:
run:
working-directory: python
@@ -43,10 +41,8 @@ jobs:
- name: Create .env for samples
run: |
echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
- name: Run sample validation
run: |
@@ -64,20 +60,19 @@ jobs:
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 }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
# Foundry configuration
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# Azure OpenAI configuration
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
AZURE_OPENAI_MODEL: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_EMBEDDING_MODEL: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME || vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
# OpenAI configuration
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
# GitHub MCP
GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
@@ -101,15 +96,14 @@ jobs:
run: |
echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=$AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_MODEL=$AZURE_OPENAI_MODEL" >> .env
echo "AZURE_OPENAI_CHAT_COMPLETION_MODEL=$AZURE_OPENAI_CHAT_COMPLETION_MODEL" >> .env
echo "AZURE_OPENAI_CHAT_MODEL=$AZURE_OPENAI_CHAT_MODEL" >> .env
echo "AZURE_OPENAI_EMBEDDING_MODEL=$AZURE_OPENAI_EMBEDDING_MODEL" >> .env
echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
echo "OPENAI_CHAT_COMPLETION_MODEL=$OPENAI_CHAT_COMPLETION_MODEL" >> .env
echo "OPENAI_CHAT_MODEL=$OPENAI_CHAT_MODEL" >> .env
echo "GITHUB_PAT=$GITHUB_PAT" >> .env
- name: Run sample validation
@@ -130,8 +124,8 @@ jobs:
env:
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
OPENAI_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
defaults:
run:
working-directory: python
@@ -150,8 +144,8 @@ jobs:
run: |
echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
echo "OPENAI_MODEL=$OPENAI_MODEL" >> .env
echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
echo "OPENAI_CHAT_COMPLETION_MODEL=$OPENAI_CHAT_COMPLETION_MODEL" >> .env
echo "OPENAI_CHAT_MODEL=$OPENAI_CHAT_MODEL" >> .env
- name: Run sample validation
run: |
@@ -169,10 +163,9 @@ jobs:
runs-on: ubuntu-latest
environment: integration
env:
AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_MODEL: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_API_VERSION: ${{ vars.AZURE_OPENAI_API_VERSION || '' }}
defaults:
run:
working-directory: python
@@ -189,10 +182,9 @@ jobs:
- name: Create .env for samples
run: |
echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_MODEL=$AZURE_OPENAI_MODEL" >> .env
echo "AZURE_OPENAI_API_VERSION=$AZURE_OPENAI_API_VERSION" >> .env
- name: Run sample validation
run: |
@@ -211,7 +203,7 @@ jobs:
environment: integration
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
ANTHROPIC_CHAT_MODEL_ID: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
ANTHROPIC_CHAT_MODEL: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
defaults:
run:
working-directory: python
@@ -229,7 +221,7 @@ jobs:
- name: Create .env for samples
run: |
echo "ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY" >> .env
echo "ANTHROPIC_CHAT_MODEL_ID=$ANTHROPIC_CHAT_MODEL_ID" >> .env
echo "ANTHROPIC_CHAT_MODEL=$ANTHROPIC_CHAT_MODEL" >> .env
- name: Run sample validation
run: |
@@ -277,7 +269,7 @@ jobs:
runs-on: ubuntu-latest
environment: integration
env:
BEDROCK_CHAT_MODEL_ID: ${{ vars.BEDROCK__CHATMODELID }}
BEDROCK_CHAT_MODEL: ${{ vars.BEDROCK__CHATMODELID }}
defaults:
run:
working-directory: python
@@ -337,11 +329,14 @@ jobs:
validate-02-agents-foundry:
name: Validate 02-agents/providers/foundry
if: false # Temporarily disabled - provider folder also contains the local Foundry sample
runs-on: ubuntu-latest
environment: integration
env:
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME || '' }}
FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION || '' }}
defaults:
run:
working-directory: python
@@ -360,6 +355,8 @@ jobs:
run: |
echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
echo "FOUNDRY_AGENT_NAME=$FOUNDRY_AGENT_NAME" >> .env
echo "FOUNDRY_AGENT_VERSION=$FOUNDRY_AGENT_VERSION" >> .env
- name: Run sample validation
run: |
@@ -448,15 +445,8 @@ jobs:
runs-on: ubuntu-latest
environment: integration
env:
# Azure AI configuration
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
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 }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
defaults:
run:
working-directory: python
@@ -475,11 +465,6 @@ jobs:
run: |
echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
- name: Run sample validation
run: |
@@ -498,12 +483,8 @@ jobs:
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 }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# A2A configuration
A2A_AGENT_HOST: http://localhost:5001/
defaults:
@@ -537,19 +518,18 @@ jobs:
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 }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# Azure OpenAI configuration
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_MODEL: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# Azure AI Search (for evaluation samples)
AZURE_SEARCH_ENDPOINT: ${{ secrets.AZURE_SEARCH_ENDPOINT }}
AZURE_SEARCH_API_KEY: ${{ secrets.AZURE_SEARCH_API_KEY }}
AZURE_SEARCH_INDEX_NAME: ${{ secrets.AZURE_SEARCH_INDEX_NAME }}
# Evaluation sample
AZURE_AI_MODEL_DEPLOYMENT_NAME_WORKFLOW: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_MODEL_WORKFLOW: ${{ vars.FOUNDRY_MODEL_WORKFLOW || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_MODEL_EVAL: ${{ vars.FOUNDRY_MODEL_EVAL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
defaults:
run:
working-directory: python
@@ -580,16 +560,15 @@ jobs:
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 }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# Azure OpenAI configuration
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_MODEL: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# OpenAI configuration
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
defaults:
run:
@@ -607,13 +586,13 @@ jobs:
- name: Create .env for samples
run: |
echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_MODEL=$AZURE_OPENAI_MODEL" >> .env
echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
echo "OPENAI_CHAT_COMPLETION_MODEL=$OPENAI_CHAT_COMPLETION_MODEL" >> .env
echo "OPENAI_CHAT_MODEL=$OPENAI_CHAT_MODEL" >> .env
- name: Run sample validation
run: |
@@ -631,18 +610,20 @@ jobs:
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
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# Azure OpenAI configuration for AF
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# OpenAI configuration
AZURE_OPENAI_MODEL: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# Azure OpenAI configuration for SK
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
# OpenAI key
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
# OpenAI configuration for SK
OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
# Copilot Studio
COPILOTSTUDIOAGENT__ENVIRONMENTID: ${{ secrets.COPILOTSTUDIOAGENT__ENVIRONMENTID }}
COPILOTSTUDIOAGENT__SCHEMANAME: ${{ secrets.COPILOTSTUDIOAGENT__SCHEMANAME }}
@@ -664,14 +645,13 @@ jobs:
- name: Create .env for samples
run: |
echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
echo "AZURE_OPENAI_MODEL=$AZURE_OPENAI_MODEL" >> .env
echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
echo "OPENAI_CHAT_COMPLETION_MODEL=$OPENAI_CHAT_COMPLETION_MODEL" >> .env
echo "OPENAI_CHAT_MODEL=$OPENAI_CHAT_MODEL" >> .env
echo "COPILOTSTUDIOAGENT__ENVIRONMENTID=$COPILOTSTUDIOAGENT__ENVIRONMENTID" >> .env
echo "COPILOTSTUDIOAGENT__SCHEMANAME=$COPILOTSTUDIOAGENT__SCHEMANAME" >> .env
echo "COPILOTSTUDIOAGENT__TENANTID=$COPILOTSTUDIOAGENT__TENANTID" >> .env
.gitignore
+3
View File
@@ -230,3 +230,6 @@ local.settings.json
# Database files
*.db
python/dotnet-ref
# Generated filtered solution files (created by eng/scripts/New-FilteredSolution.ps1)
dotnet/filtered-*.slnx
CONTRIBUTING.md
+16 -8
View File
@@ -123,22 +123,30 @@ We use and recommend the following workflow:
"issue-123" or "githubhandle-issue".
4. Make and commit your changes to your branch.
5. Add new tests corresponding to your change, if applicable.
6. Run the relevant scripts in [the section below](#development-scripts) to ensure that your build is clean and all tests are passing.
6. Run the relevant scripts in [the section below](#development-setup) to ensure that your build is clean and all tests are passing.
7. Create a PR against the repository's **main** branch.
- State in the description what issue or improvement your change is addressing.
- Verify that all the Continuous Integration checks are passing.
8. Wait for feedback or approval of your changes from the code maintainers.
9. When area owners have signed off, and all checks are green, your PR will be merged.
### Development scripts
### Development Setup
The scripts below are used to build, test, and lint within the project.
Each language has its own dev setup guide, coding standards, and build scripts:
- Python: see [python/DEV_SETUP.md](./python/DEV_SETUP.md).
- .NET:
- Build: `dotnet build`
- Test: `dotnet test`
- Linting (auto-fix): `dotnet format`
- **Python**: [Dev Setup](./python/DEV_SETUP.md) · [Coding Standard](./python/CODING_STANDARD.md) · [README](./python/README.md)
- From the `./python` directory:
- Build: `uv run poe build`
- Unit tests: `uv run poe test -A -m "not integration"`
- Integration tests: `uv run poe test -A -m integration` (requires API keys/endpoints)
- Format + lint: `uv run poe syntax`
- All checks: `uv run poe check`
- **.NET**: [README](./dotnet/README.md) · [Agent Instructions](./dotnet/AGENTS.md)
- From the `./dotnet` directory:
- Build: `dotnet build`
- Unit tests: `dotnet test --filter-query "/*UnitTests*/*/*/*"`
- Integration tests: `dotnet test --filter-query "/*IntegrationTests*/*/*/*"` (requires API keys/endpoints)
- Linting (auto-fix): `dotnet format`
### PR - CI Process
README.md
+56 -31
View File
@@ -2,7 +2,7 @@
# Welcome to Microsoft Agent Framework!
[![Microsoft Azure AI Foundry Discord](https://dcbadge.limes.pink/api/server/b5zjErwbQM?style=flat)](https://discord.gg/b5zjErwbQM)
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/b5zjErwbQM?style=flat)](https://discord.gg/b5zjErwbQM)
[![MS Learn Documentation](https://img.shields.io/badge/MS%20Learn-Documentation-blue)](https://learn.microsoft.com/en-us/agent-framework/)
[![PyPI](https://img.shields.io/pypi/v/agent-framework)](https://pypi.org/project/agent-framework/)
[![NuGet](https://img.shields.io/nuget/v/Microsoft.Agents.AI)](https://www.nuget.org/profiles/MicrosoftAgentFramework/)
@@ -28,7 +28,7 @@ Welcome to Microsoft's comprehensive multi-language framework for building, orch
Python
```bash
pip install agent-framework --pre
pip install agent-framework
# This will install all sub-packages, see `python/packages` for individual packages.
# It may take a minute on first install on Windows.
```
@@ -90,27 +90,27 @@ Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-commu
Create a simple Azure Responses Agent that writes a haiku about the Microsoft Agent Framework
```python
# pip install agent-framework --pre
# pip install agent-framework
# Use `az login` to authenticate with Azure CLI
import os
import asyncio
from agent_framework.azure import AzureOpenAIResponsesClient
from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential
async def main():
# Initialize a chat agent with Azure OpenAI Responses
# Initialize a chat agent with Microsoft Foundry
# the endpoint, deployment name, and api version can be set via environment variables
# or they can be passed in directly to the AzureOpenAIResponsesClient constructor
agent = AzureOpenAIResponsesClient(
# endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
# deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
# api_version=os.environ["AZURE_OPENAI_API_VERSION"],
# api_key=os.environ["AZURE_OPENAI_API_KEY"], # Optional if using AzureCliCredential
credential=AzureCliCredential(), # Optional, if using api_key
).as_agent(
name="HaikuBot",
instructions="You are an upbeat assistant that writes beautifully.",
# or they can be passed in directly to the FoundryChatClient constructor
agent = Agent(
client=FoundryChatClient(
credential=AzureCliCredential(),
# project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
# model=os.environ["FOUNDRY_MODEL_DEPLOYMENT_NAME"],
),
name="HaikuBot",
instructions="You are an upbeat assistant that writes beautifully.",
)
print(await agent.run("Write a haiku about Microsoft Agent Framework."))
@@ -137,24 +137,21 @@ var agent = new OpenAIClient("<apikey>")
Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
```
Create a simple Agent, using Azure OpenAI Responses with token based auth, that writes a haiku about the Microsoft Agent Framework
Create a simple Agent, using Microsoft Foundry with token-based auth, that writes a haiku about the Microsoft Agent Framework
```c#
// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
// dotnet add package Microsoft.Agents.AI.AzureAI --prerelease
// dotnet add package Azure.Identity
// Use `az login` to authenticate with Azure CLI
using System.ClientModel.Primitives;
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI;
using OpenAI.Responses;
// Replace <resource> and gpt-4o-mini with your Azure OpenAI resource name and deployment name.
var agent = new OpenAIClient(
new BearerTokenPolicy(new AzureCliCredential(), "https://ai.azure.com/.default"),
new OpenAIClientOptions() { Endpoint = new Uri("https://<resource>.openai.azure.com/openai/v1") })
.GetResponsesClient("gpt-4o-mini")
.AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
var agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
.AsAIAgent(model: deploymentName, name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
```
@@ -163,15 +160,43 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram
### Python
- [Getting Started with Agents](./python/samples/01-get-started): progressive tutorial from hello-world to hosting
- [Getting Started](./python/samples/01-get-started): progressive tutorial from hello-world to hosting
- [Agent Concepts](./python/samples/02-agents): deep-dive samples by topic (tools, middleware, providers, etc.)
- [Getting Started with Workflows](./python/samples/03-workflows): workflow creation and integration with agents
- [Workflows](./python/samples/03-workflows): workflow creation and integration with agents
- [Hosting](./python/samples/04-hosting): A2A, Azure Functions, Durable Task hosting
- [End-to-End](./python/samples/05-end-to-end): full applications, evaluation, and demos
### .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](./dotnet/samples/01-get-started): progressive tutorial from hello agent to hosting
- [Agent Concepts](./dotnet/samples/02-agents/Agents): basic agent creation and tool usage
- [Agent Providers](./dotnet/samples/02-agents/AgentProviders): samples showing different agent providers
- [Workflows](./dotnet/samples/03-workflows): advanced multi-agent patterns and workflow orchestration
- [Hosting](./dotnet/samples/04-hosting): A2A, Durable Agents, Durable Workflows
- [End-to-End](./dotnet/samples/05-end-to-end): full applications and demos
## Troubleshooting
### Authentication
| Problem | Cause | Fix |
|---------|-------|-----|
| Authentication errors when using Azure credentials | Not signed in to Azure CLI | Run `az login` before starting your app |
| API key errors | Wrong or missing API key | Verify the key and ensure it's for the correct resource/provider |
> **Tip:** `DefaultAzureCredential` is convenient for development but in production, consider using a specific credential (e.g., `ManagedIdentityCredential`) to avoid latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
### Environment Variables
The samples typically read configuration from environment variables. Common required variables:
| Variable | Used by | Purpose |
|----------|---------|---------|
| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI samples | Your Azure OpenAI resource URL |
| `AZURE_OPENAI_DEPLOYMENT_NAME` | Azure OpenAI samples | Model deployment name (e.g. `gpt-4o-mini`) |
| `AZURE_AI_PROJECT_ENDPOINT` | Microsoft Foundry samples | Your Microsoft Foundry project endpoint |
| `AZURE_AI_MODEL_DEPLOYMENT_NAME` | Microsoft Foundry samples | Model deployment name |
| `OPENAI_API_KEY` | OpenAI (non-Azure) samples | Your OpenAI platform API key |
## Contributor Resources
agent-samples/README.md → declarative-agents/agent-samples/README.md
+1 -1
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@@ -1,3 +1,3 @@
# Declarative Agents
This folder contains sample agent definitions that can be run using the declarative agent support, for python see the [declarative agent python sample folder](../python/samples/02-agents/declarative/).
This folder contains sample agent definitions that can be run using the declarative agent support, for python see the [declarative agent python sample folder](../../python/samples/02-agents/declarative/).
agent-samples/azure/AzureOpenAI.yaml → declarative-agents/agent-samples/azure/AzureOpenAI.yaml
View File
agent-samples/azure/AzureOpenAIAssistants.yaml → declarative-agents/agent-samples/azure/AzureOpenAIAssistants.yaml
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agent-samples/azure/AzureOpenAIChat.yaml → declarative-agents/agent-samples/azure/AzureOpenAIChat.yaml
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agent-samples/azure/AzureOpenAIResponses.yaml → declarative-agents/agent-samples/azure/AzureOpenAIResponses.yaml
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agent-samples/chatclient/Assistant.yaml → declarative-agents/agent-samples/chatclient/Assistant.yaml
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agent-samples/chatclient/GetWeather.yaml → declarative-agents/agent-samples/chatclient/GetWeather.yaml
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agent-samples/foundry/FoundryAgent.yaml → declarative-agents/agent-samples/foundry/FoundryAgent.yaml
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agent-samples/foundry/MicrosoftLearnAgent.yaml → declarative-agents/agent-samples/foundry/MicrosoftLearnAgent.yaml
+2 -2
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@@ -3,13 +3,13 @@ name: MicrosoftLearnAgent
description: Microsoft Learn Agent
instructions: You answer questions by searching the Microsoft Learn content only.
model:
id: =Env.AZURE_FOUNDRY_PROJECT_MODEL_ID
id: =Env.FOUNDRY_MODEL
options:
temperature: 0.9
topP: 0.95
connection:
kind: remote
endpoint: =Env.AZURE_FOUNDRY_PROJECT_ENDPOINT
endpoint: =Env.FOUNDRY_PROJECT_ENDPOINT
tools:
- kind: mcp
name: microsoft_learn
agent-samples/foundry/PersistentAgent.yaml → declarative-agents/agent-samples/foundry/PersistentAgent.yaml
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agent-samples/openai/OpenAI.yaml → declarative-agents/agent-samples/openai/OpenAI.yaml
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agent-samples/openai/OpenAIAssistants.yaml → declarative-agents/agent-samples/openai/OpenAIAssistants.yaml
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agent-samples/openai/OpenAIChat.yaml → declarative-agents/agent-samples/openai/OpenAIChat.yaml
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agent-samples/openai/OpenAIResponses.yaml → declarative-agents/agent-samples/openai/OpenAIResponses.yaml
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workflow-samples/CustomerSupport.yaml → declarative-agents/workflow-samples/CustomerSupport.yaml
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workflow-samples/DeepResearch.yaml → declarative-agents/workflow-samples/DeepResearch.yaml
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workflow-samples/Marketing.yaml → declarative-agents/workflow-samples/Marketing.yaml
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workflow-samples/MathChat.yaml → declarative-agents/workflow-samples/MathChat.yaml
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workflow-samples/README.md → declarative-agents/workflow-samples/README.md
+2 -2
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@@ -10,8 +10,8 @@ Workflow workflow = DeclarativeWorkflowBuilder.Build("Marketing.yaml", options);
```
These example workflows may be executed by the workflow
[Samples](../dotnet/samples/03-workflows/Declarative)
[Samples](../../dotnet/samples/03-workflows/Declarative)
that are present in this repository.
> See the [README.md](../dotnet/samples/03-workflows/Declarative/README.md)
> See the [README.md](../../dotnet/samples/03-workflows/Declarative/README.md)
associated with the samples for configuration details.
docs/decisions/0021-provider-leading-clients.md
+1 -1
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@@ -37,7 +37,7 @@ Key changes:
4. **New `FoundryChatClient`** in azure-ai for Azure AI Foundry Responses API access, built on `RawFoundryChatClient(RawOpenAIChatClient)`.
5. **All deprecated `AzureOpenAI*` classes** consolidated into a single file (`_deprecated_azure_openai.py`) in the azure-ai package for clean future deletion.
6. **Core's `agent_framework.openai` and `agent_framework.azure` namespaces** become lazy-loading gateways, preserving backward-compatible import paths while removing hard dependencies.
7. **Unified `model` parameter** replaces `model_id` (OpenAI), `deployment_name` (Azure OpenAI), and `model_deployment_name` (Azure AI) across all client constructors. The term `model` is intentionally generic: it naturally maps to an OpenAI model name *and* to an Azure OpenAI deployment name, making it straightforward to use `OpenAIChatClient` with either OpenAI or Azure OpenAI backends (via `AsyncAzureOpenAI`). Environment variables are similarly unified (e.g., `OPENAI_MODEL` instead of separate `OPENAI_RESPONSES_MODEL_ID` / `OPENAI_CHAT_MODEL_ID`).
7. **Unified `model` parameter** replaces `model_id` (OpenAI), `deployment_name` (Azure OpenAI), and `model_deployment_name` (Azure AI) across all client constructors. The term `model` is intentionally generic: it naturally maps to an OpenAI model name *and* to an Azure OpenAI deployment name, making it straightforward to use `OpenAIChatClient` with either OpenAI or Azure OpenAI backends (via `AsyncAzureOpenAI`). Environment variables are similarly unified (e.g., `OPENAI_MODEL` instead of separate `OPENAI_CHAT_MODEL_ID` / `OPENAI_CHAT_COMPLETION_MODEL_ID`).
8. **`FoundryAgent`** replaces the pattern of `Agent(client=AzureAIClient(...))` for connecting to pre-configured agents in Azure AI Foundry (PromptAgents and HostedAgents). The underlying `RawFoundryAgentChatClient` is an implementation detail — most users interact only with `FoundryAgent`. `AzureAIAgentClient` is separately deprecated as it refers to the V1 Agents Service API. See below for design rationale.
### Foundry Agent Design: `FoundryAgentClient` vs `FoundryAgent`
docs/decisions/0022-chat-history-persistence-consistency.md
+27 -14
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@@ -42,7 +42,7 @@ The persistence timing and `FunctionResultContent` trimming behaviors are interr
## Considered Options
- Option 1: Per-run persistence with opt-in FRC (FunctionResultContent) trimming
- Option 2: Opt-in per-service-call persistence (via `SimulateServiceStoredChatHistory`)
- Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)
## Pros and Cons of the Options
@@ -57,12 +57,12 @@ Keep the current default behavior of persisting chat history only at the end of
- Bad, because if the process crashes mid-loop, all intermediate progress from the current run is lost, not satisfying driver C.
- Bad, because this option alone does not provide a way for users to opt into per-service-call persistence, not satisfying driver E.
### Option 2: Opt-in per-service-call persistence (via `SimulateServiceStoredChatHistory`)
### Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)
Introduce an optional SimulateServiceStoredChatHistory setting to persist chat history after each individual service call within the FIC loop, matching the AI service's behavior. Trailing `FunctionResultContent` trimming is unnecessary with this approach (it is naturally handled).
Introduce an optional RequirePerServiceCallChatHistoryPersistence setting to persist chat history after each individual service call within the FIC loop, matching the AI service's behavior. Trailing `FunctionResultContent` trimming is unnecessary with this approach (it is naturally handled).
Settings:
- `SimulateServiceStoredChatHistory` = `true`
- `RequirePerServiceCallChatHistoryPersistence` = `true`
- Good, because the stored history matches the service's behavior when opting in for both timing and content, fully satisfying driver A.
- Good, because intermediate progress is preserved if the process is interrupted, satisfying driver C.
@@ -73,36 +73,49 @@ Settings:
## Decision Outcome
Chosen option: **Option 2: Opt-in per-service-call persistence (via `SimulateServiceStoredChatHistory`)**. The existing per-run persistence behavior is retained as-is, requiring no changes from users. Per-service-call persistence is available as an opt-in feature via the `SimulateServiceStoredChatHistory` setting. This satisfies drivers B (atomicity) and D (simplicity) for the common case, while fully satisfying driver A (consistency) for users who opt into simulated service-stored behavior. Users who need per-service-call persistence for recoverability (driver C) can enable it explicitly.
Chosen option: **Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)**. The existing per-run persistence behavior is retained as-is, requiring no changes from users. Per-service-call persistence is available as an opt-in feature via the `RequirePerServiceCallChatHistoryPersistence` setting. This satisfies drivers B (atomicity) and D (simplicity) for the common case, while fully satisfying driver A (consistency) for users who opt into simulated service-stored behavior. Users who need per-service-call persistence for recoverability (driver C) can enable it explicitly.
### Configuration Matrix
The behavior depends on the combination of `UseProvidedChatClientAsIs` and `SimulateServiceStoredChatHistory`:
The behavior depends on the combination of `UseProvidedChatClientAsIs` and `RequirePerServiceCallChatHistoryPersistence`:
| `UseProvidedChatClientAsIs` | `SimulateServiceStoredChatHistory` | Behavior |
| `UseProvidedChatClientAsIs` | `RequirePerServiceCallChatHistoryPersistence` | Behavior |
|---|---|---|
| `false` (default) | `false` (default) | **Per-run persistence.** Messages are persisted at the end of the full agent run via the `ChatHistoryProvider`. |
| `false` | `true` | **Per-service-call persistence (simulated).** A `ServiceStoredSimulatingChatClient` middleware is automatically injected into the chat client pipeline between `FunctionInvokingChatClient` and the leaf `IChatClient`. Messages are persisted after each service call. A sentinel `ConversationId` causes FIC to treat the conversation as service-managed. |
| `false` | `true` | **Per-service-call persistence (simulated).** A `PerServiceCallChatHistoryPersistingChatClient` middleware is automatically injected into the chat client pipeline between `FunctionInvokingChatClient` and the leaf `IChatClient`. Messages are persisted after each service call. A sentinel `ConversationId` causes FIC to treat the conversation as service-managed. |
| `true` | `false` | **Per-run persistence.** No middleware is injected because the user has provided a custom chat client stack. Messages are persisted at the end of the run. |
| `true` | `true` | **User responsibility.** The system checks whether the custom chat client stack includes a `ServiceStoredSimulatingChatClient`. If not, a warning is emitted — the user is expected to have added their own per-service-call persistence mechanism. End-of-run persistence is skipped. |
| `true` | `true` | **User responsibility.** The system checks whether the custom chat client stack includes a `PerServiceCallChatHistoryPersistingChatClient`. If not, a warning is emitted — the user is expected to have added their own per-service-call persistence mechanism. End-of-run persistence is skipped. |
### Consequences
- Good, because per-run persistence is atomic by default — chat history is only updated when the full run succeeds, satisfying driver B.
- Good, because the default mental model is simple: one run = one history update, satisfying driver D.
- Good, because users who opt into `SimulateServiceStoredChatHistory` get stored history that matches the service's behavior for both timing and content, fully satisfying driver A.
- Good, because users who opt into `RequirePerServiceCallChatHistoryPersistence` get stored history that matches the service's behavior for both timing and content, fully satisfying driver A.
- Good, because per-service-call persistence preserves intermediate progress if the process is interrupted, satisfying driver C when opted in.
- Good, because no separate `FunctionResultContent` trimming logic is needed when per-service-call persistence is active — it is naturally handled.
- Good, because conflict detection (configurable via `ThrowOnChatHistoryProviderConflict`, `WarnOnChatHistoryProviderConflict`, `ClearOnChatHistoryProviderConflict`) prevents misconfiguration when a service returns a `ConversationId` alongside a configured `ChatHistoryProvider`.
- Bad, because per-service-call persistence (when opted in) may leave chat history in an incomplete state if the run fails mid-loop (e.g., `FunctionCallContent` stored without corresponding `FunctionResultContent`), requiring manual recovery in rare cases.
- Neutral, because users who want per-service-call consistency can opt in via `SimulateServiceStoredChatHistory = true`, satisfying driver E.
- Neutral, because users who want per-service-call consistency can opt in via `RequirePerServiceCallChatHistoryPersistence = true`, satisfying driver E.
- Neutral, because increased write frequency from per-service-call persistence may impact performance for some storage backends; this can be mitigated with a caching decorator.
### Implementation Notes
#### Conversation ID Consistency
We should introduce a separate `ConversationIdPersistingChatClient`, middleware which allows us to
persist response `ConversationIds` during the FICC loop. This could be used with or without
`ServiceStoredSimulatingChatClient`.
When `RequirePerServiceCallChatHistoryPersistence` is enabled, the `PerServiceCallChatHistoryPersistingChatClient`
decorator also updates `session.ConversationId` after each service call. This handles two scenarios:
1. **Framework-managed chat history** — the decorator sets a sentinel `ConversationId` on the response
so that `FunctionInvokingChatClient` treats the conversation as service-managed (clearing accumulated
history between iterations and not injecting duplicate `FunctionCallContent` during approval processing).
2. **Service-stored chat history** — when the service returns a real `ConversationId`, the decorator
updates `session.ConversationId` immediately after each service call, rather than deferring the update
to the end of the run. This ensures intermediate ConversationId changes are captured even if the
process is interrupted mid-loop.
For some service-stored scenarios (e.g., the Conversations API with the Responses API), there is only
one thread with one ID, so every service call returns the same ConversationId and this per-call update
makes no practical difference. Enabling `RequirePerServiceCallChatHistoryPersistence` ensures consistent
per-service-call behavior across all service types regardless of how they manage ConversationIds.
docs/features/vector-stores-and-embeddings/README.md
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@@ -177,7 +177,7 @@ This feature ports the vector store abstractions, embedding generator abstractio
**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/`)
#### 2.1 — Foundry inference embedding (in `packages/foundry/`)
#### 2.2 — Ollama embedding (in `packages/ollama/`)
#### 2.3 — Anthropic embedding (in `packages/anthropic/`)
#### 2.4 — Bedrock embedding (in `packages/bedrock/`)
dotnet/.github/skills/project-structure/SKILL.md
Vendored
+2 -2
View File
@@ -12,8 +12,8 @@ dotnet/
│ ├── Microsoft.Agents.AI.Abstractions/ # Core AI agent abstractions
│ ├── Microsoft.Agents.AI.A2A/ # Agent-to-Agent (A2A) provider
│ ├── Microsoft.Agents.AI.OpenAI/ # OpenAI provider
│ ├── Microsoft.Agents.AI.AzureAI/ # Azure AI Foundry Agents (v2) provider
│ ├── Microsoft.Agents.AI.AzureAI.Persistent/ # Legacy Azure AI Foundry Agents (v1) provider
│ ├── Microsoft.Agents.AI.Foundry/ # Microsoft Foundry Agents (v2) provider
│ ├── Microsoft.Agents.AI.AzureAI.Persistent/ # Legacy Microsoft Foundry Agents (v1) provider
│ ├── Microsoft.Agents.AI.Anthropic/ # Anthropic provider
│ ├── Microsoft.Agents.AI.Workflows/ # Workflow orchestration
│ └── ... # Other packages
dotnet/.github/skills/verify-samples-tool/SKILL.md
Vendored
+214
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@@ -0,0 +1,214 @@
---
name: verify-samples-tool
description: How to use the verify-samples tool to run, verify, and manage sample definitions in the Agent Framework repository. Use this when adding, updating, or running sample verification.
---
# verify-samples Tool
The `verify-samples` project (`dotnet/eng/verify-samples/`) is an automated tool that runs sample projects and verifies their output using deterministic checks and AI-powered verification.
## Running verify-samples
```bash
cd dotnet
# Run all samples across all categories
dotnet run --project eng/verify-samples -- --log results.log --csv results.csv
# Run a specific category
dotnet run --project eng/verify-samples -- --category 02-agents --log results.log
# Run specific samples by name
dotnet run --project eng/verify-samples -- Agent_Step02_StructuredOutput Agent_Step09_AsFunctionTool
# Control parallelism (default 8)
dotnet run --project eng/verify-samples -- --parallel 8 --log results.log
# Combine options
dotnet run --project eng/verify-samples -- --category 03-workflows --parallel 4 --log results.log --csv results.csv --md results.md
```
### Required Environment Variables
The tool itself needs:
- `AZURE_OPENAI_ENDPOINT` — for the AI verification agent
- `AZURE_OPENAI_DEPLOYMENT_NAME` (optional, defaults to `gpt-5-mini`)
Individual samples require their own env vars (e.g., `AZURE_AI_PROJECT_ENDPOINT`). The tool automatically checks and skips samples with missing env vars.
### Output Files
- `--log results.log` — detailed per-sample log with stdout/stderr, AI reasoning, and a summary
- `--csv results.csv` — tabular summary with Sample, ProjectPath, Status, FailedChecks, and Failures columns
- `--md results.md` — Markdown summary with results table and collapsible failure details (suitable for GitHub PR comments)
## Sample Categories
Definitions are in the `dotnet/eng/verify-samples/` directory:
| Category | Config File | Registered Key |
|----------|-------------|----------------|
| 01-get-started | `GetStartedSamples.cs` | `01-get-started` |
| 02-agents | `AgentsSamples.cs` | `02-agents` |
| 03-workflows | `WorkflowSamples.cs` | `03-workflows` |
Categories are registered in `VerifyOptions.cs` in the `s_sampleSets` dictionary.
## SampleDefinition Properties
Each sample is defined as a `SampleDefinition` in the appropriate config file. Key properties:
```csharp
new SampleDefinition
{
// Required: Display name for the sample
Name = "Agent_Step02_StructuredOutput",
// Required: Relative path from dotnet/ to the sample project directory
ProjectPath = "samples/02-agents/Agents/Agent_Step02_StructuredOutput",
// Environment variables the sample requires (throws if missing)
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
// Environment variables with defaults that would prompt on console if unset
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
// Skip this sample with a reason (for structural issues only)
SkipReason = null, // or "Requires external service X."
// Deterministic checks: substrings that must appear in stdout
MustContain = ["=== Section Header ==="],
// Substrings that must NOT appear in stdout
MustNotContain = [],
// If true, only MustContain checks are used (no AI verification)
IsDeterministic = false,
// AI verification: natural-language descriptions of expected output
// Each entry describes one aspect to verify independently
ExpectedOutputDescription =
[
"The output should show structured person information with Name, Age, and Occupation fields.",
"The output should not contain error messages or stack traces.",
],
// Stdin inputs to feed to the sample (for interactive samples)
Inputs = ["Y", "Y", "Y"],
// Delay between stdin inputs in ms (default 2000, increase for LLM calls between inputs)
InputDelayMs = 3000,
}
```
## How to Add a New Sample Definition
1. **Check the sample's Program.cs** to understand:
- What environment variables it reads (look for `GetEnvironmentVariable`)
- Whether it needs stdin input (look for `Console.ReadLine`, `Application.GetInput`)
- Whether it has an external loop (look for `EXIT` patterns in YAML workflows)
- What output it produces (section headers, markers, expected behavior)
- Whether it exits on its own or runs as a server
2. **Choose the right verification strategy:**
- **Deterministic** (`IsDeterministic = true`): Use `MustContain` for samples with fixed output strings. No AI verification.
- **AI-verified** (default): Use `ExpectedOutputDescription` with semantic descriptions. Write expectations that are flexible enough for non-deterministic LLM output.
- **Both**: Use `MustContain` for fixed markers AND `ExpectedOutputDescription` for LLM-generated content.
3. **Set `SkipReason` only for structural issues:**
- Web servers that don't exit
- Multi-process client/server architectures
- Samples requiring external infrastructure (MCP servers you can't reach, Docker, etc.)
- Do NOT skip for missing env vars — the tool checks those dynamically.
4. **For interactive samples, provide `Inputs`:**
- Samples using `Application.GetInput(args)` need one initial input
- Samples with `Console.ReadLine()` approval loops need `"Y"` inputs
- YAML workflows with `externalLoop` need `"EXIT"` as the last input
- Set `InputDelayMs` to 3000-8000ms for samples with LLM calls between inputs
5. **Add the definition** to the appropriate config file (e.g., `AgentsSamples.cs`) in the `All` list.
6. **Register new categories** (if needed) in `VerifyOptions.cs` `s_sampleSets` dictionary.
### Writing Good ExpectedOutputDescription
- Write descriptions that are **semantically flexible** — LLM output varies between runs
- Each array entry should describe **one independent aspect** to verify
- Always include `"The output should not contain error messages or stack traces."` as the last entry
- Avoid exact wording expectations — use "should mention", "should contain information about", "should show"
- Bad: `"The output should say 'The weather in Amsterdam is cloudy with a high of 15°C'"`
- Good: `"The output should contain weather information about Amsterdam mentioning cloudy weather with a high of 15°C."`
### Example: Simple LLM Sample
```csharp
new SampleDefinition
{
Name = "Agent_With_AzureOpenAIChatCompletion",
ProjectPath = "samples/02-agents/AgentProviders/Agent_With_AzureOpenAIChatCompletion",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
ExpectedOutputDescription =
[
"The output should contain a joke about a pirate.",
"The output should not contain error messages or stack traces.",
],
},
```
### Example: Deterministic Sample
```csharp
new SampleDefinition
{
Name = "Workflow_Declarative_GenerateCode",
ProjectPath = "samples/03-workflows/Declarative/GenerateCode",
IsDeterministic = true,
MustContain = ["WORKFLOW: Parsing", "WORKFLOW: Defined"],
ExpectedOutputDescription = ["The output should show a YAML workflow being parsed and C# code being generated from it."],
},
```
### Example: Interactive Sample with Approval Loop
```csharp
new SampleDefinition
{
Name = "FoundryAgent_Hosted_MCP",
ProjectPath = "samples/02-agents/ModelContextProtocol/FoundryAgent_Hosted_MCP",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["Y", "Y", "Y", "Y", "Y"],
InputDelayMs = 5000,
ExpectedOutputDescription = ["The output should show an agent using the Microsoft Learn MCP tool with approval prompts."],
},
```
### Example: Declarative Workflow with External Loop
```csharp
new SampleDefinition
{
Name = "Workflow_Declarative_FunctionTools",
ProjectPath = "samples/03-workflows/Declarative/FunctionTools",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["What are today's specials?", "EXIT"],
InputDelayMs = 8000,
ExpectedOutputDescription = ["The output should show a workflow calling function tools to answer a question about restaurant specials."],
},
```
### Example: Skipped Sample
```csharp
new SampleDefinition
{
Name = "Agent_MCP_Server",
ProjectPath = "samples/02-agents/ModelContextProtocol/Agent_MCP_Server",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
SkipReason = "Runs as an MCP stdio server that does not exit on its own.",
},
```
dotnet/AGENTS.md
+3 -2
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@@ -29,13 +29,14 @@ using types like `IChatClient`, `FunctionInvokingChatClient`, `AITool`, `AIFunct
## Key Conventions
- **Encoding**: All new files must be saved with UTF-8 encoding with BOM (Byte Order Mark). This is required for `dotnet format` to work correctly.
- **Command output capture**: When running `dotnet build`, `dotnet test`, `dotnet format`, or similar commands, redirect output to a temp file first (e.g., `dotnet build --tl:off 2>&1 | Out-File $env:TEMP\build.log`), then analyze the file as needed. This avoids re-running expensive commands when the initial analysis misses something.
- **Encoding**: All new files must be saved with UTF-8 encoding with BOM (Byte Order Mark). This is required for `dotnet format` to work correctly. When using PowerShell `Set-Content`, always pass `-Encoding UTF8BOM` to preserve the BOM (e.g., `Set-Content $file $content -NoNewline -Encoding UTF8BOM`).
- **Copyright header**: `// Copyright (c) Microsoft. All rights reserved.` at top of all `.cs` files
- **XML docs**: Required for all public methods and classes
- **Async**: Use `Async` suffix for methods returning `Task`/`ValueTask`
- **Private classes**: Should be `sealed` unless subclassed
- **Config**: Read from environment variables with `UPPER_SNAKE_CASE` naming
- **Tests**: Add Arrange/Act/Assert comments; use Moq for mocking
- **Tests**: Add Arrange/Act/Assert comments; use Moq for mocking; test methods returning `Task`/`ValueTask` must use the `Async` suffix.
## Key Design Principles
dotnet/Directory.Build.props
+1 -1
View File
@@ -17,7 +17,7 @@
<PropertyGroup>
<IsReleaseCandidate>false</IsReleaseCandidate>
<IsGenerallyAvailable>false</IsGenerallyAvailable>
<IsReleased>false</IsReleased>
</PropertyGroup>
<PropertyGroup>
dotnet/Directory.Packages.props
+5 -5
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@@ -11,7 +11,7 @@
</PropertyGroup>
<ItemGroup>
<!-- Aspire.* -->
<PackageVersion Include="Anthropic" Version="12.8.0" />
<PackageVersion Include="Anthropic" Version="12.11.0" />
<PackageVersion Include="Anthropic.Foundry" Version="0.4.2" />
<PackageVersion Include="Aspire.Hosting" Version="$(AspireAppHostSdkVersion)" />
<PackageVersion Include="Aspire.Azure.AI.OpenAI" Version="13.0.0-preview.1.25560.3" />
@@ -22,13 +22,13 @@
<PackageVersion Include="Aspire.Microsoft.Azure.Cosmos" Version="$(AspireAppHostSdkVersion)" />
<PackageVersion Include="CommunityToolkit.Aspire.OllamaSharp" Version="13.0.0" />
<!-- Azure.* -->
<PackageVersion Include="Azure.AI.Projects" Version="2.0.0-beta.2" />
<PackageVersion Include="Azure.AI.Projects" Version="2.0.0" />
<PackageVersion Include="Azure.AI.Agents.Persistent" Version="1.2.0-beta.10" />
<PackageVersion Include="Azure.AI.OpenAI" Version="2.9.0-beta.1" />
<PackageVersion Include="Azure.Identity" Version="1.19.0" />
<PackageVersion Include="Azure.Identity" Version="1.20.0" />
<PackageVersion Include="Azure.Monitor.OpenTelemetry.Exporter" Version="1.4.0" />
<!-- Google Gemini -->
<PackageVersion Include="Google.GenAI" Version="0.11.0" />
<PackageVersion Include="Google.GenAI" Version="1.6.0" />
<PackageVersion Include="Mscc.GenerativeAI.Microsoft" Version="2.9.3" />
<!-- Microsoft.Azure.* -->
<PackageVersion Include="Microsoft.Azure.Cosmos" Version="3.54.0" />
@@ -38,7 +38,7 @@
<PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.4" />
<PackageVersion Include="Microsoft.Bcl.HashCode" Version="6.0.0" />
<PackageVersion Include="Microsoft.Bcl.Memory" Version="10.0.4" />
<PackageVersion Include="System.ClientModel" Version="1.9.0" />
<PackageVersion Include="System.ClientModel" Version="1.10.0" />
<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" />
dotnet/agent-framework-dotnet.slnx
+18 -14
View File
@@ -11,6 +11,7 @@
<Folder Name="/Samples/">
<File Path="samples/AGENTS.md" />
<File Path="samples/README.md" />
<Project Path="eng/verify-samples/verify-samples.csproj" />
</Folder>
<Folder Name="/Samples/01-get-started/">
<Project Path="samples/01-get-started/01_hello_agent/01_hello_agent.csproj" />
@@ -37,7 +38,6 @@
<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>
@@ -116,6 +116,9 @@
<File Path="samples/02-agents/AgentSkills/README.md" />
<Project Path="samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Agent_Step01_FileBasedSkills.csproj" />
<Project Path="samples/02-agents/AgentSkills/Agent_Step02_CodeDefinedSkills/Agent_Step02_CodeDefinedSkills.csproj" />
<Project Path="samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/Agent_Step03_ClassBasedSkills.csproj" />
<Project Path="samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/Agent_Step04_MixedSkills.csproj" />
<Project Path="samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/Agent_Step05_SkillsWithDI.csproj" />
</Folder>
<Folder Name="/Samples/02-agents/AGUI/Step05_StateManagement/">
<Project Path="samples/02-agents/AGUI/Step05_StateManagement/Client/Client.csproj" />
@@ -181,6 +184,7 @@
<Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/AgentWithRAG_Step02_CustomVectorStoreRAG.csproj" />
<Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/AgentWithRAG_Step03_CustomRAGDataSource.csproj" />
<Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj" />
<Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step05_Neo4jGraphRAG/AgentWithRAG_Step05_Neo4jGraphRAG.csproj" />
</Folder>
<Folder Name="/Samples/02-agents/ModelContextProtocol/">
<File Path="samples/02-agents/ModelContextProtocol/README.md" />
@@ -222,12 +226,12 @@
<Project Path="samples/03-workflows/Declarative/ToolApproval/ToolApproval.csproj" />
</Folder>
<Folder Name="/Samples/03-workflows/Declarative/Examples/">
<File Path="../workflow-samples/CustomerSupport.yaml" />
<File Path="../workflow-samples/DeepResearch.yaml" />
<File Path="../workflow-samples/Marketing.yaml" />
<File Path="../workflow-samples/MathChat.yaml" />
<File Path="../workflow-samples/README.md" />
<File Path="../workflow-samples/wttr.json" />
<File Path="../declarative-agents/workflow-samples/CustomerSupport.yaml" />
<File Path="../declarative-agents/workflow-samples/DeepResearch.yaml" />
<File Path="../declarative-agents/workflow-samples/Marketing.yaml" />
<File Path="../declarative-agents/workflow-samples/MathChat.yaml" />
<File Path="../declarative-agents/workflow-samples/README.md" />
<File Path="../declarative-agents/workflow-samples/wttr.json" />
</Folder>
<Folder Name="/Samples/03-workflows/SharedStates/">
<Project Path="samples/03-workflows/SharedStates/SharedStates.csproj" />
@@ -486,13 +490,13 @@
<Project Path="src/Microsoft.Agents.AI.AGUI/Microsoft.Agents.AI.AGUI.csproj" />
<Project Path="src/Microsoft.Agents.AI.Anthropic/Microsoft.Agents.AI.Anthropic.csproj" />
<Project Path="src/Microsoft.Agents.AI.AzureAI.Persistent/Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
<Project Path="src/Microsoft.Agents.AI.AzureAI/Microsoft.Agents.AI.AzureAI.csproj" />
<Project Path="src/Microsoft.Agents.AI.Foundry/Microsoft.Agents.AI.Foundry.csproj" />
<Project Path="src/Microsoft.Agents.AI.CopilotStudio/Microsoft.Agents.AI.CopilotStudio.csproj" />
<Project Path="src/Microsoft.Agents.AI.CosmosNoSql/Microsoft.Agents.AI.CosmosNoSql.csproj" />
<Project Path="src/Microsoft.Agents.AI.Declarative/Microsoft.Agents.AI.Declarative.csproj" />
<Project Path="src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj" />
<Project Path="src/Microsoft.Agents.AI.DurableTask/Microsoft.Agents.AI.DurableTask.csproj" />
<Project Path="src/Microsoft.Agents.AI.FoundryMemory/Microsoft.Agents.AI.FoundryMemory.csproj" />
<Project Path="src/Microsoft.Agents.AI.GitHub.Copilot/Microsoft.Agents.AI.GitHub.Copilot.csproj" />
<Project Path="src/Microsoft.Agents.AI.Hosting.A2A.AspNetCore/Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj" />
<Project Path="src/Microsoft.Agents.AI.Hosting.A2A/Microsoft.Agents.AI.Hosting.A2A.csproj" />
@@ -503,7 +507,7 @@
<Project Path="src/Microsoft.Agents.AI.Mem0/Microsoft.Agents.AI.Mem0.csproj" />
<Project Path="src/Microsoft.Agents.AI.OpenAI/Microsoft.Agents.AI.OpenAI.csproj" />
<Project Path="src/Microsoft.Agents.AI.Purview/Microsoft.Agents.AI.Purview.csproj" />
<Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.AzureAI/Microsoft.Agents.AI.Workflows.Declarative.AzureAI.csproj" />
<Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.Foundry/Microsoft.Agents.AI.Workflows.Declarative.Foundry.csproj" />
<Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.Mcp/Microsoft.Agents.AI.Workflows.Declarative.Mcp.csproj" />
<Project Path="src/Microsoft.Agents.AI.Workflows.Declarative/Microsoft.Agents.AI.Workflows.Declarative.csproj" />
<Project Path="src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj" />
@@ -514,11 +518,11 @@
<Folder Name="/Tests/IntegrationTests/">
<Project Path="tests/AgentConformance.IntegrationTests/AgentConformance.IntegrationTests.csproj" />
<Project Path="tests/AnthropicChatCompletion.IntegrationTests/AnthropicChatCompletion.IntegrationTests.csproj" />
<Project Path="tests/AzureAI.IntegrationTests/AzureAI.IntegrationTests.csproj" />
<Project Path="tests/Foundry.IntegrationTests/Foundry.IntegrationTests.csproj" />
<Project Path="tests/AzureAIAgentsPersistent.IntegrationTests/AzureAIAgentsPersistent.IntegrationTests.csproj" />
<Project Path="tests/CopilotStudio.IntegrationTests/CopilotStudio.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.DurableTask.IntegrationTests/Microsoft.Agents.AI.DurableTask.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.FoundryMemory.IntegrationTests/Microsoft.Agents.AI.FoundryMemory.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests.csproj" />
@@ -535,12 +539,12 @@
<Project Path="tests/Microsoft.Agents.AI.AGUI.UnitTests/Microsoft.Agents.AI.AGUI.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Anthropic.UnitTests/Microsoft.Agents.AI.Anthropic.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.AzureAI.UnitTests/Microsoft.Agents.AI.AzureAI.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Foundry.UnitTests/Microsoft.Agents.AI.Foundry.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.CosmosNoSql.UnitTests/Microsoft.Agents.AI.CosmosNoSql.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Declarative.UnitTests/Microsoft.Agents.AI.Declarative.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.DevUI.UnitTests/Microsoft.Agents.AI.DevUI.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.DurableTask.UnitTests/Microsoft.Agents.AI.DurableTask.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.FoundryMemory.UnitTests/Microsoft.Agents.AI.FoundryMemory.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Microsoft.Agents.AI.Hosting.A2A.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests.csproj" />
dotnet/agent-framework-release.slnf
+3 -3
View File
@@ -8,13 +8,13 @@
"src\\Microsoft.Agents.AI.Anthropic\\Microsoft.Agents.AI.Anthropic.csproj",
"src\\Microsoft.Agents.AI.GitHub.Copilot\\Microsoft.Agents.AI.GitHub.Copilot.csproj",
"src\\Microsoft.Agents.AI.AzureAI.Persistent\\Microsoft.Agents.AI.AzureAI.Persistent.csproj",
"src\\Microsoft.Agents.AI.AzureAI\\Microsoft.Agents.AI.AzureAI.csproj",
"src\\Microsoft.Agents.AI.Foundry\\Microsoft.Agents.AI.Foundry.csproj",
"src\\Microsoft.Agents.AI.CopilotStudio\\Microsoft.Agents.AI.CopilotStudio.csproj",
"src\\Microsoft.Agents.AI.CosmosNoSql\\Microsoft.Agents.AI.CosmosNoSql.csproj",
"src\\Microsoft.Agents.AI.Declarative\\Microsoft.Agents.AI.Declarative.csproj",
"src\\Microsoft.Agents.AI.DevUI\\Microsoft.Agents.AI.DevUI.csproj",
"src\\Microsoft.Agents.AI.DurableTask\\Microsoft.Agents.AI.DurableTask.csproj",
"src\\Microsoft.Agents.AI.FoundryMemory\\Microsoft.Agents.AI.FoundryMemory.csproj",
"src\\Microsoft.Agents.AI.Hosting.A2A.AspNetCore\\Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj",
"src\\Microsoft.Agents.AI.Hosting.A2A\\Microsoft.Agents.AI.Hosting.A2A.csproj",
"src\\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj",
@@ -24,7 +24,7 @@
"src\\Microsoft.Agents.AI.Mem0\\Microsoft.Agents.AI.Mem0.csproj",
"src\\Microsoft.Agents.AI.OpenAI\\Microsoft.Agents.AI.OpenAI.csproj",
"src\\Microsoft.Agents.AI.Purview\\Microsoft.Agents.AI.Purview.csproj",
"src\\Microsoft.Agents.AI.Workflows.Declarative.AzureAI\\Microsoft.Agents.AI.Workflows.Declarative.AzureAI.csproj",
"src\\Microsoft.Agents.AI.Workflows.Declarative.Foundry\\Microsoft.Agents.AI.Workflows.Declarative.Foundry.csproj",
"src\\Microsoft.Agents.AI.Workflows.Declarative\\Microsoft.Agents.AI.Workflows.Declarative.csproj",
"src\\Microsoft.Agents.AI.Workflows.Generators\\Microsoft.Agents.AI.Workflows.Generators.csproj",
"src\\Microsoft.Agents.AI.Workflows\\Microsoft.Agents.AI.Workflows.csproj",
dotnet/eng/verify-samples/AgentsSamples.cs
+1239
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File diff suppressed because it is too large Load Diff
dotnet/eng/verify-samples/ConsoleReporter.cs
+95
View File
@@ -0,0 +1,95 @@
// Copyright (c) Microsoft. All rights reserved.
namespace VerifySamples;
/// <summary>
/// Thread-safe console output with sample-name prefixes and colored status.
/// </summary>
internal sealed class ConsoleReporter
{
private readonly object _lock = new();
/// <summary>
/// Writes a complete prefixed line atomically to the console.
/// </summary>
public void WriteLineWithPrefix(string sampleName, string message, ConsoleColor? color = null)
{
lock (this._lock)
{
Console.ForegroundColor = ConsoleColor.Cyan;
Console.Write($"[{sampleName}] ");
if (color.HasValue)
{
Console.ForegroundColor = color.Value;
}
else
{
Console.ResetColor();
}
Console.WriteLine(message);
Console.ResetColor();
}
}
/// <summary>
/// Prints the final summary table and elapsed time to the console.
/// </summary>
public void PrintSummary(
IReadOnlyList<VerificationResult> orderedResults,
IReadOnlyList<(string Name, string Reason)> skipped,
TimeSpan elapsed)
{
var passCount = orderedResults.Count(r => r.Passed);
var failCount = orderedResults.Count(r => !r.Passed);
Console.WriteLine();
Console.WriteLine(new string('─', 60));
Console.ForegroundColor = ConsoleColor.White;
Console.WriteLine("SUMMARY");
Console.ResetColor();
foreach (var result in orderedResults)
{
Console.ForegroundColor = result.Passed ? ConsoleColor.Green : ConsoleColor.Red;
Console.Write(result.Passed ? " ✓ " : " ✗ ");
Console.ResetColor();
Console.WriteLine($"{result.SampleName}: {result.Summary}");
}
foreach (var (name, reason) in skipped)
{
Console.ForegroundColor = ConsoleColor.Yellow;
Console.Write(" ○ ");
Console.ResetColor();
Console.WriteLine($"{name}: Skipped — {reason}");
}
Console.WriteLine();
Console.Write("Results: ");
Console.ForegroundColor = ConsoleColor.Green;
Console.Write($"{passCount} passed");
Console.ResetColor();
if (failCount > 0)
{
Console.Write(", ");
Console.ForegroundColor = ConsoleColor.Red;
Console.Write($"{failCount} failed");
Console.ResetColor();
}
if (skipped.Count > 0)
{
Console.Write(", ");
Console.ForegroundColor = ConsoleColor.Yellow;
Console.Write($"{skipped.Count} skipped");
Console.ResetColor();
}
Console.WriteLine();
Console.ForegroundColor = ConsoleColor.DarkGray;
Console.WriteLine($"Elapsed: {elapsed.Hours:D2}:{elapsed.Minutes:D2}:{elapsed.Seconds:D2}");
Console.ResetColor();
}
}
dotnet/eng/verify-samples/CsvResultWriter.cs
+56
View File
@@ -0,0 +1,56 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Text;
namespace VerifySamples;
/// <summary>
/// Writes a CSV summary of sample verification results.
/// </summary>
internal static class CsvResultWriter
{
/// <summary>
/// Writes the results to a CSV file at the specified path.
/// </summary>
public static async Task WriteAsync(
string path,
IReadOnlyList<VerificationResult> orderedResults,
IReadOnlyList<(string Name, string Reason)> skipped,
IReadOnlyList<SampleDefinition> samples)
{
var pathLookup = samples.ToDictionary(s => s.Name, s => s.ProjectPath);
var sb = new StringBuilder();
sb.AppendLine("Sample,ProjectPath,Status,FailedChecks,Failures");
foreach (var result in orderedResults)
{
var status = result.Passed ? "PASSED" : "FAILED";
var failedChecks = result.Failures.Count;
var failures = string.Join("; ", result.Failures);
pathLookup.TryGetValue(result.SampleName, out var projectPath);
sb.AppendLine($"{CsvEscape(result.SampleName)},{CsvEscape(projectPath ?? "")},{status},{failedChecks},{CsvEscape(failures)}");
}
foreach (var (name, reason) in skipped)
{
pathLookup.TryGetValue(name, out var projectPath);
sb.AppendLine($"{CsvEscape(name)},{CsvEscape(projectPath ?? "")},SKIPPED,0,{CsvEscape(reason)}");
}
await File.WriteAllTextAsync(path, sb.ToString());
}
/// <summary>
/// Escapes a value for CSV: wraps in quotes if it contains commas, quotes, or newlines.
/// </summary>
private static string CsvEscape(string value)
{
if (value.Contains('"') || value.Contains(',') || value.Contains('\n') || value.Contains('\r'))
{
return $"\"{value.Replace("\"", "\"\"")}\"";
}
return value;
}
}
dotnet/eng/verify-samples/GetStartedSamples.cs
+105
View File
@@ -0,0 +1,105 @@
// Copyright (c) Microsoft. All rights reserved.
namespace VerifySamples;
/// <summary>
/// Defines the expected behavior for each sample in 01-get-started.
/// </summary>
internal static class GetStartedSamples
{
public static IReadOnlyList<SampleDefinition> All { get; } =
[
new SampleDefinition
{
Name = "05_first_workflow",
ProjectPath = "samples/01-get-started/05_first_workflow",
RequiredEnvironmentVariables = [],
IsDeterministic = true,
MustContain =
[
"UppercaseExecutor: HELLO, WORLD!",
"ReverseTextExecutor: !DLROW ,OLLEH",
],
},
new SampleDefinition
{
Name = "01_hello_agent",
ProjectPath = "samples/01-get-started/01_hello_agent",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
ExpectedOutputDescription =
[
"The output should contain a joke about a pirate.",
"There should be two separate joke responses — one from a non-streaming call and one from a streaming call.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "02_add_tools",
ProjectPath = "samples/01-get-started/02_add_tools",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
MustContain = [],
ExpectedOutputDescription =
[
"The output should contain information about the weather in Amsterdam.",
"The response should mention that it is cloudy with a high of 15°C (or equivalent), since this comes from a tool that returns a canned response.",
"There should be two responses — one from a non-streaming call and one from a streaming call.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "03_multi_turn",
ProjectPath = "samples/01-get-started/03_multi_turn",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
ExpectedOutputDescription =
[
"The output should contain a joke about a pirate.",
"After the initial joke, there should be a modified version that includes emojis and is told in the voice of a pirate's parrot.",
"The pattern repeats: first a non-streaming pirate joke + parrot version, then a streaming pirate joke + parrot version.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "04_memory",
ProjectPath = "samples/01-get-started/04_memory",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
MustContain =
[
">> Use session with blank memory",
">> Use deserialized session with previously created memories",
">> Read memories using memory component",
"MEMORY - User Name:",
"MEMORY - User Age:",
">> Use new session with previously created memories",
],
ExpectedOutputDescription =
[
"In the 'Use session with blank memory' section, the agent should respond to the user's messages. It may ask for the user's name or age if not yet known.",
"In the 'Use deserialized session with previously created memories' section, the agent should correctly recall that the user's name is Ruaidhrí and age is 20.",
"The 'MEMORY - User Name:' line should show 'Ruaidhrí' (or a close transliteration).",
"The 'MEMORY - User Age:' line should show '20'.",
"In the 'Use new session with previously created memories' section, the agent should know the user's name and age from the transferred memory.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "06_host_your_agent",
ProjectPath = "samples/01-get-started/06_host_your_agent",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
SkipReason = "Requires Azure Functions Core Tools runtime and starts a web server.",
},
];
}
dotnet/eng/verify-samples/LogFileWriter.cs
+153
View File
@@ -0,0 +1,153 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Text;
namespace VerifySamples;
/// <summary>
/// Incrementally writes a sequential (non-interleaved) log file, appending after each sample completes.
/// Thread-safe: multiple parallel tasks may call write methods concurrently.
/// </summary>
internal sealed class LogFileWriter : IDisposable
{
private readonly string _path;
private readonly SemaphoreSlim _writeLock = new(1, 1);
public LogFileWriter(string path)
{
this._path = path;
}
/// <inheritdoc />
public void Dispose()
{
this._writeLock.Dispose();
}
/// <summary>
/// Writes the log file header. Call once at the start of the run.
/// </summary>
public async Task WriteHeaderAsync()
{
var sb = new StringBuilder();
sb.AppendLine($"Sample Verification Log — {DateTime.UtcNow:yyyy-MM-dd HH:mm:ss} UTC");
sb.AppendLine(new string('═', 72));
sb.AppendLine();
await File.WriteAllTextAsync(this._path, sb.ToString());
}
/// <summary>
/// Appends a skipped-sample entry to the log file.
/// </summary>
public async Task WriteSkippedAsync(string name, string reason)
{
var sb = new StringBuilder();
sb.AppendLine($"── {name} ──");
sb.AppendLine($"Status: SKIPPED — {reason}");
sb.AppendLine();
await this.AppendAsync(sb.ToString());
}
/// <summary>
/// Appends a completed sample's full output section to the log file.
/// </summary>
public async Task WriteSampleResultAsync(VerificationResult result)
{
var sb = new StringBuilder();
sb.AppendLine(new string('─', 72));
sb.AppendLine($"── {result.SampleName} ──");
sb.AppendLine($"Status: {(result.Passed ? "PASSED" : "FAILED")}");
sb.AppendLine();
foreach (var line in result.LogLines)
{
sb.AppendLine(line);
}
sb.AppendLine();
if (!string.IsNullOrWhiteSpace(result.Stdout))
{
sb.AppendLine("--- stdout ---");
sb.AppendLine(result.Stdout.TrimEnd());
sb.AppendLine("--- end stdout ---");
sb.AppendLine();
}
if (!string.IsNullOrWhiteSpace(result.Stderr))
{
sb.AppendLine("--- stderr ---");
sb.AppendLine(result.Stderr.TrimEnd());
sb.AppendLine("--- end stderr ---");
sb.AppendLine();
}
if (result.Failures.Count > 0)
{
sb.AppendLine("Failures:");
foreach (var failure in result.Failures)
{
sb.AppendLine($" ✗ {failure}");
}
sb.AppendLine();
}
if (result.AIReasoning is not null)
{
sb.AppendLine("AI Reasoning:");
sb.AppendLine(result.AIReasoning);
sb.AppendLine();
}
await this.AppendAsync(sb.ToString());
}
/// <summary>
/// Appends the final summary section and elapsed time to the log file.
/// </summary>
public async Task WriteSummaryAsync(
IReadOnlyList<VerificationResult> orderedResults,
IReadOnlyList<(string Name, string Reason)> skipped,
TimeSpan elapsed)
{
var passCount = orderedResults.Count(r => r.Passed);
var failCount = orderedResults.Count(r => !r.Passed);
var sb = new StringBuilder();
sb.AppendLine(new string('═', 72));
sb.AppendLine("SUMMARY");
sb.AppendLine();
foreach (var result in orderedResults)
{
sb.AppendLine($" {(result.Passed ? "✓" : "✗")} {result.SampleName}: {result.Summary}");
}
foreach (var (name, reason) in skipped)
{
sb.AppendLine($" ○ {name}: Skipped — {reason}");
}
sb.AppendLine();
sb.AppendLine($"Results: {passCount} passed{(failCount > 0 ? $", {failCount} failed" : "")}{(skipped.Count > 0 ? $", {skipped.Count} skipped" : "")}");
sb.AppendLine($"Elapsed: {elapsed.Hours:D2}:{elapsed.Minutes:D2}:{elapsed.Seconds:D2}");
await this.AppendAsync(sb.ToString());
}
private async Task AppendAsync(string text)
{
await this._writeLock.WaitAsync();
try
{
await File.AppendAllTextAsync(this._path, text);
}
finally
{
this._writeLock.Release();
}
}
}
dotnet/eng/verify-samples/MarkdownResultWriter.cs
+98
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@@ -0,0 +1,98 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Text;
namespace VerifySamples;
/// <summary>
/// Writes a Markdown summary of sample verification results.
/// </summary>
internal static class MarkdownResultWriter
{
/// <summary>
/// Writes the results to a Markdown file at the specified path.
/// </summary>
public static async Task WriteAsync(
string path,
IReadOnlyList<VerificationResult> orderedResults,
IReadOnlyList<(string Name, string Reason)> skipped,
TimeSpan elapsed)
{
var passCount = orderedResults.Count(r => r.Passed);
var failCount = orderedResults.Count(r => !r.Passed);
var sb = new StringBuilder();
sb.AppendLine("# Sample Verification Results");
sb.AppendLine();
sb.AppendLine($"**{passCount} passed, {failCount} failed, {skipped.Count} skipped** | Elapsed: {elapsed.Hours:D2}:{elapsed.Minutes:D2}:{elapsed.Seconds:D2}");
sb.AppendLine();
// Results table
sb.AppendLine("| Sample | Status | Failed Checks | Failures |");
sb.AppendLine("|--------|--------|---------------|----------|");
foreach (var result in orderedResults)
{
var status = result.Passed ? "✅ PASSED" : "❌ FAILED";
var failedChecks = result.Failures.Count;
var failures = MdEscape(string.Join("; ", result.Failures));
sb.AppendLine($"| {MdEscape(result.SampleName)} | {status} | {failedChecks} | {failures} |");
}
foreach (var (name, reason) in skipped)
{
sb.AppendLine($"| {MdEscape(name)} | ⏭️ SKIPPED | 0 | {MdEscape(reason)} |");
}
// Collapsible AI reasoning details for failures
var failures2 = orderedResults.Where(r => !r.Passed && !string.IsNullOrEmpty(r.AIReasoning)).ToList();
if (failures2.Count > 0)
{
sb.AppendLine();
sb.AppendLine("## Failure Details");
sb.AppendLine();
foreach (var result in failures2)
{
sb.AppendLine($"<details><summary><strong>{HtmlEscape(result.SampleName)}</strong></summary>");
sb.AppendLine();
if (result.Failures.Count > 0)
{
foreach (var failure in result.Failures)
{
sb.AppendLine($"- {MdEscape(failure)}");
}
sb.AppendLine();
}
sb.AppendLine("**AI Reasoning:**");
sb.AppendLine();
sb.AppendLine("```");
sb.AppendLine(result.AIReasoning);
sb.AppendLine("```");
sb.AppendLine();
sb.AppendLine("</details>");
sb.AppendLine();
}
}
await File.WriteAllTextAsync(path, sb.ToString());
}
/// <summary>
/// Escapes pipe characters and newlines for use inside Markdown table cells.
/// </summary>
private static string MdEscape(string value)
{
return value.Replace("|", "\\|").Replace("\n", " ").Replace("\r", "");
}
/// <summary>
/// Escapes HTML special characters for use inside HTML tags.
/// </summary>
private static string HtmlEscape(string value)
{
return value.Replace("&", "&amp;").Replace("<", "&lt;").Replace(">", "&gt;").Replace("\"", "&quot;");
}
}
dotnet/eng/verify-samples/Program.cs
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// Copyright (c) Microsoft. All rights reserved.
// This tool runs the 01-get-started, 02-agents, and 03-workflows samples and verifies their output.
// Deterministic samples are verified with exact string matching.
// Non-deterministic (LLM) samples are verified using an agent-framework agent.
//
// Usage:
// dotnet run # Run all samples
// dotnet run -- 01_hello_agent 05_first_workflow # Run specific samples by name
// dotnet run -- --category 01-get-started # Run the 01-get-started category
// dotnet run -- --category 02-agents # Run the 02-agents category
// dotnet run -- --category 03-workflows # Run the 03-workflows category
// dotnet run -- --parallel 16 # Run up to 16 samples concurrently
// dotnet run -- --log results.log # Write sequential log to file
// dotnet run -- --csv results.csv # Write CSV summary to file
// dotnet run -- --md results.md # Write Markdown summary to file
//
// Required environment variables (for AI-powered samples):
// AZURE_OPENAI_ENDPOINT
// AZURE_OPENAI_DEPLOYMENT_NAME (optional, defaults to gpt-5-mini)
using System.Diagnostics;
using Azure.AI.OpenAI;
using Azure.Identity;
using VerifySamples;
var options = VerifyOptions.Parse(args);
if (options is null)
{
return 1;
}
var stopwatch = Stopwatch.StartNew();
// Resolve the dotnet/ root directory (verify-samples is at dotnet/eng/verify-samples/)
var dotnetRoot = Path.GetFullPath(Path.Combine(AppContext.BaseDirectory, "..", "..", "..", "..", ".."));
if (!File.Exists(Path.Combine(dotnetRoot, "agent-framework-dotnet.slnx")))
{
dotnetRoot = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), "..", ".."));
}
// Set up the AI verifier
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5-mini";
OpenAI.Chat.ChatClient? chatClient = null;
if (!string.IsNullOrEmpty(endpoint))
{
chatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
.GetChatClient(deploymentName);
}
// Set up optional log file writer
LogFileWriter? logWriter = null;
if (options.LogFilePath is not null)
{
logWriter = new LogFileWriter(options.LogFilePath);
await logWriter.WriteHeaderAsync();
}
try
{
// Run all samples
var reporter = new ConsoleReporter();
var verifier = new SampleVerifier(chatClient);
var orchestrator = new VerificationOrchestrator(verifier, reporter, dotnetRoot, TimeSpan.FromMinutes(3), logWriter);
var run = await orchestrator.RunAllAsync(options.Samples, options.MaxParallelism);
stopwatch.Stop();
// Print summary
var orderedResults = run.SampleOrder
.Where(run.Results.ContainsKey)
.Select(name => run.Results[name])
.ToList();
reporter.PrintSummary(orderedResults, run.Skipped, stopwatch.Elapsed);
// Write log file summary
if (logWriter is not null)
{
await logWriter.WriteSummaryAsync(orderedResults, run.Skipped, stopwatch.Elapsed);
Console.WriteLine($"Log written to: {options.LogFilePath}");
}
// Write CSV summary
if (options.CsvFilePath is not null)
{
await CsvResultWriter.WriteAsync(options.CsvFilePath, orderedResults, run.Skipped, options.Samples);
Console.WriteLine($"CSV written to: {options.CsvFilePath}");
}
// Write Markdown summary
if (options.MarkdownFilePath is not null)
{
await MarkdownResultWriter.WriteAsync(options.MarkdownFilePath, orderedResults, run.Skipped, stopwatch.Elapsed);
Console.WriteLine($"Markdown written to: {options.MarkdownFilePath}");
}
return orderedResults.Any(r => !r.Passed) ? 1 : 0;
}
finally
{
logWriter?.Dispose();
}
dotnet/eng/verify-samples/SampleDefinition.cs
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// Copyright (c) Microsoft. All rights reserved.
namespace VerifySamples;
/// <summary>
/// Describes a sample to verify, including its expected output.
/// </summary>
internal sealed class SampleDefinition
{
/// <summary>
/// Display name for the sample (e.g., "01_hello_agent").
/// </summary>
public required string Name { get; init; }
/// <summary>
/// Relative path from the dotnet/ directory to the sample project directory.
/// </summary>
public required string ProjectPath { get; init; }
/// <summary>
/// Environment variables that the sample requires for a meaningful run.
/// The runner checks these before running and will skip the sample if any are unset,
/// recording a skip reason that indicates which required variables are missing.
/// </summary>
public string[] RequiredEnvironmentVariables { get; init; } = [];
/// <summary>
/// Environment variables that the sample can use but typically has fallbacks or defaults for.
/// If these are not set, the sample might prompt or behave interactively, which could cause
/// automated verification to hang. The runner checks these and skips the sample if they are unset
/// to avoid non-deterministic or blocking behavior in automated runs.
/// </summary>
public string[] OptionalEnvironmentVariables { get; init; } = [];
/// <summary>
/// If set, the sample is skipped with this reason.
/// Use only for structural reasons (e.g., web server, multi-process, needs external service).
/// Do NOT use for missing environment variables — those are checked dynamically.
/// </summary>
public string? SkipReason { get; init; }
/// <summary>
/// Substrings that must appear in stdout for the sample to pass.
/// Used for deterministic verification.
/// </summary>
public string[] MustContain { get; init; } = [];
/// <summary>
/// Substrings that must not appear in stdout for the sample to pass.
/// </summary>
public string[] MustNotContain { get; init; } = [];
/// <summary>
/// If true, <see cref="MustContain"/> entries cover the entire expected output —
/// no AI verification is needed.
/// </summary>
public bool IsDeterministic { get; init; }
/// <summary>
/// Natural-language description of what the sample output should look like.
/// Used by the AI verifier for non-deterministic samples.
/// Each entry describes one aspect of the expected output that should be verified.
/// </summary>
public string[] ExpectedOutputDescription { get; init; } = [];
/// <summary>
/// Sequence of stdin inputs to feed to the sample process.
/// Each entry is written as a line (followed by newline) to the process stdin.
/// A <c>null</c> entry inserts a delay without writing anything.
/// Inputs are sent with a short delay between each to allow the process to prompt.
/// </summary>
public string?[] Inputs { get; init; } = [];
/// <summary>
/// Delay in milliseconds between each input line. Default is 2000ms.
/// Increase for samples that need more time between prompts (e.g., LLM calls between inputs).
/// </summary>
public int InputDelayMs { get; init; } = 2000;
}
dotnet/eng/verify-samples/SampleRunner.cs
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// Copyright (c) Microsoft. All rights reserved.
using System.Diagnostics;
namespace VerifySamples;
/// <summary>
/// Result of running a sample process.
/// </summary>
internal sealed record SampleRunResult(
string Stdout,
string Stderr,
int ExitCode,
TimeSpan Elapsed);
/// <summary>
/// Runs a sample project via <c>dotnet run</c> and captures its output.
/// </summary>
internal static class SampleRunner
{
/// <summary>
/// Runs <c>dotnet run --framework net10.0</c> in the given project directory.
/// </summary>
public static Task<SampleRunResult> RunAsync(
string projectPath,
TimeSpan timeout,
CancellationToken cancellationToken = default)
=> RunAsync(projectPath, "run --framework net10.0", timeout, inputs: null, inputDelayMs: 0, cancellationToken: cancellationToken);
/// <summary>
/// Runs <c>dotnet run --framework net10.0</c> with stdin inputs.
/// </summary>
public static Task<SampleRunResult> RunAsync(
string projectPath,
TimeSpan timeout,
string?[]? inputs,
int inputDelayMs = 2000,
CancellationToken cancellationToken = default)
=> RunAsync(projectPath, "run --framework net10.0", timeout, inputs, inputDelayMs, cancellationToken);
/// <summary>
/// Runs an arbitrary <c>dotnet</c> command in the given working directory.
/// </summary>
public static async Task<SampleRunResult> RunAsync(
string workingDirectory,
string dotnetArgs,
TimeSpan timeout,
string?[]? inputs = null,
int inputDelayMs = 0,
CancellationToken cancellationToken = default)
{
var psi = new ProcessStartInfo
{
FileName = "dotnet",
Arguments = dotnetArgs,
WorkingDirectory = workingDirectory,
RedirectStandardOutput = true,
RedirectStandardError = true,
RedirectStandardInput = inputs is { Length: > 0 },
UseShellExecute = false,
CreateNoWindow = true,
};
var sw = Stopwatch.StartNew();
using var process = new Process { StartInfo = psi };
process.Start();
var stdoutTask = process.StandardOutput.ReadToEndAsync(cancellationToken);
var stderrTask = process.StandardError.ReadToEndAsync(cancellationToken);
// Feed stdin inputs with delays if configured
if (inputs is { Length: > 0 })
{
_ = Task.Run(async () =>
{
try
{
foreach (var input in inputs)
{
await Task.Delay(inputDelayMs, cancellationToken);
if (input is not null)
{
await process.StandardInput.WriteLineAsync(input.AsMemory(), cancellationToken);
await process.StandardInput.FlushAsync(cancellationToken);
}
}
process.StandardInput.Close();
}
catch (Exception ex) when (ex is IOException or ObjectDisposedException or OperationCanceledException)
{
// Process may have exited before all inputs were sent
}
}, cancellationToken);
}
using var cts = CancellationTokenSource.CreateLinkedTokenSource(cancellationToken);
cts.CancelAfter(timeout);
try
{
await process.WaitForExitAsync(cts.Token);
}
catch (OperationCanceledException) when (!cancellationToken.IsCancellationRequested)
{
// Timeout — kill the process
try
{
process.Kill(entireProcessTree: true);
}
catch
{
// Best effort
}
sw.Stop();
return new SampleRunResult(
Stdout: await stdoutTask,
Stderr: $"TIMEOUT: Sample did not complete within {timeout.TotalSeconds}s.\n{await stderrTask}",
ExitCode: -1,
Elapsed: sw.Elapsed);
}
sw.Stop();
return new SampleRunResult(
Stdout: await stdoutTask,
Stderr: await stderrTask,
ExitCode: process.ExitCode,
Elapsed: sw.Elapsed);
}
}
dotnet/eng/verify-samples/SampleVerifier.cs
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// Copyright (c) Microsoft. All rights reserved.
using System.Text.Json.Serialization;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI.Chat;
namespace VerifySamples;
/// <summary>
/// Verifies sample output using deterministic checks and an AI agent
/// for non-deterministic output validation.
/// </summary>
internal sealed class SampleVerifier
{
private readonly AIAgent? _verifierAgent;
/// <summary>
/// Creates a verifier. If <paramref name="chatClient"/> is provided,
/// AI-based verification is available for non-deterministic samples.
/// </summary>
public SampleVerifier(ChatClient? chatClient = null)
{
if (chatClient is not null)
{
this._verifierAgent = chatClient.AsAIAgent(
instructions: """
You are a test output verifier. You will be given:
1. The actual stdout output of a program
2. A list of expectations about what the output should contain or demonstrate
Your job is to determine whether the actual output satisfies each expectation.
Be reasonable — the output comes from an LLM so exact wording won't match, but the
semantic intent should be clearly satisfied.
""",
name: "OutputVerifier");
}
}
/// <summary>
/// Verifies the output of a sample run against its definition.
/// </summary>
public async Task<VerificationResult> VerifyAsync(SampleDefinition sample, SampleRunResult run)
{
var failures = new List<string>();
// 1. Exit code check
if (run.ExitCode != 0)
{
failures.Add($"Exit code was {run.ExitCode}, expected 0. Stderr: {Truncate(run.Stderr, 500)}");
}
// 2. Must-contain checks
foreach (var expected in sample.MustContain)
{
if (!run.Stdout.Contains(expected, StringComparison.Ordinal))
{
failures.Add($"Output missing expected substring: \"{expected}\"");
}
}
// 3. Must-not-contain checks
foreach (var unexpected in sample.MustNotContain)
{
if (run.Stdout.Contains(unexpected, StringComparison.Ordinal))
{
failures.Add($"Output contains unexpected substring: \"{unexpected}\"");
}
}
// 4. AI verification for non-deterministic samples
string? aiReasoning = null;
if (!sample.IsDeterministic && sample.ExpectedOutputDescription.Length > 0)
{
if (this._verifierAgent is null)
{
failures.Add("AI verification required but no AI agent configured (missing AZURE_OPENAI_ENDPOINT).");
}
else
{
var aiResult = await this.VerifyWithAIAsync(run.Stdout, sample.ExpectedOutputDescription);
aiReasoning = aiResult.Reasoning;
foreach (var unmet in aiResult.UnmetExpectations)
{
failures.Add($"AI expectation not met: {unmet}");
}
}
}
bool passed = failures.Count == 0;
return new VerificationResult
{
SampleName = sample.Name,
Passed = passed,
Summary = passed ? "All checks passed" : $"{failures.Count} check(s) failed",
Failures = failures,
AIReasoning = aiReasoning,
};
}
private async Task<(string Reasoning, List<string> UnmetExpectations)> VerifyWithAIAsync(
string actualOutput,
string[] expectations)
{
var expectationList = string.Join("\n", expectations.Select((e, i) => $" {i + 1}. {e}"));
var prompt = $"""
Actual program output:
---
{Truncate(actualOutput, 4000)}
---
Expectations to verify:
{expectationList}
Does the output satisfy all expectations?
""";
try
{
var response = await this._verifierAgent!.RunAsync<AIVerificationResponse>(prompt);
var result = response.Result;
if (result is null)
{
return ($"AI verification returned null result. Raw: {response.Text}", ["AI verification returned null result."]);
}
var reasoning = result.Reasoning ?? "(no reasoning provided)";
// Collect unmet expectations as individual failures
var unmet = new List<string>();
if (result.ExpectationResults is { Count: > 0 })
{
foreach (var er in result.ExpectationResults.Where(er => !er.Met))
{
var detail = string.IsNullOrWhiteSpace(er.Detail) ? er.Expectation : $"{er.Expectation} — {er.Detail}";
unmet.Add(detail ?? "Unknown expectation");
}
// If the model flagged overall failure but all individual expectations were met,
// still treat as failure using the overall reasoning.
if (unmet.Count == 0 && !result.Pass)
{
unmet.Add(reasoning);
}
}
else if (!result.Pass)
{
// Fallback: no per-expectation detail but overall pass is false
unmet.Add(reasoning);
}
return (reasoning, unmet);
}
catch (Exception ex)
{
return ($"AI verification error: {ex.Message}", [$"AI verification error: {ex.Message}"]);
}
}
private static string Truncate(string text, int maxLength)
=> text.Length <= maxLength ? text : text[..maxLength] + "... (truncated)";
}
/// <summary>
/// Structured response from the AI verification agent.
/// </summary>
[System.Diagnostics.CodeAnalysis.SuppressMessage("Performance", "CA1812:Avoid uninstantiated internal classes", Justification = "Instantiated by JSON deserialization via RunAsync<T>.")]
internal sealed class AIVerificationResponse
{
/// <summary>Whether all expectations were met.</summary>
[JsonPropertyName("pass")]
public bool Pass { get; set; }
/// <summary>Brief explanation of the overall assessment.</summary>
[JsonPropertyName("reasoning")]
public string? Reasoning { get; set; }
/// <summary>Per-expectation results.</summary>
[JsonPropertyName("expectation_results")]
public List<ExpectationResult>? ExpectationResults { get; set; }
}
/// <summary>
/// Result for an individual expectation check.
/// </summary>
[System.Diagnostics.CodeAnalysis.SuppressMessage("Performance", "CA1812:Avoid uninstantiated internal classes", Justification = "Instantiated by JSON deserialization via RunAsync<T>.")]
internal sealed class ExpectationResult
{
/// <summary>The expectation text that was evaluated.</summary>
[JsonPropertyName("expectation")]
public string? Expectation { get; set; }
/// <summary>Whether this expectation was met.</summary>
[JsonPropertyName("met")]
public bool Met { get; set; }
/// <summary>Detail about how the expectation was or was not met.</summary>
[JsonPropertyName("detail")]
public string? Detail { get; set; }
}
dotnet/eng/verify-samples/VerificationOrchestrator.cs
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// Copyright (c) Microsoft. All rights reserved.
using System.Collections.Concurrent;
namespace VerifySamples;
/// <summary>
/// Orchestrates sample verification: filters, runs in parallel, and collects results.
/// </summary>
internal sealed class VerificationOrchestrator
{
private readonly SampleVerifier _verifier;
private readonly ConsoleReporter _reporter;
private readonly LogFileWriter? _logWriter;
private readonly string _dotnetRoot;
private readonly TimeSpan _timeout;
public VerificationOrchestrator(
SampleVerifier verifier,
ConsoleReporter reporter,
string dotnetRoot,
TimeSpan timeout,
LogFileWriter? logWriter = null)
{
this._verifier = verifier;
this._reporter = reporter;
this._logWriter = logWriter;
this._dotnetRoot = dotnetRoot;
this._timeout = timeout;
}
/// <summary>
/// The result of running all samples through the orchestrator.
/// </summary>
internal sealed record RunAllResult(
ConcurrentDictionary<string, VerificationResult> Results,
List<(string Name, string Reason)> Skipped,
List<string> SampleOrder);
/// <summary>
/// Filters samples, runs the runnable ones in parallel, and returns all results.
/// </summary>
public async Task<RunAllResult> RunAllAsync(
IReadOnlyList<SampleDefinition> samples,
int maxParallelism)
{
var skipped = new List<(string Name, string Reason)>();
var runnableSamples = new List<SampleDefinition>();
var sampleOrder = new List<string>();
// Separate samples into skipped and runnable
foreach (var sample in samples)
{
sampleOrder.Add(sample.Name);
if (sample.SkipReason is not null)
{
skipped.Add((sample.Name, sample.SkipReason));
this._reporter.WriteLineWithPrefix(sample.Name, $"SKIPPED — {sample.SkipReason}", ConsoleColor.Yellow);
if (this._logWriter is not null)
{
await this._logWriter.WriteSkippedAsync(sample.Name, sample.SkipReason);
}
continue;
}
var missingRequired = sample.RequiredEnvironmentVariables
.Where(v => string.IsNullOrEmpty(Environment.GetEnvironmentVariable(v)))
.ToList();
var missingOptional = sample.OptionalEnvironmentVariables
.Where(v => string.IsNullOrEmpty(Environment.GetEnvironmentVariable(v)))
.ToList();
if (missingRequired.Count > 0 || missingOptional.Count > 0)
{
var reasons = new List<string>();
if (missingRequired.Count > 0)
{
reasons.Add($"Missing required: {string.Join(", ", missingRequired)}");
}
if (missingOptional.Count > 0)
{
reasons.Add($"Missing optional (would cause console prompt hang): {string.Join(", ", missingOptional)}");
}
var skipReason = string.Join("; ", reasons);
skipped.Add((sample.Name, skipReason));
this._reporter.WriteLineWithPrefix(sample.Name, $"SKIPPED — {skipReason}", ConsoleColor.Yellow);
if (this._logWriter is not null)
{
await this._logWriter.WriteSkippedAsync(sample.Name, skipReason);
}
continue;
}
runnableSamples.Add(sample);
}
// Run samples in parallel
var results = new ConcurrentDictionary<string, VerificationResult>();
var semaphore = new SemaphoreSlim(maxParallelism);
this._reporter.WriteLineWithPrefix(
"runner", $"Running {runnableSamples.Count} samples (max {maxParallelism} parallel)...");
try
{
var tasks = runnableSamples.Select(sample => this.RunSingleAsync(sample, results, semaphore)).ToArray();
await Task.WhenAll(tasks);
}
finally
{
semaphore.Dispose();
}
return new RunAllResult(results, skipped, sampleOrder);
}
private async Task RunSingleAsync(
SampleDefinition sample,
ConcurrentDictionary<string, VerificationResult> results,
SemaphoreSlim semaphore)
{
await semaphore.WaitAsync();
try
{
var log = new List<string>();
log.Add($"[{sample.Name}] Running...");
this._reporter.WriteLineWithPrefix(sample.Name, "Running...");
var projectPath = Path.Combine(this._dotnetRoot, sample.ProjectPath);
var run = sample.Inputs.Length > 0
? await SampleRunner.RunAsync(projectPath, this._timeout, sample.Inputs, sample.InputDelayMs)
: await SampleRunner.RunAsync(projectPath, this._timeout);
log.Add($"[{sample.Name}] Completed ({run.Elapsed.TotalSeconds:F1}s, exit={run.ExitCode})");
this._reporter.WriteLineWithPrefix(
sample.Name, $"Completed ({run.Elapsed.TotalSeconds:F1}s, exit={run.ExitCode}). Verifying...");
var result = await this._verifier.VerifyAsync(sample, run);
if (result.Passed)
{
log.Add($"[{sample.Name}] PASSED");
this._reporter.WriteLineWithPrefix(sample.Name, "PASSED", ConsoleColor.Green);
}
else
{
log.Add($"[{sample.Name}] FAILED");
this._reporter.WriteLineWithPrefix(sample.Name, "FAILED", ConsoleColor.Red);
foreach (var failure in result.Failures)
{
log.Add($"[{sample.Name}] ✗ {failure}");
this._reporter.WriteLineWithPrefix(sample.Name, $" ✗ {failure}", ConsoleColor.Red);
}
}
if (result.AIReasoning is not null)
{
log.Add($"[{sample.Name}] AI: {result.AIReasoning}");
this._reporter.WriteLineWithPrefix(
sample.Name, $" AI: {Truncate(result.AIReasoning, 300)}", ConsoleColor.DarkGray);
}
var verificationResult = new VerificationResult
{
SampleName = result.SampleName,
Passed = result.Passed,
Summary = result.Summary,
Failures = result.Failures,
AIReasoning = result.AIReasoning,
Stdout = run.Stdout,
Stderr = run.Stderr,
LogLines = log,
};
results[sample.Name] = verificationResult;
if (this._logWriter is not null)
{
await this._logWriter.WriteSampleResultAsync(verificationResult);
}
}
finally
{
semaphore.Release();
}
}
private static string Truncate(string text, int maxLength)
=> text.Length <= maxLength ? text : text[..maxLength] + "...";
}
dotnet/eng/verify-samples/VerificationResult.cs
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// Copyright (c) Microsoft. All rights reserved.
namespace VerifySamples;
/// <summary>
/// The result of verifying a single sample.
/// </summary>
internal sealed class VerificationResult
{
public required string SampleName { get; init; }
public required bool Passed { get; init; }
public required string Summary { get; init; }
public List<string> Failures { get; init; } = [];
public string? AIReasoning { get; init; }
/// <summary>
/// The sample's stdout output, captured for log file output.
/// </summary>
public string? Stdout { get; init; }
/// <summary>
/// The sample's stderr output, captured for log file output.
/// </summary>
public string? Stderr { get; init; }
/// <summary>
/// Per-sample log lines, buffered during parallel execution
/// and written sequentially to the log file.
/// </summary>
public List<string> LogLines { get; init; } = [];
}
dotnet/eng/verify-samples/VerifyOptions.cs
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// Copyright (c) Microsoft. All rights reserved.
namespace VerifySamples;
/// <summary>
/// Parsed command-line options for the sample verification tool.
/// </summary>
internal sealed class VerifyOptions
{
/// <summary>
/// Maximum number of samples to run concurrently.
/// </summary>
public int MaxParallelism { get; init; } = 8;
/// <summary>
/// Path to write a CSV summary file, or <c>null</c> to skip.
/// </summary>
public string? CsvFilePath { get; init; }
/// <summary>
/// Path to write a Markdown summary file, or <c>null</c> to skip.
/// </summary>
public string? MarkdownFilePath { get; init; }
/// <summary>
/// Path to write a sequential log file, or <c>null</c> to skip.
/// </summary>
public string? LogFilePath { get; init; }
/// <summary>
/// The filtered list of samples to process.
/// </summary>
public required IReadOnlyList<SampleDefinition> Samples { get; init; }
/// <summary>
/// All known sample set registries, keyed by category name.
/// </summary>
private static readonly Dictionary<string, IReadOnlyList<SampleDefinition>> s_sampleSets =
new(StringComparer.OrdinalIgnoreCase)
{
["01-get-started"] = GetStartedSamples.All,
["02-agents"] = AgentsSamples.All,
["03-workflows"] = WorkflowSamples.All,
};
/// <summary>
/// Parses command-line arguments and resolves the sample list.
/// Returns <c>null</c> and writes to stderr if the arguments are invalid.
/// </summary>
public static VerifyOptions? Parse(string[] args)
{
var argList = args.ToList();
var categoryFilter = ExtractArg(argList, "--category");
var logFilePath = ExtractArg(argList, "--log");
var csvFilePath = ExtractArg(argList, "--csv");
var markdownFilePath = ExtractArg(argList, "--md");
int maxParallelism = 8;
var parallelArg = ExtractArg(argList, "--parallel");
if (parallelArg is not null && int.TryParse(parallelArg, out var p) && p > 0)
{
maxParallelism = p;
}
HashSet<string>? nameFilter = null;
if (argList.Count > 0)
{
nameFilter = argList.ToHashSet(StringComparer.OrdinalIgnoreCase);
}
// Build the sample list
IReadOnlyList<SampleDefinition> samples;
if (categoryFilter is not null)
{
if (!s_sampleSets.TryGetValue(categoryFilter, out var categoryList))
{
Console.Error.WriteLine(
$"Unknown category '{categoryFilter}'. Available: {string.Join(", ", s_sampleSets.Keys)}");
return null;
}
samples = categoryList;
}
else
{
samples = s_sampleSets.Values.SelectMany(s => s).ToList();
}
if (nameFilter is not null)
{
samples = samples.Where(s => nameFilter.Contains(s.Name)).ToList();
}
if (samples.Count == 0)
{
var allNames = s_sampleSets.Values.SelectMany(s => s).Select(s => s.Name);
Console.Error.WriteLine($"No matching samples found. Available: {string.Join(", ", allNames)}");
return null;
}
return new VerifyOptions
{
MaxParallelism = maxParallelism,
LogFilePath = logFilePath,
CsvFilePath = csvFilePath,
MarkdownFilePath = markdownFilePath,
Samples = samples,
};
}
private static string? ExtractArg(List<string> list, string flag)
{
var idx = list.IndexOf(flag);
if (idx < 0)
{
return null;
}
if (idx + 1 >= list.Count)
{
Console.Error.WriteLine($"Missing value for {flag}.");
list.RemoveAt(idx);
return null;
}
var value = list[idx + 1];
list.RemoveRange(idx, 2);
return value;
}
}
dotnet/eng/verify-samples/WorkflowSamples.cs
+525
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@@ -0,0 +1,525 @@
// Copyright (c) Microsoft. All rights reserved.
namespace VerifySamples;
/// <summary>
/// Defines the expected behavior for each sample in 03-workflows.
/// </summary>
internal static class WorkflowSamples
{
public static IReadOnlyList<SampleDefinition> All { get; } =
[
// ───────────────────────────────────────────────────────────────────
// _StartHere
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_StartHere_01_Streaming",
ProjectPath = "samples/03-workflows/_StartHere/01_Streaming",
RequiredEnvironmentVariables = [],
IsDeterministic = true,
MustContain =
[
"UppercaseExecutor: HELLO, WORLD!",
"ReverseTextExecutor: !DLROW ,OLLEH",
],
},
new SampleDefinition
{
Name = "Workflow_StartHere_02_AgentsInWorkflows",
ProjectPath = "samples/03-workflows/_StartHere/02_AgentsInWorkflows",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
ExpectedOutputDescription =
[
"The output should show agent responses from a translation workflow.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "Workflow_StartHere_03_AgentWorkflowPatterns",
ProjectPath = "samples/03-workflows/_StartHere/03_AgentWorkflowPatterns",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
Inputs = ["sequential"],
InputDelayMs = 3000,
ExpectedOutputDescription =
[
"The output should show a sequential workflow pattern with multiple agents executing tasks in order.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "Workflow_StartHere_04_MultiModelService",
ProjectPath = "samples/03-workflows/_StartHere/04_MultiModelService",
RequiredEnvironmentVariables = ["BEDROCK_ACCESS_KEY", "BEDROCK_SECRET_KEY", "ANTHROPIC_API_KEY", "OPENAI_API_KEY"],
SkipReason = "Requires multiple external provider API keys (Bedrock, Anthropic, OpenAI).",
},
new SampleDefinition
{
Name = "Workflow_StartHere_05_SubWorkflows",
ProjectPath = "samples/03-workflows/_StartHere/05_SubWorkflows",
RequiredEnvironmentVariables = [],
IsDeterministic = true,
MustContain =
[
"=== Sub-Workflow Demonstration ===",
"Final Output:",
"=== Main Workflow Completed ===",
"Sample Complete: Workflows can be composed hierarchically using sub-workflows",
],
},
new SampleDefinition
{
Name = "Workflow_StartHere_06_MixedWorkflowAgentsAndExecutors",
ProjectPath = "samples/03-workflows/_StartHere/06_MixedWorkflowAgentsAndExecutors",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
Inputs = ["What is 2 plus 2?"],
InputDelayMs = 3000,
ExpectedOutputDescription =
[
"The output should show agents and executors working together to process a user question.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "Workflow_StartHere_07_WriterCriticWorkflow",
ProjectPath = "samples/03-workflows/_StartHere/07_WriterCriticWorkflow",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
MustContain = ["=== Writer-Critic Iteration Workflow ==="],
ExpectedOutputDescription =
[
"The output should show a writer-critic iteration workflow with writer and critic sections.",
"The critic should either approve or request revisions.",
"The output should not contain error messages or stack traces.",
],
},
// ───────────────────────────────────────────────────────────────────
// Agents
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_Agents_CustomAgentExecutors",
ProjectPath = "samples/03-workflows/Agents/CustomAgentExecutors",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
ExpectedOutputDescription =
[
"The output should show custom workflow events including slogan generation and feedback.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "Workflow_Agents_FoundryAgent",
ProjectPath = "samples/03-workflows/Agents/FoundryAgent",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
SkipReason = "Requires Azure AI Foundry project endpoint.",
},
new SampleDefinition
{
Name = "Workflow_Agents_GroupChatToolApproval",
ProjectPath = "samples/03-workflows/Agents/GroupChatToolApproval",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
MustContain = ["Starting group chat workflow for software deployment..."],
ExpectedOutputDescription =
[
"The output should show a group chat workflow with QA and DevOps agents for software deployment.",
"There should be approval requests for tool calls.",
"The workflow should show interaction between QA and DevOps agents toward deployment.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "Workflow_Agents_WorkflowAsAnAgent",
ProjectPath = "samples/03-workflows/Agents/WorkflowAsAnAgent",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
Inputs = ["hello", "exit"],
InputDelayMs = 5000,
ExpectedOutputDescription =
[
"The output should show a conversational workflow responding to the user's hello message.",
"The output should not contain error messages or stack traces.",
],
},
// ───────────────────────────────────────────────────────────────────
// Checkpoint
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_Checkpoint_CheckpointAndRehydrate",
ProjectPath = "samples/03-workflows/Checkpoint/CheckpointAndRehydrate",
RequiredEnvironmentVariables = [],
IsDeterministic = true,
MustContain =
[
"Workflow completed with result:",
"Number of checkpoints created:",
"Hydrating a new workflow instance from the 6th checkpoint.",
],
},
new SampleDefinition
{
Name = "Workflow_Checkpoint_CheckpointAndResume",
ProjectPath = "samples/03-workflows/Checkpoint/CheckpointAndResume",
RequiredEnvironmentVariables = [],
IsDeterministic = true,
MustContain =
[
"Workflow completed with result:",
"Number of checkpoints created:",
"Restoring from the 6th checkpoint.",
],
},
new SampleDefinition
{
Name = "Workflow_Checkpoint_CheckpointWithHumanInTheLoop",
ProjectPath = "samples/03-workflows/Checkpoint/CheckpointWithHumanInTheLoop",
RequiredEnvironmentVariables = [],
Inputs = ["50", "25", "40", "45", "42", "50", "25", "40", "45", "42"],
InputDelayMs = 1000,
MustContain = ["found in"],
ExpectedOutputDescription =
[
"The output should show a number guessing game with higher/lower hints that eventually reaches the correct number.",
"The output should demonstrate checkpoint save and restore behavior.",
],
},
// ───────────────────────────────────────────────────────────────────
// Concurrent
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_Concurrent_Concurrent",
ProjectPath = "samples/03-workflows/Concurrent/Concurrent",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
ExpectedOutputDescription =
[
"The output should show results from concurrent agent processing.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "Workflow_Concurrent_MapReduce",
ProjectPath = "samples/03-workflows/Concurrent/MapReduce",
RequiredEnvironmentVariables = [],
MustContain =
[
"=== RUNNING WORKFLOW ===",
],
},
// ───────────────────────────────────────────────────────────────────
// ConditionalEdges
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_ConditionalEdges_01_EdgeCondition",
ProjectPath = "samples/03-workflows/ConditionalEdges/01_EdgeCondition",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
ExpectedOutputDescription =
[
"The output should show an email being classified as spam or not spam and processed accordingly.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "Workflow_ConditionalEdges_02_SwitchCase",
ProjectPath = "samples/03-workflows/ConditionalEdges/02_SwitchCase",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
ExpectedOutputDescription =
[
"The output should show an ambiguous email being classified as spam, not spam, or uncertain.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "Workflow_ConditionalEdges_03_MultiSelection",
ProjectPath = "samples/03-workflows/ConditionalEdges/03_MultiSelection",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
ExpectedOutputDescription =
[
"The output should show an email being classified and potentially routed to multiple handlers.",
"The output should not contain error messages or stack traces.",
],
},
// ───────────────────────────────────────────────────────────────────
// HumanInTheLoop
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_HumanInTheLoop_Basic",
ProjectPath = "samples/03-workflows/HumanInTheLoop/HumanInTheLoopBasic",
RequiredEnvironmentVariables = [],
Inputs = ["50", "25", "40", "45", "42"],
InputDelayMs = 1000,
MustContain = ["found in"],
ExpectedOutputDescription =
[
"The output should show a number guessing game with higher/lower hints that eventually reaches the correct number 42.",
],
},
// ───────────────────────────────────────────────────────────────────
// Loop
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_Loop",
ProjectPath = "samples/03-workflows/Loop",
RequiredEnvironmentVariables = [],
MustContain = ["Result:"],
},
// ───────────────────────────────────────────────────────────────────
// SharedStates
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_SharedStates",
ProjectPath = "samples/03-workflows/SharedStates",
RequiredEnvironmentVariables = [],
IsDeterministic = true,
MustContain =
[
"Total Paragraphs:",
"Total Words:",
],
},
// ───────────────────────────────────────────────────────────────────
// Visualization
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_Visualization",
ProjectPath = "samples/03-workflows/Visualization",
RequiredEnvironmentVariables = [],
IsDeterministic = true,
MustContain =
[
"Generating workflow visualization...",
"Mermaid string:",
"DiGraph string:",
],
},
// ───────────────────────────────────────────────────────────────────
// Observability
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_Observability_ApplicationInsights",
ProjectPath = "samples/03-workflows/Observability/ApplicationInsights",
RequiredEnvironmentVariables = ["APPLICATIONINSIGHTS_CONNECTION_STRING"],
SkipReason = "Requires Application Insights connection string.",
},
new SampleDefinition
{
Name = "Workflow_Observability_AspireDashboard",
ProjectPath = "samples/03-workflows/Observability/AspireDashboard",
RequiredEnvironmentVariables = [],
SkipReason = "Requires Aspire Dashboard / OTLP endpoint.",
},
new SampleDefinition
{
Name = "Workflow_Observability_WorkflowAsAnAgent",
ProjectPath = "samples/03-workflows/Observability/WorkflowAsAnAgent",
RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
SkipReason = "Interactive console with ReadLine loop; requires OTLP endpoint.",
},
// ───────────────────────────────────────────────────────────────────
// Declarative
// ───────────────────────────────────────────────────────────────────
new SampleDefinition
{
Name = "Workflow_Declarative_ConfirmInput",
ProjectPath = "samples/03-workflows/Declarative/ConfirmInput",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
Inputs = ["hello", "hello"],
InputDelayMs = 8000,
ExpectedOutputDescription = ["The output should show a confirmation prompt and a user response."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_CustomerSupport",
ProjectPath = "samples/03-workflows/Declarative/CustomerSupport",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["My laptop won't start"],
InputDelayMs = 3000,
ExpectedOutputDescription = ["The output should show a customer support workflow processing a laptop issue, with agent responses providing troubleshooting or support."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_DeepResearch",
ProjectPath = "samples/03-workflows/Declarative/DeepResearch",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
SkipReason = "Requires external weather API (wttr.in).",
},
new SampleDefinition
{
Name = "Workflow_Declarative_ExecuteCode",
ProjectPath = "samples/03-workflows/Declarative/ExecuteCode",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
Inputs = ["What is 12 * 34?"],
InputDelayMs = 5000,
ExpectedOutputDescription = ["The output should show a declarative workflow executing generated code, processing a math question and producing a result."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_ExecuteWorkflow",
ProjectPath = "samples/03-workflows/Declarative/ExecuteWorkflow",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
SkipReason = "Requires a workflow file path as a CLI argument.",
},
new SampleDefinition
{
Name = "Workflow_Declarative_FunctionTools",
ProjectPath = "samples/03-workflows/Declarative/FunctionTools",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["What are today's specials?", "EXIT"],
InputDelayMs = 8000,
ExpectedOutputDescription = ["The output should show a workflow calling function tools (e.g. a menu plugin) to answer a question about restaurant specials."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_GenerateCode",
ProjectPath = "samples/03-workflows/Declarative/GenerateCode",
IsDeterministic = true,
MustContain = ["WORKFLOW: Parsing", "WORKFLOW: Defined"],
ExpectedOutputDescription = ["The output should show a YAML workflow being parsed and C# code being generated from it."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_HostedWorkflow",
ProjectPath = "samples/03-workflows/Declarative/HostedWorkflow",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
SkipReason = "Hosts a persistent workflow server that does not exit.",
},
new SampleDefinition
{
Name = "Workflow_Declarative_InputArguments",
ProjectPath = "samples/03-workflows/Declarative/InputArguments",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["I'd like to visit Seattle", "EXIT"],
InputDelayMs = 8000,
ExpectedOutputDescription = ["The output should show a workflow capturing location input and providing travel-related information about Seattle."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_InvokeFunctionTool",
ProjectPath = "samples/03-workflows/Declarative/InvokeFunctionTool",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["What's the soup of the day?", "EXIT"],
InputDelayMs = 8000,
ExpectedOutputDescription = ["The output should show a workflow invoking a function tool (e.g. a menu plugin) to answer a question about the soup of the day."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_InvokeMcpTool",
ProjectPath = "samples/03-workflows/Declarative/InvokeMcpTool",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["Search for .NET tutorials on Microsoft Learn"],
InputDelayMs = 3000,
ExpectedOutputDescription = ["The output should show a workflow using MCP tools to search Microsoft Learn documentation and provide a summary of results."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_Marketing",
ProjectPath = "samples/03-workflows/Declarative/Marketing",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["A smart water bottle that tracks hydration"],
InputDelayMs = 3000,
ExpectedOutputDescription = ["The output should show a marketing workflow generating content about a smart water bottle product."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_StudentTeacher",
ProjectPath = "samples/03-workflows/Declarative/StudentTeacher",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["What is 18 + 27?"],
InputDelayMs = 3000,
ExpectedOutputDescription = ["The output should show a student-teacher workflow where a student asks a math question and a teacher provides the answer."],
},
new SampleDefinition
{
Name = "Workflow_Declarative_ToolApproval",
ProjectPath = "samples/03-workflows/Declarative/ToolApproval",
RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
Inputs = ["Search for .NET tutorials", "EXIT"],
InputDelayMs = 8000,
ExpectedOutputDescription = ["The output should show a workflow using an MCP tool with approval to search Microsoft Learn, followed by an exit from the input loop."],
},
];
}
dotnet/eng/verify-samples/verify-samples.csproj
+24
View File
@@ -0,0 +1,24 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<IsPackable>false</IsPackable>
<IsAotCompatible>false</IsAotCompatible>
<!-- This is a top-level console app; ConfigureAwait is unnecessary -->
<NoWarn>$(NoWarn);CA2007</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
dotnet/nuget/nuget-package.props
+7 -6
View File
@@ -2,19 +2,20 @@
<PropertyGroup>
<!-- Central version prefix - applies to all nuget packages. -->
<VersionPrefix>1.0.0</VersionPrefix>
<RCNumber>5</RCNumber>
<RCNumber>6</RCNumber>
<PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260330.1</PackageVersion>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260330.1</PackageVersion>
<GitTag>1.0.0-rc5</GitTag>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260402.1</PackageVersion>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260402.1</PackageVersion>
<PackageVersion Condition="'$(IsReleased)' == 'true'">$(VersionPrefix)</PackageVersion>
<GitTag>1.0.0</GitTag>
<Configurations>Debug;Release;Publish</Configurations>
<IsPackable>true</IsPackable>
<!-- Package validation. Baseline Version should be the latest version available on NuGet. -->
<PackageValidationBaselineVersion>1.0.0-rc4</PackageValidationBaselineVersion>
<PackageValidationBaselineVersion>1.0.0-rc5</PackageValidationBaselineVersion>
<!-- Enable validation for RC packages and GA packages -->
<EnablePackageValidation Condition="'$(IsReleaseCandidate)' == 'true' OR '$(IsGenerallyAvailable)' == 'true'">true</EnablePackageValidation>
<EnablePackageValidation Condition="'$(IsReleaseCandidate)' == 'true' OR '$(IsReleased)' == 'true'">true</EnablePackageValidation>
<!-- Validate assembly attributes only for Publish builds -->
<NoWarn Condition="'$(Configuration)' != 'Publish'">$(NoWarn);CP0003</NoWarn>
<!-- Do not validate reference assemblies -->
dotnet/samples/02-agents/AgentOpenTelemetry/Program.cs
+1 -1
View File
@@ -23,7 +23,7 @@ const string SourceName = "OpenTelemetryAspire.ConsoleApp";
const string ServiceName = "AgentOpenTelemetry";
// Configure OpenTelemetry for Aspire dashboard
var otlpEndpoint = Environment.GetEnvironmentVariable("OTEL_EXPORTER_OTLP_ENDPOINT") ?? "http://localhost:4318";
var otlpEndpoint = Environment.GetEnvironmentVariable("OTEL_EXPORTER_OTLP_ENDPOINT") ?? "http://localhost:4317";
var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
dotnet/samples/02-agents/AgentProviders/Agent_With_Anthropic/README.md
+9 -9
View File
@@ -5,8 +5,8 @@ This sample demonstrates how to create an AIAgent using Anthropic Claude models
The sample supports three deployment scenarios:
1. **Anthropic Public API** - Direct connection to Anthropic's public API
2. **Azure Foundry with API Key** - Anthropic models deployed through Azure Foundry using API key authentication
3. **Azure Foundry with Azure CLI** - Anthropic models deployed through Azure Foundry using Azure CLI credentials
2. **Microsoft Foundry with API Key** - Anthropic models deployed through Microsoft Foundry using API key authentication
3. **Microsoft Foundry with Azure CLI** - Anthropic models deployed through Microsoft Foundry using Azure CLI credentials
## Prerequisites
@@ -25,29 +25,29 @@ $env:ANTHROPIC_API_KEY="your-anthropic-api-key" # Replace with your Anthropic A
$env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5" # Optional, defaults to claude-haiku-4-5
```
### For Azure Foundry with API Key
### For Microsoft Foundry with API Key
- Azure Foundry service endpoint and deployment configured
- Microsoft Foundry service endpoint and deployment configured
- Anthropic API key
Set the following environment variables:
```powershell
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name" # Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name" # Replace with your Microsoft Foundry resource name (subdomain before .services.ai.azure.com)
$env:ANTHROPIC_API_KEY="your-anthropic-api-key" # Replace with your Anthropic API key
$env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5" # Optional, defaults to claude-haiku-4-5
```
### For Azure Foundry with Azure CLI
### For Microsoft Foundry with Azure CLI
- Azure Foundry service endpoint and deployment configured
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
Set the following environment variables:
```powershell
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name" # Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name" # Replace with your Microsoft Foundry resource name (subdomain before .services.ai.azure.com)
$env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5" # Optional, defaults to claude-haiku-4-5
```
**Note**: When using Azure Foundry with Azure CLI, make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
**Note**: When using Microsoft Foundry with Azure CLI, make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent/Program.cs
+1 -1
View File
@@ -2,7 +2,7 @@
#pragma warning disable CS0618 // Type or member is obsolete - sample uses deprecated PersistentAgentsClientExtensions
// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend.
// This sample shows how to create and use a simple AI agent with Microsoft Foundry Agents as the backend.
using Azure.AI.Agents.Persistent;
using Azure.Identity;
dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent/README.md
+3 -3
View File
@@ -13,14 +13,14 @@ Below is a comparison between the classic and new Foundry Agents approaches:
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
Set the following environment variables:
```powershell
$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
```
dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIProject/Agent_With_AzureAIProject.csproj
+1 -1
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@@ -15,7 +15,7 @@
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>
dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIProject/Program.cs
+10 -10
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@@ -1,29 +1,29 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to create and use a AI agents with Azure Foundry Agents as the backend.
// This sample shows how to create and use AI agents with Microsoft Foundry Agents as the backend.
using Azure.AI.Projects;
using Azure.AI.Projects.Agents;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.AzureAI;
using Microsoft.Agents.AI.Foundry;
var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
const string JokerName = "JokerAgent";
// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
// Get a client to create/retrieve/delete server side agents with Microsoft Foundry Agents.
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());
// Define the agent you want to create. (Prompt Agent in this case)
var agentVersionCreationOptions = new AgentVersionCreationOptions(new PromptAgentDefinition(model: deploymentName) { Instructions = "You are good at telling jokes." });
var agentVersionCreationOptions = new ProjectsAgentVersionCreationOptions(new DeclarativeAgentDefinition(model: deploymentName) { Instructions = "You are good at telling jokes." });
// Azure.AI.Agents SDK creates and manages agent by name and versions.
// You can create a server side agent version with the Azure.AI.Agents SDK client below.
var createdAgentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: JokerName, options: agentVersionCreationOptions);
var createdAgentVersion = aiProjectClient.AgentAdministrationClient.CreateAgentVersion(agentName: JokerName, options: agentVersionCreationOptions);
// Note:
// agentVersion.Id = "<agentName>:<versionNumber>",
@@ -34,15 +34,15 @@ var createdAgentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: J
FoundryAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);
// You can also create another AIAgent version by providing the same name with a different definition.
AgentVersion newJokerAgentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
ProjectsAgentVersion newJokerAgentVersion = await aiProjectClient.AgentAdministrationClient.CreateAgentVersionAsync(
JokerName,
new AgentVersionCreationOptions(new PromptAgentDefinition(model: deploymentName) { Instructions = "You are extremely hilarious at telling jokes." }));
new ProjectsAgentVersionCreationOptions(new DeclarativeAgentDefinition(model: deploymentName) { Instructions = "You are extremely hilarious at telling jokes." }));
FoundryAgent newJokerAgent = aiProjectClient.AsAIAgent(newJokerAgentVersion);
// You can also get the AIAgent latest version just providing its name.
AgentRecord jokerAgentRecord = await aiProjectClient.Agents.GetAgentAsync(JokerName);
ProjectsAgentRecord jokerAgentRecord = await aiProjectClient.AgentAdministrationClient.GetAgentAsync(JokerName);
FoundryAgent jokerAgentLatest = aiProjectClient.AsAIAgent(jokerAgentRecord);
AgentVersion latestAgentVersion = jokerAgentRecord.GetLatestVersion();
ProjectsAgentVersion latestAgentVersion = jokerAgentRecord.GetLatestVersion();
// The AIAgent version can be accessed via the GetService method.
Console.WriteLine($"Latest agent version id: {latestAgentVersion.Id}");
@@ -55,4 +55,4 @@ Console.WriteLine(await jokerAgentLatest.RunAsync("Tell me a joke about a pirate
Console.WriteLine(await jokerAgentLatest.RunAsync("Now tell me a joke about a cat and a dog using last joke as the anchor.", session));
// Cleanup by agent name removes both agent versions created.
aiProjectClient.Agents.DeleteAgent(existingJokerAgent.Name);
aiProjectClient.AgentAdministrationClient.DeleteAgent(existingJokerAgent.Name);
dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIProject/README.md
+3 -3
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@@ -13,14 +13,14 @@ Below is a comparison between the classic and new Foundry Agents approaches:
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
Set the following environment variables:
```powershell
$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
```
dotnet/samples/02-agents/AgentProviders/Agent_With_AzureFoundryModel/Program.cs
+3 -3
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@@ -1,7 +1,7 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Azure AI Foundry.
// You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in your Azure AI Foundry resource.
// This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Microsoft Foundry.
// You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in your Microsoft Foundry resource.
// Note: Ensure that you pick a model that suits your needs. For example, if you want to use function calling, ensure that the model you pick supports function calling.
using System.ClientModel;
@@ -15,7 +15,7 @@ var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? th
var apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
var model = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "Phi-4-mini-instruct";
// Since we are using the OpenAI Client SDK, we need to override the default endpoint to point to Azure Foundry.
// Since we are using the OpenAI Client SDK, we need to override the default endpoint to point to Microsoft Foundry.
var clientOptions = new OpenAIClientOptions() { Endpoint = new Uri(endpoint) };
// Create the OpenAI client with either an API key or Azure CLI credential.
dotnet/samples/02-agents/AgentProviders/Agent_With_AzureFoundryModel/README.md
+7 -7
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@@ -1,8 +1,8 @@
## Overview
This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Azure AI Foundry.
This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Microsoft Foundry.
You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in Azure AI Foundry.
You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in Microsoft Foundry.
**Note**: Ensure that you pick a model that suits your needs. For example, if you want to use function calling, ensure that the model you pick supports function calling.
@@ -11,19 +11,19 @@ You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI o
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure AI Foundry resource
- A model deployment in your Azure AI Foundry resource. This example defaults to using the `Phi-4-mini-instruct` model,
- Microsoft Foundry resource
- A model deployment in your Microsoft Foundry resource. This example defaults to using the `Phi-4-mini-instruct` model,
so if you want to use a different model, ensure that you set your `AZURE_AI_MODEL_DEPLOYMENT_NAME` environment
variable to the name of your deployed model.
- An API key or role based authentication to access the Azure AI Foundry resource
- An API key or role based authentication to access the Microsoft Foundry resource
See [here](https://learn.microsoft.com/en-us/azure/ai-foundry/quickstarts/get-started-code?tabs=csharp) for more info on setting up these prerequisites
Set the following environment variables:
```powershell
# Replace with your Azure AI Foundry resource endpoint
# Ensure that you have the "/openai/v1/" path in the URL, since this is required when using the OpenAI SDK to access Azure Foundry models.
# Replace with your Microsoft Foundry resource endpoint
# Ensure that you have the "/openai/v1/" path in the URL, since this is required when using the OpenAI SDK to access Microsoft Foundry models.
$env:AZURE_OPENAI_ENDPOINT="https://ai-foundry-<myresourcename>.services.ai.azure.com/openai/v1/"
# Optional, defaults to using Azure CLI for authentication if not provided
dotnet/samples/02-agents/AgentProviders/Agent_With_OpenAIAssistants/Program.cs
-41
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@@ -1,41 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to create and use a simple AI agent with OpenAI Assistants as the backend.
// WARNING: The Assistants API is deprecated and will be shut down.
// For more information see the OpenAI documentation: https://platform.openai.com/docs/assistants/migration
#pragma warning disable CS0618 // Type or member is obsolete - OpenAI Assistants API is deprecated but still used in this sample
using Microsoft.Agents.AI;
using OpenAI;
using OpenAI.Assistants;
var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";
const string JokerName = "Joker";
const string JokerInstructions = "You are good at telling jokes.";
// Get a client to create/retrieve server side agents with.
var assistantClient = new OpenAIClient(apiKey).GetAssistantClient();
// You can create a server side assistant with the OpenAI SDK.
var createResult = await assistantClient.CreateAssistantAsync(model, new() { Name = JokerName, Instructions = JokerInstructions });
// You can retrieve an already created server side assistant as an AIAgent.
AIAgent agent1 = await assistantClient.GetAIAgentAsync(createResult.Value.Id);
// You can also create a server side assistant and return it as an AIAgent directly.
AIAgent agent2 = await assistantClient.CreateAIAgentAsync(
model: model,
name: JokerName,
instructions: JokerInstructions);
// You can invoke the agent like any other AIAgent.
AgentSession session = await agent1.CreateSessionAsync();
Console.WriteLine(await agent1.RunAsync("Tell me a joke about a pirate.", session));
// Cleanup for sample purposes.
await assistantClient.DeleteAssistantAsync(agent1.Id);
await assistantClient.DeleteAssistantAsync(agent2.Id);
dotnet/samples/02-agents/AgentProviders/Agent_With_OpenAIAssistants/README.md
-16
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@@ -1,16 +0,0 @@
# Prerequisites
WARNING: The Assistants API is deprecated and will be shut down.
For more information see the OpenAI documentation: https://platform.openai.com/docs/assistants/migration
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- OpenAI API key
Set the following environment variables:
```powershell
$env:OPENAI_API_KEY="*****" # Replace with your OpenAI API key
$env:OPENAI_CHAT_MODEL_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
```
dotnet/samples/02-agents/AgentProviders/README.md
+1 -2
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@@ -18,14 +18,13 @@ See the README.md for each sample for the prerequisites for that sample.
|[Creating an AIAgent with Anthropic](./Agent_With_Anthropic/)|This sample demonstrates how to create an AIAgent using Anthropic Claude models as the underlying inference service|
|[Creating an AIAgent with Foundry Agents using Azure.AI.Agents.Persistent](./Agent_With_AzureAIAgentsPersistent/)|This sample demonstrates how to create a Foundry Persistent agent and expose it as an AIAgent using the Azure.AI.Agents.Persistent SDK|
|[Creating an AIAgent with Foundry Agents using Azure.AI.Project](./Agent_With_AzureAIProject/)|This sample demonstrates how to create an Foundry Project agent and expose it as an AIAgent using the Azure.AI.Project SDK|
|[Creating an AIAgent with AzureFoundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Azure Foundry to create an AIAgent|
|[Creating an AIAgent with Foundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Microsoft Foundry to create an AIAgent|
|[Creating an AIAgent with Azure OpenAI ChatCompletion](./Agent_With_AzureOpenAIChatCompletion/)|This sample demonstrates how to create an AIAgent using Azure OpenAI ChatCompletion as the underlying inference service|
|[Creating an AIAgent with Azure OpenAI Responses](./Agent_With_AzureOpenAIResponses/)|This sample demonstrates how to create an AIAgent using Azure OpenAI Responses as the underlying inference service|
|[Creating an AIAgent with a custom implementation](./Agent_With_CustomImplementation/)|This sample demonstrates how to create an AIAgent with a custom implementation|
|[Creating an AIAgent with GitHub Copilot](./Agent_With_GitHubCopilot/)|This sample demonstrates how to create an AIAgent using GitHub Copilot SDK as the underlying inference service|
|[Creating an AIAgent with Ollama](./Agent_With_Ollama/)|This sample demonstrates how to create an AIAgent using Ollama as the underlying inference service|
|[Creating an AIAgent with ONNX](./Agent_With_ONNX/)|This sample demonstrates how to create an AIAgent using ONNX as the underlying inference service|
|[Creating an AIAgent with OpenAI Assistants](./Agent_With_OpenAIAssistants/)|This sample demonstrates how to create an AIAgent using OpenAI Assistants as the underlying inference service.</br>WARNING: The Assistants API is deprecated and will be shut down. For more information see the OpenAI documentation: https://platform.openai.com/docs/assistants/migration|
|[Creating an AIAgent with OpenAI ChatCompletion](./Agent_With_OpenAIChatCompletion/)|This sample demonstrates how to create an AIAgent using OpenAI ChatCompletion as the underlying inference service|
|[Creating an AIAgent with OpenAI Responses](./Agent_With_OpenAIResponses/)|This sample demonstrates how to create an AIAgent using OpenAI Responses as the underlying inference service|
dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/README.md
+1 -1
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@@ -6,7 +6,7 @@ This sample demonstrates how to use **file-based Agent Skills** with a `ChatClie
- Discovering skills from `SKILL.md` files on disk via `AgentFileSkillsSource`
- The progressive disclosure pattern: advertise → load → read resources → run scripts
- Using the `AgentSkillsProvider` constructor with a skill directory path and script executor
- Using the `AgentSkillsProvider` constructor with a skill directory path and script runner
- Running file-based scripts (Python) via a subprocess-based executor
## Skills Included
dotnet/samples/02-agents/AgentProviders/Agent_With_OpenAIAssistants/Agent_With_OpenAIAssistants.csproj → dotnet/samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/Agent_Step03_ClassBasedSkills.csproj
+6
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@@ -6,8 +6,14 @@
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);MAAI001</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
dotnet/samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/Program.cs
+102
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@@ -0,0 +1,102 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to define Agent Skills as C# classes using AgentClassSkill.
// Class-based skills bundle all components into a single class implementation.
using System.Text.Json;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI.Responses;
// --- Configuration ---
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// --- Class-Based Skill ---
// Instantiate the skill class.
var unitConverter = new UnitConverterSkill();
// --- Skills Provider ---
var skillsProvider = new AgentSkillsProvider(unitConverter);
// --- Agent Setup ---
AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
.GetResponsesClient()
.AsAIAgent(new ChatClientAgentOptions
{
Name = "UnitConverterAgent",
ChatOptions = new()
{
Instructions = "You are a helpful assistant that can convert units.",
},
AIContextProviders = [skillsProvider],
},
model: deploymentName);
// --- Example: Unit conversion ---
Console.WriteLine("Converting units with class-based skills");
Console.WriteLine(new string('-', 60));
AgentResponse response = await agent.RunAsync(
"How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
Console.WriteLine($"Agent: {response.Text}");
/// <summary>
/// A unit-converter skill defined as a C# class.
/// </summary>
/// <remarks>
/// Class-based skills bundle all components (name, description, body, resources, scripts)
/// into a single class.
/// </remarks>
internal sealed class UnitConverterSkill : AgentClassSkill
{
private IReadOnlyList<AgentSkillResource>? _resources;
private IReadOnlyList<AgentSkillScript>? _scripts;
/// <inheritdoc/>
public override AgentSkillFrontmatter Frontmatter { get; } = new(
"unit-converter",
"Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.");
/// <inheritdoc/>
protected override string Instructions => """
Use this skill when the user asks to convert between units.
1. Review the conversion-table resource to find the factor for the requested conversion.
2. Use the convert script, passing the value and factor from the table.
3. Present the result clearly with both units.
""";
/// <inheritdoc/>
public override IReadOnlyList<AgentSkillResource>? Resources => this._resources ??=
[
CreateResource(
"conversion-table",
"""
# Conversion Tables
Formula: **result = value × factor**
| From | To | Factor |
|-------------|-------------|----------|
| miles | kilometers | 1.60934 |
| kilometers | miles | 0.621371 |
| pounds | kilograms | 0.453592 |
| kilograms | pounds | 2.20462 |
"""),
];
/// <inheritdoc/>
public override IReadOnlyList<AgentSkillScript>? Scripts => this._scripts ??=
[
CreateScript("convert", ConvertUnits),
];
private static string ConvertUnits(double value, double factor)
{
double result = Math.Round(value * factor, 4);
return JsonSerializer.Serialize(new { value, factor, result });
}
}
dotnet/samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/README.md
+49
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@@ -0,0 +1,49 @@
# Class-Based Agent Skills Sample
This sample demonstrates how to define **Agent Skills as C# classes** using `AgentClassSkill`.
## What it demonstrates
- Creating skills as classes that extend `AgentClassSkill`
- Bundling name, description, body, resources, and scripts into a single class
- Using the `AgentSkillsProvider` constructor with class-based skills
## Skills Included
### unit-converter (class-based)
A `UnitConverterSkill` class that converts between common units. Defined in `Program.cs`:
- `conversion-table` — Static resource with factor table
- `convert` — Script that performs `value × factor` conversion
## Running the Sample
### Prerequisites
- .NET 10.0 SDK
- Azure OpenAI endpoint with a deployed model
### Setup
```bash
export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
```
### Run
```bash
dotnet run
```
### Expected Output
```
Converting units with class-based skills
------------------------------------------------------------
Agent: Here are your conversions:
1. **26.2 miles → 42.16 km** (a marathon distance)
2. **75 kg → 165.35 lbs**
```
dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/Agent_Step04_MixedSkills.csproj
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@@ -0,0 +1,32 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);MAAI001</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<Compile Include="..\SubprocessScriptRunner.cs" Link="SubprocessScriptRunner.cs" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
<!-- Copy skills directory to output -->
<ItemGroup>
<None Include="skills\**\*.*">
<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
</None>
</ItemGroup>
</Project>
dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/Program.cs
+149
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@@ -0,0 +1,149 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates an advanced scenario: combining multiple skill types in a single agent
// using AgentSkillsProviderBuilder. The builder is designed for cases where the simple
// AgentSkillsProvider constructors are insufficient — for example, when you need to mix skill
// sources, apply filtering, or configure cross-cutting options in one place.
//
// Three different skill sources are registered here:
// 1. File-based: unit-converter (miles↔km, pounds↔kg) from SKILL.md on disk
// 2. Code-defined: volume-converter (gallons↔liters) using AgentInlineSkill
// 3. Class-based: temperature-converter (°F↔°C↔K) using AgentClassSkill
//
// For simpler, single-source scenarios, see the earlier steps in this sample series
// (e.g., Step01 for file-based, Step02 for code-defined, Step03 for class-based).
using System.Text.Json;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI.Responses;
// --- Configuration ---
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// --- 1. Code-Defined Skill: volume-converter ---
var volumeConverterSkill = new AgentInlineSkill(
name: "volume-converter",
description: "Convert between gallons and liters using a multiplication factor.",
instructions: """
Use this skill when the user asks to convert between gallons and liters.
1. Review the volume-conversion-table resource to find the correct factor.
2. Use the convert-volume script, passing the value and factor.
""")
.AddResource("volume-conversion-table",
"""
# Volume Conversion Table
Formula: **result = value × factor**
| From | To | Factor |
|---------|---------|---------|
| gallons | liters | 3.78541 |
| liters | gallons | 0.264172|
""")
.AddScript("convert-volume", (double value, double factor) =>
{
double result = Math.Round(value * factor, 4);
return JsonSerializer.Serialize(new { value, factor, result });
});
// --- 2. Class-Based Skill: temperature-converter ---
var temperatureConverter = new TemperatureConverterSkill();
// --- 3. Build provider combining all three source types ---
var skillsProvider = new AgentSkillsProviderBuilder()
.UseFileSkill(Path.Combine(AppContext.BaseDirectory, "skills")) // File-based: unit-converter
.UseSkill(volumeConverterSkill) // Code-defined: volume-converter
.UseSkill(temperatureConverter) // Class-based: temperature-converter
.UseFileScriptRunner(SubprocessScriptRunner.RunAsync)
.Build();
// --- Agent Setup ---
AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
.GetResponsesClient()
.AsAIAgent(new ChatClientAgentOptions
{
Name = "MultiConverterAgent",
ChatOptions = new()
{
Instructions = "You are a helpful assistant that can convert units, volumes, and temperatures.",
},
AIContextProviders = [skillsProvider],
},
model: deploymentName);
// --- Example: Use all three skills ---
Console.WriteLine("Converting with mixed skills (file + code + class)");
Console.WriteLine(new string('-', 60));
AgentResponse response = await agent.RunAsync(
"I need three conversions: " +
"1) How many kilometers is a marathon (26.2 miles)? " +
"2) How many liters is a 5-gallon bucket? " +
"3) What is 98.6°F in Celsius?");
Console.WriteLine($"Agent: {response.Text}");
/// <summary>
/// A temperature-converter skill defined as a C# class.
/// </summary>
internal sealed class TemperatureConverterSkill : AgentClassSkill
{
private IReadOnlyList<AgentSkillResource>? _resources;
private IReadOnlyList<AgentSkillScript>? _scripts;
/// <inheritdoc/>
public override AgentSkillFrontmatter Frontmatter { get; } = new(
"temperature-converter",
"Convert between temperature scales (Fahrenheit, Celsius, Kelvin).");
/// <inheritdoc/>
protected override string Instructions => """
Use this skill when the user asks to convert temperatures.
1. Review the temperature-conversion-formulas resource for the correct formula.
2. Use the convert-temperature script, passing the value, source scale, and target scale.
3. Present the result clearly with both temperature scales.
""";
/// <inheritdoc/>
public override IReadOnlyList<AgentSkillResource>? Resources => this._resources ??=
[
CreateResource(
"temperature-conversion-formulas",
"""
# Temperature Conversion Formulas
| From | To | Formula |
|-------------|-------------|---------------------------|
| Fahrenheit | Celsius | °C = (°F − 32) × 5/9 |
| Celsius | Fahrenheit | °F = (°C × 9/5) + 32 |
| Celsius | Kelvin | K = °C + 273.15 |
| Kelvin | Celsius | °C = K − 273.15 |
"""),
];
/// <inheritdoc/>
public override IReadOnlyList<AgentSkillScript>? Scripts => this._scripts ??=
[
CreateScript("convert-temperature", ConvertTemperature),
];
private static string ConvertTemperature(double value, string from, string to)
{
double result = (from.ToUpperInvariant(), to.ToUpperInvariant()) switch
{
("FAHRENHEIT", "CELSIUS") => Math.Round((value - 32) * 5.0 / 9.0, 2),
("CELSIUS", "FAHRENHEIT") => Math.Round(value * 9.0 / 5.0 + 32, 2),
("CELSIUS", "KELVIN") => Math.Round(value + 273.15, 2),
("KELVIN", "CELSIUS") => Math.Round(value - 273.15, 2),
_ => throw new ArgumentException($"Unsupported conversion: {from} → {to}")
};
return JsonSerializer.Serialize(new { value, from, to, result });
}
}
dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/README.md
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# Mixed Agent Skills Sample (Advanced)
This sample demonstrates an **advanced scenario**: combining multiple skill types in a single agent using `AgentSkillsProviderBuilder`.
> **Tip:** For simpler, single-source scenarios, use the `AgentSkillsProvider` constructors directly — see [Step01](../Agent_Step01_FileBasedSkills/) (file-based), [Step02](../Agent_Step02_CodeDefinedSkills/) (code-defined), or [Step03](../Agent_Step03_ClassBasedSkills/) (class-based).
## What it demonstrates
- Combining file-based, code-defined, and class-based skills in one provider
- Using `UseFileSkill` and `UseSkill` on the builder to register different skill types
- Aggregating skills from all sources into a single provider with automatic deduplication
## When to use `AgentSkillsProviderBuilder`
The builder is intended for advanced scenarios where the simple `AgentSkillsProvider` constructors are insufficient:
| Scenario | Builder method |
|----------|---------------|
| **Mixed skill types** — combine file-based, code-defined, and class-based skills | `UseFileSkill` + `UseSkill` / `UseSkills` |
| **Multiple file script runners** — use different script runners for different file skill directories | `UseFileSkill` / `UseFileSkills` with per-source `scriptRunner` |
| **Skill filtering** — include/exclude skills using a predicate | `UseFilter(predicate)` |
## Skills Included
### unit-converter (file-based)
Discovered from `skills/unit-converter/SKILL.md` on disk. Converts miles↔km, pounds↔kg.
### volume-converter (code-defined)
Defined as `AgentInlineSkill` in `Program.cs`. Converts gallons↔liters.
### temperature-converter (class-based)
Defined as `TemperatureConverterSkill` class in `Program.cs`. Converts °F↔°C↔K.
## Running the Sample
### Prerequisites
- .NET 10.0 SDK
- Azure OpenAI endpoint with a deployed model
### Setup
```bash
export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
```
### Run
```bash
dotnet run
```
### Expected Output
```
Converting with mixed skills (file + code + class)
------------------------------------------------------------
Agent: Here are your conversions:
1. **26.2 miles → 42.16 km** (a marathon distance)
2. **5 gallons → 18.93 liters**
3. **98.6°F → 37.0°C**
```
dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/skills/unit-converter/SKILL.md
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---
name: unit-converter
description: Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.
---
## Usage
When the user requests a unit conversion:
1. First, review `references/unit-conversion-table.md` to find the correct factor
2. Run the `scripts/convert-units.py` script with `--value <number> --factor <factor>` (e.g. `--value 26.2 --factor 1.60934`)
3. Present the converted value clearly with both units
dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/skills/unit-converter/references/unit-conversion-table.md
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@@ -0,0 +1,10 @@
# Conversion Tables
Formula: **result = value × factor**
| From | To | Factor |
|-------------|-------------|----------|
| miles | kilometers | 1.60934 |
| kilometers | miles | 0.621371 |
| pounds | kilograms | 0.453592 |
| kilograms | pounds | 2.20462 |
dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/skills/unit-converter/scripts/convert-units.py
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# Unit conversion script
# Converts a value using a multiplication factor: result = value × factor
#
# Usage:
# python scripts/convert-units.py --value 26.2 --factor 1.60934
# python scripts/convert-units.py --value 75 --factor 2.20462
import argparse
import json
def main() -> None:
parser = argparse.ArgumentParser(
description="Convert a value using a multiplication factor.",
epilog="Examples:\n"
" python scripts/convert-units.py --value 26.2 --factor 1.60934\n"
" python scripts/convert-units.py --value 75 --factor 2.20462",
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument("--value", type=float, required=True, help="The numeric value to convert.")
parser.add_argument("--factor", type=float, required=True, help="The conversion factor from the table.")
args = parser.parse_args()
result = round(args.value * args.factor, 4)
print(json.dumps({"value": args.value, "factor": args.factor, "result": result}))
if __name__ == "__main__":
main()
dotnet/samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/Agent_Step05_SkillsWithDI.csproj
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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);MAAI001;CA1812</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.DependencyInjection" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
dotnet/samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/Program.cs
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// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to use Dependency Injection (DI) with Agent Skills.
// It shows two approaches side-by-side, each handling a different conversion domain:
//
// 1. Code-defined skill (AgentInlineSkill) — converts distances (miles ↔ kilometers).
// Resources and scripts are inline delegates that resolve services from IServiceProvider.
//
// 2. Class-based skill (AgentClassSkill) — converts weights (pounds ↔ kilograms).
// Resources and scripts are encapsulated in a class, also resolving services from IServiceProvider.
//
// Both skills share the same ConversionService registered in the DI container,
// showing that DI works identically regardless of how the skill is defined.
// When prompted with a question spanning both domains, the agent uses both skills.
using System.Text.Json;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.DependencyInjection;
using OpenAI.Responses;
// --- Configuration ---
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// --- DI Container ---
// Register application services that skill resources and scripts can resolve at execution time.
ServiceCollection services = new();
services.AddSingleton<ConversionService>();
IServiceProvider serviceProvider = services.BuildServiceProvider();
// =====================================================================
// Approach 1: Code-Defined Skill with DI (AgentInlineSkill)
// =====================================================================
// Handles distance conversions (miles ↔ kilometers).
// Resources and scripts are inline delegates. Each delegate can declare
// an IServiceProvider parameter that the framework injects automatically.
var distanceSkill = new AgentInlineSkill(
name: "distance-converter",
description: "Convert between distance units. Use when asked to convert miles to kilometers or kilometers to miles.",
instructions: """
Use this skill when the user asks to convert between distance units (miles and kilometers).
1. Review the distance-table resource to find the factor for the requested conversion.
2. Use the convert script, passing the value and factor from the table.
""")
.AddResource("distance-table", (IServiceProvider serviceProvider) =>
{
var service = serviceProvider.GetRequiredService<ConversionService>();
return service.GetDistanceTable();
})
.AddScript("convert", (double value, double factor, IServiceProvider serviceProvider) =>
{
var service = serviceProvider.GetRequiredService<ConversionService>();
return service.Convert(value, factor);
});
// =====================================================================
// Approach 2: Class-Based Skill with DI (AgentClassSkill)
// =====================================================================
// Handles weight conversions (pounds ↔ kilograms).
// Resources and scripts are encapsulated in a class. Factory methods
// CreateResource and CreateScript accept delegates with IServiceProvider.
//
// Alternatively, class-based skills can accept dependencies through their
// constructor. Register the skill class itself in the ServiceCollection and
// resolve it from the container:
//
// services.AddSingleton<WeightConverterSkill>();
// var weightSkill = serviceProvider.GetRequiredService<WeightConverterSkill>();
var weightSkill = new WeightConverterSkill();
// --- Skills Provider ---
// Both skills are registered with the same provider so the agent can use either one.
var skillsProvider = new AgentSkillsProvider(distanceSkill, weightSkill);
// --- Agent Setup ---
AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
.GetResponsesClient()
.AsAIAgent(
options: new ChatClientAgentOptions
{
Name = "UnitConverterAgent",
ChatOptions = new()
{
Instructions = "You are a helpful assistant that can convert units.",
},
AIContextProviders = [skillsProvider],
},
model: deploymentName,
services: serviceProvider);
// --- Example: Unit conversion ---
// This prompt spans both domains, so the agent will use both skills.
Console.WriteLine("Converting units with DI-powered skills");
Console.WriteLine(new string('-', 60));
AgentResponse response = await agent.RunAsync(
"How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
Console.WriteLine($"Agent: {response.Text}");
// ---------------------------------------------------------------------------
// Class-Based Skill
// ---------------------------------------------------------------------------
/// <summary>
/// A weight-converter skill defined as a C# class that uses Dependency Injection.
/// </summary>
/// <remarks>
/// This skill resolves <see cref="ConversionService"/> from the DI container
/// in both its resource and script functions. This enables clean separation of
/// concerns and testability while retaining the class-based skill pattern.
/// </remarks>
internal sealed class WeightConverterSkill : AgentClassSkill
{
private IReadOnlyList<AgentSkillResource>? _resources;
private IReadOnlyList<AgentSkillScript>? _scripts;
/// <inheritdoc/>
public override AgentSkillFrontmatter Frontmatter { get; } = new(
"weight-converter",
"Convert between weight units. Use when asked to convert pounds to kilograms or kilograms to pounds.");
/// <inheritdoc/>
protected override string Instructions => """
Use this skill when the user asks to convert between weight units (pounds and kilograms).
1. Review the weight-table resource to find the factor for the requested conversion.
2. Use the convert script, passing the value and factor from the table.
3. Present the result clearly with both units.
""";
/// <inheritdoc/>
public override IReadOnlyList<AgentSkillResource>? Resources => this._resources ??=
[
CreateResource("weight-table", (IServiceProvider serviceProvider) =>
{
var service = serviceProvider.GetRequiredService<ConversionService>();
return service.GetWeightTable();
}),
];
/// <inheritdoc/>
public override IReadOnlyList<AgentSkillScript>? Scripts => this._scripts ??=
[
CreateScript("convert", (double value, double factor, IServiceProvider serviceProvider) =>
{
var service = serviceProvider.GetRequiredService<ConversionService>();
return service.Convert(value, factor);
}),
];
}
// ---------------------------------------------------------------------------
// Services
// ---------------------------------------------------------------------------
/// <summary>
/// Provides conversion rates between units.
/// In a real application this could call an external API, read from a database,
/// or apply time-varying exchange rates.
/// </summary>
internal sealed class ConversionService
{
/// <summary>
/// Returns a markdown table of supported distance conversions.
/// </summary>
public string GetDistanceTable() =>
"""
# Distance Conversions
Formula: **result = value × factor**
| From | To | Factor |
|-------------|-------------|----------|
| miles | kilometers | 1.60934 |
| kilometers | miles | 0.621371 |
""";
/// <summary>
/// Returns a markdown table of supported weight conversions.
/// </summary>
public string GetWeightTable() =>
"""
# Weight Conversions
Formula: **result = value × factor**
| From | To | Factor |
|-------------|-------------|----------|
| pounds | kilograms | 0.453592 |
| kilograms | pounds | 2.20462 |
""";
/// <summary>
/// Converts a value by the given factor and returns a JSON result.
/// </summary>
public string Convert(double value, double factor)
{
double result = Math.Round(value * factor, 4);
return JsonSerializer.Serialize(new { value, factor, result });
}
}
dotnet/samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/README.md
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# Agent Skills with Dependency Injection
This sample demonstrates how to use **Dependency Injection (DI)** with Agent Skills. It shows two approaches side-by-side, each handling a different conversion domain:
1. **Code-defined skill** (`AgentInlineSkill`) — converts **distances** (miles ↔ kilometers)
2. **Class-based skill** (`AgentClassSkill`) — converts **weights** (pounds ↔ kilograms)
Both skills resolve the same `ConversionService` from the DI container. When prompted with a question spanning both domains, the agent uses both skills.
## What It Shows
- Registering application services in a `ServiceCollection`
- Defining a **code-defined** skill (distance converter) with resources and scripts that resolve services from `IServiceProvider`
- Defining a **class-based** skill (weight converter) with resources and scripts that resolve services from `IServiceProvider`
- Passing the built `IServiceProvider` to the agent so skills can access DI services at execution time
- Running a single prompt that exercises both skills to show they work together
## How It Works
1. A `ConversionService` is registered as a singleton in the DI container
2. **Code-defined skill**: An `AgentInlineSkill` for distance conversions declares `IServiceProvider` as a parameter in its `AddResource` and `AddScript` delegates — the framework injects it automatically
3. **Class-based skill**: A `WeightConverterSkill` class extends `AgentClassSkill` for weight conversions and uses `CreateResource`/`CreateScript` factory methods with `IServiceProvider` parameters
4. Both skills resolve `ConversionService` from the provider — one for distance tables, the other for weight tables
5. A single agent is created with both skills registered, and the service provider flows through to skill execution
> **Tip:** Class-based skills can also accept dependencies through their **constructor**. Register the skill class in the `ServiceCollection` and resolve it from the container instead of calling `new` directly. This is useful when the skill itself needs injected services beyond what the resource/script delegates use.
## How It Differs from Other Samples
| Sample | Skill Type | DI Support |
|--------|------------|------------|
| [Step02](../Agent_Step02_CodeDefinedSkills/) | Code-defined (`AgentInlineSkill`) | No — static resources |
| [Step03](../Agent_Step03_ClassBasedSkills/) | Class-based (`AgentClassSkill`) | No — static resources |
| **Step05 (this)** | **Both code-defined and class-based** | **Yes — DI via `IServiceProvider`** |
## Prerequisites
- .NET 10
- An Azure OpenAI deployment
## Configuration
Set the following environment variables:
| Variable | Description |
|---|---|
| `AZURE_OPENAI_ENDPOINT` | Your Azure OpenAI endpoint URL |
| `AZURE_OPENAI_DEPLOYMENT_NAME` | Model deployment name (defaults to `gpt-4o-mini`) |
## Running the Sample
```bash
dotnet run
```
### Expected Output
```
Converting units with DI-powered skills
------------------------------------------------------------
Agent: Here are your conversions:
1. **26.2 miles → 42.16 km** (a marathon distance)
2. **75 kg → 165.35 lbs**
```
dotnet/samples/02-agents/AgentSkills/README.md
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@@ -6,19 +6,32 @@ Samples demonstrating Agent Skills capabilities. Each sample shows a different w
|--------|-------------|
| [Agent_Step01_FileBasedSkills](Agent_Step01_FileBasedSkills/) | Define skills as `SKILL.md` files on disk with reference documents. Uses a unit-converter skill. |
| [Agent_Step02_CodeDefinedSkills](Agent_Step02_CodeDefinedSkills/) | Define skills entirely in C# code using `AgentInlineSkill`, with static/dynamic resources and scripts. |
| [Agent_Step03_ClassBasedSkills](Agent_Step03_ClassBasedSkills/) | Define skills as C# classes using `AgentClassSkill`. |
| [Agent_Step04_MixedSkills](Agent_Step04_MixedSkills/) | **(Advanced)** Combine file-based, code-defined, and class-based skills using `AgentSkillsProviderBuilder`. |
| [Agent_Step05_SkillsWithDI](Agent_Step05_SkillsWithDI/) | Use Dependency Injection with both code-defined (`AgentInlineSkill`) and class-based (`AgentClassSkill`) skills. |
## Key Concepts
### File-Based vs Code-Defined Skills
### Skill Types
| Aspect | File-Based | Code-Defined |
|--------|-----------|--------------|
| Definition | `SKILL.md` files on disk | `AgentInlineSkill` instances in C# |
| Resources | All files in skill directory (filtered by extension) | `AddResource` (static value or delegate-backed) |
| Scripts | Supported via script executor delegate | `AddScript` delegates |
| Discovery | Automatic from directory path | Explicit via constructor |
| Dynamic content | No (static files only) | Yes (factory delegates) |
| Reusability | Copy skill directory | Inline or shared instances |
| Aspect | File-Based | Code-Defined | Class-Based |
|--------|-----------|--------------|-------------|
| Definition | `SKILL.md` files on disk | `AgentInlineSkill` instances in C# | Classes extending `AgentClassSkill` |
| Resources | All files in skill directory (filtered by extension) | `AddResource` (static value or delegate-backed) | `CreateResource` factory methods |
| Scripts | Supported via script runner delegate | `AddScript` delegates | `CreateScript` factory methods |
| Discovery | Automatic from directory path | Explicit via constructor | Explicit via constructor |
| Dynamic content | No (static files only) | Yes (factory delegates) | Yes (factory delegates) |
| Sharing pattern | Copy skill directory | Inline or shared instances | Package in shared assemblies/NuGet |
| DI support | No | Yes (via `IServiceProvider` parameter) | Yes (via `IServiceProvider` parameter) |
For single-source scenarios, use the `AgentSkillsProvider` constructors directly. To combine multiple skill types, use the `AgentSkillsProviderBuilder`.
### `AgentSkillsProvider` vs `AgentSkillsProviderBuilder`
For single-source scenarios, use the `AgentSkillsProvider` constructors directly — they accept a skill directory path, a set of skills, or a custom source.
Use `AgentSkillsProviderBuilder` for advanced scenarios where simple constructors are insufficient:
- **Mixed skill types** — combine file-based, code-defined, and class-based skills in one provider
- **Multiple file script runners** — use different script runners for different file skill directories
- **Skill filtering** — include or exclude skills using a predicate
See [Agent_Step04_MixedSkills](Agent_Step04_MixedSkills/) for a working example.
dotnet/samples/02-agents/AgentWithAnthropic/README.md
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@@ -18,9 +18,9 @@ Before you begin, ensure you have the following prerequisites:
**Note**: These samples use Anthropic Claude models. For more information, see [Anthropic documentation](https://docs.anthropic.com/).
## Using Anthropic with Azure Foundry
## Using Anthropic with Microsoft Foundry
To use Anthropic with Azure Foundry, you can check the sample [AgentProviders/Agent_With_Anthropic](../AgentProviders/Agent_With_Anthropic/README.md) for more details.
To use Anthropic with Microsoft Foundry, you can check the sample [AgentProviders/Agent_With_Anthropic](../AgentProviders/Agent_With_Anthropic/README.md) for more details.
## Samples
dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj
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@@ -1,4 +1,4 @@
<Project Sdk="Microsoft.NET.Sdk">
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
@@ -14,8 +14,7 @@
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.FoundryMemory\Microsoft.Agents.AI.FoundryMemory.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>
dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/Program.cs
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@@ -1,18 +1,17 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use the FoundryMemoryProvider to persist and recall memories for an agent.
// The sample stores conversation messages in an Azure AI Foundry memory store and retrieves relevant
// The sample stores conversation messages in a Microsoft Foundry memory store and retrieves relevant
// memories for subsequent invocations, even across new sessions.
//
// Note: Memory extraction in Azure AI Foundry is asynchronous and takes time. This sample demonstrates
// Note: Memory extraction in Microsoft Foundry is asynchronous and takes time. This sample demonstrates
// a simple polling approach to wait for memory updates to complete before querying.
using System.Text.Json;
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.AzureAI;
using Microsoft.Agents.AI.FoundryMemory;
using Microsoft.Agents.AI.Foundry;
string foundryEndpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? "memory-store-sample";
@@ -37,7 +36,7 @@ FoundryMemoryProvider memoryProvider = new(
memoryStoreName,
stateInitializer: _ => new(new FoundryMemoryProviderScope("sample-user-123")));
FoundryAgent agent = projectClient.AsAIAgent(
ChatClientAgent agent = projectClient.AsAIAgent(
new ChatClientAgentOptions()
{
Name = "TravelAssistantWithFoundryMemory",
@@ -62,7 +61,7 @@ await memoryProvider.EnsureStoredMemoriesDeletedAsync(session);
Console.WriteLine(await agent.RunAsync("Hi there! My name is Taylor and I'm planning a hiking trip to Patagonia in November.", session));
Console.WriteLine(await agent.RunAsync("I'm travelling with my sister and we love finding scenic viewpoints.", session));
// Memory extraction in Azure AI Foundry is asynchronous and takes time to process.
// Memory extraction in Microsoft Foundry is asynchronous and takes time to process.
// WhenUpdatesCompletedAsync polls all pending updates and waits for them to complete.
Console.WriteLine("\nWaiting for Foundry Memory to process updates...");
await memoryProvider.WhenUpdatesCompletedAsync();
dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/README.md
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@@ -1,6 +1,6 @@
# Agent with Memory Using Azure AI Foundry
# Agent with Memory Using Microsoft Foundry
This sample demonstrates how to create and run an agent that uses Azure AI Foundry's managed memory service to extract and retrieve individual memories across sessions.
This sample demonstrates how to create and run an agent that uses Microsoft Foundry's managed memory service to extract and retrieve individual memories across sessions.
## Features Demonstrated
@@ -13,7 +13,7 @@ This sample demonstrates how to create and run an agent that uses Azure AI Found
## Prerequisites
1. Azure subscription with Azure AI Foundry project
1. Azure subscription with Microsoft Foundry project
2. Azure OpenAI resource with a chat model deployment (e.g., gpt-4o-mini) and an embedding model deployment (e.g., text-embedding-ada-002)
3. .NET 10.0 SDK
4. Azure CLI logged in (`az login`)
@@ -21,7 +21,7 @@ This sample demonstrates how to create and run an agent that uses Azure AI Found
## Environment Variables
```bash
# Azure AI Foundry project endpoint and memory store name
# Microsoft Foundry project endpoint and memory store name
export AZURE_AI_PROJECT_ENDPOINT="https://your-account.services.ai.azure.com/api/projects/your-project"
export AZURE_AI_MEMORY_STORE_ID="my_memory_store"
@@ -48,10 +48,10 @@ The agent will:
## Key Differences from Mem0
| Aspect | Mem0 | Azure AI Foundry Memory |
| Aspect | Mem0 | Microsoft Foundry Memory |
|--------|------|------------------------|
| Authentication | API Key | Azure Identity (DefaultAzureCredential) |
| Scope | ApplicationId, UserId, AgentId, ThreadId | Single `Scope` string |
| Memory Types | Single memory store | User Profile + Chat Summary |
| Hosting | Mem0 cloud or self-hosted | Azure AI Foundry managed service |
| Hosting | Mem0 cloud or self-hosted | Microsoft Foundry managed service |
| Store Creation | N/A (automatic) | Explicit via `EnsureMemoryStoreCreatedAsync` |
dotnet/samples/02-agents/AgentWithMemory/README.md
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@@ -7,7 +7,7 @@ These samples show how to create an agent with the Agent Framework that uses Mem
|[Chat History memory](./AgentWithMemory_Step01_ChatHistoryMemory/)|This sample demonstrates how to enable an agent to remember messages from previous conversations.|
|[Memory with MemoryStore](./AgentWithMemory_Step02_MemoryUsingMem0/)|This sample demonstrates how to create and run an agent that uses the Mem0 service to extract and retrieve individual memories.|
|[Custom Memory Implementation](../../01-get-started/04_memory/)|This sample demonstrates how to create a custom memory component and attach it to an agent.|
|[Memory with Azure AI Foundry](./AgentWithMemory_Step04_MemoryUsingFoundry/)|This sample demonstrates how to create and run an agent that uses Azure AI Foundry's managed memory service to extract and retrieve individual memories.|
|[Memory with Microsoft Foundry](./AgentWithMemory_Step04_MemoryUsingFoundry/)|This sample demonstrates how to create and run an agent that uses Microsoft Foundry's managed memory service to extract and retrieve individual memories.|
|[Bounded Chat History with Overflow](./AgentWithMemory_Step05_BoundedChatHistory/)|This sample demonstrates how to create a bounded chat history provider that overflows older messages to a vector store and recalls them as memories.|
> **See also**: [Memory Search with Foundry Agents](../AgentsWithFoundry/Agent_Step22_MemorySearch/) - demonstrates using the built-in Memory Search tool with Azure Foundry agents.
> **See also**: [Memory Search with Foundry Agents](../AgentsWithFoundry/Agent_Step22_MemorySearch/) - demonstrates using the built-in Memory Search tool with Microsoft Foundry agents.
dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/README.md
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View File
@@ -13,7 +13,7 @@ This sample uses Qdrant for the vector store, but this can easily be swapped out
- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource.
- An existing Qdrant instance. You can use a managed service or run a local instance using Docker, but the sample assumes the instance is running locally.
**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
**Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj
+1 -1
View File
@@ -14,7 +14,7 @@
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
<ItemGroup>
dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/Program.cs
+5 -5
View File
@@ -7,7 +7,7 @@ using Azure.AI.Projects;
using Azure.AI.Projects.Agents;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.AzureAI;
using Microsoft.Agents.AI.Foundry;
using OpenAI;
using OpenAI.Files;
using OpenAI.Responses;
@@ -44,10 +44,10 @@ ClientResult<VectorStore> vectorStoreCreate = await vectorStoreClient.CreateVect
FileSearchTool fileSearchTool = new([vectorStoreCreate.Value.Id]);
#pragma warning restore OPENAI001
AgentVersion agentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
ProjectsAgentVersion agentVersion = await aiProjectClient.AgentAdministrationClient.CreateAgentVersionAsync(
"AskContoso",
new AgentVersionCreationOptions(
new PromptAgentDefinition(model: deploymentName)
new ProjectsAgentVersionCreationOptions(
new DeclarativeAgentDefinition(model: deploymentName)
{
Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
Tools = { fileSearchTool }
@@ -68,4 +68,4 @@ Console.WriteLine(await agent.RunAsync("What is the best way to maintain the Tra
// Cleanup
await fileClient.DeleteFileAsync(uploadResult.Value.Id);
await vectorStoreClient.DeleteVectorStoreAsync(vectorStoreCreate.Value.Id);
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
await aiProjectClient.AgentAdministrationClient.DeleteAgentAsync(agent.Name);
dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step05_Neo4jGraphRAG/AgentWithRAG_Step05_Neo4jGraphRAG.csproj
+54
View File
@@ -0,0 +1,54 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<ManagePackageVersionsCentrally>false</ManagePackageVersionsCentrally>
</PropertyGroup>
<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.OpenAI" Version="2.9.0-beta.1" />
<PackageReference Include="Azure.Identity" Version="1.19.0" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-rc4" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.4.0" />
<PackageReference Include="Neo4j.AgentFramework.GraphRAG" Version="0.1.0-preview.2" />
<PackageReference Include="Neo4j.Driver" Version="5.28.0" />
</ItemGroup>
<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>
dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step05_Neo4jGraphRAG/Program.cs
+77
View File
@@ -0,0 +1,77 @@
// Copyright (c) Microsoft. All rights reserved.
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using Neo4j.AgentFramework.GraphRAG;
using Neo4j.Driver;
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";
var neo4jUri = Environment.GetEnvironmentVariable("NEO4J_URI") ?? throw new InvalidOperationException("NEO4J_URI is not set.");
var neo4jUsername = Environment.GetEnvironmentVariable("NEO4J_USERNAME") ?? "neo4j";
var neo4jPassword = Environment.GetEnvironmentVariable("NEO4J_PASSWORD") ?? throw new InvalidOperationException("NEO4J_PASSWORD is not set.");
var fulltextIndex = Environment.GetEnvironmentVariable("NEO4J_FULLTEXT_INDEX_NAME") ?? "search_chunks";
const string RetrievalQuery = """
MATCH (node)-[:FROM_DOCUMENT]->(doc:Document)<-[:FILED]-(company:Company)
OPTIONAL MATCH (company)-[:FACES_RISK]->(risk:RiskFactor)
WITH node, score, company, doc, collect(DISTINCT risk.name)[0..5] AS risks
OPTIONAL MATCH (company)-[:MENTIONS]->(product:Product)
WITH node, score, company, doc, risks, collect(DISTINCT product.name)[0..5] AS products
RETURN
node.text AS text,
score,
company.name AS company,
company.ticker AS ticker,
doc.title AS title,
risks,
products
ORDER BY score DESC
""";
await using var driver = GraphDatabase.Driver(new Uri(neo4jUri), AuthTokens.Basic(neo4jUsername, neo4jPassword));
await driver.VerifyConnectivityAsync();
await using var provider = new Neo4jContextProvider(
driver,
new Neo4jContextProviderOptions
{
IndexName = fulltextIndex,
IndexType = IndexType.Fulltext,
RetrievalQuery = RetrievalQuery,
TopK = 5,
ContextPrompt = "Use the retrieved Neo4j graph context to answer accurately and call out when context is missing."
});
// 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)
.AsIChatClient()
.AsAIAgent(new ChatClientAgentOptions
{
ChatOptions = new()
{
Instructions = "You are a helpful assistant that answers questions using Neo4j graph context."
},
AIContextProviders = [provider]
});
AgentSession session = await agent.CreateSessionAsync();
foreach (var question in new[]
{
"What products does Microsoft offer?",
"What risks does Apple face?",
"Tell me about NVIDIA's AI business and risk factors."
})
{
Console.WriteLine($">> {question}\n");
Console.WriteLine(await agent.RunAsync(question, session));
Console.WriteLine();
}
dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step05_Neo4jGraphRAG/README.md
+32
View File
@@ -0,0 +1,32 @@
# Agent Framework Retrieval Augmented Generation (RAG) with Neo4j GraphRAG
This sample demonstrates how to create and run an agent that uses the [Neo4j GraphRAG context provider](https://github.com/neo4j-labs/neo4j-maf-provider) with Microsoft Agent Framework for .NET.
The sample uses a Neo4j fulltext index for retrieval and a Cypher `RetrievalQuery` to enrich results with related companies, products, and risk factors.
## Prerequisites
- .NET 10 SDK or later
- Azure OpenAI endpoint and chat deployment
- Azure CLI installed and authenticated
- A Neo4j database with chunked documents and a fulltext index such as `search_chunks`
## Environment variables
```powershell
$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
$env:NEO4J_URI="neo4j+s://your-instance.databases.neo4j.io"
$env:NEO4J_USERNAME="neo4j"
$env:NEO4J_PASSWORD="your-password"
$env:NEO4J_FULLTEXT_INDEX_NAME="search_chunks"
```
## Build and run
```powershell
dotnet build
dotnet run --framework net10.0 --no-build
```
The sample issues a few questions against the graph-backed retrieval provider and prints the responses to the console.
dotnet/samples/02-agents/AgentWithRAG/README.md
+1
View File
@@ -8,3 +8,4 @@ These samples show how to create an agent with the Agent Framework that uses Ret
|[RAG with Vector Store and custom schema](./AgentWithRAG_Step02_CustomVectorStoreRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a vector store. It also uses a custom schema for the documents stored in the vector store.|
|[RAG with custom RAG data source](./AgentWithRAG_Step03_CustomRAGDataSource/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a custom RAG data source.|
|[RAG with Foundry VectorStore service](./AgentWithRAG_Step04_FoundryServiceRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with the Foundry VectorStore service.|
|[RAG with Neo4j GraphRAG](./AgentWithRAG_Step05_Neo4jGraphRAG/)|This sample demonstrates how to create and run an agent that uses a Neo4j-backed GraphRAG context provider with graph-enriched retrieval.|
dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/README.md
+1 -1
View File
@@ -18,7 +18,7 @@ Before you begin, ensure you have the following prerequisites:
- Azure CLI installed and authenticated (for Azure credential authentication)
- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource
**Note**: This sample uses Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
**Note**: This sample uses Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
dotnet/samples/02-agents/Agents/Agent_Step07_AsMcpTool/Agent_Step07_AsMcpTool.csproj
+1 -1
View File
@@ -17,7 +17,7 @@
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>
dotnet/samples/02-agents/Agents/Agent_Step07_AsMcpTool/Program.cs
+3 -3
View File
@@ -19,10 +19,10 @@ var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYME
var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());
// Create a server side agent and expose it as an AIAgent.
AgentVersion agentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
ProjectsAgentVersion agentVersion = await aiProjectClient.AgentAdministrationClient.CreateAgentVersionAsync(
"Joker",
new AgentVersionCreationOptions(
new PromptAgentDefinition(model: deploymentName)
new ProjectsAgentVersionCreationOptions(
new DeclarativeAgentDefinition(model: deploymentName)
{
Instructions = "You are good at telling jokes, and you always start each joke with 'Aye aye, captain!'.",
})
dotnet/samples/02-agents/Agents/Agent_Step07_AsMcpTool/README.md
+2 -2
View File
@@ -20,8 +20,8 @@ To use the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector)
MCP Inspector is up and running at http://127.0.0.1:6274
```
1. Open a web browser and navigate to the URL displayed in the terminal. If not opened automatically, this will open the MCP Inspector interface.
1. In the MCP Inspector interface, add the following environment variables to allow your MCP server to access Azure AI Foundry Project to create and run the agent:
- AZURE_AI_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Azure AI Foundry Project endpoint
1. In the MCP Inspector interface, add the following environment variables to allow your MCP server to access Microsoft Foundry Project to create and run the agent:
- AZURE_AI_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Microsoft Foundry Project endpoint
- AZURE_AI_MODEL_DEPLOYMENT_NAME = gpt-4o-mini # Replace with your model deployment name
1. Find and click the `Connect` button in the MCP Inspector interface to connect to the MCP server.
1. As soon as the connection is established, open the `Tools` tab in the MCP Inspector interface and select the `Joker` tool from the list.
dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/Program.cs
+1 -1
View File
@@ -13,7 +13,7 @@ using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
// Get Azure AI Foundry configuration from environment variables
// Get Microsoft Foundry configuration from environment variables
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deploymentName = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o";
dotnet/samples/02-agents/Agents/Agent_Step13_ChatReduction/Program.cs
+1 -1
View File
@@ -3,7 +3,7 @@
// This sample shows how to use a chat history reducer to keep the context within model size limits.
// Any implementation of Microsoft.Extensions.AI.IChatReducer can be used to customize how the chat history is reduced.
// NOTE: this feature is only supported where the chat history is stored locally, such as with OpenAI Chat Completion.
// Where the chat history is stored server side, such as with Azure Foundry Agents, the service must manage the chat history size.
// Where the chat history is stored server side, such as with Microsoft Foundry Agents, the service must manage the chat history size.
using Azure.AI.OpenAI;
using Azure.Identity;

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