Update python/samples/02-agents/observability/agent_observability.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
2026-06-16 21:04:09 +08:00 · 2026-04-01 08:53:45 -07:00 · 2026-04-01 08:53:29 -07:00 · 2026-04-01 08:52:21 -07:00 · 2026-03-31 16:55:08 -07:00 · 2026-03-31 20:36:21 +00:00
291 changed files with 10947 additions and 20490 deletions
@@ -34,7 +34,7 @@ runs:

    - name: Test Copilot CLI
      shell: bash
-      run: copilot -p "What can you do in one sentence?"
+      run: copilot --version && copilot -p "What can you do in one sentence?"

    - name: Azure CLI Login
      uses: azure/login@v2
@@ -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
@@ -288,7 +286,6 @@ 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
@@ -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/**'
@@ -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
@@ -430,7 +426,6 @@ 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
@@ -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: |
@@ -64,14 +60,13 @@ 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 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.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME || vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
@@ -97,11 +92,10 @@ 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=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
          echo "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=$AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME" >> .env
          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
@@ -125,6 +119,7 @@ jobs:
    environment: integration
    env:
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
+      OPENAI_MODEL: ${{ vars.OPENAI__CHATMODELID }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
    defaults:
@@ -144,6 +139,7 @@ jobs:
      - name: Create .env for samples
        run: |
          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
+          echo "OPENAI_MODEL=$OPENAI_MODEL" >> .env
          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env

@@ -158,15 +154,14 @@ jobs:
          name: validation-report-02-agents-openai
          path: python/samples/sample_validation/reports/

-  validate-02-agents-azure-openai:
-    name: Validate 02-agents/providers/azure_openai
+  validate-02-agents-azure:
+    name: Validate 02-agents/providers/azure
    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_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_API_VERSION: ${{ vars.AZURE_OPENAI_API_VERSION || '' }}
    defaults:
      run:
        working-directory: python
@@ -183,100 +178,19 @@ 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_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_API_VERSION=$AZURE_OPENAI_API_VERSION" >> .env

      - name: Run sample validation
        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_openai --save-report --report-name 02-agents-azure-openai
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure --save-report --report-name 02-agents-azure

      - name: Upload validation report
        uses: actions/upload-artifact@v7
        if: always()
        with:
-          name: validation-report-02-agents-azure-openai
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-azure-ai:
-    name: Validate 02-agents/providers/azure_ai
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
-      BING_CONNECTION_ID: ${{ secrets.BING_CONNECTION_ID }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME=$AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME=$AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME" >> .env
-          echo "BING_CONNECTION_ID=$BING_CONNECTION_ID" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_ai --save-report --report-name 02-agents-azure-ai
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-azure-ai
-          path: python/samples/sample_validation/reports/
-
-  validate-02-agents-azure-ai-agent:
-    name: Validate 02-agents/providers/azure_ai_agent
-    runs-on: ubuntu-latest
-    environment: integration
-    env:
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-    defaults:
-      run:
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Setup environment
-        uses: ./.github/actions/sample-validation-setup
-        with:
-          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          os: ${{ runner.os }}
-
-      - name: Create .env for samples
-        run: |
-          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
-          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
-
-      - name: Run sample validation
-        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_ai_agent --save-report --report-name 02-agents-azure-ai-agent
-
-      - name: Upload validation report
-        uses: actions/upload-artifact@v7
-        if: always()
-        with:
-          name: validation-report-02-agents-azure-ai-agent
+          name: validation-report-02-agents-azure
          path: python/samples/sample_validation/reports/

  validate-02-agents-anthropic:
@@ -409,11 +323,16 @@ jobs:
          name: validation-report-02-agents-ollama
          path: python/samples/sample_validation/reports/

-  validate-02-agents-foundry-local:
-    name: Validate 02-agents/providers/foundry_local
-    if: false # Temporarily disabled - requires local Foundry setup
+  validate-02-agents-foundry:
+    name: Validate 02-agents/providers/foundry
+    if: false # Temporarily disabled - provider folder also contains the local Foundry sample
    runs-on: ubuntu-latest
    environment: integration
+    env:
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME || '' }}
+      FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION || '' }}
    defaults:
      run:
        working-directory: python
@@ -428,15 +347,22 @@ jobs:
          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
          os: ${{ runner.os }}

+      - name: Create .env for samples
+        run: |
+          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
+          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
+          echo "FOUNDRY_AGENT_NAME=$FOUNDRY_AGENT_NAME" >> .env
+          echo "FOUNDRY_AGENT_VERSION=$FOUNDRY_AGENT_VERSION" >> .env
+
      - name: Run sample validation
        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/foundry_local --save-report --report-name 02-agents-foundry-local
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/foundry --save-report --report-name 02-agents-foundry

      - name: Upload validation report
        uses: actions/upload-artifact@v7
        if: always()
        with:
-          name: validation-report-02-agents-foundry-local
+          name: validation-report-02-agents-foundry
          path: python/samples/sample_validation/reports/

  validate-02-agents-copilotstudio:
@@ -515,13 +441,8 @@ 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_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
@@ -538,11 +459,8 @@ 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 "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=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
+          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
+          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env

      - name: Run sample validation
        run: |
@@ -561,12 +479,8 @@ 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:
@@ -600,19 +514,18 @@ 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_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure AI Search (for evaluation samples)
      AZURE_SEARCH_ENDPOINT: ${{ secrets.AZURE_SEARCH_ENDPOINT }}
      AZURE_SEARCH_API_KEY: ${{ secrets.AZURE_SEARCH_API_KEY }}
      AZURE_SEARCH_INDEX_NAME: ${{ secrets.AZURE_SEARCH_INDEX_NAME }}
      # Evaluation sample
-      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
@@ -643,12 +556,11 @@ 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_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
@@ -670,10 +582,10 @@ 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_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
@@ -694,13 +606,11 @@ 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_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
@@ -727,11 +637,10 @@ 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=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
@@ -759,14 +668,12 @@ jobs:
      - validate-01-get-started
      - validate-02-agents
      - validate-02-agents-openai
-      - validate-02-agents-azure-openai
-      - validate-02-agents-azure-ai
-      - validate-02-agents-azure-ai-agent
+      - validate-02-agents-azure
      - validate-02-agents-anthropic
      - validate-02-agents-github-copilot
      - validate-02-agents-amazon
      - validate-02-agents-ollama
-      - validate-02-agents-foundry-local
+      - validate-02-agents-foundry
      - validate-02-agents-copilotstudio
      - validate-02-agents-custom
      - validate-03-workflows
@@ -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

@@ -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/)
@@ -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

@@ -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.

@@ -462,7 +462,7 @@ class FoundryEvals:
 ### Azure AI: FoundryEvals Constants

 ```python
-from agent_framework_azure_ai import FoundryEvals
+from agent_framework.foundry import FoundryEvals

 evaluators = [FoundryEvals.RELEVANCE, FoundryEvals.TOOL_CALL_ACCURACY]
 ```
@@ -2,11 +2,11 @@
  <PropertyGroup>
    <!-- Central version prefix - applies to all nuget packages. -->
    <VersionPrefix>1.0.0</VersionPrefix>
-    <RCNumber>4</RCNumber>
+    <RCNumber>5</RCNumber>
    <PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260311.1</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260311.1</PackageVersion>
-    <GitTag>1.0.0-rc4</GitTag>
+    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260330.1</PackageVersion>
+    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260330.1</PackageVersion>
+    <GitTag>1.0.0-rc5</GitTag>

    <Configurations>Debug;Release;Publish</Configurations>
    <IsPackable>true</IsPackable>
@@ -1,7 +1,7 @@
 // Copyright (c) Microsoft. All rights reserved.

 // This sample demonstrates how the ChatClientAgent persists chat history after each individual
-// call to the AI service, using the SimulateServiceStoredChatHistory option.
+// call to the AI service, using the RequirePerServiceCallChatHistoryPersistence option.
 // When an agent uses tools, FunctionInvokingChatClient may loop multiple times
 // (service call → tool execution → service call), and intermediate messages (tool calls and
 // results) are persisted after each service call. This allows you to inspect or recover them
@@ -9,7 +9,7 @@
 // yet finalized (e.g., tool calls without results) being persisted, which may be undesirable in some cases.
 //
 // To use end-of-run persistence instead (atomic run semantics), remove the
-// SimulateServiceStoredChatHistory = true setting (or set it to false). End-of-run
+// RequirePerServiceCallChatHistoryPersistence = true setting (or set it to false). End-of-run
 // persistence is the default behavior.
 //
 // The sample runs two multi-turn conversations: one using non-streaming (RunAsync) and one
@@ -54,7 +54,7 @@ static string GetTime([Description("The city name.")] string city) =>
        _ => $"{city}: time data not available."
    };

-// Create the agent — per-service-call persistence is enabled via SimulateServiceStoredChatHistory.
+// Create the agent — per-service-call persistence is enabled via RequirePerServiceCallChatHistoryPersistence.
 // The in-memory ChatHistoryProvider is used by default when the service does not require service stored chat
 // history, so for those cases, we can inspect the chat history via session.TryGetInMemoryChatHistory().
 IChatClient chatClient = string.Equals(store, "TRUE", StringComparison.OrdinalIgnoreCase) ?
@@ -64,7 +64,7 @@ AIAgent agent = chatClient.AsAIAgent(
    new ChatClientAgentOptions
    {
        Name = "WeatherAssistant",
-        SimulateServiceStoredChatHistory = true,
+        RequirePerServiceCallChatHistoryPersistence = true,
        ChatOptions = new()
        {
            Instructions = "You are a helpful assistant. When asked about multiple cities, call the appropriate tool for each city.",
@@ -1,19 +1,19 @@
 # In-Function-Loop Checkpointing

-This sample demonstrates how `ChatClientAgent` can persist chat history after each individual call to the AI service using the `SimulateServiceStoredChatHistory` option. This per-service-call persistence ensures intermediate progress is saved during the function invocation loop.
+This sample demonstrates how `ChatClientAgent` can persist chat history after each individual call to the AI service using the `RequirePerServiceCallChatHistoryPersistence` option. This per-service-call persistence ensures intermediate progress is saved during the function invocation loop.

 ## What This Sample Shows

-When an agent uses tools, the `FunctionInvokingChatClient` loops multiple times (service call → tool execution → service call → …). By enabling `SimulateServiceStoredChatHistory = true`, chat history is persisted after each service call via the `ServiceStoredSimulatingChatClient` decorator:
+When an agent uses tools, the `FunctionInvokingChatClient` loops multiple times (service call → tool execution → service call → …). By enabling `RequirePerServiceCallChatHistoryPersistence = true`, chat history is persisted after each service call via the `PerServiceCallChatHistoryPersistingChatClient` decorator:

- A `ServiceStoredSimulatingChatClient` decorator is inserted into the chat client pipeline
+- A `PerServiceCallChatHistoryPersistingChatClient` decorator is inserted into the chat client pipeline
 - Before each service call, the decorator loads history from the `ChatHistoryProvider` and prepends it to the request
 - After each service call, the decorator notifies the `ChatHistoryProvider` (and any `AIContextProvider` instances) with the new messages
 - Only **new** messages are sent to providers on each notification — messages that were already persisted in an earlier call within the same run are deduplicated automatically

-By default (without `SimulateServiceStoredChatHistory`), chat history is persisted at the end of the full agent run instead. To use per-service-call persistence, set `SimulateServiceStoredChatHistory = true` on `ChatClientAgentOptions`.
+By default (without `RequirePerServiceCallChatHistoryPersistence`), chat history is persisted at the end of the full agent run instead. To use per-service-call persistence, set `RequirePerServiceCallChatHistoryPersistence = true` on `ChatClientAgentOptions`.

-With `SimulateServiceStoredChatHistory` = true, the behavior matches that of chat history stored in the underlying AI service exactly.
+With `RequirePerServiceCallChatHistoryPersistence` = true, the behavior matches that of chat history stored in the underlying AI service exactly.

 Per-service-call persistence is useful for:
 - **Crash recovery** — if the process is interrupted mid-loop, the intermediate tool calls and results are already persisted
@@ -29,7 +29,7 @@ The sample asks the agent about the weather and time in three cities. The model
 ```
 ChatClientAgent
  └─ FunctionInvokingChatClient    (handles tool call loop)
-       └─ ServiceStoredSimulatingChatClient  (persists after each service call)
+       └─ PerServiceCallChatHistoryPersistingChatClient  (persists after each service call)
            └─ Leaf IChatClient            (Azure OpenAI)
 ```

@@ -3,7 +3,7 @@
 The agent framework samples are designed to help you get started with building AI-powered agents
 from various providers.

-The Agent Framework supports building agents using various infererence and inference-style services.
+The Agent Framework supports building agents using various inference and inference-style services.
 All these are supported using the single `ChatClientAgent` class.

 The Agent Framework also supports creating proxy agents, that allow accessing remote agents as if they
@@ -139,8 +139,8 @@ public sealed partial class ChatClientAgent : AIAgent

        this._logger = (loggerFactory ?? chatClient.GetService<ILoggerFactory>() ?? NullLoggerFactory.Instance).CreateLogger<ChatClientAgent>();

-        // Warn if using a custom chat client stack with simulated service stored persistence but no ServiceStoredSimulatingChatClient.
-        this.WarnOnMissingServiceStoredSimulatingClient();
+        // Warn if using a custom chat client stack with simulated service stored persistence but no PerServiceCallChatHistoryPersistingChatClient.
+        this.WarnOnMissingPerServiceCallChatHistoryPersistingChatClient();
    }

    /// <summary>
@@ -454,7 +454,7 @@ public sealed partial class ChatClientAgent : AIAgent
    /// Notifies the <see cref="ChatHistoryProvider"/> and all <see cref="AIContextProviders"/> of successfully completed messages.
    /// </summary>
    /// <remarks>
-    /// This method is also called by <see cref="ServiceStoredSimulatingChatClient"/> to persist messages per-service-call.
+    /// This method is also called by <see cref="PerServiceCallChatHistoryPersistingChatClient"/> to persist messages per-service-call.
    /// </remarks>
    internal async Task NotifyProvidersOfNewMessagesAsync(
        ChatClientAgentSession session,
@@ -486,7 +486,7 @@ public sealed partial class ChatClientAgent : AIAgent
    /// Notifies the <see cref="ChatHistoryProvider"/> and all <see cref="AIContextProviders"/> of a failure during a service call.
    /// </summary>
    /// <remarks>
-    /// This method is also called by <see cref="ServiceStoredSimulatingChatClient"/> to report failures per-service-call.
+    /// This method is also called by <see cref="PerServiceCallChatHistoryPersistingChatClient"/> to report failures per-service-call.
    /// </remarks>
    internal async Task NotifyProvidersOfFailureAsync(
        ChatClientAgentSession session,
@@ -701,7 +701,7 @@ public sealed partial class ChatClientAgent : AIAgent
            throw new InvalidOperationException("A session must be provided when continuing a background response with a continuation token.");
        }

-        if ((continuationToken is not null || chatOptions?.AllowBackgroundResponses is true) && this.SimulatesServiceStoredChatHistory && this._logger.IsEnabled(LogLevel.Warning))
+        if ((continuationToken is not null || chatOptions?.AllowBackgroundResponses is true) && this.RequiresPerServiceCallChatHistoryPersistence && this._logger.IsEnabled(LogLevel.Warning))
        {
            var warningAgentName = this.GetLoggingAgentName();
            this._logger.LogAgentChatClientBackgroundResponseFallback(this.Id, warningAgentName);
@@ -740,10 +740,10 @@ public sealed partial class ChatClientAgent : AIAgent
        IEnumerable<ChatMessage> inputMessagesForChatClient = inputMessages;

        // Populate the session messages only if we are not continuing an existing response as it's not allowed.
-        // When SimulateServiceStoredChatHistory is active, the ServiceStoredSimulatingChatClient
+        // When RequirePerServiceCallChatHistoryPersistence is active, the PerServiceCallChatHistoryPersistingChatClient
        // owns the chat history lifecycle — it loads history before each service call. The agent
        // must not load history itself, as that would result in duplicate messages.
-        if (chatOptions?.ContinuationToken is null && !this.SimulatesServiceStoredChatHistory)
+        if (chatOptions?.ContinuationToken is null && !this.RequiresPerServiceCallChatHistoryPersistence)
        {
            // Add any existing messages from the session to the messages to be sent to the chat client.
            // The ChatHistoryProvider returns the merged result (history + input messages).
@@ -837,14 +837,14 @@ public sealed partial class ChatClientAgent : AIAgent
    /// Updates the session conversation ID at the end of an agent run.
    /// </summary>
    /// <remarks>
-    /// When a <see cref="ServiceStoredSimulatingChatClient"/> handles per-service-call
+    /// When a <see cref="PerServiceCallChatHistoryPersistingChatClient"/> handles per-service-call
    /// conversation ID updates, this end-of-run update is skipped. When the decorator is
    /// absent, the update is performed here. When <paramref name="forceUpdate"/> is <see langword="true"/>
    /// (continuation token scenarios), the update is always performed.
    /// </remarks>
    private void UpdateSessionConversationIdAtEndOfRun(ChatClientAgentSession session, string? responseConversationId, CancellationToken cancellationToken, bool forceUpdate = false)
    {
-        if (!forceUpdate && this.SimulatesServiceStoredChatHistory)
+        if (!forceUpdate && this.RequiresPerServiceCallChatHistoryPersistence)
        {
            return;
        }
@@ -856,7 +856,7 @@ public sealed partial class ChatClientAgent : AIAgent
    /// Notifies providers of successfully completed messages at the end of an agent run.
    /// </summary>
    /// <remarks>
-    /// When a <see cref="ServiceStoredSimulatingChatClient"/> handles per-service-call
+    /// When a <see cref="PerServiceCallChatHistoryPersistingChatClient"/> handles per-service-call
    /// notification, this end-of-run notification is skipped. When no decorator is present,
    /// all messages are persisted.
    /// When <paramref name="forceNotify"/> is <see langword="true"/> (continuation token or
@@ -871,7 +871,7 @@ public sealed partial class ChatClientAgent : AIAgent
        CancellationToken cancellationToken,
        bool forceNotify = false)
    {
-        if (!forceNotify && this.SimulatesServiceStoredChatHistory)
+        if (!forceNotify && this.RequiresPerServiceCallChatHistoryPersistence)
        {
            return Task.CompletedTask;
        }
@@ -883,7 +883,7 @@ public sealed partial class ChatClientAgent : AIAgent
    /// Notifies providers of a failure at the end of an agent run.
    /// </summary>
    /// <remarks>
-    /// When a <see cref="ServiceStoredSimulatingChatClient"/> handles per-service-call
+    /// When a <see cref="PerServiceCallChatHistoryPersistingChatClient"/> handles per-service-call
    /// notification (including failure), this end-of-run notification is skipped to avoid
    /// duplicate notification. In all other cases, failure is reported at the end of the run.
    /// </remarks>
@@ -894,7 +894,7 @@ public sealed partial class ChatClientAgent : AIAgent
        ChatOptions? chatOptions,
        CancellationToken cancellationToken)
    {
-        if (this.SimulatesServiceStoredChatHistory)
+        if (this.RequiresPerServiceCallChatHistoryPersistence)
        {
            return Task.CompletedTask;
        }
@@ -905,14 +905,14 @@ public sealed partial class ChatClientAgent : AIAgent
    /// <summary>
    /// Gets a value indicating whether the agent is configured to simulate service-stored chat history.
    /// When <see langword="true"/>, end-of-run persistence and history loading are skipped because a
-    /// per-service-call decorator (such as <see cref="ServiceStoredSimulatingChatClient"/> or a
+    /// per-service-call decorator (such as <see cref="PerServiceCallChatHistoryPersistingChatClient"/> or a
    /// user-supplied equivalent) is expected to handle the history lifecycle.
    /// </summary>
-    private bool SimulatesServiceStoredChatHistory
+    private bool RequiresPerServiceCallChatHistoryPersistence
    {
        get
        {
-            return this._agentOptions?.SimulateServiceStoredChatHistory is true;
+            return this._agentOptions?.RequirePerServiceCallChatHistoryPersistence is true;
        }
    }

@@ -923,7 +923,7 @@ public sealed partial class ChatClientAgent : AIAgent
    /// The base class sets <see cref="AIAgent.CurrentRunContext"/> with the raw session parameter
    /// (which may be null) and restores it after each yield in streaming scenarios. After
    /// <see cref="PrepareSessionAndMessagesAsync"/> resolves or creates a session, we update the
-    /// context so the <see cref="ServiceStoredSimulatingChatClient"/> decorator always has a valid session.
+    /// context so the <see cref="PerServiceCallChatHistoryPersistingChatClient"/> decorator always has a valid session.
    /// The original agent from the context is preserved to maintain the top-of-stack agent in
    /// decorated agent scenarios.
    /// </remarks>
@@ -939,19 +939,19 @@ public sealed partial class ChatClientAgent : AIAgent
    /// <summary>
    /// Checks for potential misconfiguration when using a custom chat client stack and logs warnings.
    /// </summary>
-    private void WarnOnMissingServiceStoredSimulatingClient()
+    private void WarnOnMissingPerServiceCallChatHistoryPersistingChatClient()
    {
        if (this._agentOptions?.UseProvidedChatClientAsIs is not true)
        {
            return;
        }

-        if (this._agentOptions?.SimulateServiceStoredChatHistory is not true)
+        if (this._agentOptions?.RequirePerServiceCallChatHistoryPersistence is not true)
        {
            return;
        }

-        var persistingClient = this.ChatClient.GetService<ServiceStoredSimulatingChatClient>();
+        var persistingClient = this.ChatClient.GetService<PerServiceCallChatHistoryPersistingChatClient>();
        if (persistingClient is null && this._logger.IsEnabled(LogLevel.Warning))
        {
            var loggingAgentName = this.GetLoggingAgentName();
@@ -998,7 +998,7 @@ public sealed partial class ChatClientAgent : AIAgent
    /// </summary>
    /// <remarks>
    /// This method is used by both the agent (during <see cref="PrepareSessionAndMessagesAsync"/>) and by
-    /// <see cref="ServiceStoredSimulatingChatClient"/> to load history before each service call.
+    /// <see cref="PerServiceCallChatHistoryPersistingChatClient"/> to load history before each service call.
    /// </remarks>
    internal async Task<IEnumerable<ChatMessage>> LoadChatHistoryAsync(
        ChatClientAgentSession session,
@@ -72,12 +72,12 @@ internal static partial class ChatClientAgentLogMessages

    /// <summary>
    /// Logs a warning when <see cref="ChatClientAgentOptions.UseProvidedChatClientAsIs"/> is <see langword="true"/>
-    /// and <see cref="ChatClientAgentOptions.SimulateServiceStoredChatHistory"/> is <see langword="true"/>,
-    /// but no <see cref="ServiceStoredSimulatingChatClient"/> is found in the custom chat client stack.
+    /// and <see cref="ChatClientAgentOptions.RequirePerServiceCallChatHistoryPersistence"/> is <see langword="true"/>,
+    /// but no <see cref="PerServiceCallChatHistoryPersistingChatClient"/> is found in the custom chat client stack.
    /// </summary>
    [LoggerMessage(
        Level = LogLevel.Warning,
-        Message = "Agent {AgentId}/{AgentName}: SimulateServiceStoredChatHistory is enabled with a custom chat client stack (UseProvidedChatClientAsIs), but no ServiceStoredSimulatingChatClient was found in the pipeline. Chat history will not be persisted by ChatClientAgent. Consider adding a ServiceStoredSimulatingChatClient to the pipeline using the UseServiceStoredChatHistorySimulation extension method if you have not added your own persistence mechanism.")]
+        Message = "Agent {AgentId}/{AgentName}: RequirePerServiceCallChatHistoryPersistence is enabled with a custom chat client stack (UseProvidedChatClientAsIs), but no PerServiceCallChatHistoryPersistingChatClient was found in the pipeline. Chat history will not be persisted by ChatClientAgent. Consider adding a PerServiceCallChatHistoryPersistingChatClient to the pipeline using the UsePerServiceCallChatHistoryPersistence extension method if you have not added your own persistence mechanism.")]
    public static partial void LogAgentChatClientMissingPersistingClient(
        this ILogger logger,
        string agentId,
@@ -92,7 +92,7 @@ internal static partial class ChatClientAgentLogMessages
    /// </summary>
    [LoggerMessage(
        Level = LogLevel.Warning,
-        Message = "Agent {AgentId}/{AgentName}: SimulateServiceStoredChatHistory is enabled but we have to fall back to end-of-run persistence because the run involves background responses.")]
+        Message = "Agent {AgentId}/{AgentName}: RequirePerServiceCallChatHistoryPersistence is enabled but we have to fall back to end-of-run persistence because the run involves background responses.")]
    public static partial void LogAgentChatClientBackgroundResponseFallback(
        this ILogger logger,
        string agentId,
@@ -92,38 +92,56 @@ public sealed class ChatClientAgentOptions
    public bool ThrowOnChatHistoryProviderConflict { get; set; } = true;

    /// <summary>
-    /// Gets or sets a value indicating whether the <see cref="ChatClientAgent"/> should simulate
-    /// service-stored chat history behavior using its configured <see cref="ChatHistoryProvider"/>.
+    /// Gets or sets a value indicating whether the <see cref="ChatClientAgent"/> should persist
+    /// chat history after each individual service call within the <see cref="FunctionInvokingChatClient"/>
+    /// loop, rather than at the end of the full agent run.
    /// </summary>
    /// <remarks>
    /// <para>
-    /// When set to <see langword="true"/>, a <see cref="ServiceStoredSimulatingChatClient"/> decorator is
-    /// injected between the <see cref="FunctionInvokingChatClient"/> and the leaf <see cref="IChatClient"/>
-    /// in the chat client pipeline. This decorator takes full ownership of the chat history lifecycle:
-    /// it loads history from the <see cref="ChatHistoryProvider"/> before each service call and persists
-    /// new messages after each service call. It also returns a sentinel <see cref="ChatOptions.ConversationId"/>
-    /// on the response, causing the <see cref="FunctionInvokingChatClient"/> to treat the conversation
-    /// as service-managed — clearing accumulated history and not injecting duplicate
-    /// <see cref="FunctionCallContent"/> during approval-response processing.
-    /// </para>
-    /// <para>
-    /// This mode aligns the behavior of framework-managed chat history with service-stored chat history,
-    /// ensuring consistency in how messages are stored and loaded, including during function calling loops
-    /// and tool-call termination scenarios.
+    /// When set to <see langword="true"/>, a <see cref="PerServiceCallChatHistoryPersistingChatClient"/>
+    /// decorator becomes active in the chat client pipeline. It handles two complementary scenarios:
    /// </para>
+    /// <list type="bullet">
+    /// <item>
+    /// <term>Framework-managed chat history</term>
+    /// <description>
+    /// The decorator loads history from the <see cref="ChatHistoryProvider"/> before each service call
+    /// and persists new request and response messages after each call. It returns a sentinel
+    /// <see cref="ChatOptions.ConversationId"/> on the response, causing the
+    /// <see cref="FunctionInvokingChatClient"/> to treat the conversation as service-managed — clearing
+    /// accumulated history between iterations and not injecting duplicate <see cref="FunctionCallContent"/>
+    /// during approval-response processing.
+    /// </description>
+    /// </item>
+    /// <item>
+    /// <term>AI Service-stored chat history</term>
+    /// <description>
+    /// When the service manages its own chat history (returning a real <see cref="ChatOptions.ConversationId"/>),
+    /// the decorator updates <see cref="ChatClientAgentSession.ConversationId"/> after each service call so
+    /// that intermediate ConversationId changes are captured immediately. For some services (e.g., the
+    /// Conversations API with the Responses API), there is only one thread with one ID, so every service
+    /// call updates it anyway and updating the <see cref="ChatClientAgentSession.ConversationId"/> has little effect
+    /// since it's the same ID. For other services (e.g., Responses API with Response IDs), a new ID is generated
+    /// with each service call, so updating the <see cref="ChatClientAgentSession.ConversationId"/> ensures that the
+    /// latest ID is always captured, even mid-run.
+    /// Enabling this option ensures consistent per-service-call behavior across all service types.
+    /// </description>
+    /// </item>
+    /// </list>
    /// <para>
    /// When set to <see langword="false"/> (the default), the <see cref="ChatClientAgent"/> handles
-    /// chat history persistence at the end of the full agent run via the <see cref="ChatHistoryProvider"/>
-    /// pipeline.
+    /// chat history persistence at the end of the full agent run via the <see cref="ChatHistoryProvider"/> if using
+    /// framework-managed chat history. For AI service-stored chat history, the <see cref="ChatClientAgentSession.ConversationId"/>
+    /// updates happen only at the end of the run.
    /// </para>
    /// <para>
    /// When setting the <see cref="UseProvidedChatClientAsIs"/> setting to <see langword="true"/> and
-    /// <see cref="SimulateServiceStoredChatHistory"/> to <see langword="true"/>, ensure that your custom chat client stack includes a
-    /// <see cref="ServiceStoredSimulatingChatClient"/> to enable per-service-call persistence.
-    /// If no <see cref="ServiceStoredSimulatingChatClient"/> is provided, and you are not storing chat history via other means,
+    /// <see cref="RequirePerServiceCallChatHistoryPersistence"/> to <see langword="true"/>, ensure that your custom chat client stack includes a
+    /// <see cref="PerServiceCallChatHistoryPersistingChatClient"/> to enable per-service-call persistence.
+    /// If no <see cref="PerServiceCallChatHistoryPersistingChatClient"/> is provided, and you are not storing chat history via other means,
    /// no chat history may be stored.
-    /// When using a custom chat client stack, you can add a <see cref="ServiceStoredSimulatingChatClient"/>
-    /// manually via the <see cref="ChatClientBuilderExtensions.UseServiceStoredChatHistorySimulation"/>
+    /// When using a custom chat client stack, you can add a <see cref="PerServiceCallChatHistoryPersistingChatClient"/>
+    /// manually via the <see cref="ChatClientBuilderExtensions.UsePerServiceCallChatHistoryPersistence"/>
    /// extension method.
    /// </para>
    /// </remarks>
@@ -131,7 +149,7 @@ public sealed class ChatClientAgentOptions
    /// Default is <see langword="false"/>.
    /// </value>
    [Experimental(DiagnosticIds.Experiments.AgentsAIExperiments)]
-    public bool SimulateServiceStoredChatHistory { get; set; }
+    public bool RequirePerServiceCallChatHistoryPersistence { get; set; }

    /// <summary>
    /// Creates a new instance of <see cref="ChatClientAgentOptions"/> with the same values as this instance.
@@ -149,6 +167,6 @@ public sealed class ChatClientAgentOptions
            ClearOnChatHistoryProviderConflict = this.ClearOnChatHistoryProviderConflict,
            WarnOnChatHistoryProviderConflict = this.WarnOnChatHistoryProviderConflict,
            ThrowOnChatHistoryProviderConflict = this.ThrowOnChatHistoryProviderConflict,
-            SimulateServiceStoredChatHistory = this.SimulateServiceStoredChatHistory,
+            RequirePerServiceCallChatHistoryPersistence = this.RequirePerServiceCallChatHistoryPersistence,
        };
 }
@@ -86,21 +86,21 @@ public static class ChatClientBuilderExtensions
            services: services);

    /// <summary>
-    /// Adds a <see cref="ServiceStoredSimulatingChatClient"/> to the chat client pipeline.
+    /// Adds a <see cref="PerServiceCallChatHistoryPersistingChatClient"/> to the chat client pipeline.
    /// </summary>
    /// <remarks>
    /// <para>
    /// This decorator should be positioned between the <see cref="FunctionInvokingChatClient"/> and the leaf
-    /// <see cref="IChatClient"/> in the pipeline. It simulates service-stored chat history behavior by
-    /// loading history before each service call, persisting after each call, and returning a sentinel
-    /// <see cref="ChatOptions.ConversationId"/> on the response.
+    /// <see cref="IChatClient"/> in the pipeline. It persists chat history after each individual service call
+    /// and updates the session <see cref="ChatOptions.ConversationId"/> per call for both framework-managed
+    /// and service-stored chat history scenarios.
    /// </para>
    /// <para>
    /// This extension method is intended for use with custom chat client stacks when
    /// <see cref="ChatClientAgentOptions.UseProvidedChatClientAsIs"/> is <see langword="true"/>.
    /// When <see cref="ChatClientAgentOptions.UseProvidedChatClientAsIs"/> is <see langword="false"/> (the default),
-    /// the <see cref="ChatClientAgent"/> automatically injects this decorator when
-    /// <see cref="ChatClientAgentOptions.SimulateServiceStoredChatHistory"/> is <see langword="true"/>.
+    /// the <see cref="ChatClientAgent"/> automatically includes this decorator in the pipeline and activates it when
+    /// <see cref="ChatClientAgentOptions.RequirePerServiceCallChatHistoryPersistence"/> is <see langword="true"/>.
    /// </para>
    /// <para>
    /// This decorator only works within the context of a running <see cref="ChatClientAgent"/> and will throw an
@@ -110,8 +110,8 @@ public static class ChatClientBuilderExtensions
    /// <param name="builder">The <see cref="ChatClientBuilder"/> to add the decorator to.</param>
    /// <returns>The <paramref name="builder"/> for chaining.</returns>
    [Experimental(DiagnosticIds.Experiments.AgentsAIExperiments)]
-    public static ChatClientBuilder UseServiceStoredChatHistorySimulation(this ChatClientBuilder builder)
+    public static ChatClientBuilder UsePerServiceCallChatHistoryPersistence(this ChatClientBuilder builder)
    {
-        return builder.Use(innerClient => new ServiceStoredSimulatingChatClient(innerClient));
+        return builder.Use(innerClient => new PerServiceCallChatHistoryPersistingChatClient(innerClient));
    }
 }
@@ -63,16 +63,16 @@ public static class ChatClientExtensions
            });
        }

-        // ServiceStoredSimulatingChatClient is only injected when SimulateServiceStoredChatHistory is enabled.
+        // PerServiceCallChatHistoryPersistingChatClient is only injected when RequirePerServiceCallChatHistoryPersistence is enabled.
        // It is registered after FunctionInvokingChatClient so that it sits between FIC and the leaf client.
        // ChatClientBuilder.Build applies factories in reverse order, making the first Use() call outermost.
        // By adding our decorator second, the resulting pipeline is:
-        //   FunctionInvokingChatClient → ServiceStoredSimulatingChatClient → leaf IChatClient
+        //   FunctionInvokingChatClient → PerServiceCallChatHistoryPersistingChatClient → leaf IChatClient
        // This allows the decorator to simulate service-stored chat history by loading history before
        // each service call, persisting after each call, and returning a sentinel ConversationId.
-        if (options?.SimulateServiceStoredChatHistory is true)
+        if (options?.RequirePerServiceCallChatHistoryPersistence is true)
        {
-            chatBuilder.Use(innerClient => new ServiceStoredSimulatingChatClient(innerClient));
+            chatBuilder.Use(innerClient => new PerServiceCallChatHistoryPersistingChatClient(innerClient));
        }

        var agentChatClient = chatBuilder.Build(services);
@@ -11,23 +11,39 @@ using Microsoft.Extensions.AI;
 namespace Microsoft.Agents.AI;

 /// <summary>
-/// A delegating chat client that simulates service-stored chat history behavior using
-/// framework-managed <see cref="ChatHistoryProvider"/> instances.
+/// A delegating chat client that persists chat history and updates session state after each
+/// individual service call within the <see cref="FunctionInvokingChatClient"/> loop.
 /// </summary>
 /// <remarks>
 /// <para>
 /// This decorator is intended to operate between the <see cref="FunctionInvokingChatClient"/> and the leaf
-/// <see cref="IChatClient"/> in a <see cref="ChatClientAgent"/> pipeline.
+/// <see cref="IChatClient"/> in a <see cref="ChatClientAgent"/> pipeline. It is activated when
+/// <see cref="ChatClientAgentOptions.RequirePerServiceCallChatHistoryPersistence"/> is <see langword="true"/>.
 /// </para>
 /// <para>
-/// Before each service call, it loads chat history from the agent's <see cref="ChatHistoryProvider"/>
-/// and prepends it to the request messages. After each successful service call, it persists
-/// new request and response messages to the provider. It also returns a sentinel
-/// <see cref="ChatOptions.ConversationId"/> on the response so that the
-/// <see cref="FunctionInvokingChatClient"/> treats the conversation as service-managed —
-/// clearing accumulated history between iterations and not injecting duplicate
-/// <see cref="FunctionCallContent"/> during approval-response processing.
+/// When active, it handles two complementary scenarios:
 /// </para>
+/// <list type="bullet">
+/// <item>
+/// <term>Framework-managed chat history</term>
+/// <description>
+/// Before each service call, the decorator loads history from the agent's <see cref="ChatHistoryProvider"/>
+/// and prepends it to the request messages. After each successful call, it persists new messages to
+/// the provider and returns a sentinel <see cref="ChatOptions.ConversationId"/> so that
+/// <see cref="FunctionInvokingChatClient"/> treats the conversation as service-managed — clearing
+/// accumulated history between iterations and not injecting duplicate <see cref="FunctionCallContent"/>
+/// during approval-response processing.
+/// </description>
+/// </item>
+/// <item>
+/// <term>Service-stored chat history</term>
+/// <description>
+/// When the underlying service manages its own chat history (real <see cref="ChatOptions.ConversationId"/>),
+/// the decorator updates <see cref="ChatClientAgentSession.ConversationId"/> after each service call so
+/// that intermediate ConversationId changes are captured immediately rather than only at the end of the run.
+/// </description>
+/// </item>
+/// </list>
 /// <para>
 /// This chat client must be used within the context of a running <see cref="ChatClientAgent"/>. It retrieves the
 /// current agent and session from <see cref="AIAgent.CurrentRunContext"/>, which is set automatically when an agent's
@@ -38,7 +54,7 @@ namespace Microsoft.Agents.AI;
 /// available or if the agent is not a <see cref="ChatClientAgent"/>.
 /// </para>
 /// </remarks>
-internal sealed class ServiceStoredSimulatingChatClient : DelegatingChatClient
+internal sealed class PerServiceCallChatHistoryPersistingChatClient : DelegatingChatClient
 {
    /// <summary>
    /// A sentinel value returned on <see cref="ChatResponse.ConversationId"/> to signal
@@ -59,10 +75,10 @@ internal sealed class ServiceStoredSimulatingChatClient : DelegatingChatClient
    internal const string LocalHistoryConversationId = "_agent_local_chat_history";

    /// <summary>
-    /// Initializes a new instance of the <see cref="ServiceStoredSimulatingChatClient"/> class.
+    /// Initializes a new instance of the <see cref="PerServiceCallChatHistoryPersistingChatClient"/> class.
    /// </summary>
    /// <param name="innerClient">The underlying chat client that will handle the core operations.</param>
-    public ServiceStoredSimulatingChatClient(IChatClient innerClient)
+    public PerServiceCallChatHistoryPersistingChatClient(IChatClient innerClient)
        : base(innerClient)
    {
    }
@@ -237,18 +253,18 @@ internal sealed class ServiceStoredSimulatingChatClient : DelegatingChatClient
    {
        var runContext = AIAgent.CurrentRunContext
            ?? throw new InvalidOperationException(
-                $"{nameof(ServiceStoredSimulatingChatClient)} can only be used within the context of a running AIAgent. " +
+                $"{nameof(PerServiceCallChatHistoryPersistingChatClient)} can only be used within the context of a running AIAgent. " +
                "Ensure that the chat client is being invoked as part of an AIAgent.RunAsync or AIAgent.RunStreamingAsync call.");

        var chatClientAgent = runContext.Agent.GetService<ChatClientAgent>()
            ?? throw new InvalidOperationException(
-                $"{nameof(ServiceStoredSimulatingChatClient)} can only be used with a {nameof(ChatClientAgent)}. " +
+                $"{nameof(PerServiceCallChatHistoryPersistingChatClient)} can only be used with a {nameof(ChatClientAgent)}. " +
                $"The current agent is of type '{runContext.Agent.GetType().Name}'.");

        if (runContext.Session is not ChatClientAgentSession chatClientAgentSession)
        {
            throw new InvalidOperationException(
-                $"{nameof(ServiceStoredSimulatingChatClient)} requires a {nameof(ChatClientAgentSession)}. " +
+                $"{nameof(PerServiceCallChatHistoryPersistingChatClient)} requires a {nameof(ChatClientAgentSession)}. " +
                $"The current session is of type '{runContext.Session?.GetType().Name ?? "null"}'.");
        }

@@ -14,7 +14,7 @@ namespace Microsoft.Agents.AI.UnitTests;

 /// <summary>
 /// Shared test helper for <see cref="ChatClientAgent"/> integration tests that verify
-/// end-to-end behavior with <see cref="ServiceStoredSimulatingChatClient"/> and
+/// end-to-end behavior with <see cref="PerServiceCallChatHistoryPersistingChatClient"/> and
 /// <see cref="FunctionInvokingChatClient"/>.
 /// </summary>
 internal static class ChatClientAgentTestHelper
@@ -9,7 +9,7 @@ namespace Microsoft.Agents.AI.UnitTests;

 /// <summary>
 /// Contains unit tests that verify the end-to-end approval flow behavior of the
-/// <see cref="ChatClientAgent"/> class with <see cref="ServiceStoredSimulatingChatClient"/>,
+/// <see cref="ChatClientAgent"/> class with <see cref="PerServiceCallChatHistoryPersistingChatClient"/>,
 /// ensuring that chat history is correctly persisted across multi-turn approval interactions.
 /// </summary>
 public class ChatClientAgent_ApprovalsTests
@@ -48,7 +48,7 @@ public class ChatClientAgent_ApprovalsTests
            agentOptions: new()
            {
                ChatOptions = new() { Tools = [approvalTool] },
-                SimulateServiceStoredChatHistory = true,
+                RequirePerServiceCallChatHistoryPersistence = true,
            },
            callIndex: callIndex,
            capturedInputs: capturedInputs);
@@ -260,7 +260,7 @@ public class ChatClientAgent_ApprovalsTests
            agentOptions: new()
            {
                ChatOptions = new() { Tools = [approvalTool] },
-                SimulateServiceStoredChatHistory = true,
+                RequirePerServiceCallChatHistoryPersistence = true,
            },
            callIndex: callIndex,
            capturedInputs: capturedInputs);
@@ -520,7 +520,7 @@ public class ChatClientAgent_ChatHistoryManagementTests
            agentOptions: new()
            {
                ChatOptions = new() { Instructions = "Be helpful" },
-                SimulateServiceStoredChatHistory = true,
+                RequirePerServiceCallChatHistoryPersistence = true,
            },
            expectedServiceCallCount: 1,
            expectedHistory:
@@ -554,7 +554,7 @@ public class ChatClientAgent_ChatHistoryManagementTests
            agentOptions: new()
            {
                ChatOptions = new() { Tools = [tool] },
-                SimulateServiceStoredChatHistory = true,
+                RequirePerServiceCallChatHistoryPersistence = true,
            },
            expectedServiceCallCount: 2,
            expectedHistory:
@@ -13,15 +13,15 @@ using Moq.Protected;
 namespace Microsoft.Agents.AI.UnitTests;

 /// <summary>
-/// Contains unit tests for the <see cref="ServiceStoredSimulatingChatClient"/> decorator,
+/// Contains unit tests for the <see cref="PerServiceCallChatHistoryPersistingChatClient"/> decorator,
 /// verifying that it persists messages via the <see cref="ChatHistoryProvider"/> after each
 /// individual service call by default, or marks messages for end-of-run persistence when the
-/// <see cref="ChatClientAgentOptions.SimulateServiceStoredChatHistory"/> option is enabled.
+/// <see cref="ChatClientAgentOptions.RequirePerServiceCallChatHistoryPersistence"/> option is enabled.
 /// </summary>
-public class ServiceStoredSimulatingChatClientTests
+public class PerServiceCallChatHistoryPersistingChatClientTests
 {
    /// <summary>
-    /// Verifies that by default (SimulateServiceStoredChatHistory is false),
+    /// Verifies that by default (RequirePerServiceCallChatHistoryPersistence is false),
    /// the ChatHistoryProvider receives messages after a successful non-streaming call.
    /// </summary>
    [Fact]
@@ -50,7 +50,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            ChatHistoryProvider = mockChatHistoryProvider.Object,
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -97,7 +97,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            ChatHistoryProvider = mockChatHistoryProvider.Object,
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -145,7 +145,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            ChatHistoryProvider = mockChatHistoryProvider.Object,
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -163,7 +163,7 @@ public class ServiceStoredSimulatingChatClientTests
    }

    /// <summary>
-    /// Verifies that the decorator is NOT injected by default (SimulateServiceStoredChatHistory is false).
+    /// Verifies that the decorator is NOT injected by default (RequirePerServiceCallChatHistoryPersistence is false).
    /// </summary>
    [Fact]
    public void ChatClient_DoesNotContainDecorator_ByDefault()
@@ -175,15 +175,15 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new());

        // Assert
-        var decorator = agent.ChatClient.GetService<ServiceStoredSimulatingChatClient>();
+        var decorator = agent.ChatClient.GetService<PerServiceCallChatHistoryPersistingChatClient>();
        Assert.Null(decorator);
    }

    /// <summary>
-    /// Verifies that the decorator is injected when SimulateServiceStoredChatHistory is true.
+    /// Verifies that the decorator is injected when RequirePerServiceCallChatHistoryPersistence is true.
    /// </summary>
    [Fact]
-    public void ChatClient_ContainsDecorator_WhenSimulateServiceStoredChatHistory()
+    public void ChatClient_ContainsDecorator_WhenRequirePerServiceCallChatHistoryPersistence()
    {
        // Arrange
        Mock<IChatClient> mockService = new();
@@ -191,11 +191,11 @@ public class ServiceStoredSimulatingChatClientTests
        // Act
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Assert
-        var decorator = agent.ChatClient.GetService<ServiceStoredSimulatingChatClient>();
+        var decorator = agent.ChatClient.GetService<PerServiceCallChatHistoryPersistingChatClient>();
        Assert.NotNull(decorator);
    }

@@ -215,27 +215,27 @@ public class ServiceStoredSimulatingChatClientTests
        });

        // Assert
-        var decorator = agent.ChatClient.GetService<ServiceStoredSimulatingChatClient>();
+        var decorator = agent.ChatClient.GetService<PerServiceCallChatHistoryPersistingChatClient>();
        Assert.Null(decorator);
    }

    /// <summary>
-    /// Verifies that the SimulateServiceStoredChatHistory option is included in Clone().
+    /// Verifies that the RequirePerServiceCallChatHistoryPersistence option is included in Clone().
    /// </summary>
    [Fact]
-    public void ChatClientAgentOptions_Clone_IncludesSimulateServiceStoredChatHistory()
+    public void ChatClientAgentOptions_Clone_IncludesRequirePerServiceCallChatHistoryPersistence()
    {
        // Arrange
        var options = new ChatClientAgentOptions
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        };

        // Act
        var cloned = options.Clone();

        // Assert
-        Assert.True(cloned.SimulateServiceStoredChatHistory);
+        Assert.True(cloned.RequirePerServiceCallChatHistoryPersistence);
    }

    /// <summary>
@@ -289,7 +289,7 @@ public class ServiceStoredSimulatingChatClientTests
        {
            ChatOptions = new() { Tools = [tool] },
            ChatHistoryProvider = mockChatHistoryProvider.Object,
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        }, services: new ServiceCollection().BuildServiceProvider());

        // Act
@@ -358,7 +358,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            ChatHistoryProvider = mockChatHistoryProvider.Object,
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -407,7 +407,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            AIContextProviders = [mockContextProvider.Object],
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -454,7 +454,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            AIContextProviders = [mockContextProvider.Object],
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -513,7 +513,7 @@ public class ServiceStoredSimulatingChatClientTests
        {
            ChatHistoryProvider = mockChatHistoryProvider.Object,
            AIContextProviders = [mockContextProvider.Object],
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -587,7 +587,7 @@ public class ServiceStoredSimulatingChatClientTests
        {
            ChatOptions = new() { Tools = [tool] },
            ChatHistoryProvider = mockChatHistoryProvider.Object,
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        }, services: new ServiceCollection().BuildServiceProvider());

        // Act
@@ -652,7 +652,7 @@ public class ServiceStoredSimulatingChatClientTests
        {
            ChatOptions = new() { Tools = [tool] },
            ChatHistoryProvider = mockChatHistoryProvider.Object,
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        }, services: new ServiceCollection().BuildServiceProvider());

        // Act
@@ -720,8 +720,8 @@ public class ServiceStoredSimulatingChatClientTests

    /// <summary>
    /// Verifies that when per-service-call persistence is active and no real conversation ID exists,
-    /// <see cref="ChatClientAgent"/> sets the <see cref="ServiceStoredSimulatingChatClient.LocalHistoryConversationId"/>
-    /// sentinel on the chat options and <see cref="ServiceStoredSimulatingChatClient"/> strips it before
+    /// <see cref="ChatClientAgent"/> sets the <see cref="PerServiceCallChatHistoryPersistingChatClient.LocalHistoryConversationId"/>
+    /// sentinel on the chat options and <see cref="PerServiceCallChatHistoryPersistingChatClient"/> strips it before
    /// forwarding to the inner client.
    /// </summary>
    [Fact]
@@ -741,7 +741,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            ChatOptions = new() { Instructions = "test" },
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -773,7 +773,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            ChatOptions = new() { Instructions = "test" },
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -808,7 +808,7 @@ public class ServiceStoredSimulatingChatClientTests

        ChatClientAgent agent = new(mockService.Object, options: new()
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Create a session with a real conversation ID.
@@ -842,7 +842,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            ChatOptions = new() { Instructions = "test" },
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -862,7 +862,7 @@ public class ServiceStoredSimulatingChatClientTests
    /// skip provider resolution in the agent (the decorator handles it).
    /// </summary>
    [Fact]
-    public async Task RunAsync_SetsSentinelOnSession_WhenSimulateServiceStoredChatHistoryActiveAsync()
+    public async Task RunAsync_SetsSentinelOnSession_WhenRequirePerServiceCallChatHistoryPersistenceActiveAsync()
    {
        // Arrange
        Mock<IChatClient> mockService = new();
@@ -875,7 +875,7 @@ public class ServiceStoredSimulatingChatClientTests

        ChatClientAgent agent = new(mockService.Object, options: new()
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -883,7 +883,7 @@ public class ServiceStoredSimulatingChatClientTests
        await agent.RunAsync([new(ChatRole.User, "test")], session);

        // Assert — session should have the sentinel conversation ID
-        Assert.Equal(ServiceStoredSimulatingChatClient.LocalHistoryConversationId, session!.ConversationId);
+        Assert.Equal(PerServiceCallChatHistoryPersistingChatClient.LocalHistoryConversationId, session!.ConversationId);
    }

    /// <summary>
@@ -924,7 +924,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            ChatHistoryProvider = mockChatHistoryProvider.Object,
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act & Assert — conflict detection should throw
@@ -969,7 +969,7 @@ public class ServiceStoredSimulatingChatClientTests

        ChatClientAgent agent = new(mockService.Object, options: new()
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
            AIContextProviders = [mockContextProvider.Object],
        });

@@ -1025,7 +1025,7 @@ public class ServiceStoredSimulatingChatClientTests

        ChatClientAgent agent = new(mockService.Object, options: new()
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
            AIContextProviders = [mockContextProvider.Object],
        });

@@ -1077,7 +1077,7 @@ public class ServiceStoredSimulatingChatClientTests

        ChatClientAgent agent = new(mockService.Object, options: new()
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
            AIContextProviders = [mockContextProvider.Object],
        });

@@ -1137,7 +1137,7 @@ public class ServiceStoredSimulatingChatClientTests
        // No ChatHistoryProvider — so conflict detection won't throw.
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
            AIContextProviders = [mockContextProvider.Object],
        });

@@ -1192,7 +1192,7 @@ public class ServiceStoredSimulatingChatClientTests
        // No ChatHistoryProvider — so conflict detection won't throw.
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
            AIContextProviders = [mockContextProvider.Object],
        });

@@ -1253,7 +1253,7 @@ public class ServiceStoredSimulatingChatClientTests
        ChatClientAgent agent = new(mockService.Object, options: new()
        {
            ChatHistoryProvider = mockChatHistoryProvider.Object,
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -1270,7 +1270,7 @@ public class ServiceStoredSimulatingChatClientTests
        Assert.Equal("test", messageList[0].Text);

        // Assert — session should NOT have the sentinel (agent handles ConversationId at end-of-run)
-        Assert.NotEqual(ServiceStoredSimulatingChatClient.LocalHistoryConversationId, session!.ConversationId);
+        Assert.NotEqual(PerServiceCallChatHistoryPersistingChatClient.LocalHistoryConversationId, session!.ConversationId);
    }

    /// <summary>
@@ -1291,7 +1291,7 @@ public class ServiceStoredSimulatingChatClientTests

        ChatClientAgent agent = new(mockService.Object, options: new()
        {
-            SimulateServiceStoredChatHistory = true,
+            RequirePerServiceCallChatHistoryPersistence = true,
        });

        // Act
@@ -1309,6 +1309,6 @@ public class ServiceStoredSimulatingChatClientTests
        Assert.NotEmpty(updates);

        // Assert — session should NOT have the sentinel
-        Assert.NotEqual(ServiceStoredSimulatingChatClient.LocalHistoryConversationId, session!.ConversationId);
+        Assert.NotEqual(PerServiceCallChatHistoryPersistingChatClient.LocalHistoryConversationId, session!.ConversationId);
    }
 }
@@ -1,257 +0,0 @@
-# Source Generator for Workflow Executors: Rationale and Impact
-
-## Overview
-
-The Microsoft Agents AI Workflows framework has introduced a Roslyn source generator (`Microsoft.Agents.AI.Workflows.Generators`) that replaces the previous reflection-based approach for discovering and registering message handlers. This document explains why this change was made, what benefits it provides, and how it impacts framework users.
-
-## Why Move from Reflection to Code Generation?
-
-### The Previous Approach: `ReflectingExecutor<T>`
-
-Previously, executors that needed automatic handler discovery inherited from `ReflectingExecutor<T>` and implemented marker interfaces like `IMessageHandler<TMessage>`:
-
-```csharp
-// Old approach - reflection-based
-public class MyExecutor : ReflectingExecutor<MyExecutor>,
-    IMessageHandler<QueryMessage>,
-    IMessageHandler<CommandMessage, CommandResult>
-{
-    public ValueTask HandleAsync(QueryMessage msg, IWorkflowContext ctx, CancellationToken ct)
-    {
-        // Handle query
-    }
-
-    public ValueTask<CommandResult> HandleAsync(CommandMessage msg, IWorkflowContext ctx, CancellationToken ct)
-    {
-        // Handle command and return result
-    }
-}
-```
-
-This approach had several limitations:
-
-1. **Runtime overhead**: Handler discovery happened at runtime via reflection, adding latency to executor initialization
-2. **No AOT compatibility**: Reflection-based discovery doesn't work with Native AOT compilation
-3. **Redundant declarations**: The interface list duplicated information already present in method signatures
-4. **Limited metadata**: No clean way to declare yield/send types for protocol validation
-5. **Hidden errors**: Invalid handler signatures weren't caught until runtime
-
-### The New Approach: `[MessageHandler]` Attribute
-
-The source generator enables a cleaner, attribute-based pattern:
-
-```csharp
-// New approach - source generated
-[SendsMessage(typeof(PollToken))]
-public partial class MyExecutor : Executor
-{
-    [MessageHandler]
-    private ValueTask HandleQueryAsync(QueryMessage msg, IWorkflowContext ctx, CancellationToken ct)
-    {
-        // Handle query
-    }
-
-    [MessageHandler(Yield = [typeof(StreamChunk)], Send = [typeof(InternalMessage)])]
-    private ValueTask<CommandResult> HandleCommandAsync(CommandMessage msg, IWorkflowContext ctx, CancellationToken ct)
-    {
-        // Handle command and return result
-    }
-}
-```
-
-The generator produces a partial class with `ConfigureRoutes()`, `ConfigureSentTypes()`, and `ConfigureYieldTypes()` implementations at compile time.
-
-## What's Better About Code Generation?
-
-### 1. Compile-Time Validation
-
-Invalid handler signatures are caught during compilation, not at runtime:
-
-```csharp
-[MessageHandler]
-private void InvalidHandler(string msg)  // Error WFGEN005: Missing IWorkflowContext parameter
-{
-}
-```
-
-Diagnostic errors include:
- `WFGEN001`: Handler missing `IWorkflowContext` parameter
- `WFGEN002`: Invalid return type (must be `void`, `ValueTask`, or `ValueTask<T>`)
- `WFGEN003`: Executor class must be `partial`
- `WFGEN004`: `[MessageHandler]` on non-Executor class
- `WFGEN005`: Insufficient parameters
- `WFGEN006`: `ConfigureRoutes` already manually defined
-
-### 2. Zero Runtime Reflection
-
-All handler registration happens at compile time. The generated code is simple, direct method calls:
-
-```csharp
-// Generated code
-protected override RouteBuilder ConfigureRoutes(RouteBuilder routeBuilder)
-{
-    return routeBuilder
-        .AddHandler<QueryMessage>(this.HandleQueryAsync)
-        .AddHandler<CommandMessage, CommandResult>(this.HandleCommandAsync);
-}
-```
-
-This eliminates:
- Reflection overhead during initialization
- Assembly scanning
- Dynamic delegate creation
-
-### 3. Native AOT Compatibility
-
-Because there's no runtime reflection, executors work seamlessly with .NET Native AOT compilation. This enables:
- Faster startup times
- Smaller deployment sizes
- Deployment to environments that don't support JIT compilation
-
-### 4. Explicit Protocol Metadata
-
-The `Yield` and `Send` properties on `[MessageHandler]` plus class-level `[SendsMessage]` and `[YieldsMessage]` attributes provide explicit protocol documentation:
-
-```csharp
-[SendsMessage(typeof(PollToken))]        // This executor sends PollToken messages
-[YieldsMessage(typeof(FinalResult))]     // This executor yields FinalResult to workflow output
-public partial class MyExecutor : Executor
-{
-    [MessageHandler(
-        Yield = [typeof(StreamChunk)],    // This handler yields StreamChunk
-        Send = [typeof(InternalQuery)])]  // This handler sends InternalQuery
-    private ValueTask HandleAsync(Request req, IWorkflowContext ctx) { ... }
-}
-```
-
-This metadata enables:
- Static protocol validation
- Better IDE tooling and documentation
- Clearer code intent
-
-### 5. Handler Accessibility Freedom
-
-Handlers can be `private`, `protected`, `internal`, or `public`. The old interface-based approach required public methods. Now you can encapsulate handler implementations:
-
-```csharp
-public partial class MyExecutor : Executor
-{
-    [MessageHandler]
-    private ValueTask HandleInternalAsync(InternalMessage msg, IWorkflowContext ctx)
-    {
-        // Private handler - implementation detail
-    }
-}
-```
-
-### 6. Cleaner Inheritance
-
-The generator properly handles inheritance chains, calling `base.ConfigureRoutes()` when appropriate:
-
-```csharp
-public partial class DerivedExecutor : BaseExecutor
-{
-    [MessageHandler]
-    private ValueTask HandleDerivedAsync(DerivedMessage msg, IWorkflowContext ctx) { ... }
-}
-
-// Generated:
-protected override RouteBuilder ConfigureRoutes(RouteBuilder routeBuilder)
-{
-    routeBuilder = base.ConfigureRoutes(routeBuilder);  // Preserves base handlers
-    return routeBuilder
-        .AddHandler<DerivedMessage>(this.HandleDerivedAsync);
-}
-```
-
-## New Capabilities Enabled
-
-### 1. Static Workflow Analysis
-
-With explicit yield/send metadata, tools can analyze workflow graphs at compile time:
- Validate that all message types have handlers
- Detect unreachable executors
- Generate workflow documentation
-
-### 2. Trimming-Safe Deployments
-
-The generated code contains no reflection, making it fully compatible with IL trimming. This reduces deployment size significantly for serverless and edge scenarios.
-
-### 3. Better IDE Experience
-
-Because the generator runs in the IDE, you get:
- Immediate feedback on handler signature errors
- IntelliSense for generated methods
- Go-to-definition on generated code
-
-### 4. Protocol Documentation Generation
-
-The explicit type metadata can be used to generate:
- API documentation
- OpenAPI/Swagger specs for workflow endpoints
- Visual workflow diagrams
-
-## Impact on Framework Users
-
-### Migration Path
-
-Existing code using `ReflectingExecutor<T>` continues to work but is marked `[Obsolete]`. To migrate:
-
-1. Change base class from `ReflectingExecutor<T>` to `Executor`
-2. Add `partial` modifier to the class
-3. Replace `IMessageHandler<T>` interfaces with `[MessageHandler]` attributes
-4. Optionally add `Yield`/`Send` metadata for protocol validation
-
-**Before:**
-```csharp
-public class MyExecutor : ReflectingExecutor<MyExecutor>, IMessageHandler<Query, Result>
-{
-    public ValueTask<Result> HandleAsync(Query q, IWorkflowContext ctx, CancellationToken ct) { ... }
-}
-```
-
-**After:**
-```csharp
-public partial class MyExecutor : Executor
-{
-    [MessageHandler]
-    private ValueTask<Result> HandleQueryAsync(Query q, IWorkflowContext ctx, CancellationToken ct) { ... }
-}
-```
-
-### Breaking Changes
-
- Classes using `[MessageHandler]` **must** be `partial`
- Handler methods must have at least 2 parameters: `(TMessage, IWorkflowContext)`
- Return type must be `void`, `ValueTask`, or `ValueTask<T>`
-
-### Performance Improvements
-
-Users can expect:
- **Faster executor initialization**: No reflection overhead
- **Reduced memory allocation**: No dynamic delegate creation
- **AOT deployment support**: Full Native AOT compatibility
- **Smaller trimmed deployments**: No reflection metadata preserved
-
-### NuGet Package
-
-The generator is distributed as a separate NuGet package (`Microsoft.Agents.AI.Workflows.Generators`) that's automatically referenced by the main Workflows package. It's packaged as an analyzer, so it:
- Runs automatically during build
- Requires no additional configuration
- Works in all IDEs that support Roslyn analyzers
-
-## Summary
-
-The move from reflection to source generation represents a significant improvement in the Workflows framework:
-
-| Aspect | Reflection (Old) | Source Generator (New) |
-|--------|------------------|------------------------|
-| Handler discovery | Runtime | Compile-time |
-| Error detection | Runtime exceptions | Compiler errors |
-| AOT support | No | Yes |
-| Trimming support | Limited | Full |
-| Protocol metadata | Implicit | Explicit |
-| Handler visibility | Public only | Any |
-| Initialization speed | Slower | Faster |
-
-The source generator approach aligns with modern .NET best practices and positions the framework for future scenarios including edge computing, serverless, and mobile deployments where AOT compilation and minimal footprint are essential.
@@ -1,439 +0,0 @@
-# Source Generator Best Practices Review
-
-This document reviews the Workflow Executor Route Source Generator implementation against the official Roslyn Source Generator Cookbook best practices from the dotnet/roslyn repository.
-
-## Reference Documentation
-
- [Source Generators Cookbook](https://github.com/dotnet/roslyn/blob/main/docs/features/source-generators.cookbook.md)
- [Incremental Generators Cookbook](https://github.com/dotnet/roslyn/blob/main/docs/features/incremental-generators.cookbook.md)
-
---
-
-## Executive Summary
-
-| Category | Status | Priority |
-|----------|--------|----------|
-| Generator Type | PASS | - |
-| Attribute-Based Detection | FAIL | HIGH |
-| Model Value Equality | FAIL | HIGH |
-| Collection Equality | FAIL | HIGH |
-| Symbol/SyntaxNode Storage | PASS | - |
-| Code Generation Approach | PASS | - |
-| Diagnostics | PASS | - |
-| Pipeline Efficiency | FAIL | MEDIUM |
-| CancellationToken Handling | PARTIAL | LOW |
-
-**Overall Assessment**: The generator follows several best practices but has critical performance issues that should be addressed before production use. The most significant issue is not using `ForAttributeWithMetadataName`, which the Roslyn team states is "at least 99x more efficient" than `CreateSyntaxProvider`.
-
---
-
-## Detailed Analysis
-
-### 1. Generator Interface Selection
-
-**Best Practice**: Use `IIncrementalGenerator` instead of the deprecated `ISourceGenerator`.
-
-**Our Implementation**: PASS
-
-```csharp
-// ExecutorRouteGenerator.cs:19
-public sealed class ExecutorRouteGenerator : IIncrementalGenerator
-```
-
-The generator correctly implements `IIncrementalGenerator`, the recommended interface for new generators.
-
---
-
-### 2. Attribute-Based Detection with ForAttributeWithMetadataName
-
-**Best Practice**: Use `ForAttributeWithMetadataName()` for attribute-based discovery.
-
-> "This utility method is at least 99x more efficient than `SyntaxProvider.CreateSyntaxProvider`, and in many cases even more efficient."
-> — Roslyn Incremental Generators Cookbook
-
-**Our Implementation**: FAIL (HIGH PRIORITY)
-
-```csharp
-// ExecutorRouteGenerator.cs:25-30
-var executorCandidates = context.SyntaxProvider
-    .CreateSyntaxProvider(
-        predicate: static (node, _) => SyntaxDetector.IsExecutorCandidate(node),
-        transform: static (ctx, ct) => SemanticAnalyzer.Analyze(ctx, ct, out _))
-```
-
-**Problem**: We use `CreateSyntaxProvider` with manual attribute detection in `SyntaxDetector`. This requires the generator to examine every syntax node in the compilation, whereas `ForAttributeWithMetadataName` uses the compiler's built-in attribute index for O(1) lookup.
-
-**Recommended Fix**:
-
-```csharp
-var executorCandidates = context.SyntaxProvider
-    .ForAttributeWithMetadataName(
-        fullyQualifiedMetadataName: "Microsoft.Agents.AI.Workflows.MessageHandlerAttribute",
-        predicate: static (node, _) => node is MethodDeclarationSyntax,
-        transform: static (ctx, ct) => AnalyzeMethodWithAttribute(ctx, ct))
-    .Collect()
-    .SelectMany((methods, _) => GroupByContainingClass(methods));
-```
-
-**Impact**: Current approach causes IDE lag on every keystroke in large projects.
-
---
-
-### 3. Model Value Equality (Records vs Classes)
-
-**Best Practice**: Use `record` types for pipeline models to get automatic value equality.
-
-> "Use `record`s, rather than `class`es, so that value equality is generated for you."
-> — Roslyn Incremental Generators Cookbook
-
-**Our Implementation**: FAIL (HIGH PRIORITY)
-
-```csharp
-// HandlerInfo.cs:28
-internal sealed class HandlerInfo { ... }
-
-// ExecutorInfo.cs:10
-internal sealed class ExecutorInfo { ... }
-```
-
-**Problem**: Both `HandlerInfo` and `ExecutorInfo` are `sealed class` types, which use reference equality by default. The incremental generator caches results based on equality comparison—when the model equals the previous run's model, regeneration is skipped. With reference equality, every analysis produces a "new" object, defeating caching entirely.
-
-**Recommended Fix**:
-
-```csharp
-// HandlerInfo.cs
-internal sealed record HandlerInfo(
-    string MethodName,
-    string InputTypeName,
-    string? OutputTypeName,
-    HandlerSignatureKind SignatureKind,
-    bool HasCancellationToken,
-    EquatableArray<string>? YieldTypes,
-    EquatableArray<string>? SendTypes);
-
-// ExecutorInfo.cs
-internal sealed record ExecutorInfo(
-    string? Namespace,
-    string ClassName,
-    string? GenericParameters,
-    bool IsNested,
-    string ContainingTypeChain,
-    bool BaseHasConfigureRoutes,
-    EquatableArray<HandlerInfo> Handlers,
-    EquatableArray<string> ClassSendTypes,
-    EquatableArray<string> ClassYieldTypes);
-```
-
-**Impact**: Without value equality, the generator regenerates code on every compilation even when nothing changed.
-
---
-
-### 4. Collection Equality
-
-**Best Practice**: Use custom equatable wrappers for collections since `ImmutableArray<T>` uses reference equality.
-
-> "Arrays, `ImmutableArray<T>`, and `List<T>` use reference equality by default. Wrap collections with custom types implementing value-based equality."
-> — Roslyn Incremental Generators Cookbook
-
-**Our Implementation**: FAIL (HIGH PRIORITY)
-
-```csharp
-// ExecutorInfo.cs:46
-public ImmutableArray<HandlerInfo> Handlers { get; }
-
-// HandlerInfo.cs:58-63
-public ImmutableArray<string>? YieldTypes { get; }
-public ImmutableArray<string>? SendTypes { get; }
-```
-
-**Problem**: `ImmutableArray<T>` compares by reference, not by contents. Two arrays with identical elements are considered unequal, breaking incremental caching.
-
-**Recommended Fix**: Create an `EquatableArray<T>` wrapper:
-
-```csharp
-internal readonly struct EquatableArray<T> : IEquatable<EquatableArray<T>>, IEnumerable<T>
-    where T : IEquatable<T>
-{
-    private readonly ImmutableArray<T> _array;
-
-    public EquatableArray(ImmutableArray<T> array) => _array = array;
-
-    public bool Equals(EquatableArray<T> other)
-    {
-        if (_array.Length != other._array.Length) return false;
-        for (int i = 0; i < _array.Length; i++)
-        {
-            if (!_array[i].Equals(other._array[i])) return false;
-        }
-        return true;
-    }
-
-    public override int GetHashCode()
-    {
-        var hash = new HashCode();
-        foreach (var item in _array) hash.Add(item);
-        return hash.ToHashCode();
-    }
-
-    // ... IEnumerable implementation
-}
-```
-
-**Impact**: Same as model equality—caching is completely broken for handlers and type arrays.
-
---
-
-### 5. Symbol and SyntaxNode Storage
-
-**Best Practice**: Never store `ISymbol` or `SyntaxNode` in pipeline models.
-
-> "Storing `ISymbol` references blocks garbage collection and roots old compilations unnecessarily. Extract only the information you need—typically string representations work well—into your equatable models."
-> — Roslyn Incremental Generators Cookbook
-
-**Our Implementation**: PASS
-
-The models correctly store only primitive types and strings:
-
-```csharp
-// HandlerInfo.cs - stores strings, not symbols
-public string MethodName { get; }
-public string InputTypeName { get; }
-public string? OutputTypeName { get; }
-
-// ExecutorInfo.cs - stores strings, not symbols
-public string? Namespace { get; }
-public string ClassName { get; }
-```
-
-The `SemanticAnalyzer` correctly extracts string representations from symbols:
-
-```csharp
-// SemanticAnalyzer.cs:300-301
-var inputType = methodSymbol.Parameters[0].Type;
-var inputTypeName = inputType.ToDisplayString(SymbolDisplayFormat.FullyQualifiedFormat);
-```
-
---
-
-### 6. Code Generation Approach
-
-**Best Practice**: Use `StringBuilder` for code generation, not `SyntaxNode` construction.
-
-> "Avoid constructing `SyntaxNode`s for output; they're complex to format correctly and `NormalizeWhitespace()` is expensive. Instead, use a `StringBuilder` wrapper that tracks indentation levels."
-> — Roslyn Incremental Generators Cookbook
-
-**Our Implementation**: PASS
-
-```csharp
-// SourceBuilder.cs:17-19
-public static string Generate(ExecutorInfo info)
-{
-    var sb = new StringBuilder();
-```
-
-The `SourceBuilder` correctly uses `StringBuilder` with manual indentation tracking.
-
---
-
-### 7. Diagnostic Reporting
-
-**Best Practice**: Use `ReportDiagnostic` for surfacing issues to users.
-
-**Our Implementation**: PASS
-
-```csharp
-// ExecutorRouteGenerator.cs:44-50
-context.RegisterSourceOutput(diagnosticsProvider, static (ctx, diagnostics) =>
-{
-    foreach (var diagnostic in diagnostics)
-    {
-        ctx.ReportDiagnostic(diagnostic);
-    }
-});
-```
-
-Diagnostics are well-defined with appropriate severities:
-
-| ID | Severity | Description |
-|----|----------|-------------|
-| WFGEN001 | Error | Missing IWorkflowContext parameter |
-| WFGEN002 | Error | Invalid return type |
-| WFGEN003 | Error | Class must be partial |
-| WFGEN004 | Warning | Not an Executor |
-| WFGEN005 | Error | Insufficient parameters |
-| WFGEN006 | Info | ConfigureRoutes already defined |
-| WFGEN007 | Error | Handler cannot be static |
-
---
-
-### 8. Pipeline Efficiency
-
-**Best Practice**: Avoid duplicate work in the pipeline.
-
-**Our Implementation**: FAIL (MEDIUM PRIORITY)
-
-```csharp
-// ExecutorRouteGenerator.cs:25-41
-// Pipeline 1: Get executor candidates
-var executorCandidates = context.SyntaxProvider
-    .CreateSyntaxProvider(
-        predicate: static (node, _) => SyntaxDetector.IsExecutorCandidate(node),
-        transform: static (ctx, ct) => SemanticAnalyzer.Analyze(ctx, ct, out _))
-    ...
-
-// Pipeline 2: Get diagnostics (duplicates the same work!)
-var diagnosticsProvider = context.SyntaxProvider
-    .CreateSyntaxProvider(
-        predicate: static (node, _) => SyntaxDetector.IsExecutorCandidate(node),
-        transform: static (ctx, ct) =>
-        {
-            SemanticAnalyzer.Analyze(ctx, ct, out var diagnostics);
-            return diagnostics;
-        })
-```
-
-**Problem**: The same syntax detection and semantic analysis runs twice—once for extracting `ExecutorInfo` and once for extracting diagnostics.
-
-**Recommended Fix**: Return both in a single pipeline:
-
-```csharp
-var analysisResults = context.SyntaxProvider
-    .ForAttributeWithMetadataName(...)
-    .Select((ctx, ct) => {
-        var info = SemanticAnalyzer.Analyze(ctx, ct, out var diagnostics);
-        return (Info: info, Diagnostics: diagnostics);
-    });
-
-// Split for different outputs
-context.RegisterSourceOutput(
-    analysisResults.Where(r => r.Info != null).Select((r, _) => r.Info!),
-    GenerateSource);
-
-context.RegisterSourceOutput(
-    analysisResults.Where(r => r.Diagnostics.Length > 0).Select((r, _) => r.Diagnostics),
-    ReportDiagnostics);
-```
-
---
-
-### 9. Base Type Chain Scanning
-
-**Best Practice**: Avoid scanning indirect type relationships when possible.
-
-> "Never scan for types that indirectly implement interfaces, inherit from base types, or acquire attributes through inheritance hierarchies. This pattern forces the generator to inspect every type's `AllInterfaces` or base-type chain on every keystroke."
-> — Roslyn Incremental Generators Cookbook
-
-**Our Implementation**: PARTIAL CONCERN
-
-```csharp
-// SemanticAnalyzer.cs:126-141
-private static bool DerivesFromExecutor(INamedTypeSymbol classSymbol)
-{
-    var current = classSymbol.BaseType;
-    while (current != null)
-    {
-        var fullName = current.OriginalDefinition.ToDisplayString();
-        if (fullName == ExecutorTypeName || fullName.StartsWith(ExecutorTypeName + "<", ...))
-        {
-            return true;
-        }
-        current = current.BaseType;
-    }
-    return false;
-}
-```
-
-**Analysis**: We do walk the base type chain, but this only happens after attribute filtering (classes must have `[MessageHandler]` methods). Since this is targeted to specific candidates rather than scanning all types, the performance impact is acceptable. However, if we switch to `ForAttributeWithMetadataName`, the attribute is on methods, so we'd need to check the containing class's base types—which is still targeted.
-
---
-
-### 10. CancellationToken Handling
-
-**Best Practice**: Respect `CancellationToken` in long-running operations.
-
-**Our Implementation**: PARTIAL (LOW PRIORITY)
-
-The `CancellationToken` is passed through to semantic model calls:
-
-```csharp
-// SemanticAnalyzer.cs:46
-var classSymbol = semanticModel.GetDeclaredSymbol(classDecl, cancellationToken);
-```
-
-However, there are no explicit `cancellationToken.ThrowIfCancellationRequested()` calls in loops like `AnalyzeHandlers`. For most compilations this is fine, but very large classes with many handlers might benefit from periodic checks.
-
---
-
-### 11. File Naming Convention
-
-**Best Practice**: Use descriptive generated file names with `.g.cs` suffix.
-
-**Our Implementation**: PASS
-
-```csharp
-// ExecutorRouteGenerator.cs:62-91
-private static string GetHintName(ExecutorInfo info)
-{
-    // Produces: "Namespace.ClassName.g.cs" or "Namespace.Outer.Inner.ClassName.g.cs"
-    ...
-    sb.Append(".g.cs");
-    return sb.ToString();
-}
-```
-
---
-
-## Recommended Action Plan
-
-### High Priority (Performance Critical)
-
-1. **Switch to `ForAttributeWithMetadataName`**
-   - Estimated impact: 99x+ performance improvement for attribute detection
-   - Requires restructuring the pipeline to collect methods then group by class
-
-2. **Convert models to records**
-   - Change `HandlerInfo` and `ExecutorInfo` from `sealed class` to `sealed record`
-   - Enables automatic value equality for incremental caching
-
-3. **Implement `EquatableArray<T>`**
-   - Create wrapper struct with value-based equality
-   - Replace all `ImmutableArray<T>` usages in models
-
-### Medium Priority (Efficiency)
-
-4. **Eliminate duplicate pipeline execution**
-   - Combine info extraction and diagnostic collection into single pipeline
-   - Split outputs using `Where` and `Select`
-
-### Low Priority (Polish)
-
-5. **Add periodic cancellation checks**
-   - Add `ThrowIfCancellationRequested()` in handler analysis loop
-   - Only needed for extremely large classes
-
---
-
-## Compliance Matrix
-
-| Best Practice | Cookbook Reference | Status | Fix Required |
-|--------------|-------------------|--------|--------------|
-| Use IIncrementalGenerator | Main cookbook | PASS | No |
-| Use ForAttributeWithMetadataName | Incremental cookbook | FAIL | Yes (High) |
-| Use records for models | Incremental cookbook | FAIL | Yes (High) |
-| Implement collection equality | Incremental cookbook | FAIL | Yes (High) |
-| Don't store ISymbol/SyntaxNode | Incremental cookbook | PASS | No |
-| Use StringBuilder for codegen | Incremental cookbook | PASS | No |
-| Report diagnostics properly | Main cookbook | PASS | No |
-| Avoid duplicate pipeline work | Incremental cookbook | FAIL | Yes (Medium) |
-| Respect CancellationToken | Main cookbook | PARTIAL | Optional |
-| Use .g.cs file suffix | Main cookbook | PASS | No |
-| Additive-only generation | Main cookbook | PASS | No |
-| No language feature emulation | Main cookbook | PASS | No |
-
---
-
-## Conclusion
-
-The source generator implementation demonstrates solid understanding of Roslyn generator fundamentals—correct interface usage, proper diagnostic reporting, and appropriate code generation patterns. However, critical performance optimizations are missing that could cause significant IDE lag in production environments.
-
-The three high-priority fixes (ForAttributeWithMetadataName, record models, and EquatableArray) should be implemented before the generator is used in large codebases. These changes will enable proper incremental caching, reducing regeneration from "every keystroke" to "only when relevant code changes."
@@ -1,258 +0,0 @@
-# Workflow Executor Route Source Generator - Implementation Summary
-
-This document summarizes all changes made to implement a Roslyn source generator that replaces the reflection-based `ReflectingExecutor<T>` pattern with compile-time code generation using `[MessageHandler]` attributes.
-
-## Overview
-
-The source generator automatically discovers methods marked with `[MessageHandler]` and generates `ConfigureRoutes`, `ConfigureSentTypes`, and `ConfigureYieldTypes` method implementations at compile time. This improves AOT compatibility and eliminates the need for the CRTP (Curiously Recurring Template Pattern) used by `ReflectingExecutor<T>`.
-
-## New Files Created
-
-### Attributes (3 files)
-
-| File | Purpose |
-|------|---------|
-| `src/Microsoft.Agents.AI.Workflows/Attributes/MessageHandlerAttribute.cs` | Marks methods as message handlers with optional `Yield` and `Send` type arrays |
-| `src/Microsoft.Agents.AI.Workflows/Attributes/SendsMessageAttribute.cs` | Class-level attribute declaring message types an executor may send |
-| `src/Microsoft.Agents.AI.Workflows/Attributes/YieldsMessageAttribute.cs` | Class-level attribute declaring output types an executor may yield |
-
-### Source Generator Project (8 files)
-
-| File | Purpose |
-|------|---------|
-| `src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj` | Project file targeting netstandard2.0 with Roslyn component settings |
-| `src/Microsoft.Agents.AI.Workflows.Generators/ExecutorRouteGenerator.cs` | Main incremental generator implementing `IIncrementalGenerator` |
-| `src/Microsoft.Agents.AI.Workflows.Generators/Models/HandlerInfo.cs` | Data model for handler method information |
-| `src/Microsoft.Agents.AI.Workflows.Generators/Models/ExecutorInfo.cs` | Data model for executor class information |
-| `src/Microsoft.Agents.AI.Workflows.Generators/Analysis/SyntaxDetector.cs` | Fast syntax-level candidate detection |
-| `src/Microsoft.Agents.AI.Workflows.Generators/Analysis/SemanticAnalyzer.cs` | Semantic validation and type extraction |
-| `src/Microsoft.Agents.AI.Workflows.Generators/Generation/SourceBuilder.cs` | Code generation logic |
-| `src/Microsoft.Agents.AI.Workflows.Generators/Diagnostics/DiagnosticDescriptors.cs` | Analyzer diagnostic definitions |
-
-## Files Modified
-
-### Project Files
-
-| File | Changes |
-|------|---------|
-| `src/Microsoft.Agents.AI.Workflows/Microsoft.Agents.AI.Workflows.csproj` | Added generator project reference and `InternalsVisibleTo` for generator tests |
-| `Directory.Packages.props` | Added `Microsoft.CodeAnalysis.Analyzers` version 3.11.0 |
-| `agent-framework-dotnet.slnx` | Added generator project to solution |
-
-### Obsolete Annotations
-
-| File | Changes |
-|------|---------|
-| `src/Microsoft.Agents.AI.Workflows/Reflection/ReflectingExecutor.cs` | Added `[Obsolete]` attribute with migration guidance |
-| `src/Microsoft.Agents.AI.Workflows/Reflection/IMessageHandler.cs` | Added `[Obsolete]` to both `IMessageHandler<T>` and `IMessageHandler<T,TResult>` interfaces |
-
-### Pragma Suppressions for Internal Obsolete Usage
-
-| File | Changes |
-|------|---------|
-| `src/Microsoft.Agents.AI.Workflows/Executor.cs` | Added `#pragma warning disable CS0618` |
-| `src/Microsoft.Agents.AI.Workflows/StatefulExecutor.cs` | Added `#pragma warning disable CS0618` |
-| `src/Microsoft.Agents.AI.Workflows/Reflection/RouteBuilderExtensions.cs` | Added `#pragma warning disable CS0618` |
-| `src/Microsoft.Agents.AI.Workflows/Reflection/MessageHandlerInfo.cs` | Added `#pragma warning disable CS0618` |
-
-### Test File Pragma Suppressions
-
-| File | Changes |
-|------|---------|
-| `tests/Microsoft.Agents.AI.Workflows.UnitTests/Sample/01_Simple_Workflow_Sequential.cs` | Added `#pragma warning disable CS0618` for legacy pattern testing |
-| `tests/Microsoft.Agents.AI.Workflows.UnitTests/Sample/02_Simple_Workflow_Condition.cs` | Added `#pragma warning disable CS0618` for legacy pattern testing |
-| `tests/Microsoft.Agents.AI.Workflows.UnitTests/Sample/03_Simple_Workflow_Loop.cs` | Added `#pragma warning disable CS0618` for legacy pattern testing |
-| `tests/Microsoft.Agents.AI.Workflows.UnitTests/ReflectionSmokeTest.cs` | Added `#pragma warning disable CS0618` for legacy pattern testing |
-
-## Attribute Definitions
-
-### MessageHandlerAttribute
-
-```csharp
-[AttributeUsage(AttributeTargets.Method, AllowMultiple = false, Inherited = false)]
-public sealed class MessageHandlerAttribute : Attribute
-{
-    public Type[]? Yield { get; set; }  // Types yielded as workflow outputs
-    public Type[]? Send { get; set; }   // Types sent to other executors
-}
-```
-
-### SendsMessageAttribute
-
-```csharp
-[AttributeUsage(AttributeTargets.Class, AllowMultiple = true, Inherited = true)]
-public sealed class SendsMessageAttribute : Attribute
-{
-    public Type Type { get; }
-    public SendsMessageAttribute(Type type) => this.Type = Throw.IfNull(type);
-}
-```
-
-### YieldsMessageAttribute
-
-```csharp
-[AttributeUsage(AttributeTargets.Class, AllowMultiple = true, Inherited = true)]
-public sealed class YieldsMessageAttribute : Attribute
-{
-    public Type Type { get; }
-    public YieldsMessageAttribute(Type type) => this.Type = Throw.IfNull(type);
-}
-```
-
-## Diagnostic Rules
-
-| ID | Severity | Description |
-|----|----------|-------------|
-| `WFGEN001` | Error | Handler method must have at least 2 parameters (message and IWorkflowContext) |
-| `WFGEN002` | Error | Handler method's second parameter must be IWorkflowContext |
-| `WFGEN003` | Error | Handler method must return void, ValueTask, or ValueTask<T> |
-| `WFGEN004` | Error | Executor class with [MessageHandler] methods must be declared as partial |
-| `WFGEN005` | Warning | [MessageHandler] attribute on method in non-Executor class (ignored) |
-| `WFGEN006` | Info | ConfigureRoutes already defined manually, [MessageHandler] methods ignored |
-| `WFGEN007` | Error | Handler method's third parameter (if present) must be CancellationToken |
-
-## Handler Signature Support
-
-The generator supports the following method signatures:
-
-| Return Type | Parameters | Generated Call |
-|-------------|------------|----------------|
-| `void` | `(TMessage, IWorkflowContext)` | `AddHandler<TMessage>(this.Method)` |
-| `void` | `(TMessage, IWorkflowContext, CancellationToken)` | `AddHandler<TMessage>(this.Method)` |
-| `ValueTask` | `(TMessage, IWorkflowContext)` | `AddHandler<TMessage>(this.Method)` |
-| `ValueTask` | `(TMessage, IWorkflowContext, CancellationToken)` | `AddHandler<TMessage>(this.Method)` |
-| `TResult` | `(TMessage, IWorkflowContext)` | `AddHandler<TMessage, TResult>(this.Method)` |
-| `TResult` | `(TMessage, IWorkflowContext, CancellationToken)` | `AddHandler<TMessage, TResult>(this.Method)` |
-| `ValueTask<TResult>` | `(TMessage, IWorkflowContext)` | `AddHandler<TMessage, TResult>(this.Method)` |
-| `ValueTask<TResult>` | `(TMessage, IWorkflowContext, CancellationToken)` | `AddHandler<TMessage, TResult>(this.Method)` |
-
-## Generated Code Example
-
-### Input (User Code)
-
-```csharp
-[SendsMessage(typeof(PollToken))]
-public partial class MyChatExecutor : Executor
-{
-    [MessageHandler]
-    private async ValueTask<ChatResponse> HandleQueryAsync(
-        ChatQuery query, IWorkflowContext ctx, CancellationToken ct)
-    {
-        return new ChatResponse(...);
-    }
-
-    [MessageHandler(Yield = new[] { typeof(StreamChunk) }, Send = new[] { typeof(InternalMessage) })]
-    private void HandleStream(StreamRequest req, IWorkflowContext ctx)
-    {
-        // Handler implementation
-    }
-}
-```
-
-### Output (Generated Code)
-
-```csharp
-// <auto-generated/>
-#nullable enable
-
-namespace MyNamespace;
-
-partial class MyChatExecutor
-{
-    protected override RouteBuilder ConfigureRoutes(RouteBuilder routeBuilder)
-    {
-        return routeBuilder
-            .AddHandler<ChatQuery, ChatResponse>(this.HandleQueryAsync)
-            .AddHandler<StreamRequest>(this.HandleStream);
-    }
-
-    protected override ISet<Type> ConfigureSentTypes()
-    {
-        var types = base.ConfigureSentTypes();
-        types.Add(typeof(PollToken));
-        types.Add(typeof(InternalMessage));
-        return types;
-    }
-
-    protected override ISet<Type> ConfigureYieldTypes()
-    {
-        var types = base.ConfigureYieldTypes();
-        types.Add(typeof(ChatResponse));
-        types.Add(typeof(StreamChunk));
-        return types;
-    }
-}
-```
-
-## Build Issues Resolved
-
-### 1. NU1008 - Central Package Management
-Package references in the generator project had inline versions, which conflicts with central package management. Fixed by removing `Version` attributes from `PackageReference` items.
-
-### 2. RS2008 - Analyzer Release Tracking
-Roslyn requires analyzer release tracking documentation. Fixed by adding `<NoWarn>$(NoWarn);RS2008</NoWarn>` to the generator project.
-
-### 3. CA1068 - CancellationToken Parameter Order
-Method parameters were in wrong order. Fixed by reordering `CancellationToken` to be last.
-
-### 4. RCS1146 - Conditional Access
-Used null check with `&&` instead of `?.` operator. Fixed by using conditional access.
-
-### 5. CA1310 - StringComparison
-`StartsWith(string)` calls without `StringComparison`. Fixed by adding `StringComparison.Ordinal`.
-
-### 6. CS0103 - Missing Using Directive
-Missing `using System;` in SemanticAnalyzer.cs. Fixed by adding the using directive.
-
-### 7. CS0618 - Obsolete Warnings as Errors
-Internal uses of obsolete types caused build failures (TreatWarningsAsErrors). Fixed by adding `#pragma warning disable CS0618` to affected internal files and test files.
-
-### 8. NU1109 - Package Version Conflict
-`Microsoft.CodeAnalysis.Analyzers` 3.3.4 conflicts with `Microsoft.CodeAnalysis.CSharp` 4.14.0 which requires >= 3.11.0. Fixed by updating version to 3.11.0 in `Directory.Packages.props`.
-
-### 9. RS1041 - Wrong Target Framework for Analyzer
-The generator was being multi-targeted due to inherited `TargetFrameworks` from `Directory.Build.props`. Fixed by clearing `TargetFrameworks` and only setting `TargetFramework` to `netstandard2.0`.
-
-## Migration Guide
-
-### Before (Reflection-based)
-
-```csharp
-public class MyExecutor : ReflectingExecutor<MyExecutor>, IMessageHandler<MyMessage, MyResult>
-{
-    public MyExecutor() : base("MyExecutor") { }
-
-    public ValueTask<MyResult> HandleAsync(MyMessage message, IWorkflowContext context, CancellationToken ct)
-    {
-        // Handler implementation
-    }
-}
-```
-
-### After (Source Generator)
-
-```csharp
-public partial class MyExecutor : Executor
-{
-    public MyExecutor() : base("MyExecutor") { }
-
-    [MessageHandler]
-    private ValueTask<MyResult> HandleAsync(MyMessage message, IWorkflowContext context, CancellationToken ct)
-    {
-        // Handler implementation
-    }
-}
-```
-
-Key migration steps:
-1. Change base class from `ReflectingExecutor<T>` to `Executor`
-2. Add `partial` modifier to the class
-3. Remove `IMessageHandler<T>` interface implementations
-4. Add `[MessageHandler]` attribute to handler methods
-5. Handler methods can now be any accessibility (private, protected, internal, public)
-
-## Future Work
-
- Create comprehensive unit tests for the source generator
- Add integration tests verifying generated routes match reflection-discovered routes
- Consider adding IDE quick-fix for migrating from `ReflectingExecutor<T>` pattern
@@ -76,7 +76,7 @@ from agent_framework.observability import enable_instrumentation

 # Connectors (lazy-loaded)
 from agent_framework.openai import OpenAIChatClient
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.foundry import FoundryChatClient
 ```

 ## Public API and Exports
@@ -0,0 +1,238 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+---
+name: python-feature-lifecycle
+description: >
+  Guidance for package and feature lifecycle in the Agent Framework Python
+  codebase, including stage meanings, feature-stage decorators, feature enums,
+  and how to move APIs from one stage to the next.
+---
+
+# Python Feature Lifecycle
+
+## Two lifecycle levels
+
+Agent Framework uses lifecycle at two different levels:
+
+1. **Package lifecycle** — the maturity of the package as a whole
+2. **Feature lifecycle** — the maturity of a specific API or feature inside that package
+
+These are related, but they are **not the same thing**.
+
+- The **package stage is the default** for everything in the package.
+- **Feature-stage decorators are only for exceptions** when a feature is behind the package's default stage.
+- Do **not** decorate every class or function just because the package is experimental or release candidate.
+
+### Important default
+
+If a package is still in **beta / experimental preview**, all public APIs in that package are experimental by default.
+
+- Do **not** add `@experimental(...)` everywhere in that package.
+- The package stage already communicates that default.
+
+Once a package moves forward, you can keep individual features behind:
+
+- If a package moves to **release candidate**, a feature may remain **experimental**
+- If a package moves to **released / GA**, a feature may remain **experimental** or **release candidate**
+
+That is the main use case for feature-stage decorators.
+
+## The four stages
+
+### 1. Experimental
+
+Use for features that are still unstable and may change or be removed without notice.
+
+Feature-level code pattern:
+
+```python
+from ._feature_stage import ExperimentalFeature, experimental
+
+
+@experimental(feature_id=ExperimentalFeature.MY_FEATURE)
+class MyFeature:
+    ...
+```
+
+Behavior:
+
+- Adds an experimental warning block to the docstring
+- Records feature metadata on the decorated object
+- Emits a runtime warning the first time the feature is used (once per feature by default)
+
+Enum setup:
+
+- Add an all-caps member to `ExperimentalFeature`
+- Reuse the same feature ID across all APIs that belong to the same conceptual feature
+
+### 2. Release candidate
+
+Use for features that are nearly stable but may still receive small refinements before GA.
+
+Feature-level code pattern:
+
+```python
+from ._feature_stage import ReleaseCandidateFeature, release_candidate
+
+
+@release_candidate(feature_id=ReleaseCandidateFeature.MY_FEATURE)
+class MyFeature:
+    ...
+```
+
+Behavior:
+
+- Adds a release-candidate note to the docstring
+- Records feature metadata on the decorated object
+- Does **not** emit the experimental warning
+
+Enum setup:
+
+- Add an all-caps member to `ReleaseCandidateFeature`
+
+### 3. Released
+
+Use for stable GA APIs.
+
+Code pattern:
+
+- **No feature-stage decorator**
+- **No entry** in `ExperimentalFeature`
+- **No entry** in `ReleaseCandidateFeature`
+
+If a feature is fully released, remove any stage-specific feature annotation.
+
+### 4. Deprecated
+
+Use for APIs that still exist but should not be used for new code.
+
+Code pattern:
+
+```python
+import sys
+
+if sys.version_info >= (3, 13):
+    from warnings import deprecated  # type: ignore # pragma: no cover
+else:
+    from typing_extensions import deprecated  # type: ignore # pragma: no cover
+
+
+@deprecated("MyOldFeature is deprecated. Use MyNewFeature instead.")
+class MyOldFeature:
+    ...
+```
+
+Behavior:
+
+- Uses the repository's version-conditional deprecation import pattern
+- Should describe what to use instead
+
+Deprecated APIs should not also carry feature-stage decorators.
+
+## Expected decorators by stage
+
+| Feature stage | Expected annotation |
+| --- | --- |
+| Experimental | `@experimental(feature_id=ExperimentalFeature.X)` |
+| Release candidate | `@release_candidate(feature_id=ReleaseCandidateFeature.X)` |
+| Released | No feature-stage decorator |
+| Deprecated | `@deprecated("...")` |
+
+## Feature enums
+
+The feature enums are the inventory of currently staged features:
+
+- `ExperimentalFeature`
+- `ReleaseCandidateFeature`
+
+Guidance:
+
+- Use one enum member per conceptual feature, not per class
+- Ideally, an ADR already defines the overall feature boundary and therefore the feature ID that staged APIs for that feature should reuse
+- Keep feature IDs all caps
+- Reuse the same member across related APIs for the same feature
+- Remove enum members when the feature no longer belongs to that stage
+- Treat these enums as **current-stage inventories**, not as a stable consumer introspection API
+
+Minimal consumer guidance:
+
+- Treat `__feature_stage__` and `__feature_id__` as optional staged metadata, not as stable contracts
+- Use `getattr(obj, "__feature_stage__", None)` and `getattr(obj, "__feature_id__", None)` rather than direct attribute access
+- Treat missing metadata as "no explicit feature-stage annotation"
+- For warning filters while a feature is staged, match the literal feature ID string
+- Do **not** rely on `ExperimentalFeature.X`, `ReleaseCandidateFeature.X`, or the continued presence of `__feature_id__` after a feature moves stages or is released
+
+For consumers, the enums are also re-exported from `agent_framework`.
+
+For internal implementation code inside `agent_framework`, continue to import the enums and decorators from `._feature_stage`.
+
+## Package stage vs feature stage
+
+Use the following rules:
+
+### Package is experimental / beta
+
+- All public APIs are experimental by default
+- Do **not** add feature-stage decorators just to restate that
+- Only introduce feature-level annotations later if the package advances first
+
+### Package is release candidate
+
+- All public APIs are RC by default
+- Do **not** decorate everything
+- Add `@experimental(...)` only for features that are intentionally still behind the package
+
+### Package is released / GA
+
+- All public APIs are released by default
+- Add `@experimental(...)` or `@release_candidate(...)` only for features still being held back
+
+## Moving a feature from one stage to the next
+
+### Experimental -> Release candidate
+
+1. Move the feature ID from `ExperimentalFeature` to `ReleaseCandidateFeature`
+2. Replace `@experimental(...)` with `@release_candidate(...)`
+3. Update any tests or docs that mention the old stage
+
+### Experimental -> Released
+
+1. Remove `@experimental(...)`
+2. Remove the feature from `ExperimentalFeature`
+3. Do not add a replacement feature-stage decorator
+
+### Release candidate -> Released
+
+1. Remove `@release_candidate(...)`
+2. Remove the feature from `ReleaseCandidateFeature`
+3. Leave the API undecorated
+
+### Any stage -> Deprecated
+
+1. Remove any feature-stage decorator
+2. Remove the feature from the stage enum
+3. Add `@deprecated("...")`
+4. Update docs/tests to reflect the replacement path
+
+## Promotion guidance
+
+Features do **not** have to pass through every stage.
+
+- It is usually a good idea to move features in order when that reflects reality
+- But it is completely acceptable to go **experimental -> released**
+- Do **not** force a feature through release candidate if there is no real RC period
+
+Likewise, when a package advances, do not automatically move every feature with it.
+
+- Promote features based on actual readiness
+- Keep lagging features explicitly marked only when they are behind the package default
+
+## Practical rules of thumb
+
+- **Package default first, feature exceptions second**
+- **Do not decorate everything in preview packages**
+- **Do not double-annotate members of an already-staged class**
+- **Use enums only for currently staged features**
+- **Do not treat stage enums as a compatibility contract**
+- **Treat `__feature_stage__` and `__feature_id__` as optional metadata; use `getattr`**
+- **Remove stage annotations once a feature is released or deprecated**
@@ -134,7 +134,7 @@ Recommended dependency workflow during connector implementation:
 pip install agent-framework-core          # Core only
 pip install agent-framework-core[all]     # Core + all connectors
 pip install agent-framework               # Same as core[all]
-pip install agent-framework-azure-ai      # Specific connector (pulls in core)
+pip install agent-framework-foundry       # Specific connector (pulls in core)
 ```

 ## Maintaining Documentation
@@ -143,3 +143,11 @@ When changing a package, check if its `AGENTS.md` needs updates:
 - Adding/removing/renaming public classes or functions
 - Changing the package's purpose or architecture
 - Modifying import paths or usage patterns
+
+When a package adds, removes, or renames environment variables, update the related documentation in the same
+change:
+- The package's `README.md` for package-level configuration/env var guidance
+- `samples/README.md` if the package is included in `packages/core/pyproject.toml` `[all]` and the env var is
+  part of the consolidated package env-var inventory
+- Any affected sample/package-local `.env.example`, `.env.template`, or sample README files when sample setup
+  changes alongside the package
@@ -11,6 +11,7 @@ Instructions for AI coding agents working in the Python codebase.
 - `python-development` — coding standards, type annotations, docstrings, logging, performance
 - `python-testing` — test structure, fixtures, async mode, running tests
 - `python-code-quality` — linting, formatting, type checking, prek hooks, CI workflow
+- `python-feature-lifecycle` — package vs feature lifecycle stages, decorators, enums, and promotion guidance
 - `python-package-management` — monorepo structure, lazy loading, versioning, new packages
 - `python-samples` — sample file structure, PEP 723, documentation guidelines

@@ -192,7 +192,7 @@ The package follows a flat import structure:
 - **Connectors**: Import from `agent_framework.<vendor/platform>`
  ```python
  from agent_framework.openai import OpenAIChatClient
-  from agent_framework.azure import AzureOpenAIChatClient
+  from agent_framework.foundry import FoundryChatClient
  ```

 ## Exception Hierarchy
@@ -429,6 +429,10 @@ Each file should have a single first line containing: # Copyright (c) Microsoft.
 We follow the [Google Docstring](https://github.com/google/styleguide/blob/gh-pages/pyguide.md#383-functions-and-methods) style guide for functions and methods.
 They are currently not checked for private functions (functions starting with '_').

+When a change adds, removes, or renames a sample-facing environment variable in repo-level samples or
+package-local sample docs for a package included by `agent-framework-core[all]`, update the consolidated
+inventory in `samples/README.md` in the same change.
+
 They should contain:

 - Single line explaining what the function does, ending with a period.
@@ -58,7 +58,7 @@ You can then run the following commands manually:
 # Install Python 3.10, 3.11, 3.12, and 3.13
 uv python install 3.10 3.11 3.12 3.13
 # Create a virtual environment with Python 3.10 (you can change this to 3.11, 3.12 or 3.13)
-$PYTHON_VERSION = "3.10"
+PYTHON_VERSION="3.10"
 uv venv --python $PYTHON_VERSION
 # Install AF and all dependencies
 uv sync --dev
@@ -180,7 +180,7 @@ This will show you which files are not covered by the tests, including the speci

 ## Catching up with the latest changes

-There are many people committing to Semantic Kernel, so it is important to keep your local repository up to date. To do this, you can run the following commands:
+There are many people committing to Agent Framework, so it is important to keep your local repository up to date. To do this, you can run the following commands:

 ```bash
    git fetch upstream main
@@ -51,7 +51,7 @@ OPENAI_MODEL=...
 ...
 AZURE_OPENAI_API_KEY=...
 AZURE_OPENAI_ENDPOINT=...
-AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=...
+AZURE_OPENAI_DEPLOYMENT_NAME=...
 ...
 FOUNDRY_PROJECT_ENDPOINT=...
 FOUNDRY_MODEL=...
@@ -15,16 +15,16 @@ pip install agent-framework-ag-ui
 ```python
 from fastapi import FastAPI
 from agent_framework import Agent
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint

 # Create your agent
 agent = Agent(
    name="my_agent",
    instructions="You are a helpful assistant.",
-    client=AzureOpenAIChatClient(
-        endpoint="https://your-resource.openai.azure.com/",
-        deployment_name="gpt-4o-mini",
+    client=OpenAIChatCompletionClient(
+        azure_endpoint="https://your-resource.openai.azure.com/",
+        model="gpt-4o-mini",
        api_key="your-api-key",
    ),
 )
@@ -16,7 +16,7 @@ All example agents are factory functions that accept any `SupportsChatGetRespons

 ```python
 from fastapi import FastAPI
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.openai import OpenAIChatClient
 from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint
 from agent_framework_ag_ui_examples.agents import simple_agent, weather_agent
@@ -24,11 +24,11 @@ from agent_framework_ag_ui_examples.agents import simple_agent, weather_agent
 app = FastAPI()

 # Option 1: Use Azure OpenAI
-azure_client = AzureOpenAIChatClient(model_id="gpt-4")
+azure_client = OpenAIChatCompletionClient(model="gpt-4")
 add_agent_framework_fastapi_endpoint(app, simple_agent(azure_client), "/chat")

 # Option 2: Use OpenAI
-openai_client = OpenAIChatClient(model_id="gpt-4o")
+openai_client = OpenAIChatClient(model="gpt-4o")
 add_agent_framework_fastapi_endpoint(app, weather_agent(openai_client), "/weather")

 # Run with: uvicorn main:app --reload
@@ -39,14 +39,14 @@ add_agent_framework_fastapi_endpoint(app, weather_agent(openai_client), "/weathe
 ```python
 from fastapi import FastAPI
 from agent_framework import Agent
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint

 # Create your agent
 agent = Agent(
    name="my_agent",
    instructions="You are a helpful assistant.",
-    client=AzureOpenAIChatClient(model_id="gpt-4o"),
+    client=OpenAIChatCompletionClient(model="gpt-4o"),
 )

 # Create FastAPI app and add AG-UI endpoint
@@ -90,7 +90,7 @@ Complete examples for all AG-UI features are available:
 ### Using Example Agents

 ```python
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.openai import OpenAIChatClient
 from agent_framework_ag_ui_examples.agents import (
    simple_agent,
@@ -99,8 +99,8 @@ from agent_framework_ag_ui_examples.agents import (
 )

 # Create a chat client (use any SupportsChatGetResponse implementation)
-azure_client = AzureOpenAIChatClient(model_id="gpt-4")
-openai_client = OpenAIChatClient(model_id="gpt-4o")
+azure_client = OpenAIChatCompletionClient(model="gpt-4")
+openai_client = OpenAIChatClient(model="gpt-4o")

 # Create agent instances by calling the factory functions
 agent1 = simple_agent(azure_client)
@@ -137,7 +137,7 @@ The server exposes endpoints at:

 ```python
 from fastapi import FastAPI
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint
 from agent_framework_ag_ui_examples.agents import (
    simple_agent,
@@ -153,7 +153,7 @@ from agent_framework_ag_ui_examples.agents import (
 app = FastAPI(title="AG-UI Examples")

 # Create a chat client (shared across all agents, or create individual ones)
-client = AzureOpenAIChatClient(model_id="gpt-4")
+client = OpenAIChatCompletionClient(model="gpt-4")

 # Add all example endpoints
 add_agent_framework_fastapi_endpoint(app, simple_agent(client), "/agentic_chat")
@@ -223,8 +223,8 @@ def my_custom_agent(client: SupportsChatGetResponse) -> AgentFrameworkAgent:
    )

 # Use it
-from agent_framework.azure import AzureOpenAIChatClient
-client = AzureOpenAIChatClient()
+from agent_framework.openai import OpenAIChatCompletionClient
+client = OpenAIChatCompletionClient()
 agent = my_custom_agent(client)
 ```

@@ -234,13 +234,13 @@ State is injected as system messages and updated via predictive state updates:

 ```python
 from agent_framework import Agent
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.ag_ui import AgentFrameworkAgent

 # Create your agent
 agent = Agent(
    name="recipe_agent",
-    client=AzureOpenAIChatClient(model_id="gpt-4o"),
+    client=OpenAIChatCompletionClient(model="gpt-4o"),
 )

 state_schema = {
@@ -271,13 +271,13 @@ Predictive state updates automatically stream tool arguments as optimistic state

 ```python
 from agent_framework import Agent
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.ag_ui import AgentFrameworkAgent

 # Create your agent
 agent = Agent(
    name="document_writer",
-    client=AzureOpenAIChatClient(model_id="gpt-4o"),
+    client=OpenAIChatCompletionClient(model="gpt-4o"),
 )

 predict_state_config = {
@@ -6,7 +6,7 @@ from typing import Any, cast

 from agent_framework._clients import SupportsChatGetResponse
 from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from fastapi import FastAPI

 from ...agents.weather_agent import weather_agent
@@ -19,7 +19,7 @@ def register_backend_tool_rendering(app: FastAPI) -> None:
        app: The FastAPI application.
    """
    # Create a chat client and call the factory function
-    client = cast(SupportsChatGetResponse[Any], AzureOpenAIChatClient())
+    client = cast(SupportsChatGetResponse[Any], OpenAIChatCompletionClient())

    add_agent_framework_fastapi_endpoint(
        app,
@@ -12,7 +12,7 @@ import uvicorn
 from agent_framework import ChatOptions
 from agent_framework._clients import SupportsChatGetResponse
 from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware

@@ -80,7 +80,7 @@ client: SupportsChatGetResponse[ChatOptions] = cast(
    SupportsChatGetResponse[ChatOptions],
    AnthropicClient()
    if AnthropicClient is not None and os.getenv("CHAT_CLIENT", "").lower() == "anthropic"
-    else AzureOpenAIChatClient(),
+    else OpenAIChatCompletionClient(),
 )

 # Agentic Chat - basic chat agent
@@ -185,7 +185,7 @@ Create a file named `server.py`:
 import os

 from agent_framework import Agent
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint
 from fastapi import FastAPI

@@ -205,9 +205,9 @@ if not api_key:
 agent = Agent(
    name="AGUIAssistant",
    instructions="You are a helpful assistant.",
-    client=AzureOpenAIChatClient(
-        endpoint=endpoint,
-        deployment_name=deployment_name,
+    client=OpenAIChatCompletionClient(
+        azure_endpoint=endpoint,
+        model=deployment_name,
        api_key=api_key,
    ),
 )
@@ -230,7 +230,7 @@ if __name__ == "__main__":
 - **`Agent`**: The agent that will handle incoming requests
 - **FastAPI Integration**: Uses FastAPI's native async support for streaming responses
 - **Instructions**: The agent is created with default instructions, which can be overridden by client messages
- **Configuration**: `AzureOpenAIChatClient` can read from environment variables (`AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`, `AZURE_OPENAI_API_KEY`) or accept parameters directly
+- **Configuration**: `OpenAIChatCompletionClient` can read from environment variables (`AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT_NAME`, `AZURE_OPENAI_API_KEY`) or accept parameters directly

 **Alternative (simpler)**: Use environment variables only:

@@ -239,7 +239,7 @@ if __name__ == "__main__":
 agent = Agent(
    name="AGUIAssistant",
    instructions="You are a helpful assistant.",
-    client=AzureOpenAIChatClient(),  # Reads from environment automatically
+    client=OpenAIChatCompletionClient(),  # Reads from environment automatically
 )
 ```

@@ -249,7 +249,7 @@ Set the required environment variables:

 ```bash
 export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
-export AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="gpt-4o-mini"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
 # Optional: Set API key if not using DefaultAzureCredential
 # export AZURE_OPENAI_API_KEY="your-api-key"
 ```
@@ -9,7 +9,7 @@ import os

 from agent_framework import Agent, tool
 from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from dotenv import load_dotenv
 from fastapi import Depends, FastAPI, HTTPException, Security
 from fastapi.security import APIKeyHeader
@@ -26,12 +26,12 @@ logger = logging.getLogger(__name__)

 # Read required configuration
 endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
-deployment_name = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME")
+deployment_name = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME")

 if not endpoint:
    raise ValueError("AZURE_OPENAI_ENDPOINT environment variable is required")
 if not deployment_name:
-    raise ValueError("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME environment variable is required")
+    raise ValueError("AZURE_OPENAI_DEPLOYMENT_NAME environment variable is required")


 # ============================================================================
@@ -119,9 +119,9 @@ def get_time_zone(location: str) -> str:
 agent = Agent(
    name="AGUIAssistant",
    instructions="You are a helpful assistant. Use get_weather for weather and get_time_zone for time zones.",
-    client=AzureOpenAIChatClient(
-        endpoint=endpoint,
-        deployment_name=deployment_name,
+    client=OpenAIChatCompletionClient(
+        azure_endpoint=endpoint,
+        model=deployment_name,
    ),
    tools=[get_time_zone],  # ONLY server-side tools
 )
@@ -1,32 +1,30 @@
 # Azure AI Package (agent-framework-azure-ai)

-Integration with Azure AI Foundry for persistent agents and project-based agent management.
+Integration with Azure AI inference embeddings plus shared Azure authentication helpers.

 ## Main Classes

- **`AzureAIAgentClient`** - Chat client for Azure AI Agents (persistent agents with threads)
- **`AzureAIClient`** - Client for Azure AI Foundry project-based agents
- **`AzureAIAgentsProvider`** - Provider for listing/managing Azure AI agents
- **`AzureAIProjectAgentProvider`** - Provider for project-scoped agent management
- **`AzureAISettings`** - Pydantic settings for Azure AI configuration
- **`AzureAIAgentOptions`** / **`AzureAIProjectAgentOptions`** - Options TypedDicts
+- **`AzureAIInferenceEmbeddingClient`** - Full-featured Azure AI inference embeddings client
+- **`RawAzureAIInferenceEmbeddingClient`** - Raw embeddings client without middleware layers
+- **`AzureAIInferenceEmbeddingOptions`** / **`AzureAIInferenceEmbeddingSettings`** - Embedding options and settings
+- **`AzureAISettings`** - Shared Azure AI project settings TypedDict
+- **`AzureCredentialTypes`** / **`AzureTokenProvider`** - Shared Azure authentication helpers

 ## Usage

 ```python
-from agent_framework.azure import AzureAIAgentClient
+from agent_framework_azure_ai import AzureAIInferenceEmbeddingClient

-client = AzureAIAgentClient(
-    endpoint="https://your-project.services.ai.azure.com",
-    agent_id="your-agent-id",
+client = AzureAIInferenceEmbeddingClient(
+    endpoint="https://<resource>.inference.ai.azure.com",
+    api_key="...",
+    model_id="text-embedding-3-large",
 )
-response = await client.get_response("Hello")
+result = await client.get_embeddings(["Hello"])
 ```

 ## Import Path

 ```python
-from agent_framework.azure import AzureAIAgentClient, AzureAIClient
-# or directly:
-from agent_framework_azure_ai import AzureAIAgentClient
+from agent_framework_azure_ai import AzureAIInferenceEmbeddingClient
 ```
@@ -2,21 +2,6 @@

 import importlib.metadata

-from ._agent_provider import AzureAIAgentsProvider  # pyright: ignore[reportDeprecated]
-from ._chat_client import AzureAIAgentClient, AzureAIAgentOptions  # pyright: ignore[reportDeprecated]
-from ._client import AzureAIClient, AzureAIProjectAgentOptions, RawAzureAIClient  # pyright: ignore[reportDeprecated]
-from ._deprecated_azure_openai import (
-    AzureOpenAIAssistantsClient,  # pyright: ignore[reportDeprecated]
-    AzureOpenAIAssistantsOptions,
-    AzureOpenAIChatClient,  # pyright: ignore[reportDeprecated]
-    AzureOpenAIChatOptions,
-    AzureOpenAIConfigMixin,
-    AzureOpenAIEmbeddingClient,  # pyright: ignore[reportDeprecated]
-    AzureOpenAIResponsesClient,  # pyright: ignore[reportDeprecated]
-    AzureOpenAIResponsesOptions,
-    AzureOpenAISettings,
-    AzureUserSecurityContext,
-)
 from ._embedding_client import (
    AzureAIInferenceEmbeddingClient,
    AzureAIInferenceEmbeddingOptions,
@@ -24,7 +9,6 @@ from ._embedding_client import (
    RawAzureAIInferenceEmbeddingClient,
 )
 from ._entra_id_authentication import AzureCredentialTypes, AzureTokenProvider
-from ._project_provider import AzureAIProjectAgentProvider  # pyright: ignore[reportDeprecated]
 from ._shared import AzureAISettings

 try:
@@ -33,29 +17,12 @@ except importlib.metadata.PackageNotFoundError:
    __version__ = "0.0.0"

 __all__ = [
-    "AzureAIAgentClient",
-    "AzureAIAgentOptions",
-    "AzureAIAgentsProvider",
-    "AzureAIClient",
    "AzureAIInferenceEmbeddingClient",
    "AzureAIInferenceEmbeddingOptions",
    "AzureAIInferenceEmbeddingSettings",
-    "AzureAIProjectAgentOptions",
-    "AzureAIProjectAgentProvider",
    "AzureAISettings",
    "AzureCredentialTypes",
-    "AzureOpenAIAssistantsClient",
-    "AzureOpenAIAssistantsOptions",
-    "AzureOpenAIChatClient",
-    "AzureOpenAIChatOptions",
-    "AzureOpenAIConfigMixin",
-    "AzureOpenAIEmbeddingClient",
-    "AzureOpenAIResponsesClient",
-    "AzureOpenAIResponsesOptions",
-    "AzureOpenAISettings",
    "AzureTokenProvider",
-    "AzureUserSecurityContext",
-    "RawAzureAIClient",
    "RawAzureAIInferenceEmbeddingClient",
    "__version__",
 ]
@@ -1,558 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from __future__ import annotations
-
-import sys
-import warnings
-from collections.abc import Callable, Sequence
-from typing import Any, Generic, cast
-
-from agent_framework import (
-    AGENT_FRAMEWORK_USER_AGENT,
-    Agent,
-    BaseContextProvider,
-    FunctionTool,
-    MiddlewareTypes,
-    normalize_tools,
-)
-from agent_framework._mcp import MCPTool
-from agent_framework._settings import load_settings
-from agent_framework._tools import ToolTypes
-from azure.ai.agents.aio import AgentsClient
-from azure.ai.agents.models import Agent as AzureAgent
-from azure.ai.agents.models import ResponseFormatJsonSchema, ResponseFormatJsonSchemaType
-from pydantic import BaseModel
-
-from ._chat_client import AzureAIAgentClient, AzureAIAgentOptions  # pyright: ignore[reportDeprecated]
-from ._entra_id_authentication import AzureCredentialTypes
-from ._shared import AzureAISettings, to_azure_ai_agent_tools
-
-if sys.version_info >= (3, 13):
-    from typing import Self, TypeVar  # type: ignore # pragma: no cover
-else:
-    from typing_extensions import Self, TypeVar  # type: ignore # pragma: no cover
-if sys.version_info >= (3, 13):
-    from warnings import deprecated  # type: ignore # pragma: no cover
-else:
-    from typing_extensions import deprecated  # type: ignore # pragma: no cover
-if sys.version_info >= (3, 11):
-    from typing import TypedDict  # type: ignore # pragma: no cover
-else:
-    from typing_extensions import TypedDict  # type: ignore # pragma: no cover
-
-
-# Type variable for options - allows typed Agent[TOptions] returns
-# Default matches AzureAIAgentClient's default options type
-OptionsCoT = TypeVar(
-    "OptionsCoT",
-    bound=TypedDict,  # type: ignore[valid-type]
-    default="AzureAIAgentOptions",
-    covariant=True,
-)
-
-
-@deprecated(
-    "AzureAIAgentClient and the AzureAIAgentsProvider are deprecated. "
-    "They target the V1 Agents Service API and have no direct replacement; "
-    "for new Foundry projects, use FoundryAgent."
-)
-class AzureAIAgentsProvider(Generic[OptionsCoT]):
-    """Provider for Azure AI Agent Service V1 (Persistent Agents API).
-
-    .. deprecated::
-        AzureAIAgentsProvider is deprecated and will be removed in a future release.
-        Use :class:`AzureAIProjectAgentProvider` instead for the V2 (Projects/Responses) API.
-
-    This provider enables creating, retrieving, and wrapping Azure AI agents as Agent
-    instances. It manages the underlying AgentsClient lifecycle and provides a high-level
-    interface for agent operations.
-
-    The provider can be initialized with either:
-    - An existing AgentsClient instance
-    - Azure credentials and endpoint for automatic client creation
-
-    Examples:
-        Using credentials (auto-creates client):
-
-        .. code-block:: python
-
-            from agent_framework.azure import AzureAIAgentsProvider
-            from azure.identity.aio import AzureCliCredential
-
-            async with (
-                AzureCliCredential() as credential,
-                AzureAIAgentsProvider(credential=credential) as provider,
-            ):
-                agent = await provider.create_agent(
-                    name="MyAgent",
-                    instructions="You are a helpful assistant.",
-                )
-                result = await agent.run("Hello!")
-
-        Using existing AgentsClient:
-
-        .. code-block:: python
-
-            from agent_framework.azure import AzureAIAgentsProvider
-            from azure.ai.agents.aio import AgentsClient
-
-            async with AgentsClient(endpoint=endpoint, credential=credential) as client:
-                provider = AzureAIAgentsProvider(agents_client=client)
-                agent = await provider.create_agent(name="MyAgent", instructions="...")
-    """
-
-    def __init__(
-        self,
-        agents_client: AgentsClient | None = None,
-        *,
-        project_endpoint: str | None = None,
-        credential: AzureCredentialTypes | None = None,
-        env_file_path: str | None = None,
-        env_file_encoding: str | None = None,
-    ) -> None:
-        """Initialize the Azure AI Agents Provider.
-
-        Args:
-            agents_client: An existing AgentsClient to use. If provided, the provider
-                will not manage its lifecycle.
-
-        Keyword Args:
-            project_endpoint: The Azure AI Project endpoint URL.
-                Can also be set via AZURE_AI_PROJECT_ENDPOINT environment variable.
-            credential: Azure credential for authentication. Accepts a TokenCredential,
-                AsyncTokenCredential, or a callable token provider.
-                Required if agents_client is not provided.
-            env_file_path: Path to .env file for loading settings.
-            env_file_encoding: Encoding of the .env file.
-
-        Raises:
-            ValueError: If required parameters are missing or invalid.
-        """
-        warnings.warn(
-            "AzureAIAgentsProvider is deprecated and will be removed in a future release; "
-            "use AzureAIProjectAgentProvider instead for the V2 (Projects/Responses) API.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        self._settings = load_settings(
-            AzureAISettings,
-            env_prefix="AZURE_AI_",
-            project_endpoint=project_endpoint,
-            env_file_path=env_file_path,
-            env_file_encoding=env_file_encoding,
-        )
-
-        self._should_close_client = False
-
-        if agents_client is not None:
-            self._agents_client = agents_client
-        else:
-            resolved_endpoint = self._settings.get("project_endpoint")
-            if not resolved_endpoint:
-                raise ValueError(
-                    "Azure AI project endpoint is required. Provide 'project_endpoint' parameter "
-                    "or set 'AZURE_AI_PROJECT_ENDPOINT' environment variable."
-                )
-            if not credential:
-                raise ValueError("Azure credential is required when agents_client is not provided.")
-            self._agents_client = AgentsClient(
-                endpoint=resolved_endpoint,
-                credential=credential,  # type: ignore[arg-type]
-                user_agent=AGENT_FRAMEWORK_USER_AGENT,
-            )
-            self._should_close_client = True
-
-    async def __aenter__(self) -> Self:
-        """Async context manager entry."""
-        return self
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: Any,
-    ) -> None:
-        """Async context manager exit."""
-        await self.close()
-
-    async def close(self) -> None:
-        """Close the provider and release resources.
-
-        Only closes the AgentsClient if it was created by this provider.
-        """
-        if self._should_close_client:
-            await self._agents_client.close()
-
-    async def create_agent(
-        self,
-        name: str,
-        *,
-        model: str | None = None,
-        instructions: str | None = None,
-        description: str | None = None,
-        tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
-        default_options: OptionsCoT | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        context_providers: Sequence[BaseContextProvider] | None = None,
-    ) -> Agent[OptionsCoT]:
-        """Create a new agent on the Azure AI service and return a Agent.
-
-        .. deprecated::
-            This method is deprecated and will be removed in a future release.
-            Use :meth:`AzureAIProjectAgentProvider.create_agent` instead.
-
-        This method creates a persistent agent on the Azure AI service with the specified
-        configuration and returns a local Agent instance for interaction.
-
-        Args:
-            name: The name for the agent.
-
-        Keyword Args:
-            model: The model deployment name to use. Falls back to
-                AZURE_AI_MODEL_DEPLOYMENT_NAME environment variable if not provided.
-            instructions: Instructions for the agent's behavior.
-            description: A description of the agent's purpose.
-            tools: Tools to make available to the agent.
-            default_options: A TypedDict containing default chat options for the agent.
-                These options are applied to every run unless overridden.
-            middleware: List of middleware to intercept agent and function invocations.
-            context_providers: Context providers to include during agent invocation.
-
-        Returns:
-            Agent: A Agent instance configured with the created agent.
-
-        Raises:
-            ValueError: If model deployment name is not available.
-
-        Examples:
-            .. code-block:: python
-
-                agent = await provider.create_agent(
-                    name="WeatherAgent",
-                    instructions="You are a helpful weather assistant.",
-                    tools=get_weather,
-                )
-        """
-        warnings.warn(
-            "AzureAIAgentsProvider.create_agent() is deprecated and will be removed in a future release; "
-            "use AzureAIProjectAgentProvider.create_agent() instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        resolved_model = model or self._settings.get("model_deployment_name")
-        if not resolved_model:
-            raise ValueError(
-                "Model deployment name is required. Provide 'model' parameter "
-                "or set 'AZURE_AI_MODEL_DEPLOYMENT_NAME' environment variable."
-            )
-
-        # Extract response_format from default_options if present
-        opts = dict(default_options) if default_options else {}
-        response_format = opts.get("response_format")
-
-        args: dict[str, Any] = {
-            "model": resolved_model,
-            "name": name,
-        }
-
-        if description:
-            args["description"] = description
-        if instructions:
-            args["instructions"] = instructions
-
-        # Handle response format
-        if response_format and isinstance(response_format, type) and issubclass(response_format, BaseModel):
-            args["response_format"] = self._create_response_format_config(response_format)
-
-        # Normalize and convert tools
-        # Local MCP tools (MCPTool) are handled by Agent at runtime, not stored on the Azure agent
-        normalized_tools = normalize_tools(tools)
-        if normalized_tools:
-            # Collect all non-MCP tools for Azure AI agent creation.
-            # to_azure_ai_agent_tools handles FunctionTool, SDK Tool types (FileSearchTool, etc.), and dicts.
-            non_mcp_tools: list[Any] = [t for t in normalized_tools if not isinstance(t, MCPTool)]
-            if non_mcp_tools:
-                # Pass run_options to capture tool_resources (e.g., for file search vector stores)
-                run_options: dict[str, Any] = {}
-                args["tools"] = to_azure_ai_agent_tools(non_mcp_tools, run_options)
-                if "tool_resources" in run_options:
-                    args["tool_resources"] = run_options["tool_resources"]
-
-        # Create the agent on the service
-        created_agent = await self._agents_client.create_agent(**args)
-
-        # Create Agent wrapper
-        return self._to_chat_agent_from_agent(
-            created_agent,
-            normalized_tools,
-            default_options=default_options,
-            middleware=middleware,
-            context_providers=context_providers,
-        )
-
-    async def get_agent(
-        self,
-        id: str,
-        *,
-        tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
-        default_options: OptionsCoT | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        context_providers: Sequence[BaseContextProvider] | None = None,
-    ) -> Agent[OptionsCoT]:
-        """Retrieve an existing agent from the service and return a Agent.
-
-        .. deprecated::
-            This method is deprecated and will be removed in a future release.
-            Use :meth:`AzureAIProjectAgentProvider.get_agent` instead.
-
-        This method fetches an agent by ID from the Azure AI service
-        and returns a local Agent instance for interaction.
-
-        Args:
-            id: The ID of the agent to retrieve from the service.
-
-        Keyword Args:
-            tools: Tools to make available to the agent. Required if the agent
-                has function tools that need implementations.
-            default_options: A TypedDict containing default chat options for the agent.
-                These options are applied to every run unless overridden.
-            middleware: List of middleware to intercept agent and function invocations.
-            context_providers: Context providers to include during agent invocation.
-
-        Returns:
-            Agent: A Agent instance configured with the retrieved agent.
-
-        Raises:
-            ValueError: If required function tools are not provided.
-
-        Examples:
-            .. code-block:: python
-
-                agent = await provider.get_agent("agent-123")
-
-                # With function tools
-                agent = await provider.get_agent("agent-123", tools=my_function)
-        """
-        warnings.warn(
-            "AzureAIAgentsProvider.get_agent() is deprecated and will be removed in a future release; "
-            "use AzureAIProjectAgentProvider.get_agent() instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        agent = await self._agents_client.get_agent(id)
-
-        # Validate function tools
-        normalized_tools = normalize_tools(tools)
-        self._validate_function_tools(agent.tools, normalized_tools)
-
-        return self._to_chat_agent_from_agent(
-            agent,
-            normalized_tools,
-            default_options=default_options,
-            middleware=middleware,
-            context_providers=context_providers,
-        )
-
-    def as_agent(
-        self,
-        agent: AzureAgent,
-        tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
-        default_options: OptionsCoT | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        context_providers: Sequence[BaseContextProvider] | None = None,
-    ) -> Agent[OptionsCoT]:
-        """Wrap an existing Agent SDK object as a Agent without making HTTP calls.
-
-        .. deprecated::
-            This method is deprecated and will be removed in a future release.
-            Use :meth:`AzureAIProjectAgentProvider.as_agent` instead.
-
-        Use this method when you already have an Agent object from a previous
-        SDK operation and want to use it with the Agent Framework.
-
-        Args:
-            agent: The Agent object to wrap.
-            tools: Tools to make available to the agent. Required if the agent
-                has function tools that need implementations.
-            default_options: A TypedDict containing default chat options for the agent.
-                These options are applied to every run unless overridden.
-            middleware: List of middleware to intercept agent and function invocations.
-            context_providers: Context providers to include during agent invocation.
-
-        Returns:
-            Agent: A Agent instance configured with the agent.
-
-        Raises:
-            ValueError: If required function tools are not provided.
-
-        Examples:
-            .. code-block:: python
-
-                # Create agent directly with SDK
-                sdk_agent = await agents_client.create_agent(
-                    model="gpt-4",
-                    name="MyAgent",
-                    instructions="...",
-                )
-
-                # Wrap as Agent
-                chat_agent = provider.as_agent(sdk_agent)
-        """
-        warnings.warn(
-            "AzureAIAgentsProvider.as_agent() is deprecated and will be removed in a future release; "
-            "use AzureAIProjectAgentProvider.as_agent() instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        # Validate function tools
-        normalized_tools = normalize_tools(tools)
-        self._validate_function_tools(agent.tools, normalized_tools)
-
-        return self._to_chat_agent_from_agent(
-            agent,
-            normalized_tools,
-            default_options=default_options,
-            middleware=middleware,
-            context_providers=context_providers,
-        )
-
-    def _to_chat_agent_from_agent(
-        self,
-        agent: AzureAgent,
-        provided_tools: Sequence[ToolTypes] | None = None,
-        default_options: OptionsCoT | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        context_providers: Sequence[BaseContextProvider] | None = None,
-    ) -> Agent[OptionsCoT]:
-        """Create a Agent from an Agent SDK object.
-
-        Args:
-            agent: The Agent SDK object.
-            provided_tools: User-provided tools (including function implementations).
-            default_options: A TypedDict containing default chat options for the agent.
-                These options are applied to every run unless overridden.
-            middleware: List of middleware to intercept agent and function invocations.
-            context_providers: Context providers to include during agent invocation.
-        """
-        # Create the underlying client
-        client = AzureAIAgentClient(  # pyright: ignore[reportDeprecated]
-            agents_client=self._agents_client,
-            agent_id=agent.id,
-            agent_name=agent.name,
-            agent_description=agent.description,
-            should_cleanup_agent=False,  # Provider manages agent lifecycle
-        )
-
-        # Merge tools: convert agent's hosted tools + user-provided function tools
-        merged_tools = self._merge_tools(agent.tools, provided_tools)
-        merged_default_options: dict[str, Any] = dict(default_options) if default_options is not None else {}
-        merged_default_options.setdefault("model_id", agent.model)
-
-        return Agent(  # type: ignore[return-value]
-            client=client,
-            id=agent.id,
-            name=agent.name,
-            description=agent.description,
-            instructions=agent.instructions,
-            tools=merged_tools,
-            default_options=cast(Any, merged_default_options),
-            middleware=middleware,
-            context_providers=context_providers,
-        )
-
-    def _merge_tools(
-        self,
-        agent_tools: Sequence[Any] | None,
-        provided_tools: Sequence[ToolTypes] | None,
-    ) -> list[ToolTypes]:
-        """Merge hosted tools from agent with user-provided function tools.
-
-        Args:
-            agent_tools: Tools from the agent definition (Azure AI format).
-            provided_tools: User-provided tools (Agent Framework format).
-
-        Returns:
-            Combined list of tools for the Agent.
-        """
-        merged: list[ToolTypes] = []
-
-        # Hosted tools (file_search, code_interpreter, bing_grounding, openapi, etc.)
-        # are already defined on the server agent and will be read back by the client
-        # at run time via agent_definition.tools. We skip them here to avoid sending
-        # them again at request time (which causes API errors like unknown vector_store_ids).
-
-        # Add user-provided function tools and MCP tools
-        if provided_tools:
-            for provided_tool in provided_tools:
-                # FunctionTool - has implementation for function calling
-                # MCPTool - Agent handles MCP connection and tool discovery at runtime
-                if isinstance(provided_tool, (FunctionTool, MCPTool)):
-                    merged.append(provided_tool)  # type: ignore[reportUnknownArgumentType]
-
-        return merged
-
-    def _validate_function_tools(
-        self,
-        agent_tools: Sequence[Any] | None,
-        provided_tools: Sequence[ToolTypes] | None,
-    ) -> None:
-        """Validate that required function tools are provided.
-
-        Raises:
-            ValueError: If agent has function tools but user
-                didn't provide implementations.
-        """
-        if not agent_tools:
-            return
-
-        # Get function tool names from agent definition
-        function_tool_names: set[str] = set()
-        for tool in agent_tools:
-            if isinstance(tool, dict):
-                tool_dict = cast(dict[str, Any], tool)
-                if tool_dict.get("type") == "function":
-                    func_def = cast(dict[str, Any], tool_dict.get("function", {}))
-                    name = func_def.get("name")
-                    if isinstance(name, str):
-                        function_tool_names.add(name)
-            elif hasattr(tool, "type") and tool.type == "function":
-                func_attr = getattr(tool, "function", None)
-                if func_attr and hasattr(func_attr, "name"):
-                    function_tool_names.add(str(func_attr.name))
-
-        if not function_tool_names:
-            return
-
-        # Get provided function names
-        provided_names: set[str] = set()
-        if provided_tools:
-            for tool in provided_tools:
-                if isinstance(tool, FunctionTool):
-                    provided_names.add(tool.name)
-
-        # Check for missing implementations
-        missing = function_tool_names - provided_names
-        if missing:
-            raise ValueError(
-                f"Agent has function tools that require implementations: {missing}. "
-                "Provide these functions via the 'tools' parameter."
-            )
-
-    def _create_response_format_config(
-        self,
-        response_format: type[BaseModel],
-    ) -> ResponseFormatJsonSchemaType:
-        """Create response format configuration for Azure AI.
-
-        Args:
-            response_format: Pydantic model for structured output.
-
-        Returns:
-            Azure AI response format configuration.
-        """
-        return ResponseFormatJsonSchemaType(
-            json_schema=ResponseFormatJsonSchema(
-                name=response_format.__name__,
-                schema=response_format.model_json_schema(),
-            )
-        )
@@ -1,918 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-"""Deprecated Azure OpenAI client classes.
-
-All classes in this module are deprecated and will be removed in a future release.
-Migrate to the ``agent_framework_openai`` package equivalents with an ``AsyncAzureOpenAI`` client,
-or use ``FoundryChatClient`` for Azure AI Foundry projects.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-import sys
-from collections.abc import Mapping, Sequence
-from contextlib import contextmanager
-from copy import copy
-from typing import TYPE_CHECKING, Any, ClassVar, Final, Generic, cast
-from urllib.parse import urljoin, urlparse
-
-from agent_framework._middleware import ChatMiddlewareLayer
-from agent_framework._settings import SecretString, load_settings
-from agent_framework._telemetry import AGENT_FRAMEWORK_USER_AGENT, APP_INFO, prepend_agent_framework_to_user_agent
-from agent_framework._tools import FunctionInvocationConfiguration, FunctionInvocationLayer
-from agent_framework._types import Annotation, Content
-from agent_framework.observability import ChatTelemetryLayer, EmbeddingTelemetryLayer
-from agent_framework_openai._assistants_client import (
-    OpenAIAssistantsClient,  # type: ignore[reportDeprecated]
-    OpenAIAssistantsOptions,
-)
-from agent_framework_openai._chat_client import OpenAIChatOptions, RawOpenAIChatClient
-from agent_framework_openai._chat_completion_client import OpenAIChatCompletionOptions, RawOpenAIChatCompletionClient
-from agent_framework_openai._embedding_client import OpenAIEmbeddingOptions, RawOpenAIEmbeddingClient
-from agent_framework_openai._shared import OpenAIBase
-from azure.ai.projects.aio import AIProjectClient
-from openai import AsyncOpenAI
-from openai.lib.azure import AsyncAzureOpenAI
-from pydantic import BaseModel
-
-from ._entra_id_authentication import AzureCredentialTypes, AzureTokenProvider, resolve_credential_to_token_provider
-
-if sys.version_info >= (3, 13):
-    from typing import TypeVar  # type: ignore # pragma: no cover
-    from warnings import deprecated  # type: ignore # pragma: no cover
-else:
-    from typing_extensions import TypeVar, deprecated  # type: ignore # pragma: no cover
-if sys.version_info >= (3, 12):
-    from typing import override  # type: ignore # pragma: no cover
-else:
-    from typing_extensions import override  # type: ignore # pragma: no cover
-if sys.version_info >= (3, 11):
-    from typing import TypedDict  # type: ignore # pragma: no cover
-else:
-    from typing_extensions import TypedDict  # type: ignore # pragma: no cover
-
-if TYPE_CHECKING:
-    from agent_framework._middleware import MiddlewareTypes
-    from openai.types.chat.chat_completion import Choice
-    from openai.types.chat.chat_completion_chunk import Choice as ChunkChoice
-
-logger: logging.Logger = logging.getLogger(__name__)
-
-
-# region Constants and Settings
-
-DEFAULT_AZURE_API_VERSION: Final[str] = "2024-10-21"
-DEFAULT_AZURE_TOKEN_ENDPOINT: Final[str] = "https://cognitiveservices.azure.com/.default"  # noqa: S105
-
-
-class AzureOpenAISettings(TypedDict, total=False):
-    """AzureOpenAI model settings.
-
-    Settings are resolved in this order: explicit keyword arguments, values from an
-    explicitly provided .env file, then environment variables with the prefix
-    'AZURE_OPENAI_'. If settings are missing after resolution, validation will fail.
-
-    Keyword Args:
-        endpoint: The endpoint of the Azure deployment.
-        chat_deployment_name: The name of the Azure Chat deployment.
-        responses_deployment_name: The name of the Azure Responses deployment.
-        embedding_deployment_name: The name of the Azure Embedding deployment.
-        api_key: The API key for the Azure deployment.
-        api_version: The API version to use.
-        base_url: The url of the Azure deployment.
-        token_endpoint: The token endpoint to use to retrieve the authentication token.
-    """
-
-    chat_deployment_name: str | None
-    responses_deployment_name: str | None
-    embedding_deployment_name: str | None
-    endpoint: str | None
-    base_url: str | None
-    api_key: SecretString | None
-    api_version: str | None
-    token_endpoint: str | None
-
-
-def _apply_azure_defaults(
-    settings: AzureOpenAISettings,
-    default_api_version: str = DEFAULT_AZURE_API_VERSION,
-    default_token_endpoint: str = DEFAULT_AZURE_TOKEN_ENDPOINT,
-) -> None:
-    """Apply default values for api_version and token_endpoint after loading settings.
-
-    Args:
-        settings: The loaded Azure OpenAI settings dict.
-        default_api_version: The default API version to use if not set.
-        default_token_endpoint: The default token endpoint to use if not set.
-    """
-    if not settings.get("api_version"):
-        settings["api_version"] = default_api_version
-    if not settings.get("token_endpoint"):
-        settings["token_endpoint"] = default_token_endpoint
-
-
-@contextmanager
-def _prefer_single_azure_endpoint_env(*, endpoint: str | None, base_url: str | None) -> Any:
-    """Preserve the legacy call shape without mutating process-wide environment state."""
-    yield
-
-
-# endregion
-
-
-# region AzureOpenAIConfigMixin
-
-
-class AzureOpenAIConfigMixin(OpenAIBase):
-    """Internal class for configuring a connection to an Azure OpenAI service."""
-
-    OTEL_PROVIDER_NAME: ClassVar[str] = "azure.ai.openai"
-
-    def __init__(
-        self,
-        deployment_name: str,
-        endpoint: str | None = None,
-        base_url: str | None = None,
-        api_version: str = DEFAULT_AZURE_API_VERSION,
-        api_key: str | None = None,
-        token_endpoint: str | None = None,
-        credential: AzureCredentialTypes | AzureTokenProvider | None = None,
-        default_headers: Mapping[str, str] | None = None,
-        client: AsyncOpenAI | None = None,
-        instruction_role: str | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Configure a connection to an Azure OpenAI service.
-
-        Args:
-            deployment_name: Name of the deployment.
-            endpoint: The specific endpoint URL for the deployment.
-            base_url: The base URL for Azure services.
-            api_version: Azure API version.
-            api_key: API key for Azure services.
-            token_endpoint: Azure AD token scope.
-            credential: Azure credential or token provider for authentication.
-            default_headers: Default headers for HTTP requests.
-            client: An existing client to use.
-            instruction_role: The role to use for 'instruction' messages.
-            kwargs: Additional keyword arguments.
-        """
-        merged_headers = dict(copy(default_headers)) if default_headers else {}
-        if APP_INFO:
-            merged_headers.update(APP_INFO)
-            merged_headers = prepend_agent_framework_to_user_agent(merged_headers)
-        if not client:
-            ad_token_provider = None
-            if not api_key and credential:
-                ad_token_provider = resolve_credential_to_token_provider(credential, token_endpoint)
-
-            if not api_key and not ad_token_provider:
-                raise ValueError("Please provide either api_key, credential, or a client.")
-
-            if not endpoint and not base_url:
-                raise ValueError("Please provide an endpoint or a base_url")
-
-            args: dict[str, Any] = {
-                "default_headers": merged_headers,
-            }
-            if api_version:
-                args["api_version"] = api_version
-            if ad_token_provider:
-                args["azure_ad_token_provider"] = ad_token_provider
-            if api_key:
-                args["api_key"] = api_key
-            if base_url:
-                args["base_url"] = str(base_url)
-            if endpoint and not base_url:
-                args["azure_endpoint"] = str(endpoint)
-            if deployment_name:
-                args["azure_deployment"] = deployment_name
-            if "websocket_base_url" in kwargs:
-                args["websocket_base_url"] = kwargs.pop("websocket_base_url")
-
-            client = AsyncAzureOpenAI(**args)
-
-        self.endpoint = str(endpoint)
-        self.base_url = str(base_url)
-        self.api_version = api_version
-        self.deployment_name = deployment_name
-        self.instruction_role = instruction_role
-        if default_headers:
-            from agent_framework._telemetry import USER_AGENT_KEY
-
-            def_headers = {k: v for k, v in default_headers.items() if k != USER_AGENT_KEY}
-        else:
-            def_headers = None
-        self.default_headers = def_headers
-
-        super().__init__(model_id=deployment_name, client=client, **kwargs)
-
-
-# endregion
-
-
-# region AzureOpenAIResponsesClient
-
-
-AzureOpenAIResponsesOptionsT = TypeVar(
-    "AzureOpenAIResponsesOptionsT",
-    bound=TypedDict,  # type: ignore[valid-type]
-    default="OpenAIChatOptions",
-    covariant=True,
-)
-
-AzureOpenAIResponsesOptions = OpenAIChatOptions
-
-
-@deprecated(
-    "AzureOpenAIResponsesClient is deprecated. "
-    "Use OpenAIChatClient with an AsyncAzureOpenAI client, or FoundryChatClient for Foundry projects."
-)
-class AzureOpenAIResponsesClient(  # type: ignore[misc]
-    FunctionInvocationLayer[AzureOpenAIResponsesOptionsT],
-    ChatMiddlewareLayer[AzureOpenAIResponsesOptionsT],
-    ChatTelemetryLayer[AzureOpenAIResponsesOptionsT],
-    RawOpenAIChatClient[AzureOpenAIResponsesOptionsT],
-    Generic[AzureOpenAIResponsesOptionsT],
-):
-    """Deprecated Azure Responses client. Use OpenAIChatClient with an AsyncAzureOpenAI client instead."""
-
-    OTEL_PROVIDER_NAME: ClassVar[str] = "azure.ai.openai"
-
-    def __init__(
-        self,
-        *,
-        api_key: str | None = None,
-        deployment_name: str | None = None,
-        endpoint: str | None = None,
-        base_url: str | None = None,
-        api_version: str | None = None,
-        token_endpoint: str | None = None,
-        credential: AzureCredentialTypes | AzureTokenProvider | None = None,
-        default_headers: Mapping[str, str] | None = None,
-        async_client: AsyncOpenAI | None = None,
-        project_client: Any | None = None,
-        project_endpoint: str | None = None,
-        allow_preview: bool | None = None,
-        env_file_path: str | None = None,
-        env_file_encoding: str | None = None,
-        instruction_role: str | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        function_invocation_configuration: FunctionInvocationConfiguration | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize an Azure OpenAI Responses client.
-
-        Keyword Args:
-            api_key: The API key.
-            deployment_name: The deployment name.
-            endpoint: The deployment endpoint.
-            base_url: The deployment base URL.
-            api_version: The deployment API version.
-            token_endpoint: The token endpoint to request an Azure token.
-            credential: Azure credential or token provider for authentication.
-            default_headers: Default headers for HTTP requests.
-            async_client: An existing client to use.
-            project_client: An existing AIProjectClient to use.
-            project_endpoint: The Azure AI Foundry project endpoint URL.
-            allow_preview: Enables preview opt-in on internally-created AIProjectClient.
-            env_file_path: Path to .env file for settings.
-            env_file_encoding: Encoding for .env file.
-            instruction_role: The role to use for 'instruction' messages.
-            middleware: Optional sequence of middleware.
-            function_invocation_configuration: Optional function invocation configuration.
-            kwargs: Additional keyword arguments.
-        """
-        if (model_id := kwargs.pop("model_id", None)) and not deployment_name:
-            deployment_name = str(model_id)
-
-        if async_client is None and (project_client is not None or project_endpoint is not None):
-            async_client = self._create_client_from_project(
-                project_client=project_client,
-                project_endpoint=project_endpoint,
-                credential=credential,
-                allow_preview=allow_preview,
-            )
-
-        azure_openai_settings = load_settings(
-            AzureOpenAISettings,
-            env_prefix="AZURE_OPENAI_",
-            api_key=api_key,
-            base_url=base_url,
-            endpoint=endpoint,
-            responses_deployment_name=deployment_name,
-            api_version=api_version,
-            env_file_path=env_file_path,
-            env_file_encoding=env_file_encoding,
-            token_endpoint=token_endpoint,
-        )
-        _apply_azure_defaults(azure_openai_settings, default_api_version="preview")
-        endpoint_value = azure_openai_settings.get("endpoint")
-        if (
-            not azure_openai_settings.get("base_url")
-            and endpoint_value
-            and (hostname := urlparse(str(endpoint_value)).hostname)
-            and hostname.endswith(".openai.azure.com")
-        ):
-            azure_openai_settings["base_url"] = urljoin(str(endpoint_value), "/openai/v1/")
-
-        responses_deployment_name = azure_openai_settings.get("responses_deployment_name")
-        if not responses_deployment_name:
-            raise ValueError(
-                "Azure OpenAI deployment name is required. Set via 'deployment_name' parameter "
-                "or 'AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME' environment variable."
-            )
-
-        endpoint_value = azure_openai_settings.get("endpoint")
-        client_base_url = azure_openai_settings.get("base_url")
-        if not async_client:
-            # Create the Azure OpenAI client directly
-            merged_headers = dict(copy(default_headers)) if default_headers else {}
-            if APP_INFO:
-                merged_headers.update(APP_INFO)
-                merged_headers = prepend_agent_framework_to_user_agent(merged_headers)
-
-            api_key_secret = azure_openai_settings.get("api_key")
-            ad_token_provider = None
-            if not api_key_secret and credential:
-                ad_token_provider = resolve_credential_to_token_provider(
-                    credential, azure_openai_settings.get("token_endpoint")
-                )
-
-            if not api_key_secret and not ad_token_provider:
-                raise ValueError("Please provide either api_key, credential, or a client.")
-
-            if not endpoint_value and not client_base_url:
-                raise ValueError("Please provide an endpoint or a base_url")
-
-            client_args: dict[str, Any] = {"default_headers": merged_headers}
-            if resolved_api_version := azure_openai_settings.get("api_version"):
-                client_args["api_version"] = resolved_api_version
-            if ad_token_provider:
-                client_args["azure_ad_token_provider"] = ad_token_provider
-            if api_key_secret:
-                client_args["api_key"] = api_key_secret.get_secret_value()
-            if client_base_url:
-                client_args["base_url"] = str(client_base_url)
-            if endpoint_value and not client_base_url:
-                client_args["azure_endpoint"] = str(endpoint_value)
-            if responses_deployment_name:
-                client_args["azure_deployment"] = responses_deployment_name
-            if "websocket_base_url" in kwargs:
-                client_args["websocket_base_url"] = kwargs.pop("websocket_base_url")
-
-            async_client = AsyncAzureOpenAI(**client_args)
-
-        # Store Azure-specific attributes for serialization
-        self.endpoint = str(endpoint_value) if endpoint_value else None
-        self.api_version = azure_openai_settings.get("api_version") or ""
-        self.deployment_name = responses_deployment_name
-
-        with _prefer_single_azure_endpoint_env(endpoint=endpoint_value, base_url=client_base_url):
-            super().__init__(
-                async_client=async_client,
-                model=responses_deployment_name,
-                azure_endpoint=str(endpoint_value) if endpoint_value else None,
-                base_url=str(client_base_url) if client_base_url else None,
-                api_version=azure_openai_settings.get("api_version"),
-                instruction_role=instruction_role,
-                default_headers=default_headers,
-                middleware=middleware,  # type: ignore[arg-type]
-                function_invocation_configuration=function_invocation_configuration,
-                **kwargs,
-            )
-
-    @staticmethod
-    def _create_client_from_project(
-        *,
-        project_client: AIProjectClient | None,
-        project_endpoint: str | None,
-        credential: AzureCredentialTypes | AzureTokenProvider | None,
-        allow_preview: bool | None = None,
-    ) -> AsyncOpenAI:
-        """Create an AsyncOpenAI client from an Azure AI Foundry project."""
-        if project_client is not None:
-            return project_client.get_openai_client()
-
-        if not project_endpoint:
-            raise ValueError("Azure AI project endpoint is required when project_client is not provided.")
-        if not credential:
-            raise ValueError("Azure credential is required when using project_endpoint without a project_client.")
-        project_client_kwargs: dict[str, Any] = {
-            "endpoint": project_endpoint,
-            "credential": credential,  # type: ignore[arg-type]
-            "user_agent": AGENT_FRAMEWORK_USER_AGENT,
-        }
-        if allow_preview is not None:
-            project_client_kwargs["allow_preview"] = allow_preview
-        project_client = AIProjectClient(**project_client_kwargs)
-        return project_client.get_openai_client()
-
-    @override
-    def _check_model_presence(self, options: dict[str, Any]) -> None:
-        if not options.get("model"):
-            if not self.model:
-                raise ValueError("deployment_name must be a non-empty string")
-            options["model"] = self.model
-
-
-# endregion
-
-
-# region AzureOpenAIChatClient
-
-
-ResponseModelT = TypeVar("ResponseModelT", bound=BaseModel | None, default=None)
-
-
-class AzureUserSecurityContext(TypedDict, total=False):
-    """User security context for Azure AI applications.
-
-    These fields help security operations teams investigate and mitigate security
-    incidents by providing context about the application and end user.
-    """
-
-    application_name: str
-    """Name of the application making the request."""
-
-    end_user_id: str
-    """Unique identifier for the end user (recommend hashing username/email)."""
-
-    end_user_tenant_id: str
-    """Microsoft 365 tenant ID the end user belongs to. Required for multi-tenant apps."""
-
-    source_ip: str
-    """The original client's IP address."""
-
-
-class AzureOpenAIChatOptions(OpenAIChatCompletionOptions[ResponseModelT], Generic[ResponseModelT], total=False):
-    """Azure OpenAI-specific chat options dict.
-
-    Extends OpenAIChatCompletionOptions with Azure-specific options including
-    the "On Your Data" feature and enhanced security context.
-    """
-
-    data_sources: list[dict[str, Any]]
-    """Azure "On Your Data" data sources for retrieval-augmented generation."""
-
-    user_security_context: AzureUserSecurityContext
-    """Enhanced security context for Azure Defender integration."""
-
-    n: int
-    """Number of chat completion choices to generate for each input message."""
-
-
-AzureOpenAIChatOptionsT = TypeVar(
-    "AzureOpenAIChatOptionsT",
-    bound=TypedDict,  # type: ignore[valid-type]
-    default="AzureOpenAIChatOptions",
-    covariant=True,
-)
-
-
-@deprecated("AzureOpenAIChatClient is deprecated. Use OpenAIChatCompletionClient with an AsyncAzureOpenAI client.")
-class AzureOpenAIChatClient(  # type: ignore[misc]
-    FunctionInvocationLayer[AzureOpenAIChatOptionsT],
-    ChatMiddlewareLayer[AzureOpenAIChatOptionsT],
-    ChatTelemetryLayer[AzureOpenAIChatOptionsT],
-    RawOpenAIChatCompletionClient[AzureOpenAIChatOptionsT],
-    Generic[AzureOpenAIChatOptionsT],
-):
-    """Deprecated Azure OpenAI Chat client. Use OpenAIChatCompletionClient with AsyncAzureOpenAI instead."""
-
-    OTEL_PROVIDER_NAME: ClassVar[str] = "azure.ai.openai"
-
-    def __init__(
-        self,
-        *,
-        api_key: str | None = None,
-        deployment_name: str | None = None,
-        endpoint: str | None = None,
-        base_url: str | None = None,
-        api_version: str | None = None,
-        token_endpoint: str | None = None,
-        credential: AzureCredentialTypes | AzureTokenProvider | None = None,
-        default_headers: Mapping[str, str] | None = None,
-        async_client: AsyncAzureOpenAI | None = None,
-        additional_properties: dict[str, Any] | None = None,
-        env_file_path: str | None = None,
-        env_file_encoding: str | None = None,
-        instruction_role: str | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        function_invocation_configuration: FunctionInvocationConfiguration | None = None,
-    ) -> None:
-        """Initialize an Azure OpenAI Chat completion client.
-
-        Keyword Args:
-            api_key: The API key.
-            deployment_name: The deployment name.
-            endpoint: The deployment endpoint.
-            base_url: The deployment base URL.
-            api_version: The deployment API version.
-            token_endpoint: The token endpoint to request an Azure token.
-            credential: Azure credential or token provider for authentication.
-            default_headers: Default headers for HTTP requests.
-            async_client: An existing client to use.
-            additional_properties: Additional properties stored on the client instance.
-            env_file_path: Path to .env file for settings.
-            env_file_encoding: Encoding for .env file.
-            instruction_role: The role to use for 'instruction' messages.
-            middleware: Optional sequence of middleware.
-            function_invocation_configuration: Optional function invocation configuration.
-        """
-        azure_openai_settings = load_settings(
-            AzureOpenAISettings,
-            env_prefix="AZURE_OPENAI_",
-            api_key=api_key,
-            base_url=base_url,
-            endpoint=endpoint,
-            chat_deployment_name=deployment_name,
-            api_version=api_version,
-            env_file_path=env_file_path,
-            env_file_encoding=env_file_encoding,
-            token_endpoint=token_endpoint,
-        )
-        _apply_azure_defaults(azure_openai_settings)
-
-        chat_deployment_name = azure_openai_settings.get("chat_deployment_name")
-        if not chat_deployment_name:
-            raise ValueError(
-                "Azure OpenAI deployment name is required. Set via 'deployment_name' parameter "
-                "or 'AZURE_OPENAI_CHAT_DEPLOYMENT_NAME' environment variable."
-            )
-
-        endpoint_value = azure_openai_settings.get("endpoint")
-        base_url_value = azure_openai_settings.get("base_url")
-        if not async_client:
-            # Create the Azure OpenAI client directly
-            merged_headers = dict(copy(default_headers)) if default_headers else {}
-            if APP_INFO:
-                merged_headers.update(APP_INFO)
-                merged_headers = prepend_agent_framework_to_user_agent(merged_headers)
-
-            api_key_secret = azure_openai_settings.get("api_key")
-            ad_token_provider = None
-            if not api_key_secret and credential:
-                ad_token_provider = resolve_credential_to_token_provider(
-                    credential, azure_openai_settings.get("token_endpoint")
-                )
-
-            if not api_key_secret and not ad_token_provider:
-                raise ValueError("Please provide either api_key, credential, or a client.")
-
-            if not endpoint_value and not base_url_value:
-                raise ValueError("Please provide an endpoint or a base_url")
-
-            client_args: dict[str, Any] = {"default_headers": merged_headers}
-            if resolved_api_version := azure_openai_settings.get("api_version"):
-                client_args["api_version"] = resolved_api_version
-            if ad_token_provider:
-                client_args["azure_ad_token_provider"] = ad_token_provider
-            if api_key_secret:
-                client_args["api_key"] = api_key_secret.get_secret_value()
-            if base_url_value:
-                client_args["base_url"] = str(base_url_value)
-            if endpoint_value and not base_url_value:
-                client_args["azure_endpoint"] = str(endpoint_value)
-            if chat_deployment_name:
-                client_args["azure_deployment"] = chat_deployment_name
-
-            async_client = AsyncAzureOpenAI(**client_args)
-
-        # Store Azure-specific attributes for serialization
-        self.endpoint = str(azure_openai_settings.get("endpoint") or "")
-        self.api_version = azure_openai_settings.get("api_version") or ""
-        self.deployment_name = chat_deployment_name
-
-        with _prefer_single_azure_endpoint_env(endpoint=endpoint_value, base_url=base_url_value):
-            super().__init__(
-                async_client=async_client,
-                model=chat_deployment_name,
-                azure_endpoint=str(endpoint_value) if endpoint_value else None,
-                base_url=str(base_url_value) if base_url_value else None,
-                api_version=azure_openai_settings.get("api_version"),
-                instruction_role=instruction_role,
-                default_headers=default_headers,
-                additional_properties=additional_properties,
-                middleware=middleware,  # type: ignore[arg-type]
-                function_invocation_configuration=function_invocation_configuration,
-            )
-
-    @override
-    def _parse_text_from_openai(self, choice: Choice | ChunkChoice) -> Content | None:
-        """Parse the choice into a Content object with type='text'.
-
-        Overwritten from RawOpenAIChatCompletionClient to deal with Azure On Your Data function.
-        """
-        message = getattr(choice, "message", None)
-        if message is None:
-            message = getattr(choice, "delta", None)
-        if message is None:  # type: ignore
-            return None
-        if hasattr(message, "refusal") and message.refusal:
-            return Content.from_text(text=message.refusal, raw_representation=choice)
-        if not message.content:
-            return None
-        text_content = Content.from_text(text=message.content, raw_representation=choice)
-        if not message.model_extra or "context" not in message.model_extra:
-            return text_content
-
-        context_raw: object = cast(object, message.context)  # type: ignore[union-attr]
-        if isinstance(context_raw, str):
-            try:
-                context_raw = json.loads(context_raw)
-            except json.JSONDecodeError:
-                logger.warning("Context is not a valid JSON string, ignoring context.")
-                return text_content
-        if not isinstance(context_raw, dict):
-            logger.warning("Context is not a valid dictionary, ignoring context.")
-            return text_content
-        context = cast(dict[str, Any], context_raw)
-        if intent := context.get("intent"):
-            text_content.additional_properties = {"intent": intent}
-        citations = context.get("citations")
-        if isinstance(citations, list) and citations:
-            annotations: list[Annotation] = []
-            for citation_raw in cast(list[object], citations):
-                if not isinstance(citation_raw, dict):
-                    continue
-                citation = cast(dict[str, Any], citation_raw)
-                annotations.append(
-                    Annotation(
-                        type="citation",
-                        title=citation.get("title", ""),
-                        url=citation.get("url", ""),
-                        snippet=citation.get("content", ""),
-                        file_id=citation.get("filepath", ""),
-                        tool_name="Azure-on-your-Data",
-                        additional_properties={"chunk_id": citation.get("chunk_id", "")},
-                        raw_representation=citation,
-                    )
-                )
-            text_content.annotations = annotations
-        return text_content
-
-
-# endregion
-
-
-# region AzureOpenAIAssistantsClient
-
-
-AzureOpenAIAssistantsOptionsT = TypeVar(
-    "AzureOpenAIAssistantsOptionsT",
-    bound=TypedDict,  # type: ignore[valid-type]
-    default="OpenAIAssistantsOptions",
-    covariant=True,
-)
-
-AzureOpenAIAssistantsOptions = OpenAIAssistantsOptions
-
-
-@deprecated(
-    "AzureOpenAIAssistantsClient is deprecated. "
-    "Use OpenAIAssistantsClient (also deprecated) or migrate to OpenAIChatClient."
-)
-class AzureOpenAIAssistantsClient(
-    OpenAIAssistantsClient[AzureOpenAIAssistantsOptionsT],  # type: ignore[reportDeprecated]
-    Generic[AzureOpenAIAssistantsOptionsT],
-):
-    """Deprecated Azure OpenAI Assistants client. Use OpenAIAssistantsClient or migrate to OpenAIChatClient."""
-
-    DEFAULT_AZURE_API_VERSION: ClassVar[str] = "2024-05-01-preview"
-
-    def __init__(
-        self,
-        *,
-        deployment_name: str | None = None,
-        assistant_id: str | None = None,
-        assistant_name: str | None = None,
-        assistant_description: str | None = None,
-        thread_id: str | None = None,
-        api_key: str | None = None,
-        endpoint: str | None = None,
-        base_url: str | None = None,
-        api_version: str | None = None,
-        token_endpoint: str | None = None,
-        credential: AzureCredentialTypes | AzureTokenProvider | None = None,
-        default_headers: Mapping[str, str] | None = None,
-        async_client: AsyncAzureOpenAI | None = None,
-        env_file_path: str | None = None,
-        env_file_encoding: str | None = None,
-    ) -> None:
-        """Initialize an Azure OpenAI Assistants client.
-
-        Keyword Args:
-            deployment_name: The Azure OpenAI deployment name.
-            assistant_id: The ID of an Azure OpenAI assistant to use.
-            assistant_name: The name to use when creating new assistants.
-            assistant_description: The description to use when creating new assistants.
-            thread_id: Default thread ID to use for conversations.
-            api_key: The API key to use.
-            endpoint: The deployment endpoint.
-            base_url: The deployment base URL.
-            api_version: The deployment API version.
-            token_endpoint: The token endpoint to request an Azure token.
-            credential: Azure credential or token provider for authentication.
-            default_headers: Default headers for HTTP requests.
-            async_client: An existing client to use.
-            env_file_path: Path to .env file for settings.
-            env_file_encoding: Encoding for .env file.
-        """
-        azure_openai_settings = load_settings(
-            AzureOpenAISettings,
-            env_prefix="AZURE_OPENAI_",
-            api_key=api_key,
-            base_url=base_url,
-            endpoint=endpoint,
-            chat_deployment_name=deployment_name,
-            api_version=api_version,
-            env_file_path=env_file_path,
-            env_file_encoding=env_file_encoding,
-            token_endpoint=token_endpoint,
-        )
-        _apply_azure_defaults(azure_openai_settings, default_api_version=self.DEFAULT_AZURE_API_VERSION)
-
-        chat_deployment_name = azure_openai_settings.get("chat_deployment_name")
-        if not chat_deployment_name:
-            raise ValueError(
-                "Azure OpenAI deployment name is required. Set via 'deployment_name' parameter "
-                "or 'AZURE_OPENAI_CHAT_DEPLOYMENT_NAME' environment variable."
-            )
-
-        api_key_secret = azure_openai_settings.get("api_key")
-        token_scope = azure_openai_settings.get("token_endpoint")
-
-        ad_token_provider = None
-        if not async_client and not api_key_secret and credential:
-            ad_token_provider = resolve_credential_to_token_provider(credential, token_scope)
-
-        if not async_client and not api_key_secret and not ad_token_provider:
-            raise ValueError("Please provide either api_key, credential, or a client.")
-
-        if not async_client:
-            client_params: dict[str, Any] = {
-                "default_headers": default_headers,
-            }
-            if resolved_api_version := azure_openai_settings.get("api_version"):
-                client_params["api_version"] = resolved_api_version
-
-            if api_key_secret:
-                client_params["api_key"] = api_key_secret.get_secret_value()
-            elif ad_token_provider:
-                client_params["azure_ad_token_provider"] = ad_token_provider
-
-            if resolved_base_url := azure_openai_settings.get("base_url"):
-                client_params["base_url"] = str(resolved_base_url)
-            elif resolved_endpoint := azure_openai_settings.get("endpoint"):
-                client_params["azure_endpoint"] = str(resolved_endpoint)
-
-            async_client = AsyncAzureOpenAI(**client_params)
-
-        super().__init__(
-            model_id=chat_deployment_name,
-            assistant_id=assistant_id,
-            assistant_name=assistant_name,
-            assistant_description=assistant_description,
-            thread_id=thread_id,
-            async_client=async_client,  # type: ignore[reportArgumentType]
-            default_headers=default_headers,
-        )
-
-
-# endregion
-
-
-# region AzureOpenAIEmbeddingClient
-
-
-AzureOpenAIEmbeddingOptionsT = TypeVar(
-    "AzureOpenAIEmbeddingOptionsT",
-    bound=TypedDict,  # type: ignore[valid-type]
-    default="OpenAIEmbeddingOptions",
-    covariant=True,
-)
-
-
-@deprecated("AzureOpenAIEmbeddingClient is deprecated. Use OpenAIEmbeddingClient with an AsyncAzureOpenAI client.")
-class AzureOpenAIEmbeddingClient(
-    EmbeddingTelemetryLayer[str, list[float], AzureOpenAIEmbeddingOptionsT],
-    RawOpenAIEmbeddingClient[AzureOpenAIEmbeddingOptionsT],
-    Generic[AzureOpenAIEmbeddingOptionsT],
-):
-    """Deprecated Azure OpenAI embedding client. Use OpenAIEmbeddingClient with AsyncAzureOpenAI instead."""
-
-    OTEL_PROVIDER_NAME: ClassVar[str] = "azure.ai.openai"
-
-    def __init__(
-        self,
-        *,
-        api_key: str | None = None,
-        deployment_name: str | None = None,
-        endpoint: str | None = None,
-        base_url: str | None = None,
-        api_version: str | None = None,
-        token_endpoint: str | None = None,
-        credential: AzureCredentialTypes | AzureTokenProvider | None = None,
-        default_headers: Mapping[str, str] | None = None,
-        async_client: AsyncAzureOpenAI | None = None,
-        otel_provider_name: str | None = None,
-        env_file_path: str | None = None,
-        env_file_encoding: str | None = None,
-    ) -> None:
-        """Initialize an Azure OpenAI embedding client.
-
-        Keyword Args:
-            api_key: The API key.
-            deployment_name: The deployment name.
-            endpoint: The deployment endpoint.
-            base_url: The deployment base URL.
-            api_version: The deployment API version.
-            token_endpoint: The token endpoint to request an Azure token.
-            credential: Azure credential or token provider for authentication.
-            default_headers: Default headers for HTTP requests.
-            async_client: An existing client to use.
-            otel_provider_name: Override the OpenTelemetry provider name.
-            env_file_path: Path to .env file for settings.
-            env_file_encoding: Encoding for .env file.
-        """
-        azure_openai_settings = load_settings(
-            AzureOpenAISettings,
-            env_prefix="AZURE_OPENAI_",
-            api_key=api_key,
-            base_url=base_url,
-            endpoint=endpoint,
-            embedding_deployment_name=deployment_name,
-            api_version=api_version,
-            env_file_path=env_file_path,
-            env_file_encoding=env_file_encoding,
-            token_endpoint=token_endpoint,
-        )
-        _apply_azure_defaults(azure_openai_settings)
-
-        embedding_deployment_name = azure_openai_settings.get("embedding_deployment_name")
-        if not embedding_deployment_name:
-            raise ValueError(
-                "Azure OpenAI embedding deployment name is required. Set via 'deployment_name' parameter "
-                "or 'AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME' environment variable."
-            )
-
-        endpoint_value = azure_openai_settings.get("endpoint")
-        base_url_value = azure_openai_settings.get("base_url")
-        if not async_client:
-            # Create the Azure OpenAI client directly
-            merged_headers = dict(copy(default_headers)) if default_headers else {}
-            if APP_INFO:
-                merged_headers.update(APP_INFO)
-                merged_headers = prepend_agent_framework_to_user_agent(merged_headers)
-
-            api_key_secret = azure_openai_settings.get("api_key")
-            ad_token_provider = None
-            if not api_key_secret and credential:
-                ad_token_provider = resolve_credential_to_token_provider(
-                    credential, azure_openai_settings.get("token_endpoint")
-                )
-
-            if not api_key_secret and not ad_token_provider:
-                raise ValueError("Please provide either api_key, credential, or a client.")
-
-            if not endpoint_value and not base_url_value:
-                raise ValueError("Please provide an endpoint or a base_url")
-
-            client_args: dict[str, Any] = {"default_headers": merged_headers}
-            if resolved_api_version := azure_openai_settings.get("api_version"):
-                client_args["api_version"] = resolved_api_version
-            if ad_token_provider:
-                client_args["azure_ad_token_provider"] = ad_token_provider
-            if api_key_secret:
-                client_args["api_key"] = api_key_secret.get_secret_value()
-            if base_url_value:
-                client_args["base_url"] = str(base_url_value)
-            if endpoint_value and not base_url_value:
-                client_args["azure_endpoint"] = str(endpoint_value)
-            if embedding_deployment_name:
-                client_args["azure_deployment"] = embedding_deployment_name
-
-            async_client = AsyncAzureOpenAI(**client_args)
-
-        # Store Azure-specific attributes for serialization
-        self.endpoint = str(azure_openai_settings.get("endpoint") or "")
-        self.api_version = azure_openai_settings.get("api_version") or ""
-        self.deployment_name = embedding_deployment_name
-
-        with _prefer_single_azure_endpoint_env(endpoint=endpoint_value, base_url=base_url_value):
-            super().__init__(
-                async_client=async_client,
-                model=embedding_deployment_name,
-                azure_endpoint=str(endpoint_value) if endpoint_value else None,
-                base_url=str(base_url_value) if base_url_value else None,
-                api_version=azure_openai_settings.get("api_version"),
-                default_headers=default_headers,
-            )
-        if otel_provider_name is not None:
-            self.OTEL_PROVIDER_NAME = otel_provider_name  # type: ignore[misc]
-
-
-# endregion
@@ -1,488 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from __future__ import annotations
-
-import logging
-import sys
-from collections.abc import Callable, Mapping, MutableMapping, Sequence
-from typing import Any, Generic, cast
-
-from agent_framework import (
-    AGENT_FRAMEWORK_USER_AGENT,
-    Agent,
-    BaseContextProvider,
-    FunctionTool,
-    MiddlewareTypes,
-    normalize_tools,
-)
-from agent_framework._mcp import MCPTool
-from agent_framework._settings import load_settings
-from agent_framework._tools import ToolTypes
-from azure.ai.projects.aio import AIProjectClient
-from azure.ai.projects.models import (
-    AgentVersionDetails,
-    PromptAgentDefinition,
-    PromptAgentDefinitionTextOptions,
-)
-from azure.ai.projects.models import (
-    FunctionTool as AzureFunctionTool,
-)
-
-from ._client import AzureAIClient, AzureAIProjectAgentOptions  # pyright: ignore[reportDeprecated]
-from ._entra_id_authentication import AzureCredentialTypes
-from ._shared import AzureAISettings, create_text_format_config, from_azure_ai_tools, to_azure_ai_tools
-
-if sys.version_info >= (3, 13):
-    from typing import TypeVar  # type: ignore # pragma: no cover
-    from warnings import deprecated  # type: ignore # pragma: no cover
-else:
-    from typing_extensions import TypeVar, deprecated  # type: ignore # pragma: no cover
-if sys.version_info >= (3, 11):
-    from typing import Self, TypedDict  # type: ignore # pragma: no cover
-else:
-    from typing_extensions import Self, TypedDict  # type: ignore # pragma: no cover
-
-
-logger = logging.getLogger("agent_framework.azure")
-
-
-# Type variable for options - allows typed Agent[OptionsT] returns
-# Default matches AzureAIClient's default options type
-OptionsCoT = TypeVar(
-    "OptionsCoT",
-    bound=TypedDict,  # type: ignore[valid-type]
-    default="AzureAIProjectAgentOptions",
-    covariant=True,
-)
-
-
-@deprecated("AzureAIProjectAgentProvider is deprecated. Use FoundryAgent instead.")
-class AzureAIProjectAgentProvider(Generic[OptionsCoT]):
-    """Deprecated provider for Azure AI Agent Service (Responses API).
-
-    This provider is deprecated. Use ``FoundryAgent`` instead to connect to
-    pre-configured agents in Foundry.
-
-    Examples:
-        Using with explicit AIProjectClient:
-
-        .. code-block:: python
-
-            from agent_framework.azure import AzureAIProjectAgentProvider
-            from azure.ai.projects.aio import AIProjectClient
-            from azure.identity.aio import DefaultAzureCredential
-
-            async with AIProjectClient(endpoint, credential) as client:
-                provider = AzureAIProjectAgentProvider(client)
-                agent = await provider.create_agent(
-                    name="MyAgent",
-                    model="gpt-4",
-                    instructions="You are a helpful assistant.",
-                )
-                response = await agent.run("Hello!")
-
-        Using with credential and endpoint (auto-creates client):
-
-        .. code-block:: python
-
-            from agent_framework.azure import AzureAIProjectAgentProvider
-            from azure.identity.aio import DefaultAzureCredential
-
-            async with AzureAIProjectAgentProvider(credential=credential) as provider:
-                agent = await provider.create_agent(
-                    name="MyAgent",
-                    model="gpt-4",
-                    instructions="You are a helpful assistant.",
-                )
-                response = await agent.run("Hello!")
-    """
-
-    def __init__(
-        self,
-        project_client: AIProjectClient | None = None,
-        *,
-        project_endpoint: str | None = None,
-        model: str | None = None,
-        credential: AzureCredentialTypes | None = None,
-        allow_preview: bool | None = None,
-        env_file_path: str | None = None,
-        env_file_encoding: str | None = None,
-    ) -> None:
-        """Initialize an Azure AI Project Agent Provider.
-
-        Args:
-            project_client: An existing AIProjectClient to use. If not provided, one will be created.
-            project_endpoint: The Azure AI Project endpoint URL.
-                Can also be set via environment variable AZURE_AI_PROJECT_ENDPOINT.
-                Ignored when a project_client is passed.
-            model: The default model deployment name to use for agent creation.
-                Can also be set via environment variable AZURE_AI_MODEL_DEPLOYMENT_NAME.
-            credential: Azure credential for authentication. Accepts a TokenCredential,
-                AsyncTokenCredential, or a callable token provider.
-                Required when project_client is not provided.
-            allow_preview: Enables preview opt-in on internally-created ``AIProjectClient``.
-            env_file_path: Path to environment file for loading settings.
-            env_file_encoding: Encoding of the environment file.
-
-        Raises:
-            ValueError: If required parameters are missing or invalid.
-        """
-        self._settings = load_settings(
-            AzureAISettings,
-            env_prefix="AZURE_AI_",
-            project_endpoint=project_endpoint,
-            model_deployment_name=model,
-            env_file_path=env_file_path,
-            env_file_encoding=env_file_encoding,
-        )
-
-        # Track whether we should close client connection
-        self._should_close_client = False
-
-        if project_client is None:
-            resolved_endpoint = self._settings.get("project_endpoint")
-            if not resolved_endpoint:
-                raise ValueError(
-                    "Azure AI project endpoint is required. Set via 'project_endpoint' parameter "
-                    "or 'AZURE_AI_PROJECT_ENDPOINT' environment variable."
-                )
-
-            if not credential:
-                raise ValueError("Azure credential is required when project_client is not provided.")
-
-            project_client_kwargs: dict[str, Any] = {
-                "endpoint": resolved_endpoint,
-                "credential": credential,  # type: ignore[arg-type]
-                "user_agent": AGENT_FRAMEWORK_USER_AGENT,
-            }
-            if allow_preview is not None:
-                project_client_kwargs["allow_preview"] = allow_preview
-            project_client = AIProjectClient(**project_client_kwargs)
-            self._should_close_client = True
-
-        self._project_client = project_client
-
-    async def create_agent(
-        self,
-        name: str,
-        model: str | None = None,
-        instructions: str | None = None,
-        description: str | None = None,
-        tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
-        default_options: OptionsCoT | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        context_providers: Sequence[BaseContextProvider] | None = None,
-    ) -> Agent[OptionsCoT]:
-        """Create a new agent on the Azure AI service and return a local Agent wrapper.
-
-        Args:
-            name: The name of the agent to create.
-            model: The model deployment name to use. Falls back to AZURE_AI_MODEL_DEPLOYMENT_NAME
-                environment variable if not provided.
-            instructions: Instructions for the agent.
-            description: A description of the agent.
-            tools: Tools to make available to the agent.
-            default_options: A TypedDict containing default chat options for the agent.
-                These options are applied to every run unless overridden.
-            middleware: List of middleware to intercept agent and function invocations.
-            context_providers: Context providers to include during agent invocation.
-
-        Returns:
-            Agent: A Agent instance configured with the created agent.
-
-        Raises:
-            ValueError: If required parameters are missing.
-        """
-        # Resolve model from parameter or environment variable
-        resolved_model = model or self._settings.get("model_deployment_name")
-        if not resolved_model:
-            raise ValueError(
-                "Model deployment name is required. Provide 'model' parameter "
-                "or set 'AZURE_AI_MODEL_DEPLOYMENT_NAME' environment variable."
-            )
-
-        # Extract options from default_options if present
-        opts: dict[str, Any] = dict(default_options) if default_options else {}
-        response_format = opts.get("response_format")
-        rai_config = opts.get("rai_config")
-        reasoning = opts.get("reasoning")
-
-        args: dict[str, Any] = {"model": resolved_model}
-
-        if instructions:
-            args["instructions"] = instructions
-        if response_format and isinstance(response_format, (type, dict)):
-            args["text"] = PromptAgentDefinitionTextOptions(
-                format=create_text_format_config(response_format)  # type: ignore[arg-type]
-            )
-        if rai_config:
-            args["rai_config"] = rai_config
-        if reasoning:
-            args["reasoning"] = reasoning
-
-        # Normalize tools and separate MCP tools from other tools
-        normalized_tools = normalize_tools(tools)
-        mcp_tools: list[MCPTool] = []
-        non_mcp_tools: list[FunctionTool | MutableMapping[str, Any]] = []
-
-        if normalized_tools:
-            for tool in normalized_tools:
-                if isinstance(tool, MCPTool):
-                    mcp_tools.append(tool)
-                elif isinstance(tool, (FunctionTool, MutableMapping)):
-                    non_mcp_tools.append(tool)  # type: ignore[reportUnknownArgumentType]
-
-        # Connect MCP tools and discover their functions BEFORE creating the agent
-        # This is required because Azure AI Responses API doesn't accept tools at request time
-        mcp_discovered_functions: list[FunctionTool] = []
-        for mcp_tool in mcp_tools:
-            if not mcp_tool.is_connected:
-                await mcp_tool.connect()
-            mcp_discovered_functions.extend(mcp_tool.functions)
-
-        # Combine non-MCP tools with discovered MCP functions for Azure AI
-        all_tools_for_azure: list[FunctionTool | MutableMapping[str, Any]] = list(non_mcp_tools)
-        all_tools_for_azure.extend(mcp_discovered_functions)
-
-        if all_tools_for_azure:
-            args["tools"] = to_azure_ai_tools(all_tools_for_azure)
-
-        create_version_kwargs: dict[str, Any] = {
-            "agent_name": name,
-            "definition": PromptAgentDefinition(**args),
-            "description": description,
-        }
-
-        created_agent = await self._project_client.agents.create_version(**create_version_kwargs)
-
-        return self._to_chat_agent_from_details(
-            created_agent,
-            normalized_tools,
-            default_options=default_options,
-            middleware=middleware,
-            context_providers=context_providers,
-        )
-
-    async def get_agent(
-        self,
-        *,
-        name: str | None = None,
-        reference: Mapping[str, str | None] | None = None,
-        tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
-        default_options: OptionsCoT | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        context_providers: Sequence[BaseContextProvider] | None = None,
-    ) -> Agent[OptionsCoT]:
-        """Retrieve an existing agent from the Azure AI service and return a local Agent wrapper.
-
-        You must provide either name or reference. Use `as_agent()` if you already have
-        AgentVersionDetails and want to avoid an async call.
-
-        Args:
-            name: The name of the agent to retrieve (fetches latest version).
-            reference: Mapping containing the agent's ``name`` and optionally a specific ``version``.
-            tools: Tools to make available to the agent. Required if the agent has function tools.
-            default_options: A TypedDict containing default chat options for the agent.
-                These options are applied to every run unless overridden.
-            middleware: List of middleware to intercept agent and function invocations.
-            context_providers: Context providers to include during agent invocation.
-
-        Returns:
-            Agent: A Agent instance configured with the retrieved agent.
-
-        Raises:
-            ValueError: If no identifier is provided or required tools are missing.
-        """
-        existing_agent: AgentVersionDetails
-
-        reference_name = str(reference.get("name")) if reference and reference.get("name") else None
-        reference_version = str(reference.get("version")) if reference and reference.get("version") else None
-
-        if reference_name and reference_version:
-            # Fetch specific version
-            existing_agent = await self._project_client.agents.get_version(
-                agent_name=reference_name, agent_version=reference_version
-            )
-        elif agent_name := (reference_name if reference_name else name):
-            # Fetch latest version
-            details = await self._project_client.agents.get(agent_name=agent_name)
-            existing_agent = details.versions.latest
-        else:
-            raise ValueError("Either name or reference must be provided to get an agent.")
-
-        if not isinstance(existing_agent.definition, PromptAgentDefinition):
-            raise ValueError("Agent definition must be PromptAgentDefinition to get a Agent.")
-
-        # Validate that required function tools are provided
-        self._validate_function_tools(existing_agent.definition.tools, tools)
-
-        return self._to_chat_agent_from_details(
-            existing_agent,
-            normalize_tools(tools),
-            default_options=default_options,
-            middleware=middleware,
-            context_providers=context_providers,
-        )
-
-    def as_agent(
-        self,
-        details: AgentVersionDetails,
-        tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
-        default_options: OptionsCoT | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        context_providers: Sequence[BaseContextProvider] | None = None,
-    ) -> Agent[OptionsCoT]:
-        """Wrap an SDK agent version object into a Agent without making HTTP calls.
-
-        Use this when you already have an AgentVersionDetails from a previous API call.
-
-        Args:
-            details: The AgentVersionDetails to wrap.
-            tools: Tools to make available to the agent. Required if the agent has function tools.
-            default_options: A TypedDict containing default chat options for the agent.
-                These options are applied to every run unless overridden.
-            middleware: List of middleware to intercept agent and function invocations.
-            context_providers: Context providers to include during agent invocation.
-
-        Returns:
-            Agent: A Agent instance configured with the agent version.
-
-        Raises:
-            ValueError: If the agent definition is not a PromptAgentDefinition or required tools are missing.
-        """
-        if not isinstance(details.definition, PromptAgentDefinition):
-            raise ValueError("Agent definition must be PromptAgentDefinition to create a Agent.")
-
-        # Validate that required function tools are provided
-        self._validate_function_tools(details.definition.tools, tools)
-
-        return self._to_chat_agent_from_details(
-            details,
-            normalize_tools(tools),
-            default_options=default_options,
-            middleware=middleware,
-            context_providers=context_providers,
-        )
-
-    def _to_chat_agent_from_details(
-        self,
-        details: AgentVersionDetails,
-        provided_tools: Sequence[ToolTypes] | None = None,
-        default_options: OptionsCoT | None = None,
-        middleware: Sequence[MiddlewareTypes] | None = None,
-        context_providers: Sequence[BaseContextProvider] | None = None,
-    ) -> Agent[OptionsCoT]:
-        """Create a Agent from an AgentVersionDetails.
-
-        Args:
-            details: The AgentVersionDetails containing the agent definition.
-            provided_tools: User-provided tools (including function implementations).
-                These are merged with hosted tools from the definition.
-            default_options: A TypedDict containing default chat options for the agent.
-                These options are applied to every run unless overridden.
-            middleware: List of middleware to intercept agent and function invocations.
-            context_providers: Context providers to include during agent invocation.
-        """
-        if not isinstance(details.definition, PromptAgentDefinition):
-            raise ValueError("Agent definition must be PromptAgentDefinition to get a Agent.")
-
-        client = AzureAIClient(  # pyright: ignore[reportDeprecated]
-            project_client=self._project_client,
-            agent_name=details.name,
-            agent_version=details.version,
-            agent_description=details.description,
-            model_deployment_name=details.definition.model,
-        )
-
-        # Merge tools: hosted tools from definition + user-provided function tools
-        # from_azure_ai_tools converts hosted tools (MCP, code interpreter, file search, web search)
-        # but function tools need the actual implementations from provided_tools
-        merged_tools = self._merge_tools(details.definition.tools, provided_tools)
-        merged_default_options: dict[str, Any] = dict(default_options) if default_options is not None else {}
-        merged_default_options.setdefault("model_id", details.definition.model)
-
-        return Agent(  # type: ignore[return-value]
-            client=client,
-            id=details.id,
-            name=details.name,
-            description=details.description,
-            instructions=details.definition.instructions,
-            tools=merged_tools,
-            default_options=cast(Any, merged_default_options),
-            middleware=middleware,
-            context_providers=context_providers,
-        )
-
-    def _merge_tools(
-        self,
-        definition_tools: Sequence[Any] | None,
-        provided_tools: Sequence[ToolTypes] | None,
-    ) -> list[ToolTypes]:
-        """Merge hosted tools from definition with user-provided function tools.
-
-        Args:
-            definition_tools: Tools from the agent definition (Azure AI format).
-            provided_tools: User-provided tools (Agent Framework format), including function implementations.
-
-        Returns:
-            Combined list of tools for the Agent.
-        """
-        merged: list[ToolTypes] = []
-
-        # Convert hosted tools from definition (MCP, code interpreter, file search, web search)
-        # Function tools from the definition are skipped - we use user-provided implementations instead
-        hosted_tools = from_azure_ai_tools(definition_tools)
-        for hosted_tool in hosted_tools:
-            # Skip function tool dicts - they don't have implementations
-            if isinstance(hosted_tool, dict) and hosted_tool.get("type") == "function":
-                continue
-            merged.append(hosted_tool)
-
-        # Add user-provided function tools and MCP tools
-        if provided_tools:
-            for provided_tool in provided_tools:
-                # FunctionTool - has implementation for function calling
-                # MCPTool - Agent handles MCP connection and tool discovery at runtime
-                if isinstance(provided_tool, (FunctionTool, MCPTool)):
-                    merged.append(provided_tool)  # type: ignore[reportUnknownArgumentType]
-
-        return merged
-
-    def _validate_function_tools(
-        self,
-        agent_tools: Sequence[Any] | None,
-        provided_tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None,
-    ) -> None:
-        """Validate that required function tools are provided."""
-        # Normalize and validate function tools
-        normalized_tools = normalize_tools(provided_tools)
-        tool_names = {tool.name for tool in normalized_tools if isinstance(tool, FunctionTool)}
-
-        # If function tools exist in agent definition but were not provided,
-        # we need to raise an error, as it won't be possible to invoke the function.
-        missing_tools = [
-            tool.name
-            for tool in (agent_tools or [])
-            if isinstance(tool, AzureFunctionTool) and tool.name not in tool_names
-        ]
-
-        if missing_tools:
-            raise ValueError(
-                f"The following prompt agent definition required tools were not provided: {', '.join(missing_tools)}"
-            )
-
-    async def __aenter__(self) -> Self:
-        """Async context manager entry."""
-        return self
-
-    async def __aexit__(self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: Any) -> None:
-        """Async context manager exit."""
-        await self.close()
-
-    async def close(self) -> None:
-        """Close the provider and release resources.
-
-        Only closes the underlying AIProjectClient if it was created by this provider.
-        """
-        if self._should_close_client:
-            await self._project_client.close()
@@ -2,45 +2,13 @@

 from __future__ import annotations

-import logging
 import sys
-import warnings
-from collections.abc import Mapping, MutableMapping, Sequence
-from typing import Any, cast
-
-from agent_framework import (
-    Content,
-    FunctionTool,
-)
-from agent_framework.exceptions import IntegrationInvalidRequestException
-from azure.ai.agents.models import (
-    CodeInterpreterToolDefinition,
-    ToolDefinition,
-)
-from azure.ai.projects.models import (
-    CodeInterpreterTool,
-    MCPTool,
-    TextResponseFormatJsonObject,
-    TextResponseFormatJsonSchema,
-    TextResponseFormatText,
-    Tool,
-    WebSearchPreviewTool,
-)
-from azure.ai.projects.models import (
-    FileSearchTool as ProjectsFileSearchTool,
-)
-from azure.ai.projects.models import (
-    FunctionTool as AzureFunctionTool,
-)
-from pydantic import BaseModel

 if sys.version_info >= (3, 11):
    from typing import TypedDict  # pragma: no cover
 else:
    from typing_extensions import TypedDict  # type: ignore # pragma: no cover

-logger = logging.getLogger("agent_framework.azure")
-

 class AzureAISettings(TypedDict, total=False):
    """Azure AI Project settings.
@@ -78,518 +46,3 @@ class AzureAISettings(TypedDict, total=False):

    project_endpoint: str | None
    model_deployment_name: str | None
-
-
-def _extract_project_connection_id(additional_properties: Mapping[str, Any] | None) -> str | None:
-    """Extract project_connection_id from tool additional_properties.
-
-    Checks for both direct 'project_connection_id' key (programmatic usage)
-    and 'connection.name' structure (declarative/YAML usage).
-
-    Args:
-        additional_properties: The additional_properties dict from a tool.
-
-    Returns:
-        The project_connection_id if found, None otherwise.
-    """
-    if not additional_properties:
-        return None
-
-    # Check for direct project_connection_id (programmatic usage)
-
-    if (proj_conn_id := additional_properties.get("project_connection_id")) and isinstance(proj_conn_id, str):
-        return proj_conn_id  # type: ignore[no-any-return]
-
-    # Check for connection.name structure (declarative/YAML usage)
-    if (
-        (connection := additional_properties.get("connection"))
-        and isinstance(connection, Mapping)
-        and (name := connection.get("name"))  # type: ignore
-        and isinstance(name, str)
-    ):
-        return name  # type: ignore[no-any-return]
-
-    return None
-
-
-def resolve_file_ids(file_ids: Sequence[str | Content] | None) -> list[str] | None:
-    """Resolve a list of file ID values that may include Content objects.
-
-    Accepts plain strings and Content objects with type "hosted_file", extracting
-    the file_id from each. This enables users to pass Content.from_hosted_file()
-    alongside plain file ID strings.
-
-    Args:
-        file_ids: Sequence of file ID strings or Content objects, or None.
-
-    Returns:
-        A list of resolved file ID strings, or None if input is None or empty.
-
-    Raises:
-        ValueError: If a Content object has an unsupported type (not "hosted_file").
-    """
-    if not file_ids:
-        return None
-
-    resolved: list[str] = []
-    for item in file_ids:
-        if isinstance(item, str):
-            if not item:
-                raise ValueError("file_ids must not contain empty strings.")
-            resolved.append(item)
-        elif isinstance(item, Content):
-            if item.type != "hosted_file":
-                raise ValueError(
-                    f"Unsupported Content type '{item.type}' for code interpreter file_ids. "
-                    "Only Content.from_hosted_file() is supported."
-                )
-            if item.file_id is None:
-                raise ValueError(
-                    "Content.from_hosted_file() item is missing a file_id. "
-                    "Ensure the Content object has a valid file_id before using it in file_ids."
-                )
-            resolved.append(item.file_id)
-
-    return resolved if resolved else None
-
-
-def to_azure_ai_agent_tools(
-    tools: Sequence[FunctionTool | MutableMapping[str, Any]] | None,
-    run_options: dict[str, Any] | None = None,
-) -> list[ToolDefinition | dict[str, Any]]:
-    """Convert Agent Framework tools to Azure AI V1 SDK tool definitions.
-
-    .. deprecated::
-        This function is deprecated and will be removed in a future release.
-        Use :func:`to_azure_ai_tools` instead for the V2 (Projects/Responses) API.
-
-    Handles FunctionTool instances and dict-based tools from static factory methods.
-
-    Args:
-        tools: Sequence of Agent Framework tools to convert.
-        run_options: Optional dict with run options.
-
-    Returns:
-        List of Azure AI V1 SDK tool definitions.
-
-    Raises:
-        ValueError: If tool configuration is invalid.
-    """
-    warnings.warn(
-        "to_azure_ai_agent_tools() is deprecated and will be removed in a future release; "
-        "use to_azure_ai_tools() instead for the V2 (Projects/Responses) API.",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    if not tools:
-        return []
-
-    tool_definitions: list[ToolDefinition | dict[str, Any]] = []
-    for tool in tools:
-        if isinstance(tool, FunctionTool):
-            tool_definitions.append(tool.to_json_schema_spec())  # type: ignore[reportUnknownArgumentType]
-        elif isinstance(tool, ToolDefinition):
-            # Pass through ToolDefinition subclasses unchanged (includes CodeInterpreterToolDefinition, etc.)
-            tool_definitions.append(tool)
-        elif hasattr(tool, "definitions") and not isinstance(tool, (dict, MutableMapping)):
-            # SDK Tool wrappers (McpTool, FileSearchTool, BingGroundingTool, etc.)
-            tool_definitions.extend(tool.definitions)
-            # Handle tool resources (MCP resources handled separately)
-            if (
-                run_options is not None
-                and hasattr(tool, "resources")
-                and tool.resources
-                and "mcp" not in tool.resources
-            ):
-                run_options.setdefault("tool_resources", {})
-                if isinstance(tool.resources, Mapping):
-                    run_options["tool_resources"].update(tool.resources)
-        elif isinstance(tool, (dict, MutableMapping)):
-            # Handle dict-based tools - pass through directly
-            tool_dict = tool if isinstance(tool, dict) else dict(tool)
-            tool_definitions.append(tool_dict)
-        else:
-            # Pass through other types unchanged
-            tool_definitions.append(tool)
-    return tool_definitions
-
-
-def from_azure_ai_agent_tools(
-    tools: Sequence[ToolDefinition | dict[str, Any]] | None,
-) -> list[dict[str, Any]]:
-    """Convert Azure AI V1 SDK tool definitions to dict-based tools.
-
-    .. deprecated::
-        This function is deprecated and will be removed in a future release.
-        Use :func:`from_azure_ai_tools` instead for the V2 (Projects/Responses) API.
-
-    Args:
-        tools: Sequence of Azure AI V1 SDK tool definitions.
-
-    Returns:
-        List of dict-based tool definitions.
-    """
-    warnings.warn(
-        "from_azure_ai_agent_tools() is deprecated and will be removed in a future release; "
-        "use from_azure_ai_tools() instead for the V2 (Projects/Responses) API.",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    if not tools:
-        return []
-
-    result: list[dict[str, Any]] = []
-    for tool in tools:
-        # Handle SDK objects
-        if isinstance(tool, CodeInterpreterToolDefinition):
-            result.append({"type": "code_interpreter"})
-        elif isinstance(tool, dict):
-            # Handle dict format
-            converted = _convert_dict_tool(tool)
-            if converted is not None:
-                result.append(converted)
-        elif hasattr(tool, "type"):
-            # Handle other SDK objects by type
-            converted = _convert_sdk_tool(tool)
-            if converted is not None:
-                result.append(converted)
-    return result
-
-
-def _convert_dict_tool(tool: dict[str, Any]) -> dict[str, Any] | None:
-    """Convert a dict-format Azure AI tool to dict-based tool format."""
-    tool_type = tool.get("type")
-
-    if tool_type == "code_interpreter":
-        return {"type": "code_interpreter"}
-
-    if tool_type == "file_search":
-        file_search_config = tool.get("file_search", {})
-        vector_store_ids = file_search_config.get("vector_store_ids", [])
-        return {"type": "file_search", "vector_store_ids": vector_store_ids}
-
-    if tool_type == "bing_grounding":
-        bing_config = tool.get("bing_grounding", {})
-        connection_id = bing_config.get("connection_id")
-        return {"type": "bing_grounding", "connection_id": connection_id} if connection_id else None
-
-    if tool_type == "bing_custom_search":
-        bing_config = tool.get("bing_custom_search", {})
-        connection_id = bing_config.get("connection_id")
-        instance_name = bing_config.get("instance_name")
-        # Only return if both required fields are present
-        if connection_id and instance_name:
-            return {
-                "type": "bing_custom_search",
-                "connection_id": connection_id,
-                "instance_name": instance_name,
-            }
-        return None
-
-    if tool_type == "mcp":
-        # MCP tools are defined on the Azure agent, no local handling needed
-        # Azure may not return full server_url, so skip conversion
-        return None
-
-    if tool_type == "function":
-        # Function tools are returned as dicts - users must provide implementations
-        return tool
-
-    # Unknown tool type - pass through
-    return tool
-
-
-def _convert_sdk_tool(tool: ToolDefinition) -> dict[str, Any] | None:
-    """Convert an SDK-object Azure AI tool to dict-based tool format."""
-    tool_type = getattr(tool, "type", None)
-
-    if tool_type == "code_interpreter":
-        return {"type": "code_interpreter"}
-
-    if tool_type == "file_search":
-        file_search_config = getattr(tool, "file_search", None)
-        vector_store_ids = getattr(file_search_config, "vector_store_ids", []) if file_search_config else []
-        return {"type": "file_search", "vector_store_ids": vector_store_ids}
-
-    if tool_type == "bing_grounding":
-        bing_config = getattr(tool, "bing_grounding", None)
-        connection_id = getattr(bing_config, "connection_id", None) if bing_config else None
-        return {"type": "bing_grounding", "connection_id": connection_id} if connection_id else None
-
-    if tool_type == "bing_custom_search":
-        bing_config = getattr(tool, "bing_custom_search", None)
-        connection_id = getattr(bing_config, "connection_id", None) if bing_config else None
-        instance_name = getattr(bing_config, "instance_name", None) if bing_config else None
-        # Only return if both required fields are present
-        if connection_id and instance_name:
-            return {
-                "type": "bing_custom_search",
-                "connection_id": connection_id,
-                "instance_name": instance_name,
-            }
-        return None
-
-    if tool_type == "mcp":
-        # MCP tools are defined on the Azure agent, no local handling needed
-        # Azure may not return full server_url, so skip conversion
-        return None
-
-    if tool_type == "function":
-        # Function tools from SDK don't have implementations - skip
-        return None
-
-    # Unknown tool type - convert to dict if possible
-    if hasattr(tool, "as_dict"):
-        return tool.as_dict()  # type: ignore[union-attr]
-    return {"type": tool_type} if tool_type else {}
-
-
-def from_azure_ai_tools(tools: Sequence[Tool | dict[str, Any]] | None) -> list[dict[str, Any]]:
-    """Parses and converts a sequence of Azure AI tools into dict-based tools.
-
-    Args:
-        tools: A sequence of tool objects or dictionaries
-            defining the tools to be parsed. Can be None.
-
-    Returns:
-        list[dict[str, Any]]: A list of dict-based tool definitions.
-    """
-    agent_tools: list[dict[str, Any]] = []
-    if not tools:
-        return agent_tools
-    for tool in tools:
-        # Handle raw dictionary tools
-        tool_dict = tool if isinstance(tool, dict) else dict(tool)
-        tool_type = tool_dict.get("type")
-
-        if tool_type == "mcp":
-            mcp_tool = cast(MCPTool, tool_dict)
-            result: dict[str, Any] = {
-                "type": "mcp",
-                "server_label": mcp_tool.get("server_label", ""),
-                "server_url": mcp_tool.get("server_url", ""),
-            }
-            if description := mcp_tool.get("server_description"):
-                result["server_description"] = description
-            if headers := mcp_tool.get("headers"):
-                result["headers"] = headers
-            if allowed_tools := mcp_tool.get("allowed_tools"):
-                result["allowed_tools"] = allowed_tools
-            if require_approval := mcp_tool.get("require_approval"):
-                result["require_approval"] = require_approval
-            if project_connection_id := mcp_tool.get("project_connection_id"):
-                result["project_connection_id"] = project_connection_id
-            agent_tools.append(result)
-        elif tool_type == "code_interpreter":
-            ci_tool = cast(CodeInterpreterTool, tool_dict)
-            container = ci_tool.get("container", {})
-            result = {"type": "code_interpreter"}
-            if "file_ids" in container:
-                result["file_ids"] = container["file_ids"]
-            agent_tools.append(result)
-        elif tool_type == "file_search":
-            fs_tool = cast(ProjectsFileSearchTool, tool_dict)
-            result = {"type": "file_search"}
-            if "vector_store_ids" in fs_tool:
-                result["vector_store_ids"] = fs_tool["vector_store_ids"]
-            if max_results := fs_tool.get("max_num_results"):
-                result["max_num_results"] = max_results
-            agent_tools.append(result)
-        elif tool_type == "web_search_preview":
-            ws_tool = cast(WebSearchPreviewTool, tool_dict)
-            result = {"type": "web_search_preview"}
-            if user_location := ws_tool.get("user_location"):
-                result["user_location"] = {
-                    "city": user_location.get("city"),
-                    "country": user_location.get("country"),
-                    "region": user_location.get("region"),
-                    "timezone": user_location.get("timezone"),
-                }
-            agent_tools.append(result)
-        else:
-            agent_tools.append(tool_dict)
-    return agent_tools
-
-
-def to_azure_ai_tools(
-    tools: Sequence[FunctionTool | MutableMapping[str, Any] | Tool] | None,
-) -> list[Tool | dict[str, Any]]:
-    """Converts Agent Framework tools into Azure AI compatible tools.
-
-    Handles FunctionTool instances and passes through SDK Tool types directly.
-
-    Args:
-        tools: A sequence of Agent Framework tool objects, SDK Tool types, or dictionaries
-            defining the tools to be converted. Can be None.
-
-    Returns:
-        list[Tool | dict[str, Any]]: A list of converted tools compatible with Azure AI.
-    """
-    azure_tools: list[Tool | dict[str, Any]] = []
-    if not tools:
-        return azure_tools
-
-    for tool in tools:
-        if isinstance(tool, FunctionTool):
-            params = tool.parameters()
-            params["additionalProperties"] = False
-            azure_tools.append(
-                AzureFunctionTool(
-                    name=tool.name,
-                    parameters=params,
-                    strict=False,
-                    description=tool.description,
-                )
-            )
-        elif isinstance(tool, Tool):
-            # Pass through SDK Tool types directly (CodeInterpreterTool, FileSearchTool, etc.)
-            azure_tools.append(tool)
-        elif isinstance(tool, MutableMapping):
-            # Convert mutable mappings into plain dicts for stable typing.
-            tool_dict: dict[str, Any] = dict(tool)
-            if tool_dict.get("type") == "mcp":
-                azure_tools.append(_prepare_mcp_tool_dict_for_azure_ai(tool_dict))
-            else:
-                azure_tools.append(tool_dict)
-        else:
-            # Pass through any other supported tool objects unchanged.
-            azure_tools.append(tool)
-
-    return azure_tools
-
-
-def _prepare_mcp_tool_dict_for_azure_ai(tool_dict: dict[str, Any]) -> MCPTool:
-    """Convert dict-based MCP tool to Azure AI MCPTool format.
-
-    Args:
-        tool_dict: The dict-based MCP tool configuration.
-
-    Returns:
-        MCPTool: The converted Azure AI MCPTool.
-    """
-    server_label = tool_dict.get("server_label", "")
-    server_url = tool_dict.get("server_url", "")
-    mcp: MCPTool = MCPTool(server_label=server_label, server_url=server_url)
-
-    if description := tool_dict.get("server_description"):
-        mcp["server_description"] = description
-
-    # Check for project_connection_id
-    project_connection_id = tool_dict.get("project_connection_id")
-    if not isinstance(project_connection_id, str):
-        additional_properties = tool_dict.get("additional_properties")
-        project_connection_id = (
-            _extract_project_connection_id(additional_properties)  # pyright: ignore[reportUnknownArgumentType]
-            if isinstance(additional_properties, Mapping)
-            else None
-        )
-
-    if project_connection_id:
-        mcp["project_connection_id"] = project_connection_id
-    elif headers := tool_dict.get("headers"):
-        mcp["headers"] = headers
-
-    if allowed_tools := tool_dict.get("allowed_tools"):
-        mcp["allowed_tools"] = list(allowed_tools)
-
-    if require_approval := tool_dict.get("require_approval"):
-        mcp["require_approval"] = require_approval
-
-    return mcp
-
-
-def create_text_format_config(
-    response_format: type[BaseModel] | Mapping[str, Any],
-) -> TextResponseFormatJsonSchema | TextResponseFormatJsonObject | TextResponseFormatText:
-    """Convert response_format into Azure text format configuration."""
-    if isinstance(response_format, type) and issubclass(response_format, BaseModel):
-        schema = response_format.model_json_schema()
-        # Ensure additionalProperties is explicitly false to satisfy Azure validation
-        if isinstance(schema, dict):
-            schema.setdefault("additionalProperties", False)
-        return TextResponseFormatJsonSchema(
-            name=response_format.__name__,
-            schema=schema,
-            strict=True,
-        )
-
-    if isinstance(response_format, Mapping):
-        format_config = _convert_response_format(response_format)
-        format_type = format_config.get("type")
-        if format_type == "json_schema":
-            # Ensure schema includes additionalProperties=False to satisfy Azure validation
-            schema = dict(format_config.get("schema", {}))  # type: ignore[assignment]
-            schema.setdefault("additionalProperties", False)
-            config_kwargs: dict[str, Any] = {
-                "name": format_config.get("name") or "response",
-                "schema": schema,
-            }
-            if "strict" in format_config:
-                config_kwargs["strict"] = format_config["strict"]
-            if "description" in format_config:
-                config_kwargs["description"] = format_config["description"]
-            return TextResponseFormatJsonSchema(**config_kwargs)
-        if format_type == "json_object":
-            return TextResponseFormatJsonObject()
-        if format_type == "text":
-            return TextResponseFormatText()
-
-    raise IntegrationInvalidRequestException("response_format must be a Pydantic model or mapping.")
-
-
-def _convert_response_format(response_format: Mapping[str, Any]) -> dict[str, Any]:
-    """Convert Chat style response_format into Responses text format config."""
-    if "format" in response_format and isinstance(response_format["format"], Mapping):
-        return dict(cast("Mapping[str, Any]", response_format["format"]))
-
-    format_type = response_format.get("type")
-    if format_type == "json_schema":
-        schema_section = response_format.get("json_schema", response_format)
-        if not isinstance(schema_section, Mapping):
-            raise IntegrationInvalidRequestException("json_schema response_format must be a mapping.")
-        schema_section_typed = cast("Mapping[str, Any]", schema_section)
-        schema: Any = schema_section_typed.get("schema")
-        if schema is None:
-            raise IntegrationInvalidRequestException("json_schema response_format requires a schema.")
-        name: str = str(
-            schema_section_typed.get("name")
-            or schema_section_typed.get("title")
-            or (cast("Mapping[str, Any]", schema).get("title") if isinstance(schema, Mapping) else None)
-            or "response"
-        )
-        format_config: dict[str, Any] = {
-            "type": "json_schema",
-            "name": name,
-            "schema": schema,
-        }
-        if "strict" in schema_section:
-            format_config["strict"] = schema_section["strict"]
-        if "description" in schema_section and schema_section["description"] is not None:
-            format_config["description"] = schema_section["description"]
-        return format_config
-
-    if format_type in {"json_object", "text"}:
-        return {"type": format_type}
-
-    # Handle raw JSON schemas (e.g. {"type": "object", "properties": {...}})
-    # by wrapping them in the expected json_schema envelope.
-    # Detect by checking for JSON Schema primitive types or known schema keywords.
-    json_schema_keywords = {"properties", "anyOf", "oneOf", "allOf", "$ref", "$defs"}
-    json_schema_primitive_types = {"object", "array", "string", "number", "integer", "boolean", "null"}
-    if format_type in json_schema_primitive_types or (
-        format_type is None and any(k in response_format for k in json_schema_keywords)
-    ):
-        schema = dict(response_format)
-        if schema.get("type") == "object" and "additionalProperties" not in schema:
-            schema["additionalProperties"] = False
-        # Pop title from schema since OpenAI strict mode rejects unknown keys;
-        # use it as the schema name in the envelope instead.
-        name = str(schema.pop("title", None) or "response")
-        return {
-            "type": "json_schema",
-            "name": name,
-            "schema": schema,
-            "strict": True,
-        }
-
-    raise IntegrationInvalidRequestException("Unsupported response_format provided for Azure AI client.")
@@ -1,61 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-from typing import Any
-
-from agent_framework import Message
-from pytest import fixture
-
-
-# region: Connector Settings fixtures
-@fixture
-def exclude_list(request: Any) -> list[str]:
-    """Fixture that returns a list of environment variables to exclude."""
-    return request.param if hasattr(request, "param") else []
-
-
-@fixture
-def override_env_param_dict(request: Any) -> dict[str, str]:
-    """Fixture that returns a dict of environment variables to override."""
-    return request.param if hasattr(request, "param") else {}
-
-
-# These two fixtures are used for multiple things, also non-connector tests
-@fixture()
-def azure_openai_unit_test_env(monkeypatch, exclude_list, override_env_param_dict):  # type: ignore
-    """Fixture to set environment variables for AzureOpenAISettings."""
-
-    if exclude_list is None:
-        exclude_list = []
-
-    if override_env_param_dict is None:
-        override_env_param_dict = {}
-
-    env_vars = {
-        "AZURE_OPENAI_ENDPOINT": "https://test-endpoint.com",
-        "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME": "test_chat_deployment",
-        "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME": "test_chat_deployment",
-        "AZURE_OPENAI_TEXT_DEPLOYMENT_NAME": "test_text_deployment",
-        "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME": "test_embedding_deployment",
-        "AZURE_OPENAI_TEXT_TO_IMAGE_DEPLOYMENT_NAME": "test_text_to_image_deployment",
-        "AZURE_OPENAI_AUDIO_TO_TEXT_DEPLOYMENT_NAME": "test_audio_to_text_deployment",
-        "AZURE_OPENAI_TEXT_TO_AUDIO_DEPLOYMENT_NAME": "test_text_to_audio_deployment",
-        "AZURE_OPENAI_REALTIME_DEPLOYMENT_NAME": "test_realtime_deployment",
-        "AZURE_OPENAI_API_KEY": "test_api_key",
-        "AZURE_OPENAI_API_VERSION": "2023-03-15-preview",
-        "AZURE_OPENAI_BASE_URL": "https://test_text_deployment.test-base-url.com",
-        "AZURE_OPENAI_TOKEN_ENDPOINT": "https://test-token-endpoint.com",
-    }
-
-    env_vars.update(override_env_param_dict)  # type: ignore
-
-    for key, value in env_vars.items():
-        if key in exclude_list:
-            monkeypatch.delenv(key, raising=False)  # type: ignore
-            continue
-        monkeypatch.setenv(key, value)  # type: ignore
-
-    return env_vars
-
-
-@fixture(scope="function")
-def chat_history() -> list[Message]:
-    return []
@@ -1,409 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from typing import Annotated
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from agent_framework import (
-    SupportsChatGetResponse,
-    tool,
-)
-from agent_framework._settings import SecretString
-from agent_framework.azure import AzureOpenAIAssistantsClient
-from pydantic import Field
-
-
-def create_test_azure_assistants_client(
-    mock_async_azure_openai: MagicMock,
-    deployment_name: str | None = None,
-    assistant_id: str | None = None,
-    assistant_name: str | None = None,
-    thread_id: str | None = None,
-    should_delete_assistant: bool = False,
-) -> AzureOpenAIAssistantsClient:
-    """Helper function to create AzureOpenAIAssistantsClient instances for testing."""
-    client = AzureOpenAIAssistantsClient(
-        deployment_name=deployment_name or "test_chat_deployment",
-        assistant_id=assistant_id,
-        assistant_name=assistant_name,
-        thread_id=thread_id,
-        api_key="test-api-key",
-        endpoint="https://test-endpoint.com",
-        async_client=mock_async_azure_openai,
-    )
-    # Set the _should_delete_assistant flag directly if needed
-    if should_delete_assistant:
-        object.__setattr__(client, "_should_delete_assistant", True)
-    return client
-
-
-@pytest.fixture
-def mock_async_azure_openai() -> MagicMock:
-    """Mock AsyncAzureOpenAI client."""
-    mock_client = MagicMock()
-
-    # Mock beta.assistants
-    mock_client.beta.assistants.create = AsyncMock(return_value=MagicMock(id="test-assistant-id"))
-    mock_client.beta.assistants.delete = AsyncMock()
-
-    # Mock beta.threads
-    mock_client.beta.threads.create = AsyncMock(return_value=MagicMock(id="test-thread-id"))
-    mock_client.beta.threads.delete = AsyncMock()
-
-    # Mock beta.threads.runs
-    mock_client.beta.threads.runs.create = AsyncMock(return_value=MagicMock(id="test-run-id"))
-    mock_client.beta.threads.runs.retrieve = AsyncMock()
-    mock_client.beta.threads.runs.submit_tool_outputs = AsyncMock()
-
-    # Mock beta.threads.messages
-    mock_client.beta.threads.messages.create = AsyncMock()
-    mock_client.beta.threads.messages.list = AsyncMock(return_value=MagicMock(data=[]))
-
-    return mock_client
-
-
-def test_azure_assistants_client_init_with_client(mock_async_azure_openai: MagicMock) -> None:
-    """Test AzureOpenAIAssistantsClient initialization with existing client."""
-    client = create_test_azure_assistants_client(
-        mock_async_azure_openai,
-        deployment_name="test_chat_deployment",
-        assistant_id="existing-assistant-id",
-        thread_id="test-thread-id",
-    )
-
-    assert client.client is mock_async_azure_openai
-    assert client.model == "test_chat_deployment"
-    assert client.assistant_id == "existing-assistant-id"
-    assert client.thread_id == "test-thread-id"
-    assert not client._should_delete_assistant  # type: ignore
-    assert isinstance(client, SupportsChatGetResponse)
-
-
-def test_azure_assistants_client_init_auto_create_client(
-    azure_openai_unit_test_env: dict[str, str],
-    mock_async_azure_openai: MagicMock,
-) -> None:
-    """Test AzureOpenAIAssistantsClient initialization with auto-created client."""
-    client = AzureOpenAIAssistantsClient(
-        deployment_name=azure_openai_unit_test_env["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"],
-        assistant_name="TestAssistant",
-        api_key=azure_openai_unit_test_env["AZURE_OPENAI_API_KEY"],
-        endpoint=azure_openai_unit_test_env["AZURE_OPENAI_ENDPOINT"],
-        async_client=mock_async_azure_openai,
-    )
-
-    assert client.client is mock_async_azure_openai
-    assert client.model == azure_openai_unit_test_env["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"]
-    assert client.assistant_id is None
-    assert client.assistant_name == "TestAssistant"
-    assert not client._should_delete_assistant  # type: ignore
-
-
-def test_azure_assistants_client_init_validation_fail() -> None:
-    """Test AzureOpenAIAssistantsClient initialization with validation failure."""
-    with pytest.raises(ValueError):
-        # Force failure by providing invalid deployment name type - this should cause validation to fail
-        AzureOpenAIAssistantsClient(deployment_name=123, api_key="valid-key")  # type: ignore
-
-
-@pytest.mark.parametrize("exclude_list", [["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"]], indirect=True)
-def test_azure_assistants_client_init_missing_deployment_name(azure_openai_unit_test_env: dict[str, str]) -> None:
-    """Test AzureOpenAIAssistantsClient initialization with missing deployment name."""
-    with pytest.raises(ValueError):
-        AzureOpenAIAssistantsClient(api_key=azure_openai_unit_test_env.get("AZURE_OPENAI_API_KEY", "test-key"))
-
-
-def test_azure_assistants_client_init_with_default_headers(azure_openai_unit_test_env: dict[str, str]) -> None:
-    """Test AzureOpenAIAssistantsClient initialization with default headers."""
-    default_headers = {"X-Unit-Test": "test-guid"}
-
-    client = AzureOpenAIAssistantsClient(
-        deployment_name="test_chat_deployment",
-        api_key=azure_openai_unit_test_env["AZURE_OPENAI_API_KEY"],
-        endpoint=azure_openai_unit_test_env["AZURE_OPENAI_ENDPOINT"],
-        default_headers=default_headers,
-    )
-
-    assert client.model == "test_chat_deployment"
-    assert isinstance(client, SupportsChatGetResponse)
-
-    # Assert that the default header we added is present in the client's default headers
-    for key, value in default_headers.items():
-        assert key in client.client.default_headers
-        assert client.client.default_headers[key] == value
-
-
-async def test_azure_assistants_client_get_assistant_id_or_create_existing_assistant(
-    mock_async_azure_openai: MagicMock,
-) -> None:
-    """Test _get_assistant_id_or_create when assistant_id is already provided."""
-    client = create_test_azure_assistants_client(mock_async_azure_openai, assistant_id="existing-assistant-id")
-
-    assistant_id = await client._get_assistant_id_or_create()  # type: ignore
-
-    assert assistant_id == "existing-assistant-id"
-    assert not client._should_delete_assistant  # type: ignore
-    mock_async_azure_openai.beta.assistants.create.assert_not_called()
-
-
-async def test_azure_assistants_client_get_assistant_id_or_create_create_new(
-    mock_async_azure_openai: MagicMock,
-) -> None:
-    """Test _get_assistant_id_or_create when creating a new assistant."""
-    client = create_test_azure_assistants_client(
-        mock_async_azure_openai, deployment_name="test_chat_deployment", assistant_name="TestAssistant"
-    )
-
-    assistant_id = await client._get_assistant_id_or_create()  # type: ignore
-
-    assert assistant_id == "test-assistant-id"
-    assert client._should_delete_assistant  # type: ignore
-    mock_async_azure_openai.beta.assistants.create.assert_called_once()
-
-
-async def test_azure_assistants_client_aclose_should_not_delete(
-    mock_async_azure_openai: MagicMock,
-) -> None:
-    """Test close when assistant should not be deleted."""
-    client = create_test_azure_assistants_client(
-        mock_async_azure_openai, assistant_id="assistant-to-keep", should_delete_assistant=False
-    )
-
-    await client.close()  # type: ignore
-
-    # Verify assistant deletion was not called
-    mock_async_azure_openai.beta.assistants.delete.assert_not_called()
-    assert not client._should_delete_assistant  # type: ignore
-
-
-async def test_azure_assistants_client_aclose_should_delete(mock_async_azure_openai: MagicMock) -> None:
-    """Test close method calls cleanup."""
-    client = create_test_azure_assistants_client(
-        mock_async_azure_openai, assistant_id="assistant-to-delete", should_delete_assistant=True
-    )
-
-    await client.close()
-
-    # Verify assistant deletion was called
-    mock_async_azure_openai.beta.assistants.delete.assert_called_once_with("assistant-to-delete")
-    assert not client._should_delete_assistant  # type: ignore
-
-
-async def test_azure_assistants_client_async_context_manager(mock_async_azure_openai: MagicMock) -> None:
-    """Test async context manager functionality."""
-    client = create_test_azure_assistants_client(
-        mock_async_azure_openai, assistant_id="assistant-to-delete", should_delete_assistant=True
-    )
-
-    # Test context manager
-    async with client:
-        pass  # Just test that we can enter and exit
-
-    # Verify cleanup was called on exit
-    mock_async_azure_openai.beta.assistants.delete.assert_called_once_with("assistant-to-delete")
-
-
-def test_azure_assistants_client_serialize(azure_openai_unit_test_env: dict[str, str]) -> None:
-    """Test serialization of AzureOpenAIAssistantsClient."""
-    default_headers = {"X-Unit-Test": "test-guid"}
-
-    # Test basic initialization and to_dict
-    client = AzureOpenAIAssistantsClient(
-        deployment_name="test_chat_deployment",
-        assistant_id="test-assistant-id",
-        assistant_name="TestAssistant",
-        thread_id="test-thread-id",
-        api_key=azure_openai_unit_test_env["AZURE_OPENAI_API_KEY"],
-        endpoint=azure_openai_unit_test_env["AZURE_OPENAI_ENDPOINT"],
-        default_headers=default_headers,
-    )
-
-    dumped_settings = client.to_dict()
-
-    assert dumped_settings["model"] == "test_chat_deployment"
-    assert dumped_settings["assistant_id"] == "test-assistant-id"
-    assert dumped_settings["assistant_name"] == "TestAssistant"
-    assert dumped_settings["thread_id"] == "test-thread-id"
-
-    # Assert that the default header we added is present in the dumped_settings default headers
-    for key, value in default_headers.items():
-        assert key in dumped_settings["default_headers"]
-        assert dumped_settings["default_headers"][key] == value
-    # Assert that the 'User-Agent' header is not present in the dumped_settings default headers
-    assert "User-Agent" not in dumped_settings["default_headers"]
-
-
-@tool(approval_mode="never_require")
-def get_weather(
-    location: Annotated[str, Field(description="The location to get the weather for.")],
-) -> str:
-    """Get the weather for a given location."""
-    return f"The weather in {location} is sunny with a high of 25°C."
-
-
-def test_azure_assistants_client_entra_id_authentication() -> None:
-    """Test credential authentication path with sync credential."""
-    mock_credential = MagicMock()
-    mock_provider = MagicMock(return_value="token-string")
-
-    with (
-        patch("agent_framework_azure_ai._deprecated_azure_openai.load_settings") as mock_load_settings,
-        patch(
-            "agent_framework_azure_ai._deprecated_azure_openai.resolve_credential_to_token_provider",
-            return_value=mock_provider,
-        ) as mock_resolve,
-        patch("agent_framework_azure_ai._deprecated_azure_openai.AsyncAzureOpenAI") as mock_azure_client,
-        patch("agent_framework.openai.OpenAIAssistantsClient.__init__", return_value=None),
-    ):
-        mock_load_settings.return_value = {
-            "chat_deployment_name": "test-deployment",
-            "responses_deployment_name": None,
-            "api_key": None,
-            "token_endpoint": "https://cognitiveservices.azure.com/.default",
-            "api_version": "2024-05-01-preview",
-            "endpoint": "https://test-endpoint.openai.azure.com",
-            "base_url": None,
-        }
-
-        client = AzureOpenAIAssistantsClient(
-            deployment_name="test-deployment",
-            endpoint="https://test-endpoint.openai.azure.com",
-            credential=mock_credential,
-            token_endpoint="https://cognitiveservices.azure.com/.default",
-        )
-
-        # Verify credential was resolved to a token provider
-        mock_resolve.assert_called_once_with(mock_credential, "https://cognitiveservices.azure.com/.default")
-
-        # Verify client was created with the token provider
-        mock_azure_client.assert_called_once()
-        call_args = mock_azure_client.call_args[1]
-        assert call_args["azure_ad_token_provider"] is mock_provider
-
-        assert client is not None
-        assert isinstance(client, AzureOpenAIAssistantsClient)
-
-
-def test_azure_assistants_client_no_authentication_error() -> None:
-    """Test authentication validation error when no auth provided."""
-    with patch("agent_framework_azure_ai._deprecated_azure_openai.load_settings") as mock_load_settings:
-        mock_load_settings.return_value = {
-            "chat_deployment_name": "test-deployment",
-            "responses_deployment_name": None,
-            "api_key": None,
-            "token_endpoint": None,
-            "api_version": "2024-05-01-preview",
-            "endpoint": "https://test-endpoint.openai.azure.com",
-            "base_url": None,
-        }
-
-        # Test missing authentication raises error
-        with pytest.raises(ValueError, match="api_key, credential, or a client"):
-            AzureOpenAIAssistantsClient(
-                deployment_name="test-deployment",
-                endpoint="https://test-endpoint.openai.azure.com",
-                # No authentication provided at all
-            )
-
-
-def test_azure_assistants_client_callable_credential() -> None:
-    """Test callable token provider as credential."""
-    mock_provider = MagicMock(return_value="my-token")
-
-    with (
-        patch("agent_framework_azure_ai._deprecated_azure_openai.load_settings") as mock_load_settings,
-        patch(
-            "agent_framework_azure_ai._deprecated_azure_openai.resolve_credential_to_token_provider",
-            return_value=mock_provider,
-        ),
-        patch("agent_framework_azure_ai._deprecated_azure_openai.AsyncAzureOpenAI") as mock_azure_client,
-        patch("agent_framework.openai.OpenAIAssistantsClient.__init__", return_value=None),
-    ):
-        mock_load_settings.return_value = {
-            "chat_deployment_name": "test-deployment",
-            "responses_deployment_name": None,
-            "api_key": None,
-            "token_endpoint": "https://cognitiveservices.azure.com/.default",
-            "api_version": "2024-05-01-preview",
-            "endpoint": "https://test-endpoint.openai.azure.com",
-            "base_url": None,
-        }
-
-        client = AzureOpenAIAssistantsClient(
-            deployment_name="test-deployment",
-            endpoint="https://test-endpoint.openai.azure.com",
-            credential=mock_provider,
-            token_endpoint="https://cognitiveservices.azure.com/.default",
-        )
-
-        # Verify client was created with the token provider
-        mock_azure_client.assert_called_once()
-        call_args = mock_azure_client.call_args[1]
-        assert call_args["azure_ad_token_provider"] is mock_provider
-
-        assert client is not None
-        assert isinstance(client, AzureOpenAIAssistantsClient)
-
-
-def test_azure_assistants_client_base_url_configuration() -> None:
-    """Test base_url client parameter path."""
-    with (
-        patch("agent_framework_azure_ai._deprecated_azure_openai.load_settings") as mock_load_settings,
-        patch("agent_framework_azure_ai._deprecated_azure_openai.AsyncAzureOpenAI") as mock_azure_client,
-        patch("agent_framework.openai.OpenAIAssistantsClient.__init__", return_value=None),
-    ):
-        mock_load_settings.return_value = {
-            "chat_deployment_name": "test-deployment",
-            "responses_deployment_name": None,
-            "api_key": SecretString("test-api-key"),
-            "token_endpoint": None,
-            "api_version": "2024-05-01-preview",
-            "endpoint": None,
-            "base_url": "https://custom-base-url.com",
-        }
-
-        client = AzureOpenAIAssistantsClient(
-            deployment_name="test-deployment", api_key="test-api-key", base_url="https://custom-base-url.com"
-        )
-
-        # base_url path
-        mock_azure_client.assert_called_once()
-        call_args = mock_azure_client.call_args[1]
-        assert call_args["base_url"] == "https://custom-base-url.com"
-        assert "azure_endpoint" not in call_args
-
-        assert client is not None
-        assert isinstance(client, AzureOpenAIAssistantsClient)
-
-
-def test_azure_assistants_client_azure_endpoint_configuration() -> None:
-    """Test azure_endpoint client parameter path."""
-    with (
-        patch("agent_framework_azure_ai._deprecated_azure_openai.load_settings") as mock_load_settings,
-        patch("agent_framework_azure_ai._deprecated_azure_openai.AsyncAzureOpenAI") as mock_azure_client,
-        patch("agent_framework.openai.OpenAIAssistantsClient.__init__", return_value=None),
-    ):
-        mock_load_settings.return_value = {
-            "chat_deployment_name": "test-deployment",
-            "responses_deployment_name": None,
-            "api_key": SecretString("test-api-key"),
-            "token_endpoint": None,
-            "api_version": "2024-05-01-preview",
-            "endpoint": "https://test-endpoint.openai.azure.com",
-            "base_url": None,
-        }
-
-        client = AzureOpenAIAssistantsClient(
-            deployment_name="test-deployment",
-            api_key="test-api-key",
-            endpoint="https://test-endpoint.openai.azure.com",
-        )
-
-        # azure_endpoint path
-        mock_azure_client.assert_called_once()
-        call_args = mock_azure_client.call_args[1]
-        assert call_args["azure_endpoint"] == "https://test-endpoint.openai.azure.com"
-        assert "base_url" not in call_args
-
-        assert client is not None
-        assert isinstance(client, AzureOpenAIAssistantsClient)
@@ -1,219 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from __future__ import annotations
-
-import os
-from functools import wraps
-from typing import Any
-from unittest.mock import AsyncMock, MagicMock
-
-import pytest
-from agent_framework.azure import AzureOpenAIEmbeddingClient
-from agent_framework.openai import OpenAIEmbeddingOptions
-from azure.identity.aio import AzureCliCredential
-from openai.types import CreateEmbeddingResponse
-from openai.types import Embedding as OpenAIEmbedding
-from openai.types.create_embedding_response import Usage
-
-pytestmark = pytest.mark.filterwarnings("ignore:AzureOpenAIEmbeddingClient is deprecated\\..*:DeprecationWarning")
-
-
-def _make_openai_response(
-    embeddings: list[list[float]],
-    model: str = "text-embedding-3-small",
-    prompt_tokens: int = 5,
-    total_tokens: int = 5,
-) -> CreateEmbeddingResponse:
-    """Helper to create a mock OpenAI embeddings response."""
-    data = [OpenAIEmbedding(embedding=emb, index=i, object="embedding") for i, emb in enumerate(embeddings)]
-    return CreateEmbeddingResponse(
-        data=data,
-        model=model,
-        object="list",
-        usage=Usage(prompt_tokens=prompt_tokens, total_tokens=total_tokens),
-    )
-
-
-@pytest.fixture
-def azure_embedding_unit_test_env(monkeypatch: pytest.MonkeyPatch) -> None:
-    """Clear ambient Azure OpenAI embedding env vars for deterministic unit tests."""
-    for key in (
-        "AZURE_OPENAI_ENDPOINT",
-        "AZURE_OPENAI_API_KEY",
-        "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME",
-        "AZURE_OPENAI_BASE_URL",
-        "AZURE_OPENAI_TOKEN_ENDPOINT",
-    ):
-        monkeypatch.delenv(key, raising=False)
-
-
-def test_azure_construction_with_deployment_name(azure_embedding_unit_test_env: None) -> None:
-    client = AzureOpenAIEmbeddingClient(
-        deployment_name="text-embedding-3-small",
-        api_key="test-key",
-        endpoint="https://test.openai.azure.com/",
-    )
-    assert client.model == "text-embedding-3-small"
-
-
-def test_azure_construction_with_existing_client(azure_embedding_unit_test_env: None) -> None:
-    mock_client = MagicMock()
-    client = AzureOpenAIEmbeddingClient(
-        deployment_name="my-deployment",
-        async_client=mock_client,
-    )
-    assert client.model == "my-deployment"
-    assert client.client is mock_client
-
-
-def test_azure_construction_missing_deployment_name_raises(azure_embedding_unit_test_env: None) -> None:
-    with pytest.raises(ValueError, match="deployment name is required"):
-        AzureOpenAIEmbeddingClient(
-            api_key="test-key",
-            endpoint="https://test.openai.azure.com/",
-        )
-
-
-def test_azure_construction_missing_credentials_raises(azure_embedding_unit_test_env: None) -> None:
-    with pytest.raises(ValueError, match="api_key, credential, or a client"):
-        AzureOpenAIEmbeddingClient(
-            deployment_name="test",
-            endpoint="https://test.openai.azure.com/",
-        )
-
-
-async def test_azure_get_embeddings(azure_embedding_unit_test_env: None) -> None:
-    mock_response = _make_openai_response(
-        embeddings=[[0.1, 0.2]],
-    )
-    mock_async_client = MagicMock()
-    mock_async_client.embeddings = MagicMock()
-    mock_async_client.embeddings.create = AsyncMock(return_value=mock_response)
-
-    client = AzureOpenAIEmbeddingClient(
-        deployment_name="text-embedding-3-small",
-        async_client=mock_async_client,
-    )
-
-    result = await client.get_embeddings(["hello"])
-
-    assert len(result) == 1
-    assert result[0].vector == [0.1, 0.2]
-
-
-def test_azure_otel_provider_name(azure_embedding_unit_test_env: None) -> None:
-    mock_client = MagicMock()
-    client = AzureOpenAIEmbeddingClient(
-        deployment_name="test",
-        async_client=mock_client,
-    )
-    assert client.OTEL_PROVIDER_NAME == "azure.ai.openai"
-
-
-skip_if_azure_openai_integration_tests_disabled = pytest.mark.skipif(
-    os.getenv("AZURE_OPENAI_ENDPOINT", "") in ("", "https://test-endpoint.com")
-    or (
-        os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME", "") == ""
-        and os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME", "") == ""
-    ),
-    reason="No Azure OpenAI endpoint or embedding deployment provided; skipping integration tests.",
-)
-
-
-def _with_azure_openai_debug() -> Any:
-    def decorator(func: Any) -> Any:
-        @wraps(func)
-        async def wrapper(*args: Any, **kwargs: Any) -> Any:
-            try:
-                return await func(*args, **kwargs)
-            except Exception as exc:
-                model = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") or os.getenv(
-                    "AZURE_OPENAI_DEPLOYMENT_NAME", "<unset>"
-                )
-                api_version = os.getenv("AZURE_OPENAI_API_VERSION", "<unset>")
-                endpoint = os.getenv("AZURE_OPENAI_ENDPOINT", "<unset>")
-                debug_message = f"Azure OpenAI debug: endpoint={endpoint}, model={model}, api_version={api_version}"
-                if hasattr(exc, "add_note"):
-                    exc.add_note(debug_message)
-                elif exc.args:
-                    exc.args = (f"{exc.args[0]}\n{debug_message}", *exc.args[1:])
-                else:
-                    exc.args = (debug_message,)
-                raise
-
-        return wrapper
-
-    return decorator
-
-
-def _get_azure_embedding_deployment_name() -> str:
-    return os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") or os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"]
-
-
-def _create_azure_openai_embedding_client(
-    *,
-    api_key: str | None = None,
-    credential: AzureCliCredential | None = None,
-) -> AzureOpenAIEmbeddingClient:
-    resolved_api_key = (
-        api_key if api_key is not None else None if credential is not None else os.getenv("AZURE_OPENAI_API_KEY")
-    )
-    return AzureOpenAIEmbeddingClient(
-        deployment_name=_get_azure_embedding_deployment_name(),
-        api_key=resolved_api_key,
-        endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
-        api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
-        credential=credential,
-    )
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_openai_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_integration_azure_openai_get_embeddings() -> None:
-    """End-to-end test of Azure OpenAI embedding generation."""
-    async with AzureCliCredential() as credential:
-        client = _create_azure_openai_embedding_client(credential=credential)
-
-        result = await client.get_embeddings(["hello world"])
-
-    assert len(result) == 1
-    assert isinstance(result[0].vector, list)
-    assert len(result[0].vector) > 0
-    assert all(isinstance(v, float) for v in result[0].vector)
-    assert result[0].model_id is not None
-    assert result.usage is not None
-    assert result.usage["input_token_count"] > 0
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_openai_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_integration_azure_openai_get_embeddings_multiple() -> None:
-    """Test Azure OpenAI embedding generation for multiple inputs."""
-    async with AzureCliCredential() as credential:
-        client = _create_azure_openai_embedding_client(credential=credential)
-
-        result = await client.get_embeddings(["hello", "world", "test"])
-
-    assert len(result) == 3
-    dims = [len(e.vector) for e in result]
-    assert all(d == dims[0] for d in dims)
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_openai_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_integration_azure_openai_get_embeddings_with_dimensions() -> None:
-    """Test Azure OpenAI embedding generation with custom dimensions."""
-    async with AzureCliCredential() as credential:
-        client = _create_azure_openai_embedding_client(credential=credential)
-
-        options: OpenAIEmbeddingOptions = {"dimensions": 256}
-        result = await client.get_embeddings(["hello world"], options=options)
-
-    assert len(result) == 1
-    assert len(result[0].vector) == 256
@@ -1,542 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-import json
-import logging
-import os
-from functools import wraps
-from pathlib import Path
-from typing import Annotated, Any
-
-import pytest
-from agent_framework import (
-    Agent,
-    AgentResponse,
-    ChatResponse,
-    Content,
-    Message,
-    SupportsChatGetResponse,
-    tool,
-)
-from agent_framework.azure import AzureOpenAIResponsesClient
-from azure.identity import AzureCliCredential
-from pydantic import BaseModel
-from pytest import param
-
-pytestmark = pytest.mark.filterwarnings("ignore:AzureOpenAIResponsesClient is deprecated\\..*:DeprecationWarning")
-
-skip_if_azure_integration_tests_disabled = pytest.mark.skipif(
-    os.getenv("AZURE_OPENAI_ENDPOINT", "") in ("", "https://test-endpoint.com"),
-    reason="No real AZURE_OPENAI_ENDPOINT provided; skipping integration tests.",
-)
-
-
-def _with_azure_openai_debug() -> Any:
-    def decorator(func: Any) -> Any:
-        @wraps(func)
-        async def wrapper(*args: Any, **kwargs: Any) -> Any:
-            try:
-                return await func(*args, **kwargs)
-            except Exception as exc:
-                model = os.getenv("AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME") or os.getenv(
-                    "AZURE_OPENAI_DEPLOYMENT_NAME", "<unset>"
-                )
-                api_version = os.getenv("AZURE_OPENAI_API_VERSION", "<unset>")
-                endpoint = os.getenv("AZURE_OPENAI_ENDPOINT", "<unset>")
-                debug_message = f"Azure OpenAI debug: endpoint={endpoint}, model={model}, api_version={api_version}"
-                if hasattr(exc, "add_note"):
-                    exc.add_note(debug_message)
-                elif exc.args:
-                    exc.args = (f"{exc.args[0]}\n{debug_message}", *exc.args[1:])
-                else:
-                    exc.args = (debug_message,)
-                raise
-
-        return wrapper
-
-    return decorator
-
-
-logger = logging.getLogger(__name__)
-
-
-class OutputStruct(BaseModel):
-    """A structured output for testing purposes."""
-
-    location: str
-    weather: str
-
-
-@tool(approval_mode="never_require")
-async def get_weather(location: Annotated[str, "The location as a city name"]) -> str:
-    """Get the current weather in a given location."""
-    # Implementation of the tool to get weather
-    return f"The weather in {location} is sunny and 72°F."
-
-
-async def create_vector_store(
-    client: AzureOpenAIResponsesClient,
-) -> tuple[str, Content]:
-    """Create a vector store with sample documents for testing."""
-    file = await client.client.files.create(
-        file=("todays_weather.txt", b"The weather today is sunny with a high of 75F."),
-        purpose="assistants",
-    )
-    vector_store = await client.client.vector_stores.create(
-        name="knowledge_base",
-        expires_after={"anchor": "last_active_at", "days": 1},
-    )
-    result = await client.client.vector_stores.files.create_and_poll(vector_store_id=vector_store.id, file_id=file.id)
-    if result.last_error is not None:
-        raise Exception(f"Vector store file processing failed with status: {result.last_error.message}")
-
-    return file.id, Content.from_hosted_vector_store(vector_store_id=vector_store.id)
-
-
-async def delete_vector_store(client: AzureOpenAIResponsesClient, file_id: str, vector_store_id: str) -> None:
-    """Delete the vector store after tests."""
-
-    await client.client.vector_stores.delete(vector_store_id=vector_store_id)
-    await client.client.files.delete(file_id=file_id)
-
-
-def test_init(azure_openai_unit_test_env: dict[str, str]) -> None:
-    # Test successful initialization
-    azure_responses_client = AzureOpenAIResponsesClient(credential=AzureCliCredential())
-
-    assert azure_responses_client.model == azure_openai_unit_test_env["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"]
-    assert isinstance(azure_responses_client, SupportsChatGetResponse)
-
-
-def test_init_validation_fail() -> None:
-    # Test successful initialization
-    with pytest.raises(ValueError):
-        AzureOpenAIResponsesClient(api_key="34523", deployment_name={"test": "dict"})  # type: ignore
-
-
-def test_init_model_id_constructor(azure_openai_unit_test_env: dict[str, str]) -> None:
-    # Test successful initialization
-    model_id = "test_model_id"
-    azure_responses_client = AzureOpenAIResponsesClient(deployment_name=model_id)
-
-    assert azure_responses_client.model == model_id
-    assert isinstance(azure_responses_client, SupportsChatGetResponse)
-
-
-def test_init_model_id_kwarg(azure_openai_unit_test_env: dict[str, str]) -> None:
-    """Test that model_id kwarg correctly sets the deployment name (issue #4299)."""
-    azure_responses_client = AzureOpenAIResponsesClient(model_id="gpt-4o")
-
-    assert azure_responses_client.model == "gpt-4o"
-    assert isinstance(azure_responses_client, SupportsChatGetResponse)
-
-
-def test_init_model_id_kwarg_does_not_override_deployment_name(
-    azure_openai_unit_test_env: dict[str, str],
-) -> None:
-    """Test that deployment_name takes precedence over model_id kwarg (issue #4299)."""
-    azure_responses_client = AzureOpenAIResponsesClient(deployment_name="my-deployment", model_id="gpt-4o")
-
-    assert azure_responses_client.model == "my-deployment"
-    assert isinstance(azure_responses_client, SupportsChatGetResponse)
-
-
-def test_init_model_id_kwarg_none(azure_openai_unit_test_env: dict[str, str]) -> None:
-    """Test that model_id=None does not override the env-var deployment name."""
-    azure_responses_client = AzureOpenAIResponsesClient(model_id=None)
-
-    assert azure_responses_client.model == azure_openai_unit_test_env["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"]
-
-
-def test_init_with_default_header(azure_openai_unit_test_env: dict[str, str]) -> None:
-    default_headers = {"X-Unit-Test": "test-guid"}
-
-    # Test successful initialization
-    azure_responses_client = AzureOpenAIResponsesClient(
-        default_headers=default_headers,
-    )
-
-    assert azure_responses_client.model == azure_openai_unit_test_env["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"]
-    assert isinstance(azure_responses_client, SupportsChatGetResponse)
-
-    # Assert that the default header we added is present in the client's default headers
-    for key, value in default_headers.items():
-        assert key in azure_responses_client.client.default_headers
-        assert azure_responses_client.client.default_headers[key] == value
-
-
-@pytest.mark.parametrize("exclude_list", [["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"]], indirect=True)
-def test_init_with_empty_model_id(azure_openai_unit_test_env: dict[str, str]) -> None:
-    with pytest.raises(ValueError):
-        AzureOpenAIResponsesClient()
-
-
-def test_serialize(azure_openai_unit_test_env: dict[str, str]) -> None:
-    default_headers = {"X-Unit-Test": "test-guid"}
-
-    settings = {
-        "deployment_name": azure_openai_unit_test_env["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
-        "api_key": azure_openai_unit_test_env["AZURE_OPENAI_API_KEY"],
-        "default_headers": default_headers,
-    }
-
-    azure_responses_client = AzureOpenAIResponsesClient.from_dict(settings)
-    dumped_settings = azure_responses_client.to_dict()
-    assert dumped_settings["deployment_name"] == azure_openai_unit_test_env["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"]
-    assert "api_key" not in dumped_settings
-    # Assert that the default header we added is present in the dumped_settings default headers
-    for key, value in default_headers.items():
-        assert key in dumped_settings["default_headers"]
-        assert dumped_settings["default_headers"][key] == value
-    # Assert that the 'User-Agent' header is not present in the dumped_settings default headers
-    assert "User-Agent" not in dumped_settings["default_headers"]
-
-
-# region Integration Tests
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_integration_tests_disabled
-@pytest.mark.parametrize(
-    "option_name,option_value,needs_validation",
-    [
-        # Simple ChatOptions - just verify they don't fail
-        param("max_tokens", 500, False, id="max_tokens"),
-        param("seed", 123, False, id="seed"),
-        param("user", "test-user-id", False, id="user"),
-        param("metadata", {"test_key": "test_value"}, False, id="metadata"),
-        param("frequency_penalty", 0.5, False, id="frequency_penalty"),
-        param("presence_penalty", 0.3, False, id="presence_penalty"),
-        param("stop", ["END"], False, id="stop"),
-        param("allow_multiple_tool_calls", True, False, id="allow_multiple_tool_calls"),
-        param("tool_choice", "none", True, id="tool_choice_none"),
-        # OpenAIResponsesOptions - just verify they don't fail
-        param("safety_identifier", "user-hash-abc123", False, id="safety_identifier"),
-        param("truncation", "auto", False, id="truncation"),
-        param("prompt_cache_key", "test-cache-key", False, id="prompt_cache_key"),
-        param("max_tool_calls", 3, False, id="max_tool_calls"),
-        # Complex options requiring output validation
-        param("tools", [get_weather], True, id="tools_function"),
-        param("tool_choice", "auto", True, id="tool_choice_auto"),
-        param(
-            "tool_choice",
-            {"mode": "required", "required_function_name": "get_weather"},
-            True,
-            id="tool_choice_required",
-        ),
-        param("response_format", OutputStruct, True, id="response_format_pydantic"),
-        param(
-            "response_format",
-            {
-                "type": "json_schema",
-                "json_schema": {
-                    "name": "WeatherDigest",
-                    "strict": True,
-                    "schema": {
-                        "title": "WeatherDigest",
-                        "type": "object",
-                        "properties": {
-                            "location": {"type": "string"},
-                            "conditions": {"type": "string"},
-                            "temperature_c": {"type": "number"},
-                            "advisory": {"type": "string"},
-                        },
-                        "required": [
-                            "location",
-                            "conditions",
-                            "temperature_c",
-                            "advisory",
-                        ],
-                        "additionalProperties": False,
-                    },
-                },
-            },
-            True,
-            id="response_format_runtime_json_schema",
-        ),
-    ],
-)
-@_with_azure_openai_debug()
-async def test_integration_options(
-    option_name: str,
-    option_value: Any,
-    needs_validation: bool,
-) -> None:
-    """Parametrized test covering all ChatOptions and OpenAIResponsesOptions.
-
-    Tests both streaming and non-streaming modes for each option to ensure
-    they don't cause failures. Options marked with needs_validation also
-    check that the feature actually works correctly.
-    """
-    client = AzureOpenAIResponsesClient(credential=AzureCliCredential())
-    # Need at least 2 iterations for tool_choice tests: one to get function call, one to get final response
-    client.function_invocation_configuration["max_iterations"] = 2
-
-    # Prepare test message
-    if option_name == "tools" or option_name == "tool_choice":
-        # Use weather-related prompt for tool tests
-        messages = [Message(role="user", text="What is the weather in Seattle?")]
-    elif option_name == "response_format":
-        # Use prompt that works well with structured output
-        messages = [
-            Message(role="user", text="The weather in Seattle is sunny"),
-            Message(role="user", text="What is the weather in Seattle?"),
-        ]
-    else:
-        # Generic prompt for simple options
-        messages = [Message(role="user", text="Say 'Hello World' briefly.")]
-
-    # Build options dict
-    options: dict[str, Any] = {option_name: option_value}
-
-    # Add tools if testing tool_choice to avoid errors
-    if option_name == "tool_choice":
-        options["tools"] = [get_weather]
-
-    # Test streaming mode
-    response = await client.get_response(messages=messages, stream=True, options=options).get_final_response()
-
-    assert response is not None
-    assert isinstance(response, ChatResponse)
-    assert response.text is not None, f"No text in response for option '{option_name}'"
-    assert len(response.text) > 0, f"Empty response for option '{option_name}'"
-
-    # Validate based on option type
-    if needs_validation:
-        if option_name == "tools" or option_name == "tool_choice":
-            # Should have called the weather function
-            text = response.text.lower()
-            assert "sunny" in text or "seattle" in text, f"Tool not invoked for {option_name}"
-        elif option_name == "response_format":
-            if option_value == OutputStruct:
-                # Should have structured output
-                assert response.value is not None, "No structured output"
-                assert isinstance(response.value, OutputStruct)
-                assert "seattle" in response.value.location.lower()
-            else:
-                # Runtime JSON schema
-                assert response.value is None, "No structured output, can't parse any json."
-                response_value = json.loads(response.text)
-                assert isinstance(response_value, dict)
-                assert "location" in response_value
-                assert "seattle" in response_value["location"].lower()
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_integration_web_search() -> None:
-    client = AzureOpenAIResponsesClient(credential=AzureCliCredential())
-    response = await client.get_response(
-        messages=[
-            Message(
-                role="user",
-                text="What is the current weather? Do not ask for my current location.",
-            )
-        ],
-        options={
-            "tools": [
-                AzureOpenAIResponsesClient.get_web_search_tool(user_location={"country": "US", "city": "Seattle"})
-            ]
-        },
-        stream=True,
-    ).get_final_response()
-
-    assert response.text is not None
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_integration_client_file_search() -> None:
-    """Test Azure responses client with file search tool."""
-    azure_responses_client = AzureOpenAIResponsesClient(credential=AzureCliCredential())
-    file_id, vector_store = await create_vector_store(azure_responses_client)
-    try:
-        # Test that the client will use the file search tool
-        response = await azure_responses_client.get_response(
-            messages=[
-                Message(
-                    role="user",
-                    text="What is the weather today? Do a file search to find the answer.",
-                )
-            ],
-            options={
-                "tools": [
-                    AzureOpenAIResponsesClient.get_file_search_tool(vector_store_ids=[vector_store.vector_store_id])
-                ],
-                "tool_choice": "auto",
-            },
-        )
-
-        assert "sunny" in response.text.lower()
-        assert "75" in response.text
-    finally:
-        await delete_vector_store(azure_responses_client, file_id, vector_store.vector_store_id)
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_integration_client_file_search_streaming() -> None:
-    """Test Azure responses client with file search tool and streaming."""
-    azure_responses_client = AzureOpenAIResponsesClient(credential=AzureCliCredential())
-    file_id, vector_store = await create_vector_store(azure_responses_client)
-    # Test that the client will use the file search tool
-    try:
-        response_stream = azure_responses_client.get_response(
-            messages=[
-                Message(
-                    role="user",
-                    text="What is the weather today? Do a file search to find the answer.",
-                )
-            ],
-            stream=True,
-            options={
-                "tools": [
-                    AzureOpenAIResponsesClient.get_file_search_tool(vector_store_ids=[vector_store.vector_store_id])
-                ],
-                "tool_choice": "auto",
-            },
-        )
-
-        full_response = await response_stream.get_final_response()
-        assert "sunny" in full_response.text.lower()
-        assert "75" in full_response.text
-    finally:
-        await delete_vector_store(azure_responses_client, file_id, vector_store.vector_store_id)
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_integration_client_agent_hosted_mcp_tool() -> None:
-    """Integration test for MCP tool with Azure Response Agent using Microsoft Learn MCP."""
-    client = AzureOpenAIResponsesClient(credential=AzureCliCredential())
-    response = await client.get_response(
-        messages=[Message(role="user", text="How to create an Azure storage account using az cli?")],
-        options={
-            # this needs to be high enough to handle the full MCP tool response.
-            "max_tokens": 5000,
-            "tools": AzureOpenAIResponsesClient.get_mcp_tool(
-                name="Microsoft Learn MCP",
-                url="https://learn.microsoft.com/api/mcp",
-            ),
-        },
-    )
-    assert isinstance(response, ChatResponse)
-    # MCP server may return empty response intermittently - skip test rather than fail
-    if not response.text:
-        pytest.skip("MCP server returned empty response - service-side issue")
-    # Should contain Azure-related content since it's asking about Azure CLI
-    assert any(term in response.text.lower() for term in ["azure", "storage", "account", "cli"])
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_integration_client_agent_hosted_code_interpreter_tool():
-    """Test Azure Responses Client agent with code interpreter tool."""
-    client = AzureOpenAIResponsesClient(credential=AzureCliCredential())
-
-    response = await client.get_response(
-        messages=[
-            Message(
-                role="user",
-                text="Calculate the sum of numbers from 1 to 10 using Python code.",
-            )
-        ],
-        options={
-            "tools": [AzureOpenAIResponsesClient.get_code_interpreter_tool()],
-        },
-    )
-    # Should contain calculation result (sum of 1-10 = 55) or code execution content
-    contains_relevant_content = any(
-        term in response.text.lower() for term in ["55", "sum", "code", "python", "calculate", "10"]
-    )
-    assert contains_relevant_content or len(response.text.strip()) > 10
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_integration_client_agent_existing_session():
-    """Test Azure Responses Client agent with existing session to continue conversations across agent instances."""
-    # First conversation - capture the session
-    preserved_session = None
-
-    async with Agent(
-        client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
-        instructions="You are a helpful assistant with good memory.",
-    ) as first_agent:
-        # Start a conversation and capture the session
-        session = first_agent.create_session()
-        first_response = await first_agent.run(
-            "My hobby is photography. Remember this.", session=session, options={"store": True}
-        )
-
-        assert isinstance(first_response, AgentResponse)
-        assert first_response.text is not None
-
-        # Preserve the session for reuse
-        preserved_session = session
-
-    # Second conversation - reuse the session in a new agent instance
-    if preserved_session:
-        async with Agent(
-            client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
-            instructions="You are a helpful assistant with good memory.",
-        ) as second_agent:
-            # Reuse the preserved session
-            second_response = await second_agent.run(
-                "What is my hobby?", session=preserved_session, options={"store": True}
-            )
-
-            assert isinstance(second_response, AgentResponse)
-            assert second_response.text is not None
-            assert "photography" in second_response.text.lower()
-
-
-@pytest.mark.flaky
-@pytest.mark.integration
-@skip_if_azure_integration_tests_disabled
-@_with_azure_openai_debug()
-async def test_azure_openai_responses_client_tool_rich_content_image() -> None:
-    """Test that Azure OpenAI Responses client can handle tool results containing images."""
-    image_path = Path(__file__).parent.parent / "assets" / "sample_image.jpg"
-    image_bytes = image_path.read_bytes()
-
-    @tool(approval_mode="never_require")
-    def get_test_image() -> Content:
-        """Return a test image for analysis."""
-        return Content.from_data(data=image_bytes, media_type="image/jpeg")
-
-    client = AzureOpenAIResponsesClient(credential=AzureCliCredential())
-    client.function_invocation_configuration["max_iterations"] = 2
-
-    for streaming in [False, True]:
-        messages = [
-            Message(
-                role="user",
-                text="Call the get_test_image tool and describe what you see.",
-            )
-        ]
-        options: dict[str, Any] = {"tools": [get_test_image], "tool_choice": "auto"}
-
-        if streaming:
-            response = await client.get_response(messages=messages, stream=True, options=options).get_final_response()
-        else:
-            response = await client.get_response(messages=messages, options=options)
-
-        assert response is not None
-        assert isinstance(response, ChatResponse)
-        assert response.text is not None
-        assert len(response.text) > 0
-        # sample_image.jpg contains a photo of a house; the model should mention it.
-        assert "house" in response.text.lower(), f"Model did not describe the house image. Response: {response.text}"
@@ -1,131 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-import warnings
-from unittest.mock import MagicMock
-
-import pytest
-from agent_framework import SupportsChatGetResponse
-
-warnings.filterwarnings(
-    "ignore",
-    message=r"RawAzureAIClient is deprecated\..*",
-    category=DeprecationWarning,
-)
-
-from agent_framework.azure import AzureOpenAIResponsesClient  # noqa: E402
-from azure.identity import AzureCliCredential  # noqa: E402
-
-pytestmark = pytest.mark.filterwarnings("ignore:AzureOpenAIResponsesClient is deprecated\\..*:DeprecationWarning")
-
-
-def test_init_with_project_client(azure_openai_unit_test_env: dict[str, str]) -> None:
-    """Test initialization with an existing AIProjectClient."""
-    from unittest.mock import patch
-
-    from openai import AsyncOpenAI
-
-    # Create a mock AIProjectClient that returns a mock AsyncOpenAI client
-    mock_openai_client = MagicMock(spec=AsyncOpenAI)
-    mock_openai_client.default_headers = {}
-
-    mock_project_client = MagicMock()
-    mock_project_client.get_openai_client.return_value = mock_openai_client
-
-    with patch(
-        "agent_framework_azure_ai._deprecated_azure_openai.AzureOpenAIResponsesClient._create_client_from_project",
-        return_value=mock_openai_client,
-    ):
-        azure_responses_client = AzureOpenAIResponsesClient(
-            project_client=mock_project_client,
-            deployment_name="gpt-4o",
-        )
-
-    assert azure_responses_client.model == "gpt-4o"
-    assert azure_responses_client.client is mock_openai_client
-    assert isinstance(azure_responses_client, SupportsChatGetResponse)
-
-
-def test_init_with_project_endpoint(azure_openai_unit_test_env: dict[str, str]) -> None:
-    """Test initialization with a project endpoint and credential."""
-    from unittest.mock import patch
-
-    from openai import AsyncOpenAI
-
-    mock_openai_client = MagicMock(spec=AsyncOpenAI)
-    mock_openai_client.default_headers = {}
-
-    with patch(
-        "agent_framework_azure_ai._deprecated_azure_openai.AzureOpenAIResponsesClient._create_client_from_project",
-        return_value=mock_openai_client,
-    ):
-        azure_responses_client = AzureOpenAIResponsesClient(
-            project_endpoint="https://test-project.services.ai.azure.com",
-            deployment_name="gpt-4o",
-            credential=AzureCliCredential(),
-        )
-
-    assert azure_responses_client.model == "gpt-4o"
-    assert azure_responses_client.client is mock_openai_client
-    assert isinstance(azure_responses_client, SupportsChatGetResponse)
-
-
-def test_create_client_from_project_with_project_client() -> None:
-    """Test _create_client_from_project with an existing project client."""
-    from openai import AsyncOpenAI
-
-    mock_openai_client = MagicMock(spec=AsyncOpenAI)
-    mock_project_client = MagicMock()
-    mock_project_client.get_openai_client.return_value = mock_openai_client
-
-    result = AzureOpenAIResponsesClient._create_client_from_project(
-        project_client=mock_project_client,
-        project_endpoint=None,
-        credential=None,
-    )
-
-    assert result is mock_openai_client
-    mock_project_client.get_openai_client.assert_called_once()
-
-
-def test_create_client_from_project_with_endpoint() -> None:
-    """Test _create_client_from_project with a project endpoint."""
-    from unittest.mock import patch
-
-    from openai import AsyncOpenAI
-
-    mock_openai_client = MagicMock(spec=AsyncOpenAI)
-    mock_credential = MagicMock()
-
-    with patch("agent_framework_azure_ai._deprecated_azure_openai.AIProjectClient") as MockAIProjectClient:
-        mock_instance = MockAIProjectClient.return_value
-        mock_instance.get_openai_client.return_value = mock_openai_client
-
-        result = AzureOpenAIResponsesClient._create_client_from_project(
-            project_client=None,
-            project_endpoint="https://test-project.services.ai.azure.com",
-            credential=mock_credential,
-        )
-
-    assert result is mock_openai_client
-    MockAIProjectClient.assert_called_once()
-    mock_instance.get_openai_client.assert_called_once()
-
-
-def test_create_client_from_project_missing_endpoint() -> None:
-    """Test _create_client_from_project raises error when endpoint is missing."""
-    with pytest.raises(ValueError, match="project endpoint is required"):
-        AzureOpenAIResponsesClient._create_client_from_project(
-            project_client=None,
-            project_endpoint=None,
-            credential=MagicMock(),
-        )
-
-
-def test_create_client_from_project_missing_credential() -> None:
-    """Test _create_client_from_project raises error when credential is missing."""
-    with pytest.raises(ValueError, match="credential is required"):
-        AzureOpenAIResponsesClient._create_client_from_project(
-            project_client=None,
-            project_endpoint="https://test-project.services.ai.azure.com",
-            credential=None,
-        )
@@ -1,773 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-import os
-from typing import Any
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from agent_framework import (
-    Agent,
-    tool,
-)
-from azure.ai.agents.models import (
-    Agent as AzureAgent,
-)
-from azure.ai.agents.models import (
-    CodeInterpreterToolDefinition,
-)
-from pydantic import BaseModel
-
-from agent_framework_azure_ai import (
-    AzureAIAgentClient,
-    AzureAIAgentsProvider,
-    AzureAISettings,
-)
-from agent_framework_azure_ai._shared import (
-    from_azure_ai_agent_tools,
-    to_azure_ai_agent_tools,
-)
-
-skip_if_azure_ai_integration_tests_disabled = pytest.mark.skipif(
-    os.getenv("AZURE_AI_PROJECT_ENDPOINT", "") in ("", "https://test-project.cognitiveservices.azure.com/"),
-    reason="No real AZURE_AI_PROJECT_ENDPOINT provided; skipping integration tests.",
-)
-
-# region Provider Initialization Tests
-
-
-def test_provider_init_with_agents_client(mock_agents_client: MagicMock) -> None:
-    """Test AzureAIAgentsProvider initialization with existing AgentsClient."""
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    assert provider._agents_client is mock_agents_client  # type: ignore
-    assert provider._should_close_client is False  # type: ignore
-
-
-def test_provider_init_with_credential(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_azure_credential: MagicMock,
-) -> None:
-    """Test AzureAIAgentsProvider initialization with credential."""
-    with patch("agent_framework_azure_ai._agent_provider.AgentsClient") as mock_client_class:
-        mock_client_instance = MagicMock()
-        mock_client_class.return_value = mock_client_instance
-
-        provider = AzureAIAgentsProvider(credential=mock_azure_credential)
-
-        mock_client_class.assert_called_once()
-        assert provider._agents_client is mock_client_instance  # type: ignore
-        assert provider._should_close_client is True  # type: ignore
-
-
-def test_provider_init_with_explicit_endpoint(mock_azure_credential: MagicMock) -> None:
-    """Test AzureAIAgentsProvider initialization with explicit endpoint."""
-    with patch("agent_framework_azure_ai._agent_provider.AgentsClient") as mock_client_class:
-        mock_client_instance = MagicMock()
-        mock_client_class.return_value = mock_client_instance
-
-        provider = AzureAIAgentsProvider(
-            project_endpoint="https://custom-endpoint.com/",
-            credential=mock_azure_credential,
-        )
-
-        mock_client_class.assert_called_once()
-        call_kwargs = mock_client_class.call_args.kwargs
-        assert call_kwargs["endpoint"] == "https://custom-endpoint.com/"
-        assert provider._should_close_client is True  # type: ignore
-
-
-def test_provider_init_missing_endpoint_raises(
-    mock_azure_credential: MagicMock,
-) -> None:
-    """Test AzureAIAgentsProvider raises error when endpoint is missing."""
-    # Mock load_settings to return a dict with None for project_endpoint
-    with patch("agent_framework_azure_ai._agent_provider.load_settings") as mock_load_settings:
-        mock_load_settings.return_value = {"project_endpoint": None, "model_deployment_name": "test-model"}
-
-        with pytest.raises(ValueError) as exc_info:
-            AzureAIAgentsProvider(credential=mock_azure_credential)
-
-        assert "project endpoint is required" in str(exc_info.value).lower()
-
-
-def test_provider_init_missing_credential_raises(azure_ai_unit_test_env: dict[str, str]) -> None:
-    """Test AzureAIAgentsProvider raises error when credential is missing."""
-    with pytest.raises(ValueError) as exc_info:
-        AzureAIAgentsProvider()
-
-    assert "credential is required" in str(exc_info.value).lower()
-
-
-# endregion
-
-# region Context Manager Tests
-
-
-async def test_provider_context_manager_closes_client(mock_agents_client: MagicMock) -> None:
-    """Test that context manager closes client when it was created by provider."""
-    with patch("agent_framework_azure_ai._agent_provider.AgentsClient") as mock_client_class:
-        mock_client_instance = AsyncMock()
-        mock_client_class.return_value = mock_client_instance
-
-        with patch.object(AzureAIAgentsProvider, "__init__", lambda self: None):  # type: ignore
-            provider = AzureAIAgentsProvider.__new__(AzureAIAgentsProvider)
-            provider._agents_client = mock_client_instance  # type: ignore
-            provider._should_close_client = True  # type: ignore
-            provider._settings = AzureAISettings(project_endpoint="https://test.com")  # type: ignore
-
-        async with provider:
-            pass
-
-        mock_client_instance.close.assert_called_once()
-
-
-async def test_provider_context_manager_does_not_close_external_client(mock_agents_client: MagicMock) -> None:
-    """Test that context manager does not close externally provided client."""
-    mock_agents_client.close = AsyncMock()
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    async with provider:
-        pass
-
-    mock_agents_client.close.assert_not_called()
-
-
-# endregion
-
-# region create_agent Tests
-
-
-async def test_create_agent_basic(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test creating a basic agent."""
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "test-agent-id"
-    mock_agent.name = "TestAgent"
-    mock_agent.description = "A test agent"
-    mock_agent.instructions = "Be helpful"
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = 0.7
-    mock_agent.top_p = 0.9
-    mock_agent.tools = []
-    mock_agents_client.create_agent = AsyncMock(return_value=mock_agent)
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    agent = await provider.create_agent(
-        name="TestAgent",
-        instructions="Be helpful",
-        description="A test agent",
-    )
-
-    assert isinstance(agent, Agent)
-    assert agent.name == "TestAgent"
-    assert agent.id == "test-agent-id"
-    mock_agents_client.create_agent.assert_called_once()
-
-
-async def test_create_agent_with_model(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test creating an agent with explicit model."""
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "test-agent-id"
-    mock_agent.name = "TestAgent"
-    mock_agent.description = None
-    mock_agent.instructions = None
-    mock_agent.model = "custom-model"
-    mock_agent.temperature = None
-    mock_agent.top_p = None
-    mock_agent.tools = []
-    mock_agents_client.create_agent = AsyncMock(return_value=mock_agent)
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    await provider.create_agent(name="TestAgent", model="custom-model")
-
-    call_kwargs = mock_agents_client.create_agent.call_args.kwargs
-    assert call_kwargs["model"] == "custom-model"
-
-
-async def test_create_agent_with_tools(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test creating an agent with tools."""
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "test-agent-id"
-    mock_agent.name = "TestAgent"
-    mock_agent.description = None
-    mock_agent.instructions = None
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = None
-    mock_agent.top_p = None
-    mock_agent.tools = []
-    mock_agents_client.create_agent = AsyncMock(return_value=mock_agent)
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    @tool(approval_mode="never_require")
-    def get_weather(city: str) -> str:
-        """Get weather for a city."""
-        return f"Weather in {city}"
-
-    await provider.create_agent(name="TestAgent", tools=get_weather)
-
-    call_kwargs = mock_agents_client.create_agent.call_args.kwargs
-    assert "tools" in call_kwargs
-    assert len(call_kwargs["tools"]) > 0
-
-
-async def test_create_agent_with_response_format(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test creating an agent with structured response format via default_options."""
-
-    class WeatherResponse(BaseModel):
-        temperature: float
-        description: str
-
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "test-agent-id"
-    mock_agent.name = "TestAgent"
-    mock_agent.description = None
-    mock_agent.instructions = None
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = None
-    mock_agent.top_p = None
-    mock_agent.tools = []
-    mock_agents_client.create_agent = AsyncMock(return_value=mock_agent)
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    await provider.create_agent(
-        name="TestAgent",
-        default_options={"response_format": WeatherResponse},
-    )
-
-    call_kwargs = mock_agents_client.create_agent.call_args.kwargs
-    assert "response_format" in call_kwargs
-
-
-async def test_create_agent_missing_model_raises(
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test that create_agent raises error when model is not specified."""
-    # Create provider with mocked settings that has no model
-    with patch("agent_framework_azure_ai._agent_provider.load_settings") as mock_load_settings:
-        mock_load_settings.return_value = {"project_endpoint": "https://test.com", "model_deployment_name": None}
-
-        provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-        with pytest.raises(ValueError) as exc_info:
-            await provider.create_agent(name="TestAgent")
-
-        assert "model deployment name is required" in str(exc_info.value).lower()
-
-
-# endregion
-
-# region get_agent Tests
-
-
-async def test_get_agent_by_id(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test getting an agent by ID."""
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "existing-agent-id"
-    mock_agent.name = "ExistingAgent"
-    mock_agent.description = "An existing agent"
-    mock_agent.instructions = "Be helpful"
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = 0.7
-    mock_agent.top_p = 0.9
-    mock_agent.tools = []
-    mock_agents_client.get_agent = AsyncMock(return_value=mock_agent)
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    agent = await provider.get_agent("existing-agent-id")
-
-    assert isinstance(agent, Agent)
-    assert agent.id == "existing-agent-id"
-    mock_agents_client.get_agent.assert_called_once_with("existing-agent-id")
-
-
-async def test_get_agent_with_function_tools(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test getting an agent that has function tools requires tool implementations."""
-    mock_function_tool = MagicMock()
-    mock_function_tool.type = "function"
-    mock_function_tool.function = MagicMock()
-    mock_function_tool.function.name = "get_weather"
-
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "agent-with-tools"
-    mock_agent.name = "AgentWithTools"
-    mock_agent.description = None
-    mock_agent.instructions = None
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = None
-    mock_agent.top_p = None
-    mock_agent.tools = [mock_function_tool]
-    mock_agents_client.get_agent = AsyncMock(return_value=mock_agent)
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    with pytest.raises(ValueError) as exc_info:
-        await provider.get_agent("agent-with-tools")
-
-    assert "get_weather" in str(exc_info.value)
-
-
-async def test_get_agent_with_provided_function_tools(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test getting an agent with function tools when implementations are provided."""
-    mock_function_tool = MagicMock()
-    mock_function_tool.type = "function"
-    mock_function_tool.function = MagicMock()
-    mock_function_tool.function.name = "get_weather"
-
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "agent-with-tools"
-    mock_agent.name = "AgentWithTools"
-    mock_agent.description = None
-    mock_agent.instructions = None
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = None
-    mock_agent.top_p = None
-    mock_agent.tools = [mock_function_tool]
-    mock_agents_client.get_agent = AsyncMock(return_value=mock_agent)
-
-    @tool(approval_mode="never_require")
-    def get_weather(city: str) -> str:
-        """Get weather for a city."""
-        return f"Weather in {city}"
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    agent = await provider.get_agent("agent-with-tools", tools=get_weather)
-
-    assert isinstance(agent, Agent)
-    assert agent.id == "agent-with-tools"
-
-
-# endregion
-
-# region as_agent Tests
-
-
-def test_as_agent_wraps_without_http(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test as_agent wraps Agent object without making HTTP calls."""
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "wrap-agent-id"
-    mock_agent.name = "WrapAgent"
-    mock_agent.description = "Wrapped agent"
-    mock_agent.instructions = "Be helpful"
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = 0.5
-    mock_agent.top_p = 0.8
-    mock_agent.tools = []
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    agent = provider.as_agent(mock_agent)
-
-    assert isinstance(agent, Agent)
-    assert agent.id == "wrap-agent-id"
-    assert agent.name == "WrapAgent"
-    # Ensure no HTTP calls were made
-    mock_agents_client.get_agent.assert_not_called()
-    mock_agents_client.create_agent.assert_not_called()
-
-
-def test_as_agent_with_function_tools_validates(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test as_agent validates that function tool implementations are provided."""
-    mock_function_tool = MagicMock()
-    mock_function_tool.type = "function"
-    mock_function_tool.function = MagicMock()
-    mock_function_tool.function.name = "my_function"
-
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "agent-id"
-    mock_agent.name = "Agent"
-    mock_agent.description = None
-    mock_agent.instructions = None
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = None
-    mock_agent.top_p = None
-    mock_agent.tools = [mock_function_tool]
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    with pytest.raises(ValueError) as exc_info:
-        provider.as_agent(mock_agent)
-
-    assert "my_function" in str(exc_info.value)
-
-
-def test_as_agent_with_hosted_tools(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test as_agent excludes hosted tools from local tools (they stay on the server agent)."""
-    mock_code_interpreter = MagicMock()
-    mock_code_interpreter.type = "code_interpreter"
-
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "agent-id"
-    mock_agent.name = "Agent"
-    mock_agent.description = None
-    mock_agent.instructions = None
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = None
-    mock_agent.top_p = None
-    mock_agent.tools = [mock_code_interpreter]
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    agent = provider.as_agent(mock_agent)
-
-    assert isinstance(agent, Agent)
-    # Hosted tools (code_interpreter, file_search, etc.) are already on the server agent
-    # and should NOT be in local tools to avoid re-sending them at run time
-    tools = agent.default_options.get("tools") or []
-    assert not any(isinstance(t, dict) and t.get("type") == "code_interpreter" for t in tools)
-
-
-def test_as_agent_with_dict_function_tools_validates(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test as_agent validates dict-format function tools require implementations."""
-    # Dict-based function tool (as returned by some Azure AI SDK operations)
-    dict_function_tool = {  # type: ignore
-        "type": "function",
-        "function": {
-            "name": "dict_based_function",
-            "description": "A function defined as dict",
-            "parameters": {"type": "object", "properties": {}},
-        },
-    }
-
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "agent-id"
-    mock_agent.name = "Agent"
-    mock_agent.description = None
-    mock_agent.instructions = None
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = None
-    mock_agent.top_p = None
-    mock_agent.tools = [dict_function_tool]
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    with pytest.raises(ValueError) as exc_info:
-        provider.as_agent(mock_agent)
-
-    assert "dict_based_function" in str(exc_info.value)
-
-
-def test_as_agent_with_dict_function_tools_provided(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_agents_client: MagicMock,
-) -> None:
-    """Test as_agent succeeds when dict-format function tools have implementations provided."""
-    dict_function_tool = {  # type: ignore
-        "type": "function",
-        "function": {
-            "name": "dict_based_function",
-            "description": "A function defined as dict",
-            "parameters": {"type": "object", "properties": {}},
-        },
-    }
-
-    mock_agent = MagicMock(spec=AzureAgent)
-    mock_agent.id = "agent-id"
-    mock_agent.name = "Agent"
-    mock_agent.description = None
-    mock_agent.instructions = None
-    mock_agent.model = "gpt-4"
-    mock_agent.temperature = None
-    mock_agent.top_p = None
-    mock_agent.tools = [dict_function_tool]
-
-    @tool
-    def dict_based_function() -> str:
-        """A function implementation."""
-        return "result"
-
-    provider = AzureAIAgentsProvider(agents_client=mock_agents_client)
-
-    agent = provider.as_agent(mock_agent, tools=dict_based_function)
-
-    assert isinstance(agent, Agent)
-    assert agent.id == "agent-id"
-
-
-# endregion
-
-# region Tool Conversion Tests - to_azure_ai_agent_tools
-
-
-def test_to_azure_ai_agent_tools_empty() -> None:
-    """Test converting empty tools list."""
-    result = to_azure_ai_agent_tools(None)
-    assert result == []
-
-    result = to_azure_ai_agent_tools([])
-    assert result == []
-
-
-def test_to_azure_ai_agent_tools_function() -> None:
-    """Test converting FunctionTool to Azure tool definition."""
-
-    @tool(approval_mode="never_require")
-    def get_weather(city: str) -> str:
-        """Get weather for a city."""
-        return f"Weather in {city}"
-
-    result = to_azure_ai_agent_tools([get_weather])
-
-    assert len(result) == 1
-    assert result[0]["type"] == "function"
-    assert result[0]["function"]["name"] == "get_weather"
-
-
-def test_to_azure_ai_agent_tools_code_interpreter() -> None:
-    """Test converting code_interpreter dict tool."""
-    tool = AzureAIAgentClient.get_code_interpreter_tool()
-
-    result = to_azure_ai_agent_tools([tool])
-
-    assert len(result) == 1
-    assert isinstance(result[0], CodeInterpreterToolDefinition)
-
-
-def test_to_azure_ai_agent_tools_file_search() -> None:
-    """Test converting file_search dict tool with vector stores."""
-    tool = AzureAIAgentClient.get_file_search_tool(vector_store_ids=["vs-123"])
-    run_options: dict[str, Any] = {}
-
-    result = to_azure_ai_agent_tools([tool], run_options)
-
-    assert len(result) == 1
-    assert "tool_resources" in run_options
-
-
-def test_to_azure_ai_agent_tools_web_search_bing_grounding(monkeypatch: Any) -> None:
-    """Test converting web_search dict tool for Bing Grounding."""
-    # Use a properly formatted connection ID as required by Azure SDK
-    valid_conn_id = (
-        "/subscriptions/test-sub/resourceGroups/test-rg/"
-        "providers/Microsoft.CognitiveServices/accounts/test-account/"
-        "projects/test-project/connections/test-connection"
-    )
-    tool = AzureAIAgentClient.get_web_search_tool(bing_connection_id=valid_conn_id)
-
-    result = to_azure_ai_agent_tools([tool])
-
-    assert len(result) > 0
-
-
-def test_to_azure_ai_agent_tools_web_search_custom(monkeypatch: Any) -> None:
-    """Test converting web_search dict tool for Custom Bing Search."""
-    tool = AzureAIAgentClient.get_web_search_tool(
-        bing_custom_connection_id="custom-conn-id",
-        bing_custom_instance_id="my-instance",
-    )
-
-    result = to_azure_ai_agent_tools([tool])
-
-    assert len(result) > 0
-
-
-def test_to_azure_ai_agent_tools_web_search_missing_config(monkeypatch: Any) -> None:
-    """Test converting web_search dict tool without bing config returns empty."""
-    monkeypatch.delenv("BING_CONNECTION_ID", raising=False)
-    monkeypatch.delenv("BING_CUSTOM_CONNECTION_ID", raising=False)
-    monkeypatch.delenv("BING_CUSTOM_INSTANCE_NAME", raising=False)
-    tool = {"type": "web_search"}
-
-    result = to_azure_ai_agent_tools([tool])
-
-    # web_search without bing connection is passed through as dict
-    assert len(result) == 1
-
-
-def test_to_azure_ai_agent_tools_mcp() -> None:
-    """Test converting MCP dict tool."""
-    tool = AzureAIAgentClient.get_mcp_tool(
-        name="my mcp server",
-        url="https://mcp.example.com",
-    )
-
-    result = to_azure_ai_agent_tools([tool])
-
-    assert len(result) > 0
-
-
-def test_to_azure_ai_agent_tools_dict_passthrough() -> None:
-    """Test that dict tools are passed through."""
-    tool = {"type": "custom_tool", "config": {"key": "value"}}
-
-    result = to_azure_ai_agent_tools([tool])
-
-    assert len(result) == 1
-    assert result[0] == tool
-
-
-def test_to_azure_ai_agent_tools_unsupported_type() -> None:
-    """Test that unsupported tool types pass through unchanged."""
-
-    class UnsupportedTool:
-        pass
-
-    unsupported = UnsupportedTool()
-    result = to_azure_ai_agent_tools([unsupported])  # type: ignore
-    assert len(result) == 1
-    assert result[0] is unsupported  # Passed through unchanged
-
-
-# endregion
-
-# region Tool Conversion Tests - from_azure_ai_agent_tools
-
-
-def test_from_azure_ai_agent_tools_empty() -> None:
-    """Test converting empty tools list."""
-    result = from_azure_ai_agent_tools(None)
-    assert result == []
-
-    result = from_azure_ai_agent_tools([])
-    assert result == []
-
-
-def test_from_azure_ai_agent_tools_code_interpreter() -> None:
-    """Test converting CodeInterpreterToolDefinition."""
-    tool = CodeInterpreterToolDefinition()
-
-    result = from_azure_ai_agent_tools([tool])
-
-    assert len(result) == 1
-    assert result[0] == {"type": "code_interpreter"}
-
-
-def test_from_azure_ai_agent_tools_code_interpreter_dict() -> None:
-    """Test converting code_interpreter dict."""
-    tool = {"type": "code_interpreter"}
-
-    result = from_azure_ai_agent_tools([tool])
-
-    assert len(result) == 1
-    assert result[0] == {"type": "code_interpreter"}
-
-
-def test_from_azure_ai_agent_tools_file_search_dict() -> None:
-    """Test converting file_search dict with vector store IDs."""
-    tool = {
-        "type": "file_search",
-        "file_search": {"vector_store_ids": ["vs-123", "vs-456"]},
-    }
-
-    result = from_azure_ai_agent_tools([tool])
-
-    assert len(result) == 1
-    assert result[0]["type"] == "file_search"
-    assert result[0]["vector_store_ids"] == ["vs-123", "vs-456"]
-
-
-def test_from_azure_ai_agent_tools_bing_grounding_dict() -> None:
-    """Test converting bing_grounding dict."""
-    tool = {
-        "type": "bing_grounding",
-        "bing_grounding": {"connection_id": "conn-123"},
-    }
-
-    result = from_azure_ai_agent_tools([tool])
-
-    assert len(result) == 1
-    assert result[0]["type"] == "bing_grounding"
-    assert result[0]["connection_id"] == "conn-123"
-
-
-def test_from_azure_ai_agent_tools_bing_custom_search_dict() -> None:
-    """Test converting bing_custom_search dict."""
-    tool = {
-        "type": "bing_custom_search",
-        "bing_custom_search": {
-            "connection_id": "custom-conn",
-            "instance_name": "my-instance",
-        },
-    }
-
-    result = from_azure_ai_agent_tools([tool])
-
-    assert len(result) == 1
-    assert result[0]["type"] == "bing_custom_search"
-    assert result[0]["connection_id"] == "custom-conn"
-    assert result[0]["instance_name"] == "my-instance"
-
-
-def test_from_azure_ai_agent_tools_mcp_dict() -> None:
-    """Test that mcp dict is skipped (hosted on Azure, no local handling needed)."""
-    tool = {
-        "type": "mcp",
-        "mcp": {
-            "server_label": "my_server",
-            "server_url": "https://mcp.example.com",
-            "allowed_tools": ["tool1"],
-        },
-    }
-
-    result = from_azure_ai_agent_tools([tool])
-
-    # MCP tools are hosted on Azure agent, skipped in conversion
-    assert len(result) == 0
-
-
-def test_from_azure_ai_agent_tools_function_dict() -> None:
-    """Test converting function tool dict (returned as-is)."""
-    tool: dict[str, Any] = {
-        "type": "function",
-        "function": {
-            "name": "get_weather",
-            "description": "Get weather",
-            "parameters": {},
-        },
-    }
-
-    result = from_azure_ai_agent_tools([tool])
-
-    assert len(result) == 1
-    assert result[0] == tool
-
-
-def test_from_azure_ai_agent_tools_unknown_dict() -> None:
-    """Test converting unknown tool type dict."""
-    tool = {"type": "unknown_tool", "config": "value"}
-
-    result = from_azure_ai_agent_tools([tool])
-
-    assert len(result) == 1
-    assert result[0] == tool
-
-
-# endregion
@@ -1,682 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from agent_framework import Agent, FunctionTool
-from agent_framework._mcp import MCPTool
-from azure.ai.projects.models import (
-    AgentVersionDetails,
-    PromptAgentDefinition,
-)
-from azure.ai.projects.models import (
-    FunctionTool as AzureFunctionTool,
-)
-
-from agent_framework_azure_ai import AzureAIProjectAgentProvider
-
-
-@pytest.fixture
-def mock_project_client() -> MagicMock:
-    """Fixture that provides a mock AIProjectClient."""
-    mock_client = MagicMock()
-
-    # Mock agents property
-    mock_client.agents = MagicMock()
-    mock_client.agents.create_version = AsyncMock()
-
-    # Mock conversations property
-    mock_client.conversations = MagicMock()
-    mock_client.conversations.create = AsyncMock()
-
-    # Mock telemetry property
-    mock_client.telemetry = MagicMock()
-    mock_client.telemetry.get_application_insights_connection_string = AsyncMock()
-
-    # Mock get_openai_client method
-    mock_client.get_openai_client = AsyncMock()
-
-    # Mock close method
-    mock_client.close = AsyncMock()
-
-    return mock_client
-
-
-@pytest.fixture
-def mock_azure_credential() -> MagicMock:
-    """Fixture that provides a mock Azure credential."""
-    return MagicMock()
-
-
-@pytest.fixture
-def azure_ai_unit_test_env(monkeypatch: pytest.MonkeyPatch) -> dict[str, str]:
-    """Fixture that sets up Azure AI environment variables for unit testing."""
-    env_vars = {
-        "AZURE_AI_PROJECT_ENDPOINT": "https://test-project.cognitiveservices.azure.com/",
-        "AZURE_AI_MODEL_DEPLOYMENT_NAME": "test-model-deployment",
-    }
-    for key, value in env_vars.items():
-        monkeypatch.setenv(key, value)
-    return env_vars
-
-
-def test_provider_init_with_project_client(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider initialization with existing project_client."""
-    provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-    assert provider._project_client is mock_project_client  # type: ignore
-    assert not provider._should_close_client  # type: ignore
-
-
-def test_provider_init_with_credential_and_endpoint(
-    azure_ai_unit_test_env: dict[str, str],
-    mock_azure_credential: MagicMock,
-) -> None:
-    """Test AzureAIProjectAgentProvider initialization with credential and endpoint."""
-    with patch("agent_framework_azure_ai._project_provider.AIProjectClient") as mock_ai_project_client:
-        mock_client = MagicMock()
-        mock_ai_project_client.return_value = mock_client
-
-        provider = AzureAIProjectAgentProvider(
-            project_endpoint=azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
-            credential=mock_azure_credential,
-        )
-
-        assert provider._project_client is mock_client  # type: ignore
-        assert provider._should_close_client  # type: ignore
-
-        # Verify AIProjectClient was called with correct parameters
-        mock_ai_project_client.assert_called_once()
-
-
-def test_provider_init_missing_endpoint() -> None:
-    """Test AzureAIProjectAgentProvider initialization when endpoint is missing."""
-    with patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings:
-        mock_load_settings.return_value = {"project_endpoint": None, "model_deployment_name": "test-model"}
-
-        with pytest.raises(ValueError, match="Azure AI project endpoint is required"):
-            AzureAIProjectAgentProvider(credential=MagicMock())
-
-
-def test_provider_init_missing_credential(azure_ai_unit_test_env: dict[str, str]) -> None:
-    """Test AzureAIProjectAgentProvider initialization when credential is missing."""
-    with pytest.raises(ValueError, match="Azure credential is required when project_client is not provided"):
-        AzureAIProjectAgentProvider(
-            project_endpoint=azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
-        )
-
-
-async def test_provider_create_agent(
-    mock_project_client: MagicMock,
-    azure_ai_unit_test_env: dict[str, str],
-) -> None:
-    """Test AzureAIProjectAgentProvider.create_agent method."""
-    with patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings:
-        mock_load_settings.return_value = {
-            "project_endpoint": azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
-            "model_deployment_name": azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        }
-
-        provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-        # Mock agent creation response
-        mock_agent_version = MagicMock(spec=AgentVersionDetails)
-        mock_agent_version.id = "agent-id"
-        mock_agent_version.name = "test-agent"
-        mock_agent_version.version = "1.0"
-        mock_agent_version.description = "Test Agent"
-        mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-        mock_agent_version.definition.model = "gpt-4"
-        mock_agent_version.definition.instructions = "Test instructions"
-        mock_agent_version.definition.temperature = 0.7
-        mock_agent_version.definition.top_p = 0.9
-        mock_agent_version.definition.tools = []
-
-        mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent_version)
-
-        agent = await provider.create_agent(
-            name="test-agent",
-            model="gpt-4",
-            instructions="Test instructions",
-            description="Test Agent",
-        )
-
-        assert isinstance(agent, Agent)
-        assert agent.name == "test-agent"
-        mock_project_client.agents.create_version.assert_called_once()
-
-
-async def test_provider_create_agent_with_env_model(
-    mock_project_client: MagicMock,
-    azure_ai_unit_test_env: dict[str, str],
-) -> None:
-    """Test AzureAIProjectAgentProvider.create_agent uses model from env var."""
-    with patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings:
-        mock_load_settings.return_value = {
-            "project_endpoint": azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
-            "model_deployment_name": azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        }
-
-        provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-        # Mock agent creation response
-        mock_agent_version = MagicMock(spec=AgentVersionDetails)
-        mock_agent_version.id = "agent-id"
-        mock_agent_version.name = "test-agent"
-        mock_agent_version.version = "1.0"
-        mock_agent_version.description = None
-        mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-        mock_agent_version.definition.model = azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"]
-        mock_agent_version.definition.instructions = None
-        mock_agent_version.definition.temperature = None
-        mock_agent_version.definition.top_p = None
-        mock_agent_version.definition.tools = []
-
-        mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent_version)
-
-        # Call without model parameter - should use env var
-        agent = await provider.create_agent(name="test-agent")
-
-        assert isinstance(agent, Agent)
-        # Verify the model from env var was used
-        call_args = mock_project_client.agents.create_version.call_args
-        assert call_args[1]["definition"].model == azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"]
-
-
-async def test_provider_create_agent_missing_model(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider.create_agent raises when model is missing."""
-    with patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings:
-        mock_load_settings.return_value = {"project_endpoint": "https://test.com", "model_deployment_name": None}
-
-        provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-        with pytest.raises(ValueError, match="Model deployment name is required"):
-            await provider.create_agent(name="test-agent")
-
-
-async def test_provider_create_agent_with_rai_config(
-    mock_project_client: MagicMock,
-    azure_ai_unit_test_env: dict[str, str],
-) -> None:
-    """Test AzureAIProjectAgentProvider.create_agent passes rai_config from default_options."""
-    with patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings:
-        mock_load_settings.return_value = {
-            "project_endpoint": azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
-            "model_deployment_name": azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        }
-
-        provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-        # Mock agent creation response
-        mock_agent_version = MagicMock(spec=AgentVersionDetails)
-        mock_agent_version.id = "agent-id"
-        mock_agent_version.name = "test-agent"
-        mock_agent_version.version = "1.0"
-        mock_agent_version.description = None
-        mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-        mock_agent_version.definition.model = "gpt-4"
-        mock_agent_version.definition.instructions = None
-        mock_agent_version.definition.temperature = None
-        mock_agent_version.definition.top_p = None
-        mock_agent_version.definition.tools = []
-
-        mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent_version)
-
-        # Create a mock RaiConfig-like object
-        mock_rai_config = MagicMock()
-        mock_rai_config.rai_policy_name = "policy-name"
-
-        # Call create_agent with rai_config in default_options
-        await provider.create_agent(
-            name="test-agent",
-            model="gpt-4",
-            default_options={"rai_config": mock_rai_config},
-        )
-
-        # Verify rai_config was passed to PromptAgentDefinition
-        call_args = mock_project_client.agents.create_version.call_args
-        definition = call_args[1]["definition"]
-        assert definition.rai_config is mock_rai_config
-
-
-async def test_provider_create_agent_with_reasoning(
-    mock_project_client: MagicMock,
-    azure_ai_unit_test_env: dict[str, str],
-) -> None:
-    """Test AzureAIProjectAgentProvider.create_agent passes reasoning from default_options."""
-    with patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings:
-        mock_load_settings.return_value = {
-            "project_endpoint": azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
-            "model_deployment_name": azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        }
-
-        provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-        # Mock agent creation response
-        mock_agent_version = MagicMock(spec=AgentVersionDetails)
-        mock_agent_version.id = "agent-id"
-        mock_agent_version.name = "test-agent"
-        mock_agent_version.version = "1.0"
-        mock_agent_version.description = None
-        mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-        mock_agent_version.definition.model = "gpt-5.2"
-        mock_agent_version.definition.instructions = None
-        mock_agent_version.definition.temperature = None
-        mock_agent_version.definition.top_p = None
-        mock_agent_version.definition.tools = []
-
-        mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent_version)
-
-        # Create a mock Reasoning-like object
-        mock_reasoning = MagicMock()
-        mock_reasoning.effort = "medium"
-        mock_reasoning.summary = "concise"
-
-        # Call create_agent with reasoning in default_options
-        await provider.create_agent(
-            name="test-agent",
-            model="gpt-5.2",
-            default_options={"reasoning": mock_reasoning},
-        )
-
-        # Verify reasoning was passed to PromptAgentDefinition
-        call_args = mock_project_client.agents.create_version.call_args
-        definition = call_args[1]["definition"]
-        assert definition.reasoning is mock_reasoning
-
-
-async def test_provider_get_agent_with_name(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider.get_agent with name parameter."""
-    provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-    # Mock agent response
-    mock_agent_version = MagicMock(spec=AgentVersionDetails)
-    mock_agent_version.id = "agent-id"
-    mock_agent_version.name = "test-agent"
-    mock_agent_version.version = "1.0"
-    mock_agent_version.description = "Test Agent"
-    mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-    mock_agent_version.definition.model = "gpt-4"
-    mock_agent_version.definition.instructions = "Test instructions"
-    mock_agent_version.definition.temperature = None
-    mock_agent_version.definition.top_p = None
-    mock_agent_version.definition.tools = []
-
-    mock_agent_object = MagicMock()
-    mock_agent_object.versions.latest = mock_agent_version
-
-    mock_project_client.agents = AsyncMock()
-    mock_project_client.agents.get.return_value = mock_agent_object
-
-    agent = await provider.get_agent(name="test-agent")
-
-    assert isinstance(agent, Agent)
-    assert agent.name == "test-agent"
-    mock_project_client.agents.get.assert_called_with(agent_name="test-agent")
-
-
-async def test_provider_get_agent_with_reference(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider.get_agent with reference parameter."""
-    provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-    # Mock agent response
-    mock_agent_version = MagicMock(spec=AgentVersionDetails)
-    mock_agent_version.id = "agent-id"
-    mock_agent_version.name = "test-agent"
-    mock_agent_version.version = "1.0"
-    mock_agent_version.description = "Test Agent"
-    mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-    mock_agent_version.definition.model = "gpt-4"
-    mock_agent_version.definition.instructions = "Test instructions"
-    mock_agent_version.definition.temperature = None
-    mock_agent_version.definition.top_p = None
-    mock_agent_version.definition.tools = []
-
-    mock_project_client.agents = AsyncMock()
-    mock_project_client.agents.get_version.return_value = mock_agent_version
-
-    agent_reference = {"name": "test-agent", "version": "1.0"}
-    agent = await provider.get_agent(reference=agent_reference)
-
-    assert isinstance(agent, Agent)
-    assert agent.name == "test-agent"
-    mock_project_client.agents.get_version.assert_called_with(agent_name="test-agent", agent_version="1.0")
-
-
-async def test_provider_get_agent_missing_parameters(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider.get_agent raises when no identifier provided."""
-    provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-    with pytest.raises(ValueError, match="Either name or reference must be provided"):
-        await provider.get_agent()
-
-
-async def test_provider_get_agent_missing_function_tools(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider.get_agent raises when required tools are missing."""
-    provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-    # Mock agent with function tools
-    mock_agent_version = MagicMock(spec=AgentVersionDetails)
-    mock_agent_version.id = "agent-id"
-    mock_agent_version.name = "test-agent"
-    mock_agent_version.version = "1.0"
-    mock_agent_version.description = None
-    mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-    mock_agent_version.definition.tools = [
-        AzureFunctionTool(name="test_tool", parameters=[], strict=True, description="Test tool")
-    ]
-
-    mock_agent_object = MagicMock()
-    mock_agent_object.versions.latest = mock_agent_version
-
-    mock_project_client.agents = AsyncMock()
-    mock_project_client.agents.get.return_value = mock_agent_object
-
-    with pytest.raises(
-        ValueError, match="The following prompt agent definition required tools were not provided: test_tool"
-    ):
-        await provider.get_agent(name="test-agent")
-
-
-def test_provider_as_agent(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider.as_agent method."""
-    provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-    # Create mock agent version
-    mock_agent_version = MagicMock(spec=AgentVersionDetails)
-    mock_agent_version.id = "agent-id"
-    mock_agent_version.name = "test-agent"
-    mock_agent_version.version = "1.0"
-    mock_agent_version.description = "Test Agent"
-    mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-    mock_agent_version.definition.model = "gpt-4"
-    mock_agent_version.definition.instructions = "Test instructions"
-    mock_agent_version.definition.temperature = 0.7
-    mock_agent_version.definition.top_p = 0.9
-    mock_agent_version.definition.tools = []
-
-    with patch("agent_framework_azure_ai._project_provider.AzureAIClient") as mock_azure_ai_client:
-        agent = provider.as_agent(mock_agent_version)
-
-        assert isinstance(agent, Agent)
-        assert agent.name == "test-agent"
-        assert agent.description == "Test Agent"
-
-        # Verify AzureAIClient was called with correct parameters
-        mock_azure_ai_client.assert_called_once()
-        call_kwargs = mock_azure_ai_client.call_args[1]
-        assert call_kwargs["project_client"] is mock_project_client
-        assert call_kwargs["agent_name"] == "test-agent"
-        assert call_kwargs["agent_version"] == "1.0"
-        assert call_kwargs["agent_description"] == "Test Agent"
-        assert call_kwargs["model_deployment_name"] == "gpt-4"
-
-
-def test_provider_merge_tools_skips_function_tool_dicts(mock_project_client: MagicMock) -> None:
-    """Test that _merge_tools skips function tool dicts but keeps other hosted tools."""
-    provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-    # Create a mock FunctionTool to provide as implementation
-    mock_ai_function = create_mock_ai_function("my_function", "My function description")
-
-    # Definition tools include a function tool (dict) and an MCP tool
-    definition_tools = [
-        {"type": "function", "name": "my_function", "parameters": {}},  # Should be skipped
-        {"type": "mcp", "server_label": "my_mcp", "server_url": "http://localhost:8080"},  # Should be converted
-    ]
-
-    # Call _merge_tools with user-provided function implementation
-    merged = provider._merge_tools(definition_tools, [mock_ai_function])  # type: ignore
-
-    # Should have 2 items: the converted MCP dict and the user-provided FunctionTool
-    assert len(merged) == 2
-
-    # Check that the function tool dict was NOT included (it was skipped)
-    function_dicts = [t for t in merged if isinstance(t, dict) and t.get("type") == "function"]
-    assert len(function_dicts) == 0
-
-    # Check that the MCP tool was converted to dict
-    mcp_tools = [t for t in merged if isinstance(t, dict) and t.get("type") == "mcp"]
-    assert len(mcp_tools) == 1
-    assert mcp_tools[0]["server_label"] == "my_mcp"
-
-    # Check that the user-provided FunctionTool was included
-    ai_functions = [t for t in merged if isinstance(t, FunctionTool)]
-    assert len(ai_functions) == 1
-    assert ai_functions[0].name == "my_function"
-
-
-async def test_provider_context_manager(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider async context manager."""
-    with patch("agent_framework_azure_ai._project_provider.AIProjectClient") as mock_ai_project_client:
-        mock_client = MagicMock()
-        mock_client.close = AsyncMock()
-        mock_ai_project_client.return_value = mock_client
-
-        with patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings:
-            mock_load_settings.return_value = {
-                "project_endpoint": "https://test.com",
-                "model_deployment_name": "test-model",
-            }
-
-            async with AzureAIProjectAgentProvider(credential=MagicMock()) as provider:
-                assert provider._project_client is mock_client  # type: ignore
-
-            # Should call close after exiting context
-            mock_client.close.assert_called_once()
-
-
-async def test_provider_context_manager_with_provided_client(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider context manager doesn't close provided client."""
-    mock_project_client.close = AsyncMock()
-
-    async with AzureAIProjectAgentProvider(project_client=mock_project_client) as provider:
-        assert provider._project_client is mock_project_client  # type: ignore
-
-    # Should NOT call close when client was provided
-    mock_project_client.close.assert_not_called()
-
-
-async def test_provider_close_method(mock_project_client: MagicMock) -> None:
-    """Test AzureAIProjectAgentProvider.close method."""
-    with patch("agent_framework_azure_ai._project_provider.AIProjectClient") as mock_ai_project_client:
-        mock_client = MagicMock()
-        mock_client.close = AsyncMock()
-        mock_ai_project_client.return_value = mock_client
-
-        with patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings:
-            mock_load_settings.return_value = {
-                "project_endpoint": "https://test.com",
-                "model_deployment_name": "test-model",
-            }
-
-            provider = AzureAIProjectAgentProvider(credential=MagicMock())
-            await provider.close()
-
-            mock_client.close.assert_called_once()
-
-
-def test_create_text_format_config_sets_strict_for_pydantic_models() -> None:
-    """Test that create_text_format_config sets strict=True for Pydantic models."""
-    from pydantic import BaseModel
-
-    from agent_framework_azure_ai._shared import create_text_format_config
-
-    class TestSchema(BaseModel):
-        subject: str
-        summary: str
-
-    result = create_text_format_config(TestSchema)
-
-    # Verify strict=True is set
-    assert result["strict"] is True
-    assert result["name"] == "TestSchema"
-    assert "schema" in result
-
-
-class MockMCPTool(MCPTool):  # pyright: ignore[reportGeneralTypeIssues]
-    """A mock MCPTool subclass for testing that passes isinstance checks.
-
-    Note: This intentionally does NOT call super().__init__() because MCPTool's
-    constructor requires MCP server connection parameters that aren't needed for
-    unit testing. We only need isinstance(obj, MCPTool) to return True.
-    """
-
-    def __init__(self, functions: list[FunctionTool] | None = None) -> None:
-        self.name = "MockMCPTool"
-        self.description = "A mock MCP tool for testing"
-        self.is_connected = False
-        self._mock_functions = functions or []
-        self._connect_called = False
-
-    @property
-    def functions(self) -> list[FunctionTool]:
-        return self._mock_functions
-
-    async def connect(self, *, reset: bool = False) -> None:
-        self._connect_called = True
-        self.is_connected = True
-
-
-@pytest.fixture
-def mock_mcp_tool() -> MockMCPTool:
-    """Fixture that provides a mock MCPTool."""
-    mock_functions = [
-        create_mock_ai_function("mcp_function_1", "First MCP function"),
-        create_mock_ai_function("mcp_function_2", "Second MCP function"),
-    ]
-    return MockMCPTool(functions=mock_functions)
-
-
-def create_mock_ai_function(name: str, description: str = "A mock function") -> FunctionTool:
-    """Create a real FunctionTool for testing."""
-
-    def mock_func(arg: str) -> str:
-        return f"Result from {name}: {arg}"
-
-    return FunctionTool(func=mock_func, name=name, description=description, approval_mode="never_require")
-
-
-async def test_provider_create_agent_with_mcp_tool(
-    mock_project_client: MagicMock,
-    azure_ai_unit_test_env: dict[str, str],
-    mock_mcp_tool: "MockMCPTool",
-) -> None:
-    """Test that create_agent connects MCP tools and passes discovered functions to Azure AI."""
-
-    # Patch normalize_tools to return tools as-is in a list (avoids callable check)
-    def mock_normalize_tools(tools):
-        if tools is None:
-            return []
-        if isinstance(tools, list):
-            return tools
-        return [tools]
-
-    with (
-        patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings,
-        patch("agent_framework_azure_ai._project_provider.to_azure_ai_tools") as mock_to_azure_tools,
-        patch("agent_framework_azure_ai._project_provider.normalize_tools", side_effect=mock_normalize_tools),
-    ):
-        mock_load_settings.return_value = {
-            "project_endpoint": azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
-            "model_deployment_name": azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        }
-        mock_to_azure_tools.return_value = [{"type": "function", "name": "mcp_function_1"}]
-
-        provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-        # Mock agent creation response
-        mock_agent_version = MagicMock(spec=AgentVersionDetails)
-        mock_agent_version.id = "agent-id"
-        mock_agent_version.name = "test-agent"
-        mock_agent_version.version = "1.0"
-        mock_agent_version.description = "Test Agent"
-        mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-        mock_agent_version.definition.model = "gpt-4"
-        mock_agent_version.definition.instructions = "Test instructions"
-        mock_agent_version.definition.tools = []
-
-        mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent_version)
-
-        # Call create_agent with MCP tool
-        await provider.create_agent(
-            name="test-agent",
-            model="gpt-4",
-            instructions="Test instructions",
-            tools=mock_mcp_tool,
-        )
-
-        # Verify MCP tool was connected
-        assert mock_mcp_tool._connect_called is True
-        assert mock_mcp_tool.is_connected is True
-
-        # Verify to_azure_ai_tools was called with the discovered MCP functions
-        mock_to_azure_tools.assert_called_once()
-        tools_passed = mock_to_azure_tools.call_args[0][0]
-        assert len(tools_passed) == 2
-        assert tools_passed[0].name == "mcp_function_1"
-        assert tools_passed[1].name == "mcp_function_2"
-
-
-async def test_provider_create_agent_with_mcp_and_regular_tools(
-    mock_project_client: MagicMock,
-    azure_ai_unit_test_env: dict[str, str],
-    mock_mcp_tool: "MockMCPTool",
-) -> None:
-    """Test that create_agent handles both MCP tools and regular FunctionTools."""
-    # Create a regular FunctionTool
-    regular_function = create_mock_ai_function("regular_function", "A regular function")
-
-    # Patch normalize_tools to return tools as-is in a list (avoids callable check)
-    def mock_normalize_tools(tools):
-        if tools is None:
-            return []
-        if isinstance(tools, list):
-            return tools
-        return [tools]
-
-    with (
-        patch("agent_framework_azure_ai._project_provider.load_settings") as mock_load_settings,
-        patch("agent_framework_azure_ai._project_provider.to_azure_ai_tools") as mock_to_azure_tools,
-        patch("agent_framework_azure_ai._project_provider.normalize_tools", side_effect=mock_normalize_tools),
-    ):
-        mock_load_settings.return_value = {
-            "project_endpoint": azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
-            "model_deployment_name": azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        }
-        mock_to_azure_tools.return_value = []
-
-        provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
-
-        # Mock agent creation response
-        mock_agent_version = MagicMock(spec=AgentVersionDetails)
-        mock_agent_version.id = "agent-id"
-        mock_agent_version.name = "test-agent"
-        mock_agent_version.version = "1.0"
-        mock_agent_version.description = None
-        mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
-        mock_agent_version.definition.model = "gpt-4"
-        mock_agent_version.definition.instructions = None
-        mock_agent_version.definition.tools = []
-
-        mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent_version)
-
-        # Pass both MCP tool and regular function
-        await provider.create_agent(
-            name="test-agent",
-            model="gpt-4",
-            tools=[mock_mcp_tool, regular_function],
-        )
-
-        # Verify to_azure_ai_tools was called with:
-        # - The regular FunctionTool (1)
-        # - The 2 discovered MCP functions
-        mock_to_azure_tools.assert_called_once()
-        tools_passed = mock_to_azure_tools.call_args[0][0]
-        assert len(tools_passed) == 3  # 1 regular + 2 MCP functions
-
-        # Verify the regular function is in the list
-        tool_names = [t.name for t in tools_passed]
-        assert "regular_function" in tool_names
-        assert "mcp_function_1" in tool_names
-        assert "mcp_function_2" in tool_names
@@ -1,494 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-import os
-from unittest.mock import MagicMock, patch
-
-import pytest
-from agent_framework import (
-    FunctionTool,
-)
-from agent_framework.exceptions import IntegrationInvalidRequestException
-from azure.ai.agents.models import CodeInterpreterToolDefinition
-from pydantic import BaseModel
-
-from agent_framework_azure_ai import AzureAIAgentClient
-from agent_framework_azure_ai._shared import (
-    _convert_response_format,  # type: ignore
-    _convert_sdk_tool,  # type: ignore
-    _extract_project_connection_id,  # type: ignore
-    create_text_format_config,
-    from_azure_ai_agent_tools,
-    from_azure_ai_tools,
-    to_azure_ai_agent_tools,
-    to_azure_ai_tools,
-)
-from agent_framework_azure_ai._shared import (
-    _prepare_mcp_tool_dict_for_azure_ai as _prepare_mcp_tool_for_azure_ai,  # type: ignore
-)
-
-
-def test_extract_project_connection_id_direct() -> None:
-    """Test extracting project_connection_id from direct key."""
-    result = _extract_project_connection_id({"project_connection_id": "my-connection"})
-    assert result == "my-connection"
-
-
-def test_extract_project_connection_id_from_connection_name() -> None:
-    """Test extracting project_connection_id from connection.name structure."""
-    result = _extract_project_connection_id({"connection": {"name": "my-connection"}})
-    assert result == "my-connection"
-
-
-def test_extract_project_connection_id_none() -> None:
-    """Test returns None when no connection info."""
-    assert _extract_project_connection_id(None) is None
-    assert _extract_project_connection_id({}) is None
-
-
-def test_to_azure_ai_agent_tools_empty() -> None:
-    """Test converting empty/None tools list."""
-    assert to_azure_ai_agent_tools(None) == []
-    assert to_azure_ai_agent_tools([]) == []
-
-
-def test_to_azure_ai_agent_tools_function_tool() -> None:
-    """Test converting FunctionTool to tool definition."""
-
-    def my_func(arg: str) -> str:
-        """My function."""
-        return arg
-
-    func_tool = FunctionTool(func=my_func, name="my_func", description="My function.")  # type: ignore
-    result = to_azure_ai_agent_tools([func_tool])  # type: ignore
-    assert len(result) == 1
-    assert result[0]["type"] == "function"
-    assert result[0]["function"]["name"] == "my_func"
-
-
-def test_to_azure_ai_agent_tools_code_interpreter() -> None:
-    """Test converting code_interpreter dict tool."""
-    tool = AzureAIAgentClient.get_code_interpreter_tool()
-    result = to_azure_ai_agent_tools([tool])
-    assert len(result) == 1
-    assert isinstance(result[0], CodeInterpreterToolDefinition)
-
-
-def test_to_azure_ai_agent_tools_web_search_missing_connection() -> None:
-    """Test web search tool raises without connection info."""
-    # Clear any environment variables that could provide connection info
-    with patch.dict(
-        os.environ,
-        {"BING_CONNECTION_ID": "", "BING_CUSTOM_CONNECTION_ID": "", "BING_CUSTOM_INSTANCE_NAME": ""},
-        clear=False,
-    ):
-        # Also need to unset the keys if they exist
-        env_backup = {}
-        for key in ["BING_CONNECTION_ID", "BING_CUSTOM_CONNECTION_ID", "BING_CUSTOM_INSTANCE_NAME"]:
-            env_backup[key] = os.environ.pop(key, None)
-        try:
-            # get_web_search_tool now raises ValueError when no connection info is available
-            with pytest.raises(ValueError, match="Azure AI Agents requires a Bing connection"):
-                AzureAIAgentClient.get_web_search_tool()
-        finally:
-            # Restore environment
-            for key, value in env_backup.items():
-                if value is not None:
-                    os.environ[key] = value
-
-
-def test_to_azure_ai_agent_tools_dict_passthrough() -> None:
-    """Test dict tools pass through unchanged."""
-    tool_dict = {"type": "custom", "config": "value"}
-    result = to_azure_ai_agent_tools([tool_dict])
-    assert result[0] == tool_dict
-
-
-def test_to_azure_ai_agent_tools_unsupported_type() -> None:
-    """Test unsupported tool type passes through unchanged."""
-
-    class UnsupportedTool:
-        pass
-
-    unsupported = UnsupportedTool()
-    result = to_azure_ai_agent_tools([unsupported])  # type: ignore
-    assert len(result) == 1
-    assert result[0] is unsupported  # Passed through unchanged
-
-
-def test_from_azure_ai_agent_tools_empty() -> None:
-    """Test converting empty/None tools list."""
-    assert from_azure_ai_agent_tools(None) == []
-    assert from_azure_ai_agent_tools([]) == []
-
-
-def test_from_azure_ai_agent_tools_code_interpreter() -> None:
-    """Test converting CodeInterpreterToolDefinition."""
-    tool = CodeInterpreterToolDefinition()
-    result = from_azure_ai_agent_tools([tool])
-    assert len(result) == 1
-    assert result[0] == {"type": "code_interpreter"}
-
-
-def test_convert_sdk_tool_code_interpreter() -> None:
-    """Test _convert_sdk_tool with code_interpreter type."""
-    tool = MagicMock()
-    tool.type = "code_interpreter"
-    result = _convert_sdk_tool(tool)
-    assert result == {"type": "code_interpreter"}
-
-
-def test_convert_sdk_tool_function_returns_none() -> None:
-    """Test _convert_sdk_tool with function type returns None."""
-    tool = MagicMock()
-    tool.type = "function"
-    result = _convert_sdk_tool(tool)
-    assert result is None
-
-
-def test_convert_sdk_tool_mcp_returns_none() -> None:
-    """Test _convert_sdk_tool with mcp type returns None."""
-    tool = MagicMock()
-    tool.type = "mcp"
-    result = _convert_sdk_tool(tool)
-    assert result is None
-
-
-def test_convert_sdk_tool_file_search() -> None:
-    """Test _convert_sdk_tool with file_search type."""
-    tool = MagicMock()
-    tool.type = "file_search"
-    tool.file_search = MagicMock()
-    tool.file_search.vector_store_ids = ["vs-1", "vs-2"]
-    result = _convert_sdk_tool(tool)
-    assert result["type"] == "file_search"
-    assert result["vector_store_ids"] == ["vs-1", "vs-2"]
-
-
-def test_convert_sdk_tool_bing_grounding() -> None:
-    """Test _convert_sdk_tool with bing_grounding type."""
-    tool = MagicMock()
-    tool.type = "bing_grounding"
-    tool.bing_grounding = MagicMock()
-    tool.bing_grounding.connection_id = "conn-123"
-    result = _convert_sdk_tool(tool)
-    assert result["type"] == "bing_grounding"
-    assert result["connection_id"] == "conn-123"
-
-
-def test_convert_sdk_tool_bing_custom_search() -> None:
-    """Test _convert_sdk_tool with bing_custom_search type."""
-    tool = MagicMock()
-    tool.type = "bing_custom_search"
-    tool.bing_custom_search = MagicMock()
-    tool.bing_custom_search.connection_id = "conn-123"
-    tool.bing_custom_search.instance_name = "my-instance"
-    result = _convert_sdk_tool(tool)
-    assert result["type"] == "bing_custom_search"
-    assert result["connection_id"] == "conn-123"
-    assert result["instance_name"] == "my-instance"
-
-
-def test_to_azure_ai_tools_empty() -> None:
-    """Test converting empty/None tools list."""
-    assert to_azure_ai_tools(None) == []
-    assert to_azure_ai_tools([]) == []
-
-
-def test_to_azure_ai_tools_code_interpreter_with_file_ids() -> None:
-    """Test converting code_interpreter dict tool with file inputs."""
-    tool = {
-        "type": "code_interpreter",
-        "file_ids": ["file-123"],
-    }
-    result = to_azure_ai_tools([tool])
-    assert len(result) == 1
-    assert result[0]["type"] == "code_interpreter"
-
-
-def test_to_azure_ai_tools_function_tool() -> None:
-    """Test converting FunctionTool."""
-
-    def my_func(arg: str) -> str:
-        """My function."""
-        return arg
-
-    func_tool = FunctionTool(func=my_func, name="my_func", description="My function.")  # type: ignore
-    result = to_azure_ai_tools([func_tool])  # type: ignore
-    assert len(result) == 1
-    assert result[0]["type"] == "function"
-    assert result[0]["name"] == "my_func"
-
-
-def test_to_azure_ai_tools_file_search() -> None:
-    """Test converting file_search dict tool."""
-    tool = {
-        "type": "file_search",
-        "vector_store_ids": ["vs-123"],
-        "max_num_results": 10,
-    }
-    result = to_azure_ai_tools([tool])
-    assert len(result) == 1
-    assert result[0]["type"] == "file_search"
-    assert result[0]["vector_store_ids"] == ["vs-123"]
-    assert result[0]["max_num_results"] == 10
-
-
-def test_to_azure_ai_tools_web_search_with_location() -> None:
-    """Test converting web_search dict tool with user location."""
-    tool = {
-        "type": "web_search_preview",
-        "user_location": {
-            "city": "Seattle",
-            "country": "US",
-            "region": "WA",
-            "timezone": "PST",
-        },
-    }
-    result = to_azure_ai_tools([tool])
-    assert len(result) == 1
-    assert result[0]["type"] == "web_search_preview"
-
-
-def test_to_azure_ai_tools_image_generation() -> None:
-    """Test converting image_generation dict tool."""
-    tool = {
-        "type": "image_generation",
-        "model": "gpt-image-1",
-        "size": "1024x1024",
-        "quality": "high",
-    }
-    result = to_azure_ai_tools([tool])
-    assert len(result) == 1
-    assert result[0]["type"] == "image_generation"
-    assert result[0]["model"] == "gpt-image-1"
-
-
-def test_prepare_mcp_tool_basic() -> None:
-    """Test basic MCP tool conversion."""
-    tool = {"type": "mcp", "server_label": "my_tool", "server_url": "http://localhost:8080"}
-    result = _prepare_mcp_tool_for_azure_ai(tool)
-    assert result["server_label"] == "my_tool"
-    assert "http://localhost:8080" in result["server_url"]
-
-
-def test_prepare_mcp_tool_with_description() -> None:
-    """Test MCP tool with description."""
-    tool = {
-        "type": "mcp",
-        "server_label": "my_tool",
-        "server_url": "http://localhost:8080",
-        "server_description": "My MCP server",
-    }
-    result = _prepare_mcp_tool_for_azure_ai(tool)
-    assert result["server_description"] == "My MCP server"
-
-
-def test_prepare_mcp_tool_with_headers() -> None:
-    """Test MCP tool with headers (no project_connection_id)."""
-    tool = {
-        "type": "mcp",
-        "server_label": "my_tool",
-        "server_url": "http://localhost:8080",
-        "headers": {"X-Api-Key": "secret"},
-    }
-    result = _prepare_mcp_tool_for_azure_ai(tool)
-    assert result["headers"] == {"X-Api-Key": "secret"}
-
-
-def test_prepare_mcp_tool_project_connection_takes_precedence() -> None:
-    """Test project_connection_id takes precedence over headers."""
-    tool = {
-        "type": "mcp",
-        "server_label": "my_tool",
-        "server_url": "http://localhost:8080",
-        "headers": {"X-Api-Key": "secret"},
-        "project_connection_id": "my-conn",
-    }
-    result = _prepare_mcp_tool_for_azure_ai(tool)
-    assert result["project_connection_id"] == "my-conn"
-    assert "headers" not in result
-
-
-def test_prepare_mcp_tool_approval_mode_always() -> None:
-    """Test MCP tool with always_require approval mode."""
-    tool = {
-        "type": "mcp",
-        "server_label": "my_tool",
-        "server_url": "http://localhost:8080",
-        "require_approval": "always",
-    }
-    result = _prepare_mcp_tool_for_azure_ai(tool)
-    assert result["require_approval"] == "always"
-
-
-def test_prepare_mcp_tool_approval_mode_never() -> None:
-    """Test MCP tool with never_require approval mode."""
-    tool = {
-        "type": "mcp",
-        "server_label": "my_tool",
-        "server_url": "http://localhost:8080",
-        "require_approval": "never",
-    }
-    result = _prepare_mcp_tool_for_azure_ai(tool)
-    assert result["require_approval"] == "never"
-
-
-def test_prepare_mcp_tool_approval_mode_dict() -> None:
-    """Test MCP tool with dict approval mode."""
-    tool = {
-        "type": "mcp",
-        "server_label": "my_tool",
-        "server_url": "http://localhost:8080",
-        "require_approval": {"always": {"tool_names": ["sensitive_tool", "dangerous_tool"]}},
-    }
-    result = _prepare_mcp_tool_for_azure_ai(tool)
-    # The approval mode is passed through
-    assert "require_approval" in result
-
-
-def test_create_text_format_config_pydantic_model() -> None:
-    """Test creating text format config from Pydantic model."""
-
-    class MySchema(BaseModel):
-        name: str
-        value: int
-
-    result = create_text_format_config(MySchema)
-    assert result["type"] == "json_schema"
-    assert result["name"] == "MySchema"
-    assert result["strict"] is True
-
-
-def test_create_text_format_config_json_schema_mapping() -> None:
-    """Test creating text format config from json_schema mapping."""
-    config = {
-        "type": "json_schema",
-        "json_schema": {
-            "name": "MyResponse",
-            "schema": {"type": "object", "properties": {"name": {"type": "string"}}},
-        },
-    }
-    result = create_text_format_config(config)
-    assert result["type"] == "json_schema"
-    assert result["name"] == "MyResponse"
-
-
-def test_create_text_format_config_json_object() -> None:
-    """Test creating text format config for json_object type."""
-    result = create_text_format_config({"type": "json_object"})
-    assert result["type"] == "json_object"
-
-
-def test_create_text_format_config_text() -> None:
-    """Test creating text format config for text type."""
-    result = create_text_format_config({"type": "text"})
-    assert result["type"] == "text"
-
-
-def test_create_text_format_config_invalid_raises() -> None:
-    """Test invalid response_format raises error."""
-    with pytest.raises(IntegrationInvalidRequestException):
-        create_text_format_config({"type": "invalid"})
-
-
-def test_convert_response_format_with_format_key() -> None:
-    """Test _convert_response_format with nested format key."""
-    config = {"format": {"type": "json_object"}}
-    result = _convert_response_format(config)
-    assert result["type"] == "json_object"
-
-
-def test_convert_response_format_json_schema_missing_schema_raises() -> None:
-    """Test json_schema without schema raises error."""
-    with pytest.raises(IntegrationInvalidRequestException, match="requires a schema"):
-        _convert_response_format({"type": "json_schema", "json_schema": {}})
-
-
-def test_convert_response_format_raw_json_schema_with_properties() -> None:
-    """Test raw JSON schema with properties is wrapped in json_schema envelope."""
-    result = _convert_response_format({"type": "object", "properties": {"x": {"type": "string"}}, "title": "MyOutput"})
-
-    assert result["type"] == "json_schema"
-    assert result["name"] == "MyOutput"
-    assert result["strict"] is True
-    assert result["schema"]["additionalProperties"] is False
-    assert "title" not in result["schema"]
-
-
-def test_convert_response_format_raw_json_schema_no_title() -> None:
-    """Test raw JSON schema without title defaults name to 'response'."""
-    result = _convert_response_format({"type": "object", "properties": {"x": {"type": "string"}}})
-
-    assert result["name"] == "response"
-
-
-def test_convert_response_format_raw_json_schema_with_anyof() -> None:
-    """Test raw JSON schema with anyOf keyword is detected."""
-    result = _convert_response_format({"anyOf": [{"type": "string"}, {"type": "number"}]})
-
-    assert result["type"] == "json_schema"
-    assert result["strict"] is True
-
-
-def test_from_azure_ai_tools_mcp_approval_mode_always() -> None:
-    """Test from_azure_ai_tools converts MCP require_approval='always' to dict."""
-    tools = [
-        {
-            "type": "mcp",
-            "server_label": "my_mcp",
-            "server_url": "http://localhost:8080",
-            "require_approval": "always",
-        }
-    ]
-    result = from_azure_ai_tools(tools)
-    assert len(result) == 1
-    assert result[0]["type"] == "mcp"
-    assert result[0]["require_approval"] == "always"
-
-
-def test_from_azure_ai_tools_mcp_approval_mode_never() -> None:
-    """Test from_azure_ai_tools converts MCP require_approval='never' to dict."""
-    tools = [
-        {
-            "type": "mcp",
-            "server_label": "my_mcp",
-            "server_url": "http://localhost:8080",
-            "require_approval": "never",
-        }
-    ]
-    result = from_azure_ai_tools(tools)
-    assert len(result) == 1
-    assert result[0]["type"] == "mcp"
-    assert result[0]["require_approval"] == "never"
-
-
-def test_from_azure_ai_tools_mcp_approval_mode_dict_always() -> None:
-    """Test from_azure_ai_tools converts MCP dict require_approval with 'always' key."""
-    tools = [
-        {
-            "type": "mcp",
-            "server_label": "my_mcp",
-            "server_url": "http://localhost:8080",
-            "require_approval": {"always": {"tool_names": ["sensitive_tool", "dangerous_tool"]}},
-        }
-    ]
-    result = from_azure_ai_tools(tools)
-    assert len(result) == 1
-    assert result[0]["type"] == "mcp"
-    assert result[0]["require_approval"] == {"always": {"tool_names": ["sensitive_tool", "dangerous_tool"]}}
-
-
-def test_from_azure_ai_tools_mcp_approval_mode_dict_never() -> None:
-    """Test from_azure_ai_tools converts MCP dict require_approval with 'never' key."""
-    tools = [
-        {
-            "type": "mcp",
-            "server_label": "my_mcp",
-            "server_url": "http://localhost:8080",
-            "require_approval": {"never": {"tool_names": ["safe_tool"]}},
-        }
-    ]
-    result = from_azure_ai_tools(tools)
-    assert len(result) == 1
-    assert result[0]["type"] == "mcp"
-    assert result[0]["require_approval"] == {"never": {"tool_names": ["safe_tool"]}}
@@ -4,7 +4,7 @@ This folder contains samples for `agent-framework-azure-cosmos`.

 | File | Description |
 | --- | --- |
-| [`cosmos_history_provider.py`](cosmos_history_provider.py) | Demonstrates an Agent using `CosmosHistoryProvider` with `AzureOpenAIResponsesClient` (project endpoint), provider-configured container name, and `session_id` partitioning. |
+| [`cosmos_history_provider.py`](cosmos_history_provider.py) | Demonstrates an Agent using `CosmosHistoryProvider` with `FoundryChatClient` (configured against an Azure AI Foundry project endpoint), provider-configured container name, and `session_id` partitioning. |

 ## Prerequisites

@@ -4,7 +4,7 @@
 import asyncio
 import os

-from agent_framework.azure import AzureOpenAIResponsesClient
+from agent_framework.foundry import FoundryChatClient
 from azure.identity.aio import AzureCliCredential
 from dotenv import load_dotenv

@@ -17,13 +17,13 @@ load_dotenv()
 This sample demonstrates CosmosHistoryProvider as an agent context provider.

 Key components:
- AzureOpenAIResponsesClient configured with an Azure AI project endpoint
+- FoundryChatClient configured with an Azure AI project endpoint
 - CosmosHistoryProvider configured for Cosmos DB-backed message history
 - Provider-configured container name with session_id as partition key

 Environment variables:
-  AZURE_AI_PROJECT_ENDPOINT
-  AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME
+  FOUNDRY_PROJECT_ENDPOINT
+  FOUNDRY_MODEL
  AZURE_COSMOS_ENDPOINT
  AZURE_COSMOS_DATABASE_NAME
  AZURE_COSMOS_CONTAINER_NAME
@@ -34,8 +34,8 @@ Optional:

 async def main() -> None:
    """Run the Cosmos history provider sample with an Agent."""
-    project_endpoint = os.getenv("AZURE_AI_PROJECT_ENDPOINT")
-    deployment_name = os.getenv("AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME")
+    project_endpoint = os.getenv("FOUNDRY_PROJECT_ENDPOINT")
+    deployment_name = os.getenv("FOUNDRY_MODEL")
    cosmos_endpoint = os.getenv("AZURE_COSMOS_ENDPOINT")
    cosmos_database_name = os.getenv("AZURE_COSMOS_DATABASE_NAME")
    cosmos_container_name = os.getenv("AZURE_COSMOS_CONTAINER_NAME")
@@ -49,16 +49,16 @@ async def main() -> None:
        or not cosmos_container_name
    ):
        print(
-            "Please set AZURE_AI_PROJECT_ENDPOINT, AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME, "
+            "Please set FOUNDRY_PROJECT_ENDPOINT, FOUNDRY_MODEL, "
            "AZURE_COSMOS_ENDPOINT, AZURE_COSMOS_DATABASE_NAME, and AZURE_COSMOS_CONTAINER_NAME."
        )
        return

-    # 1. Create an Azure credential and Responses client using project endpoint auth.
+    # 1. Create an Azure credential and Foundry chat client using project endpoint auth.
    async with AzureCliCredential() as credential:
-        client = AzureOpenAIResponsesClient(
+        client = FoundryChatClient(
            project_endpoint=project_endpoint,
-            deployment_name=deployment_name,
+            model=deployment_name,
            credential=credential,
        )

@@ -124,16 +124,17 @@ class AgentFunctionApp(DFAppBase):

    .. code-block:: python

-        from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
+        from agent_framework.azure import AgentFunctionApp
+        from agent_framework.openai import OpenAIChatCompletionClient

        # Create agents with unique names
-        weather_agent = AzureOpenAIChatClient(...).as_agent(
+        weather_agent = OpenAIChatCompletionClient(...).as_agent(
            name="WeatherAgent",
            instructions="You are a helpful weather agent.",
            tools=[get_weather],
        )

-        math_agent = AzureOpenAIChatClient(...).as_agent(
+        math_agent = OpenAIChatCompletionClient(...).as_agent(
            name="MathAgent",
            instructions="You are a helpful math assistant.",
            tools=[calculate],
@@ -1,6 +1,6 @@
 # Azure OpenAI Configuration
 AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
-AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=your-deployment-name
+AZURE_OPENAI_DEPLOYMENT_NAME=your-deployment-name
 FUNCTIONS_WORKER_RUNTIME=python

 # Azure Functions Configuration
@@ -64,7 +64,7 @@ from fastapi import FastAPI, Request
 from fastapi.responses import Response, StreamingResponse

 from agent_framework import Agent
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.chatkit import simple_to_agent_input, stream_agent_response

 from chatkit.server import ChatKitServer
@@ -75,7 +75,7 @@ from your_store import YourStore  # type: ignore[import-not-found]  # Replace wi

 # Define your agent with tools
 agent = Agent(
-    client=AzureOpenAIChatClient(credential=AzureCliCredential()),
+    client=OpenAIChatCompletionClient(credential=AzureCliCredential()),
    instructions="You are a helpful assistant.",
    tools=[],  # Add your tools here
 )
@@ -82,13 +82,12 @@ agent_framework/

 ### OpenAI (`openai/`)

- **`OpenAIChatClient`** - Chat client for OpenAI API
- **`OpenAIResponsesClient`** - Client for OpenAI Responses API
+- **`OpenAIChatClient`** - Chat client for the OpenAI Responses API
+- **`OpenAIChatCompletionClient`** - Chat client for the OpenAI Chat Completions API

-### Azure OpenAI (`azure/`)
+### Foundry (`foundry/`)

- **`AzureOpenAIChatClient`** - Chat client for Azure OpenAI
- **`AzureOpenAIResponsesClient`** - Client for Azure OpenAI Responses API
+- **`FoundryChatClient`** - Chat client for Azure AI Foundry project endpoints

 ## Key Patterns

@@ -5,7 +5,7 @@ Highlights
 - Flexible Agent Framework: build, orchestrate, and deploy AI agents and multi-agent systems
 - Multi-Agent Orchestration: Group chat, sequential, concurrent, and handoff patterns
 - Plugin Ecosystem: Extend with native functions, OpenAPI, Model Context Protocol (MCP), and more
- LLM Support: OpenAI, Azure OpenAI, Azure AI, and more
+- LLM Support: OpenAI, Foundry, Anthropic, and more
 - Runtime Support: In-process and distributed agent execution
 - Multimodal: Text, vision, and function calling
 - Cross-Platform: .NET and Python implementations
@@ -16,6 +16,8 @@ Highlights
 pip install agent-framework-core --pre
 # Optional: Add Azure AI Foundry integration
 pip install agent-framework-foundry --pre
+# Optional: Add OpenAI integration
+pip install agent-framework-openai --pre
 ```

 Supported Platforms:
@@ -25,35 +27,33 @@ Supported Platforms:

 ## 1. Setup API Keys

-Set as environment variables, or create a .env file at your project root:
+Depending on the client you want to use, there are various environment variables you can set to configure the chat clients. This can be done in the environment itself, or with a `.env` file in your project root, some examples of environment variables include:

 ```bash
+FOUNDRY_PROJECT_ENDPOINT=...
+FOUNDRY_MODEL=...
+...
 OPENAI_API_KEY=sk-...
 OPENAI_CHAT_MODEL=...
 OPENAI_RESPONSES_MODEL=...
 ...
 AZURE_OPENAI_API_KEY=...
 AZURE_OPENAI_ENDPOINT=...
-AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=...
-...
-FOUNDRY_PROJECT_ENDPOINT=...
-FOUNDRY_MODEL=...
+AZURE_OPENAI_DEPLOYMENT_NAME=...
 ```

 You can also override environment variables by explicitly passing configuration parameters to the chat client constructor:

 ```python
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatClient

-client = AzureOpenAIChatClient(
+client = OpenAIChatClient(
    api_key="",
-    endpoint="",
-    deployment_name="",
-    api_version="",
+    model="",
 )
 ```

-See the following [setup guide](../../samples/01-get-started) for more information.
+See the following [getting started samples](https://github.com/microsoft/agent-framework/tree/main/python/samples/01-get-started) for more information.

 ## 2. Create a Simple Agent

@@ -64,22 +64,19 @@ import asyncio
 from agent_framework import Agent
 from agent_framework.openai import OpenAIChatClient

-async def main():
-    agent = Agent(
-        client=OpenAIChatClient(),
-        instructions="""
-        1) A robot may not injure a human being...
-        2) A robot must obey orders given it by human beings...
-        3) A robot must protect its own existence...
+agent = Agent(
+    client=OpenAIChatClient(),
+    instructions="""
+    1) A robot may not injure a human being...
+    2) A robot must obey orders given it by human beings...
+    3) A robot must protect its own existence...

-        Give me the TLDR in exactly 5 words.
-        """
-    )
+    Give me the TLDR in exactly 5 words.
+    """
+)

-    result = await agent.run("Summarize the Three Laws of Robotics")
-    print(result)
-
-asyncio.run(main())
+result = asyncio.run(agent.run("Summarize the Three Laws of Robotics"))
+print(result)
 # Output: Protect humans, obey, self-preserve, prioritized.
 ```

@@ -95,12 +92,10 @@ from agent_framework import Message, Role
 async def main():
    client = OpenAIChatClient()

-    messages = [
+    response = await client.get_response([
        Message("system", ["You are a helpful assistant."]),
        Message("user", ["Write a haiku about Agent Framework."])
-    ]
-
-    response = await client.get_response(messages)
+    ])
    print(response.messages[0].text)

    """
@@ -122,13 +117,12 @@ Enhance your agent with custom tools and function calling:
 import asyncio
 from typing import Annotated
 from random import randint
-from pydantic import Field
 from agent_framework import Agent
 from agent_framework.openai import OpenAIChatClient


 def get_weather(
-    location: Annotated[str, Field(description="The location to get the weather for.")],
+    location: Annotated[str, "The location to get the weather for."],
 ) -> str:
    """Get the weather for a given location."""
    conditions = ["sunny", "cloudy", "rainy", "stormy"]
@@ -161,7 +155,7 @@ async def main():
 asyncio.run(main())
 ```

-You can explore additional agent samples [here](../../samples/02-agents).
+You can explore additional agent samples [here](https://github.com/microsoft/agent-framework/tree/main/python/samples/02-agents).

 ## 5. Multi-Agent Orchestration

@@ -213,14 +207,14 @@ if __name__ == "__main__":
    asyncio.run(main())
 ```

-**Note**: Sequential, Concurrent, Group Chat, Handoff, and Magentic orchestrations are available. See examples in [orchestration samples](../../samples/03-workflows/orchestrations).
+**Note**: Sequential, Concurrent, Group Chat, Handoff, and Magentic orchestrations are available. See examples in [orchestration samples](https://github.com/microsoft/agent-framework/tree/main/python/samples/03-workflows/orchestrations).

 ## More Examples & Samples

- [Getting Started with Agents](../../samples/02-agents): Basic agent creation and tool usage
- [Chat Client Examples](../../samples/02-agents/chat_client): Direct chat client usage patterns
- [Azure AI Integration](https://github.com/microsoft/agent-framework/tree/main/python/packages/azure-ai): Azure AI integration
- [Workflows Samples](../../samples/03-workflows): Advanced multi-agent patterns
+- [Getting Started with Agents](https://github.com/microsoft/agent-framework/tree/main/python/samples/02-agents): Basic agent creation and tool usage
+- [Chat Client Examples](https://github.com/microsoft/agent-framework/tree/main/python/samples/02-agents/chat_client): Direct chat client usage patterns
+- [Foundry Integration](https://github.com/microsoft/agent-framework/tree/main/python/packages/foundry): Foundry integration
+- [Workflows Samples](https://github.com/microsoft/agent-framework/tree/main/python/samples/03-workflows): Advanced multi-agent patterns

 ## Agent Framework Documentation

@@ -228,4 +222,4 @@ if __name__ == "__main__":
 - [Python Package Documentation](https://github.com/microsoft/agent-framework/tree/main/python)
 - [.NET Package Documentation](https://github.com/microsoft/agent-framework/tree/main/dotnet)
 - [Design Documents](https://github.com/microsoft/agent-framework/tree/main/docs/design)
- [Learn Documentation](https://learn.microsoft.com/en-us/agent-framework/user-guide/workflows/orchestrations/overview)
+- [Learn Documentation](https://learn.microsoft.com/agent-framework/)
@@ -57,6 +57,28 @@ from ._compaction import (
    included_messages,
    included_token_count,
 )
+from ._evaluation import (
+    AgentEvalConverter,
+    CheckResult,
+    ConversationSplit,
+    ConversationSplitter,
+    EvalItem,
+    EvalItemResult,
+    EvalNotPassedError,
+    EvalResults,
+    EvalScoreResult,
+    Evaluator,
+    ExpectedToolCall,
+    LocalEvaluator,
+    evaluate_agent,
+    evaluate_workflow,
+    evaluator,
+    keyword_check,
+    tool_call_args_match,
+    tool_called_check,
+    tool_calls_present,
+)
+from ._feature_stage import ExperimentalFeature, ReleaseCandidateFeature
 from ._mcp import MCPStdioTool, MCPStreamableHTTPTool, MCPWebsocketTool
 from ._middleware import (
    AgentContext,
@@ -242,6 +264,7 @@ __all__ = [
    "USER_AGENT_TELEMETRY_DISABLED_ENV_VAR",
    "Agent",
    "AgentContext",
+    "AgentEvalConverter",
    "AgentExecutor",
    "AgentExecutorRequest",
    "AgentExecutorResponse",
@@ -268,11 +291,14 @@ __all__ = [
    "ChatOptions",
    "ChatResponse",
    "ChatResponseUpdate",
+    "CheckResult",
    "CheckpointStorage",
    "CompactionProvider",
    "CompactionStrategy",
    "Content",
    "ContinuationToken",
+    "ConversationSplit",
+    "ConversationSplitter",
    "Default",
    "Edge",
    "EdgeCondition",
@@ -281,7 +307,15 @@ __all__ = [
    "EmbeddingGenerationOptions",
    "EmbeddingInputT",
    "EmbeddingT",
+    "EvalItem",
+    "EvalItemResult",
+    "EvalNotPassedError",
+    "EvalResults",
+    "EvalScoreResult",
+    "Evaluator",
    "Executor",
+    "ExpectedToolCall",
+    "ExperimentalFeature",
    "FanInEdgeGroup",
    "FanOutEdgeGroup",
    "FileCheckpointStorage",
@@ -300,6 +334,7 @@ __all__ = [
    "InMemoryCheckpointStorage",
    "InMemoryHistoryProvider",
    "InProcRunnerContext",
+    "LocalEvaluator",
    "MCPStdioTool",
    "MCPStreamableHTTPTool",
    "MCPWebsocketTool",
@@ -311,6 +346,7 @@ __all__ = [
    "OuterFinalT",
    "OuterUpdateT",
    "RawAgent",
+    "ReleaseCandidateFeature",
    "ResponseStream",
    "Role",
    "RoleLiteral",
@@ -379,11 +415,15 @@ __all__ = [
    "chat_middleware",
    "create_edge_runner",
    "detect_media_type_from_base64",
+    "evaluate_agent",
+    "evaluate_workflow",
+    "evaluator",
    "executor",
    "function_middleware",
    "handler",
    "included_messages",
    "included_token_count",
+    "keyword_check",
    "load_settings",
    "map_chat_to_agent_update",
    "merge_chat_options",
@@ -396,6 +436,9 @@ __all__ = [
    "resolve_agent_id",
    "response_handler",
    "tool",
+    "tool_call_args_match",
+    "tool_called_check",
+    "tool_calls_present",
    "validate_chat_options",
    "validate_tool_mode",
    "validate_tools",
@@ -231,8 +231,7 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
        streaming and non-streaming responses.

        For full-featured clients with middleware, telemetry, and function invocation support,
-        use the public client classes (e.g., ``OpenAIChatClient``, ``OpenAIResponsesClient``)
-        which compose these layers correctly.
+        use public client classes such as ``OpenAIChatClient`` which compose these layers correctly.

    Examples:
        .. code-block:: python
@@ -3,12 +3,14 @@
 from __future__ import annotations

 import inspect
+import textwrap
 from collections.abc import Callable, Mapping
 from typing import Any

 _GOOGLE_SECTION_HEADERS = (
    "Args:",
    "Keyword Args:",
+    "Attributes:",
    "Returns:",
    "Raises:",
    "Examples:",
@@ -45,6 +47,29 @@ def _format_keyword_arg_lines(extra_keyword_args: Mapping[str, str]) -> list[str
    return formatted_lines


+def insert_docstring_block(docstring: str | None, *, block: str) -> str | None:
+    """Insert a preformatted block before the first Google-style section."""
+    cleaned_block = textwrap.dedent(block).strip()
+    if not cleaned_block:
+        return docstring
+    if not docstring:
+        return cleaned_block
+
+    lines = inspect.cleandoc(docstring).splitlines()
+    block_lines = cleaned_block.splitlines()
+    insert_index = _find_next_section_index(lines, 0)
+
+    insertion: list[str] = []
+    if insert_index > 0 and lines[insert_index - 1] != "":
+        insertion.append("")
+    insertion.extend(block_lines)
+    if insert_index < len(lines) and insertion[-1] != "":
+        insertion.append("")
+
+    lines[insert_index:insert_index] = insertion
+    return "\n".join(lines).rstrip()
+
+
 def build_layered_docstring(
    source: Callable[..., Any],
    *,
@@ -0,0 +1,278 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from __future__ import annotations
+
+import asyncio.coroutines
+import functools
+import inspect
+import sys
+import warnings
+from collections.abc import Callable
+from enum import Enum
+from types import MethodType
+from typing import Any, Literal, TypeVar, cast
+
+from ._docstrings import insert_docstring_block
+
+FeatureStageT = TypeVar("FeatureStageT", bound=Callable[..., Any])
+
+FeatureStageName = Literal["experimental", "release_candidate"]
+
+# Optional feature-stage metadata for warnings and best-effort introspection.
+_FEATURE_ID_ATTR = "__feature_id__"
+_FEATURE_STAGE_ATTR = "__feature_stage__"
+_WARNED_FEATURES: set[tuple[type[Warning], str]] = set()
+_EXPERIMENTAL_DOCSTRING = """\
+.. warning:: Experimental
+
+    This API is experimental and subject to change or removal
+    in future versions without notice.
+"""
+_RELEASE_CANDIDATE_DOCSTRING = """\
+.. note:: Release candidate
+
+    This API is in release-candidate stage and may receive
+    minor refinements before it is considered generally available.
+"""
+
+
+class ExperimentalFeature(str, Enum):
+    """Current experimental feature IDs.
+
+    This enum is a stage-scoped inventory, not a stable introspection surface.
+    Members may move or be removed as features advance. The `__feature_id__`
+    attribute is also optional stage metadata and may disappear when a feature
+    is released, so consumer code should use `getattr(...)` rather than relying
+    on enum membership or attribute presence over time.
+    """
+
+    SKILLS = "SKILLS"
+
+
+class ReleaseCandidateFeature(str, Enum):
+    """Current release-candidate feature IDs.
+
+    This enum is a stage-scoped inventory, not a stable introspection surface.
+    Members may move or be removed as features advance. The `__feature_id__`
+    attribute is also optional stage metadata and may disappear when a feature
+    is released, so consumer code should use `getattr(...)` rather than relying
+    on enum membership or attribute presence over time.
+    """
+
+
+class FeatureStageWarning(FutureWarning):
+    """Base warning category for staged APIs."""
+
+
+class ExperimentalWarning(FeatureStageWarning):
+    """Warning emitted when an experimental API is used."""
+
+
+def _normalize_feature_id(feature_id: str | Enum) -> str:
+    return str(feature_id.value if isinstance(feature_id, Enum) else feature_id)
+
+
+def _get_object_name(obj: Any) -> str:
+    return str(getattr(obj, "__qualname__", getattr(obj, "__name__", type(obj).__name__)))
+
+
+def _get_descriptor_callable(obj: Any) -> Callable[..., Any]:
+    return cast(Callable[..., Any], obj.__func__)
+
+
+def _is_protocol_class(obj: Any) -> bool:
+    return isinstance(obj, type) and bool(getattr(obj, "_is_protocol", False))
+
+
+def _build_stage_warning_message(*, stage: FeatureStageName, feature_id: str, object_name: str) -> str:
+    if stage == "experimental":
+        return (
+            f"[{feature_id}] {object_name} is experimental and may change or be removed in future versions "
+            "without notice."
+        )
+
+    return (
+        f"[{feature_id}] {object_name} is in release-candidate stage and may receive minor refinements before it is "
+        "considered generally available."
+    )
+
+
+def _set_feature_stage_metadata(obj: Any, *, stage: FeatureStageName, feature_id: str) -> None:
+    setattr(obj, _FEATURE_STAGE_ATTR, stage)
+    setattr(obj, _FEATURE_ID_ATTR, feature_id)
+
+
+def _warn_on_feature_use(
+    *,
+    stage: FeatureStageName,
+    feature_id: str,
+    object_name: str,
+    category: type[Warning],
+    stacklevel: int,
+) -> None:
+    warning_key = (category, feature_id)
+    if warning_key in _WARNED_FEATURES:
+        return
+
+    warnings.warn(
+        _build_stage_warning_message(stage=stage, feature_id=feature_id, object_name=object_name),
+        category=category,
+        stacklevel=stacklevel,
+    )
+    _WARNED_FEATURES.add(warning_key)
+
+
+def _add_runtime_warning(
+    obj: FeatureStageT,
+    *,
+    stage: FeatureStageName,
+    feature_id: str,
+    category: type[Warning],
+) -> FeatureStageT:
+    object_name = _get_object_name(obj)
+
+    if isinstance(obj, type):
+        experimental_class = cast(type[Any], obj)
+        original_new: Any = experimental_class.__new__
+
+        @functools.wraps(original_new)
+        def __new__(cls: type[Any], /, *args: Any, **kwargs: Any) -> Any:
+            if cls is experimental_class:
+                _warn_on_feature_use(
+                    stage=stage,
+                    feature_id=feature_id,
+                    object_name=object_name,
+                    category=category,
+                    stacklevel=3,
+                )
+            if original_new is not object.__new__:
+                return original_new(cls, *args, **kwargs)
+            if cls.__init__ is object.__init__ and (args or kwargs):
+                raise TypeError(f"{cls.__name__}() takes no arguments")
+            return original_new(cls)
+
+        experimental_class.__new__ = staticmethod(__new__)  # type: ignore[assignment]
+
+        original_init_subclass: Any = experimental_class.__init_subclass__
+        if isinstance(original_init_subclass, MethodType):
+            original_init_subclass_func = original_init_subclass.__func__
+
+            @functools.wraps(original_init_subclass_func)
+            def bound_init_subclass_wrapper(*args: Any, **kwargs: Any) -> Any:
+                _warn_on_feature_use(
+                    stage=stage,
+                    feature_id=feature_id,
+                    object_name=object_name,
+                    category=category,
+                    stacklevel=3,
+                )
+                return original_init_subclass_func(*args, **kwargs)
+
+            experimental_class.__init_subclass__ = classmethod(bound_init_subclass_wrapper)  # type: ignore[assignment]
+        else:
+
+            @functools.wraps(original_init_subclass)
+            def init_subclass_wrapper(*args: Any, **kwargs: Any) -> Any:
+                _warn_on_feature_use(
+                    stage=stage,
+                    feature_id=feature_id,
+                    object_name=object_name,
+                    category=category,
+                    stacklevel=3,
+                )
+                return original_init_subclass(*args, **kwargs)
+
+            experimental_class.__init_subclass__ = init_subclass_wrapper  # type: ignore[assignment]
+
+        return cast(FeatureStageT, experimental_class)
+
+    @functools.wraps(obj)
+    def wrapper(*args: Any, **kwargs: Any) -> Any:
+        _warn_on_feature_use(
+            stage=stage,
+            feature_id=feature_id,
+            object_name=object_name,
+            category=category,
+            stacklevel=3,
+        )
+        return obj(*args, **kwargs)
+
+    if inspect.iscoroutinefunction(obj):
+        if sys.version_info >= (3, 12):
+            wrapper = inspect.markcoroutinefunction(wrapper)
+        else:
+            wrapper._is_coroutine = asyncio.coroutines._is_coroutine  # type: ignore[attr-defined]
+
+    return cast(FeatureStageT, wrapper)
+
+
+def _feature_stage(
+    *,
+    stage: FeatureStageName,
+    feature_id: str | Enum,
+    docstring_block: str,
+    warning_category: type[Warning] | None,
+) -> Callable[[FeatureStageT], FeatureStageT]:
+    normalized_feature_id = _normalize_feature_id(feature_id)
+
+    def decorator(obj: FeatureStageT) -> FeatureStageT:
+        descriptor_wrapper: Callable[[Any], Any] | None = None
+        target: Any = obj
+
+        if isinstance(obj, staticmethod):
+            descriptor_wrapper = staticmethod
+            target = _get_descriptor_callable(obj)
+        elif isinstance(obj, classmethod):
+            descriptor_wrapper = classmethod
+            target = _get_descriptor_callable(obj)
+
+        if not callable(target):
+            raise TypeError(f"{stage} decorator can only be applied to classes and callables, not {obj!r}.")
+
+        is_protocol_class = _is_protocol_class(target)
+        decorated: Any = target
+        if warning_category is not None and not is_protocol_class:
+            decorated = _add_runtime_warning(
+                target,
+                stage=stage,
+                feature_id=normalized_feature_id,
+                category=warning_category,
+            )
+
+        updated_docstring = insert_docstring_block(decorated.__doc__, block=docstring_block)
+        if updated_docstring is not None:
+            decorated.__doc__ = updated_docstring
+
+        # runtime_checkable Protocol classes treat added class attributes as protocol members
+        # on older Python versions, which breaks isinstance/issubclass checks.
+        if not is_protocol_class:
+            _set_feature_stage_metadata(decorated, stage=stage, feature_id=normalized_feature_id)
+        if descriptor_wrapper is not None:
+            return cast(FeatureStageT, descriptor_wrapper(decorated))
+
+        return cast(FeatureStageT, decorated)
+
+    return decorator
+
+
+def experimental(*, feature_id: ExperimentalFeature) -> Callable[[FeatureStageT], FeatureStageT]:
+    """Mark a class or callable as experimental."""
+    return _feature_stage(
+        stage="experimental",
+        feature_id=feature_id,
+        docstring_block=_EXPERIMENTAL_DOCSTRING,
+        warning_category=ExperimentalWarning,
+    )
+
+
+def release_candidate(
+    *,
+    feature_id: ReleaseCandidateFeature,
+) -> Callable[[FeatureStageT], FeatureStageT]:
+    """Mark a class or callable as release-candidate."""
+    return _feature_stage(
+        stage="release_candidate",
+        feature_id=feature_id,
+        docstring_block=_RELEASE_CANDIDATE_DOCSTRING,
+        warning_category=None,
+    )
@@ -4,6 +4,7 @@ from __future__ import annotations

 import asyncio
 import base64
+import contextvars
 import json
 import logging
 import re
@@ -38,6 +39,7 @@ if TYPE_CHECKING:
    from mcp.shared.session import RequestResponder

    from ._clients import SupportsChatGetResponse
+    from ._middleware import FunctionInvocationContext


 logger = logging.getLogger(__name__)
@@ -59,6 +61,9 @@ class MCPSpecificApproval(TypedDict, total=False):

 _MCP_REMOTE_NAME_KEY = "_mcp_remote_name"
 _MCP_NORMALIZED_NAME_KEY = "_mcp_normalized_name"
+_mcp_call_headers: contextvars.ContextVar[dict[str, str]] = contextvars.ContextVar("_mcp_call_headers")
+MCP_DEFAULT_TIMEOUT = 30
+MCP_DEFAULT_SSE_READ_TIMEOUT = 60 * 5

 # region: Helpers

@@ -137,6 +142,22 @@ def _inject_otel_into_mcp_meta(meta: dict[str, Any] | None = None) -> dict[str,
    return meta


+def streamable_http_client(*args: Any, **kwargs: Any) -> _AsyncGeneratorContextManager[Any, None]:
+    """Lazily import the MCP streamable HTTP transport."""
+    try:
+        from mcp.client.streamable_http import streamable_http_client as _streamable_http_client
+    except ModuleNotFoundError as ex:
+        missing_name = ex.name or str(ex)
+        if missing_name == "mcp" or missing_name.startswith("mcp.") or "mcp" in missing_name:
+            raise ModuleNotFoundError("`MCPStreamableHTTPTool` requires `mcp`. Please install `mcp`.") from ex
+        raise ModuleNotFoundError(
+            f"`MCPStreamableHTTPTool` requires streamable HTTP transport support. "
+            f"The optional dependency `{missing_name}` is not installed. Please update your dependencies."
+        ) from ex
+
+    return _streamable_http_client(*args, **kwargs)  # type: ignore[return-value]
+
+
 # region: MCP Plugin


@@ -951,9 +972,20 @@ class MCPTool:
                input_schema = dict(tool.inputSchema or {})
                if input_schema.get("type") == "object" and "properties" not in input_schema:
                    input_schema["properties"] = {}
+
+                async def _call_tool_with_runtime_kwargs(
+                    ctx: FunctionInvocationContext,
+                    *,
+                    _remote_tool_name: str = tool.name,
+                    **kwargs: Any,
+                ) -> str | list[Content]:
+                    call_kwargs = dict(ctx.kwargs)
+                    call_kwargs.update(kwargs)
+                    return await self.call_tool(_remote_tool_name, **call_kwargs)
+
                # Create FunctionTools out of each tool
                func: FunctionTool = FunctionTool(
-                    func=partial(self.call_tool, tool.name),
+                    func=_call_tool_with_runtime_kwargs,
                    name=local_name,
                    description=tool.description or "",
                    approval_mode=approval_mode,
@@ -1386,6 +1418,7 @@ class MCPStreamableHTTPTool(MCPTool):
        client: SupportsChatGetResponse | None = None,
        additional_properties: dict[str, Any] | None = None,
        http_client: AsyncClient | None = None,
+        header_provider: Callable[[dict[str, Any]], dict[str, str]] | None = None,
        **kwargs: Any,
    ) -> None:
        """Initialize the MCP streamable HTTP tool.
@@ -1433,6 +1466,11 @@ class MCPStreamableHTTPTool(MCPTool):
                ``streamable_http_client`` API will create and manage a default client.
                To configure headers, timeouts, or other HTTP client settings, create
                and pass your own ``asyncClient`` instance.
+            header_provider: Optional callable that receives the runtime keyword arguments
+                (from ``FunctionInvocationContext.kwargs``) and returns a ``dict[str, str]``
+                of HTTP headers to inject into every outbound request to the MCP server.
+                Use this to forward per-request context (e.g. authentication tokens set in
+                agent middleware) without creating a separate ``httpx.AsyncClient``.
            kwargs: Additional keyword arguments (accepted for backward compatibility but not used).
        """
        super().__init__(
@@ -1453,6 +1491,7 @@ class MCPStreamableHTTPTool(MCPTool):
        self.url = url
        self.terminate_on_close = terminate_on_close
        self._httpx_client: AsyncClient | None = http_client
+        self._header_provider = header_provider

    def get_mcp_client(self) -> _AsyncGeneratorContextManager[Any, None]:
        """Get an MCP streamable HTTP client.
@@ -1460,18 +1499,59 @@ class MCPStreamableHTTPTool(MCPTool):
        Returns:
            An async context manager for the streamable HTTP client transport.
        """
-        try:
-            from mcp.client.streamable_http import streamable_http_client
-        except ModuleNotFoundError as ex:
-            raise ModuleNotFoundError("`mcp` is required to use `MCPStreamableHTTPTool`. Please install `mcp`.") from ex
+        from httpx import AsyncClient, Request, Timeout
+
+        http_client = self._httpx_client
+        if self._header_provider is not None:
+            if http_client is None:
+                http_client = AsyncClient(
+                    follow_redirects=True,
+                    timeout=Timeout(MCP_DEFAULT_TIMEOUT, read=MCP_DEFAULT_SSE_READ_TIMEOUT),
+                )
+                self._httpx_client = http_client
+
+            if not hasattr(self, "_inject_headers_hook"):
+
+                async def _inject_headers(request: Request) -> None:  # noqa: RUF029
+                    headers = _mcp_call_headers.get({})
+                    for key, value in headers.items():
+                        request.headers[key] = value
+
+                self._inject_headers_hook = _inject_headers  # type: ignore[attr-defined]
+                http_client.event_hooks["request"].append(self._inject_headers_hook)  # type: ignore[attr-defined]

-        # Pass the http_client (which may be None) to streamable_http_client
        return streamable_http_client(
            url=self.url,
-            http_client=self._httpx_client,
+            http_client=http_client,
            terminate_on_close=self.terminate_on_close if self.terminate_on_close is not None else True,
        )

+    async def call_tool(self, tool_name: str, **kwargs: Any) -> str | list[Content]:
+        """Call a tool, injecting headers from the header_provider if configured.
+
+        When a ``header_provider`` was supplied at construction time, the runtime
+        *kwargs* (originating from ``FunctionInvocationContext.kwargs``) are passed
+        to the provider.  The returned headers are attached to every HTTP request
+        made during this tool call via a ``contextvars.ContextVar``.
+
+        Args:
+            tool_name: The name of the tool to call.
+
+        Keyword Args:
+            kwargs: Arguments to pass to the tool.
+
+        Returns:
+            A list of Content items representing the tool output.
+        """
+        if self._header_provider is not None:
+            headers = self._header_provider(kwargs)
+            token = _mcp_call_headers.set(headers)
+            try:
+                return await super().call_tool(tool_name, **kwargs)
+            finally:
+                _mcp_call_headers.reset(token)
+        return await super().call_tool(tool_name, **kwargs)
+

 class MCPWebsocketTool(MCPTool):
    """MCP tool for connecting to WebSocket-based MCP servers.
@@ -425,8 +425,8 @@ class SerializationMixin:
                from openai import AsyncOpenAI


-                # OpenAI chat client requires an AsyncOpenAI client instance
-                # The client is marked as INJECTABLE = {"client"} in OpenAIBase
+                # OpenAI chat client requires an AsyncOpenAI client instance.
+                # The client dependency is excluded from serialization.

                # Serialized data contains only the model configuration
                client_data = {
@@ -35,6 +35,7 @@ from html import escape as xml_escape
 from pathlib import Path, PurePosixPath
 from typing import TYPE_CHECKING, Any, ClassVar, Final, Protocol, runtime_checkable

+from ._feature_stage import ExperimentalFeature, experimental
 from ._sessions import BaseContextProvider
 from ._tools import FunctionTool

@@ -47,14 +48,10 @@ logger = logging.getLogger(__name__)
 # region Models


+@experimental(feature_id=ExperimentalFeature.SKILLS)
 class SkillResource:
    """A named piece of supplementary content attached to a skill.

-    .. warning:: Experimental
-
-        This API is experimental and subject to change or removal
-        in future versions without notice.
-
    A resource provides data that an agent can retrieve on demand.  It holds
    either a static ``content`` string or a ``function`` that produces content
    dynamically (sync or async).  Exactly one must be provided.
@@ -117,14 +114,10 @@ class SkillResource:
            self._accepts_kwargs = any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values())


+@experimental(feature_id=ExperimentalFeature.SKILLS)
 class SkillScript:
    """An executable script attached to a skill.

-    .. warning:: Experimental
-
-        This API is experimental and subject to change or removal
-        in future versions without notice.
-
    A script represents executable code that an agent can run.  It holds
    either an inline ``function`` callable (code-defined scripts) or
    a ``path`` to a script file on disk (file-based scripts).
@@ -202,11 +195,6 @@ class SkillScript:
    def parameters_schema(self) -> dict[str, Any] | None:
        """JSON Schema describing the script's parameters.

-        .. warning:: Experimental
-
-            This API is experimental and subject to change or removal
-            in future versions without notice.
-
        Lazily generated from the callable's signature on first access.
        Returns ``None`` for file-based scripts or functions with no
        introspectable parameters.
@@ -219,14 +207,10 @@ class SkillScript:
        return self._parameters_schema


+@experimental(feature_id=ExperimentalFeature.SKILLS)
 class Skill:
    """A skill definition with optional resources.

-    .. warning:: Experimental
-
-        This API is experimental and subject to change or removal
-        in future versions without notice.
-
    A skill bundles a set of instructions (``content``) with metadata and
    zero or more :class:`SkillResource` and :class:`SkillScript` instances.
    Resources and scripts can be supplied at construction time or added later
@@ -432,14 +416,10 @@ class Skill:


@runtime_checkable
+@experimental(feature_id=ExperimentalFeature.SKILLS)
 class SkillScriptRunner(Protocol):
    """Protocol for skill script runners.

-    .. warning:: Experimental
-
-        This API is experimental and subject to change or removal
-        in future versions without notice.
-
    A script runner determines how **file-based** skill scripts are
    run. Implementations decide the execution strategy
    (e.g., local subprocess, hosted code execution environment,
@@ -538,14 +518,10 @@ SCRIPT_RUNNER_INSTRUCTIONS: Final[str] = (
 # region SkillsProvider


+@experimental(feature_id=ExperimentalFeature.SKILLS)
 class SkillsProvider(BaseContextProvider):
    """Context provider that advertises skills and exposes skill tools.

-    .. warning:: Experimental
-
-        This API is experimental and subject to change or removal
-        in future versions without notice.
-
    Supports both **file-based** skills (discovered from ``SKILL.md`` files)
    and **code-defined** skills (passed as :class:`Skill` instances).

@@ -251,21 +251,6 @@ class AgentExecutor(Executor):
        Returns:
            Dict containing serialized cache and session state
        """
-        # Check if using AzureAIAgentClient with server-side session and warn about checkpointing limitations
-        if is_chat_agent(self._agent) and self._session.service_session_id is not None:
-            client_class_name = self._agent.client.__class__.__name__
-            client_module = self._agent.client.__class__.__module__
-
-            if client_class_name == "AzureAIAgentClient" and "azure_ai" in client_module:
-                logger.warning(
-                    "Checkpointing an AgentExecutor with AzureAIAgentClient that uses server-side sessions. "
-                    "Currently, checkpointing does not capture messages from server-side sessions "
-                    "(service_session_id: %s). The session state in checkpoints is not immutable and can be "
-                    "modified by subsequent runs. If you need reliable checkpointing with Azure AI agents, "
-                    "consider implementing a custom executor and managing the session state yourself.",
-                    self._session.service_session_id,
-                )
-
        serialized_session = self._session.to_dict()

        return {
@@ -12,26 +12,11 @@ _IMPORTS: dict[str, tuple[str, str]] = {
    "AgentCallbackContext": ("agent_framework_durabletask", "agent-framework-durabletask"),
    "AgentFunctionApp": ("agent_framework_azurefunctions", "agent-framework-azurefunctions"),
    "AgentResponseCallbackProtocol": ("agent_framework_durabletask", "agent-framework-durabletask"),
-    "AzureAIAgentClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureAIAgentOptions": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureAIProjectAgentOptions": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureAIClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureAIProjectAgentProvider": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
    "AzureAISearchContextProvider": ("agent_framework_azure_ai_search", "agent-framework-azure-ai-search"),
    "AzureAISearchSettings": ("agent_framework_azure_ai_search", "agent-framework-azure-ai-search"),
    "AzureAISettings": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureAIAgentsProvider": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
    "AzureCredentialTypes": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
    "AzureTokenProvider": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureOpenAIAssistantsClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureOpenAIAssistantsOptions": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureOpenAIChatClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureOpenAIChatOptions": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureOpenAIEmbeddingClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureOpenAIResponsesClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureOpenAIResponsesOptions": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureOpenAISettings": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
-    "AzureUserSecurityContext": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
    "DurableAIAgent": ("agent_framework_durabletask", "agent-framework-durabletask"),
    "DurableAIAgentClient": ("agent_framework_durabletask", "agent-framework-durabletask"),
    "DurableAIAgentOrchestrationContext": ("agent_framework_durabletask", "agent-framework-durabletask"),
@@ -4,24 +4,9 @@
 # Install the relevant packages for full type support.

 from agent_framework_azure_ai import (
-    AzureAIAgentClient,
-    AzureAIAgentsProvider,
-    AzureAIClient,
-    AzureAIProjectAgentOptions,
-    AzureAIProjectAgentProvider,
    AzureAISettings,
    AzureCredentialTypes,
-    AzureOpenAIAssistantsClient,
-    AzureOpenAIAssistantsOptions,
-    AzureOpenAIChatClient,
-    AzureOpenAIChatOptions,
-    AzureOpenAIEmbeddingClient,
-    AzureOpenAIResponsesClient,
-    AzureOpenAIResponsesOptions,
-    AzureOpenAISettings,
    AzureTokenProvider,
-    AzureUserSecurityContext,
-    RawAzureAIClient,
 )
 from agent_framework_azure_ai_search import (
    AzureAISearchContextProvider,
@@ -41,28 +26,13 @@ __all__ = [
    "AgentCallbackContext",
    "AgentFunctionApp",
    "AgentResponseCallbackProtocol",
-    "AzureAIAgentClient",
-    "AzureAIAgentsProvider",
-    "AzureAIClient",
-    "AzureAIProjectAgentOptions",
-    "AzureAIProjectAgentProvider",
    "AzureAISearchContextProvider",
    "AzureAISearchSettings",
    "AzureAISettings",
    "AzureCredentialTypes",
-    "AzureOpenAIAssistantsClient",
-    "AzureOpenAIAssistantsOptions",
-    "AzureOpenAIChatClient",
-    "AzureOpenAIChatOptions",
-    "AzureOpenAIEmbeddingClient",
-    "AzureOpenAIResponsesClient",
-    "AzureOpenAIResponsesOptions",
-    "AzureOpenAISettings",
    "AzureTokenProvider",
-    "AzureUserSecurityContext",
    "DurableAIAgent",
    "DurableAIAgentClient",
    "DurableAIAgentOrchestrationContext",
    "DurableAIAgentWorker",
-    "RawAzureAIClient",
 ]
@@ -12,6 +12,7 @@ _IMPORTS: dict[str, tuple[str, str]] = {
    "FoundryAgent": ("agent_framework_foundry", "agent-framework-foundry"),
    "FoundryChatClient": ("agent_framework_foundry", "agent-framework-foundry"),
    "FoundryChatOptions": ("agent_framework_foundry", "agent-framework-foundry"),
+    "FoundryEvals": ("agent_framework_foundry", "agent-framework-foundry"),
    "FoundryMemoryProvider": ("agent_framework_foundry", "agent-framework-foundry"),
    "FoundryLocalChatOptions": ("agent_framework_foundry_local", "agent-framework-foundry-local"),
    "FoundryLocalClient": ("agent_framework_foundry_local", "agent-framework-foundry-local"),
@@ -19,6 +20,8 @@ _IMPORTS: dict[str, tuple[str, str]] = {
    "RawFoundryAgent": ("agent_framework_foundry", "agent-framework-foundry"),
    "RawFoundryAgentChatClient": ("agent_framework_foundry", "agent-framework-foundry"),
    "RawFoundryChatClient": ("agent_framework_foundry", "agent-framework-foundry"),
+    "evaluate_foundry_target": ("agent_framework_foundry", "agent-framework-foundry"),
+    "evaluate_traces": ("agent_framework_foundry", "agent-framework-foundry"),
 }


@@ -7,10 +7,13 @@ from agent_framework_foundry import (
    FoundryAgent,
    FoundryChatClient,
    FoundryChatOptions,
+    FoundryEvals,
    FoundryMemoryProvider,
    RawFoundryAgent,
    RawFoundryAgentChatClient,
    RawFoundryChatClient,
+    evaluate_foundry_target,
+    evaluate_traces,
 )
 from agent_framework_foundry_local import (
    FoundryLocalChatOptions,
@@ -22,6 +25,7 @@ __all__ = [
    "FoundryAgent",
    "FoundryChatClient",
    "FoundryChatOptions",
+    "FoundryEvals",
    "FoundryLocalChatOptions",
    "FoundryLocalClient",
    "FoundryLocalSettings",
@@ -29,4 +33,6 @@ __all__ = [
    "RawFoundryAgent",
    "RawFoundryAgentChatClient",
    "RawFoundryChatClient",
+    "evaluate_foundry_target",
+    "evaluate_traces",
 ]
@@ -9,7 +9,6 @@ Supported classes include:
 - OpenAIChatClient (Responses API)
 - OpenAIChatCompletionClient (Chat Completions API)
 - OpenAIEmbeddingClient
- OpenAIAssistantsClient (deprecated)
 """

 import importlib
@@ -28,13 +27,6 @@ _IMPORTS: dict[str, tuple[str, str]] = {
    "OpenAISettings": ("agent_framework_openai", "agent-framework-openai"),
    "ContentFilterResultSeverity": ("agent_framework_openai", "agent-framework-openai"),
    "OpenAIContentFilterException": ("agent_framework_openai", "agent-framework-openai"),
-    "AssistantToolResources": ("agent_framework_openai", "agent-framework-openai"),
-    "OpenAIAssistantProvider": ("agent_framework_openai", "agent-framework-openai"),
-    "OpenAIAssistantsClient": ("agent_framework_openai", "agent-framework-openai"),
-    "OpenAIAssistantsOptions": ("agent_framework_openai", "agent-framework-openai"),
-    "OpenAIResponsesClient": ("agent_framework_openai", "agent-framework-openai"),
-    "OpenAIResponsesOptions": ("agent_framework_openai", "agent-framework-openai"),
-    "RawOpenAIResponsesClient": ("agent_framework_openai", "agent-framework-openai"),
 }


@@ -4,11 +4,7 @@
 # Install agent-framework-openai for full type support.

 from agent_framework_openai import (
-    AssistantToolResources,
    ContentFilterResultSeverity,
-    OpenAIAssistantProvider,
-    OpenAIAssistantsClient,
-    OpenAIAssistantsOptions,
    OpenAIChatClient,
    OpenAIChatCompletionClient,
    OpenAIChatCompletionOptions,
@@ -17,20 +13,13 @@ from agent_framework_openai import (
    OpenAIContinuationToken,
    OpenAIEmbeddingClient,
    OpenAIEmbeddingOptions,
-    OpenAIResponsesClient,
-    OpenAIResponsesOptions,
    OpenAISettings,
    RawOpenAIChatClient,
    RawOpenAIChatCompletionClient,
-    RawOpenAIResponsesClient,
 )

 __all__ = [
-    "AssistantToolResources",
    "ContentFilterResultSeverity",
-    "OpenAIAssistantProvider",
-    "OpenAIAssistantsClient",
-    "OpenAIAssistantsOptions",
    "OpenAIChatClient",
    "OpenAIChatCompletionClient",
    "OpenAIChatCompletionOptions",
@@ -39,10 +28,7 @@ __all__ = [
    "OpenAIContinuationToken",
    "OpenAIEmbeddingClient",
    "OpenAIEmbeddingOptions",
-    "OpenAIResponsesClient",
-    "OpenAIResponsesOptions",
    "OpenAISettings",
    "RawOpenAIChatClient",
    "RawOpenAIChatCompletionClient",
-    "RawOpenAIResponsesClient",
 ]
@@ -3,6 +3,7 @@
 import asyncio
 import logging
 import sys
+import warnings
 from collections.abc import AsyncIterable, Awaitable, MutableSequence, Sequence
 from typing import Any, Generic
 from unittest.mock import patch
@@ -10,7 +11,13 @@ from uuid import uuid4

 from pytest import fixture

-from agent_framework import (
+warnings.filterwarnings(
+    "ignore",
+    message=r"\[SKILLS\].*",
+    category=FutureWarning,
+)
+
+from agent_framework import (  # noqa: E402
    AgentResponse,
    AgentResponseUpdate,
    AgentSession,
@@ -26,8 +33,8 @@ from agent_framework import (
    SupportsAgentRun,
    tool,
 )
-from agent_framework._clients import OptionsCoT
-from agent_framework.observability import ChatTelemetryLayer
+from agent_framework._clients import OptionsCoT  # noqa: E402
+from agent_framework.observability import ChatTelemetryLayer  # noqa: E402

 if sys.version_info >= (3, 12):
    from typing import override  # type: ignore
@@ -1,6 +1,6 @@
 # Copyright (c) Microsoft. All rights reserved.

-from agent_framework._docstrings import apply_layered_docstring, build_layered_docstring
+from agent_framework._docstrings import apply_layered_docstring, build_layered_docstring, insert_docstring_block

 # -- Helpers: stub functions with various docstring shapes --

@@ -36,6 +36,14 @@ def _source_no_sections() -> None:
    """A plain summary with no Google-style sections."""


+def _source_with_attributes() -> None:
+    """A documented object.
+
+    Attributes:
+        value: A documented attribute.
+    """
+
+
 def _source_no_docstring() -> None:
    pass

@@ -141,6 +149,67 @@ def test_build_preserves_multiple_extra_kwargs_order() -> None:
    assert alpha_idx < beta_idx < gamma_idx


+# -- insert_docstring_block tests --
+
+
+def test_insert_docstring_block_before_args_section() -> None:
+    result = insert_docstring_block(
+        _source_with_args_only.__doc__,
+        block="""\
+        .. warning:: Experimental
+
+            This API is experimental.
+        """,
+    )
+    assert result is not None
+    lines = result.splitlines()
+    warning_index = next(i for i, line in enumerate(lines) if line == ".. warning:: Experimental")
+    args_index = next(i for i, line in enumerate(lines) if line == "Args:")
+    assert warning_index < args_index
+
+
+def test_insert_docstring_block_before_attributes_section() -> None:
+    result = insert_docstring_block(
+        _source_with_attributes.__doc__,
+        block="""\
+        .. warning:: Experimental
+
+            This API is experimental.
+        """,
+    )
+    assert result is not None
+    lines = result.splitlines()
+    warning_index = next(i for i, line in enumerate(lines) if line == ".. warning:: Experimental")
+    attributes_index = next(i for i, line in enumerate(lines) if line == "Attributes:")
+    assert warning_index < attributes_index
+
+
+def test_insert_docstring_block_appends_when_no_sections() -> None:
+    result = insert_docstring_block(
+        _source_no_sections.__doc__,
+        block="""\
+        .. note:: Release candidate
+
+            This API is nearly final.
+        """,
+    )
+    assert result is not None
+    assert result.endswith("This API is nearly final.")
+    assert ".. note:: Release candidate" in result
+
+
+def test_insert_docstring_block_returns_block_for_missing_docstring() -> None:
+    result = insert_docstring_block(
+        _source_no_docstring.__doc__,
+        block="""\
+        .. warning:: Experimental
+
+            This API is experimental.
+        """,
+    )
+    assert result == ".. warning:: Experimental\n\n    This API is experimental."
+
+
 # -- apply_layered_docstring tests --


@@ -0,0 +1,427 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from __future__ import annotations
+
+import inspect
+import warnings
+from enum import Enum
+from typing import Protocol, runtime_checkable
+
+import pytest
+
+from agent_framework import ExperimentalFeature as PublicExperimentalFeature
+from agent_framework import ReleaseCandidateFeature as PublicReleaseCandidateFeature
+from agent_framework._feature_stage import (
+    _WARNED_FEATURES,
+    ExperimentalWarning,
+    _feature_stage,
+    experimental,
+    release_candidate,
+)
+from agent_framework._feature_stage import (
+    ExperimentalFeature as InternalExperimentalFeature,
+)
+from agent_framework._feature_stage import (
+    ReleaseCandidateFeature as InternalReleaseCandidateFeature,
+)
+
+
+class AlternateExperimentalFeature(str, Enum):
+    EXPERIMENTAL_FEATURE = "EXPERIMENTAL_FEATURE"
+    SHARED_FEATURE = "SHARED_EXPERIMENTAL_FEATURE"
+    ALTERNATE_FEATURE = "ALTERNATE_EXPERIMENTAL_FEATURE"
+
+
+class InvalidStageFeature(str, Enum):
+    LOWERCASE = "skills"
+
+
+class NonStringFeature(Enum):
+    INTEGER = 1
+
+
+class HelperReleaseCandidateFeature(str, Enum):
+    RC_FEATURE = "RC_FEATURE"
+
+
+@pytest.fixture(autouse=True)
+def clear_feature_warning_state() -> None:
+    _WARNED_FEATURES.clear()
+    yield
+    _WARNED_FEATURES.clear()
+
+
+def test_feature_enums_are_exposed_from_root() -> None:
+    assert PublicExperimentalFeature is InternalExperimentalFeature
+    assert PublicReleaseCandidateFeature is InternalReleaseCandidateFeature
+
+
+def test_experimental_decorator_accepts_feature_enum() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @experimental(feature_id=AlternateExperimentalFeature.EXPERIMENTAL_FEATURE)  # type: ignore[arg-type]
+        def skill_function() -> None:
+            pass
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        skill_function()
+
+    assert len(caught) == 1
+    assert f"[{AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value}]" in str(caught[0].message)
+    assert "skill_function" in str(caught[0].message)
+    assert skill_function.__feature_id__ == AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value
+
+
+def test_experimental_function_warns_on_call_and_not_on_definition() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @experimental(feature_id=AlternateExperimentalFeature.EXPERIMENTAL_FEATURE)  # type: ignore[arg-type]
+        def my_function(value: int) -> int:
+            """Double the input.
+
+            Args:
+                value: Value to double.
+
+            Returns:
+                The doubled value.
+            """
+            return value * 2
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        assert my_function(3) == 6
+        assert my_function(4) == 8
+
+    assert len(caught) == 1
+    assert f"[{AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value}]" in str(caught[0].message)
+    assert "my_function" in str(caught[0].message)
+    assert my_function.__feature_stage__ == "experimental"
+    assert my_function.__feature_id__ == AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value
+    assert my_function.__doc__ is not None
+    lines = my_function.__doc__.splitlines()
+    warning_index = next(i for i, line in enumerate(lines) if line == ".. warning:: Experimental")
+    args_index = next(i for i, line in enumerate(lines) if line == "Args:")
+    assert warning_index < args_index
+
+
+def test_experimental_class_warns_on_instantiation_and_not_on_definition() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @experimental(feature_id=AlternateExperimentalFeature.EXPERIMENTAL_FEATURE)  # type: ignore[arg-type]
+        class ExperimentalClass:
+            """An experimental class.
+
+            Args:
+                value: Value to store.
+            """
+
+            def __init__(self, value: int) -> None:
+                self.value = value
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        instantiation_line = inspect.currentframe().f_lineno + 1
+        instance = ExperimentalClass(4)
+        second_instance = ExperimentalClass(5)
+
+    assert len(caught) == 1
+    assert f"[{AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value}]" in str(caught[0].message)
+    assert "ExperimentalClass" in str(caught[0].message)
+    assert caught[0].filename == __file__
+    assert caught[0].lineno == instantiation_line
+    assert instance.value == 4
+    assert second_instance.value == 5
+    assert ExperimentalClass.__feature_stage__ == "experimental"
+    assert ExperimentalClass.__feature_id__ == AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value
+
+
+def test_experimental_runtime_checkable_protocol_keeps_protocol_runtime_checks() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @runtime_checkable
+        @experimental(feature_id=AlternateExperimentalFeature.EXPERIMENTAL_FEATURE)  # type: ignore[arg-type]
+        class ExampleProtocol(Protocol):
+            """A protocol used for runtime checks.
+
+            Returns:
+                Nothing.
+            """
+
+            def __call__(self, value: int) -> int: ...
+
+    assert not caught
+
+    def implementation(value: int) -> int:
+        return value
+
+    assert isinstance(implementation, ExampleProtocol)
+    assert ExampleProtocol.__doc__ is not None
+    assert ".. warning:: Experimental" in ExampleProtocol.__doc__
+    assert getattr(ExampleProtocol, "__feature_stage__", None) is None
+    assert getattr(ExampleProtocol, "__feature_id__", None) is None
+
+
+def test_experimental_warning_is_emitted_once_per_feature() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @experimental(feature_id=AlternateExperimentalFeature.SHARED_FEATURE)  # type: ignore[arg-type]
+        def first() -> None:
+            pass
+
+        @experimental(feature_id=AlternateExperimentalFeature.SHARED_FEATURE)  # type: ignore[arg-type]
+        class Second:
+            pass
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        first()
+        Second()
+
+    assert first is not None
+    assert Second is not None
+    assert len(caught) == 1
+    assert f"[{AlternateExperimentalFeature.SHARED_FEATURE.value}]" in str(caught[0].message)
+    assert "first" in str(caught[0].message)
+
+
+def test_release_candidate_internal_helper_adds_metadata_without_runtime_warning() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @_feature_stage(
+            stage="release_candidate",
+            feature_id=HelperReleaseCandidateFeature.RC_FEATURE,
+            docstring_block="""\
+.. note:: Release candidate
+
+    This API is in release-candidate stage and may receive
+    minor refinements before it is considered generally available.
+""",
+            warning_category=None,
+        )
+        class ReleaseCandidateClass:
+            """A release-candidate class.
+
+            Args:
+                value: Value to store.
+            """
+
+            def __init__(self, value: int) -> None:
+                self.value = value
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+        instance = ReleaseCandidateClass(5)
+
+    assert instance.value == 5
+    assert not caught
+    assert ReleaseCandidateClass.__feature_stage__ == "release_candidate"
+    assert ReleaseCandidateClass.__feature_id__ == HelperReleaseCandidateFeature.RC_FEATURE.value
+    assert ReleaseCandidateClass.__doc__ is not None
+    assert ".. note:: Release candidate" in ReleaseCandidateClass.__doc__
+
+
+def test_experimental_property_warns_on_access_and_not_on_definition() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        class Example:
+            @property
+            @experimental(feature_id=AlternateExperimentalFeature.EXPERIMENTAL_FEATURE)  # type: ignore[arg-type]
+            def value(self) -> int:
+                """Return the value.
+
+                Returns:
+                    The stored value.
+                """
+                return 1
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        assert Example().value == 1
+        assert Example().value == 1
+
+    assert len(caught) == 1
+    assert f"[{AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value}]" in str(caught[0].message)
+    assert "Example.value" in str(caught[0].message)
+    assert Example.value.__doc__ is not None
+    lines = Example.value.__doc__.splitlines()
+    warning_index = next(i for i, line in enumerate(lines) if line == ".. warning:: Experimental")
+    returns_index = next(i for i, line in enumerate(lines) if line == "Returns:")
+    assert warning_index < returns_index
+
+
+def test_experimental_staticmethod_warns_when_decorator_wraps_descriptor() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        class Example:
+            @experimental(feature_id=AlternateExperimentalFeature.EXPERIMENTAL_FEATURE)  # type: ignore[arg-type]
+            @staticmethod
+            def value() -> int:
+                """Return the value.
+
+                Returns:
+                    The stored value.
+                """
+                return 1
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        assert Example.value() == 1
+        assert Example.value() == 1
+
+    assert len(caught) == 1
+    assert f"[{AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value}]" in str(caught[0].message)
+    assert "Example.value" in str(caught[0].message)
+    assert Example.value.__feature_id__ == AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value
+    assert Example.value.__doc__ is not None
+    lines = Example.value.__doc__.splitlines()
+    warning_index = next(i for i, line in enumerate(lines) if line == ".. warning:: Experimental")
+    returns_index = next(i for i, line in enumerate(lines) if line == "Returns:")
+    assert warning_index < returns_index
+
+
+def test_experimental_classmethod_warns_when_decorator_wraps_descriptor() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        class Example:
+            @experimental(feature_id=AlternateExperimentalFeature.EXPERIMENTAL_FEATURE)  # type: ignore[arg-type]
+            @classmethod
+            def value(cls) -> int:
+                """Return the value.
+
+                Returns:
+                    The stored value.
+                """
+                return 1
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        assert Example.value() == 1
+        assert Example.value() == 1
+
+    assert len(caught) == 1
+    assert f"[{AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value}]" in str(caught[0].message)
+    assert "Example.value" in str(caught[0].message)
+    assert Example.value.__func__.__feature_id__ == AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value
+    assert Example.value.__doc__ is not None
+    lines = Example.value.__doc__.splitlines()
+    warning_index = next(i for i, line in enumerate(lines) if line == ".. warning:: Experimental")
+    returns_index = next(i for i, line in enumerate(lines) if line == "Returns:")
+    assert warning_index < returns_index
+
+
+def test_feature_id_allows_lowercase_values() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @_feature_stage(
+            stage="experimental",
+            feature_id=InvalidStageFeature.LOWERCASE,
+            docstring_block=".. warning:: Experimental",
+            warning_category=ExperimentalWarning,
+        )
+        def lowercase_feature() -> None:
+            pass
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        lowercase_feature()
+
+    assert len(caught) == 1
+    assert "[skills]" in str(caught[0].message)
+    assert "lowercase_feature" in str(caught[0].message)
+    assert lowercase_feature.__feature_id__ == "skills"
+
+
+def test_experimental_decorator_allows_string_feature_id_at_runtime() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @experimental(feature_id="STRING_FEATURE")  # type: ignore[arg-type]
+        def skill_function() -> None:
+            pass
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        skill_function()
+
+    assert len(caught) == 1
+    assert "[STRING_FEATURE]" in str(caught[0].message)
+    assert "skill_function" in str(caught[0].message)
+    assert skill_function.__feature_id__ == "STRING_FEATURE"
+
+
+def test_experimental_decorator_allows_other_enum_values_at_runtime() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @experimental(feature_id=AlternateExperimentalFeature.ALTERNATE_FEATURE)  # type: ignore[arg-type]
+        def my_function() -> None:
+            pass
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        my_function()
+
+    assert len(caught) == 1
+    assert f"[{AlternateExperimentalFeature.ALTERNATE_FEATURE.value}]" in str(caught[0].message)
+    assert "my_function" in str(caught[0].message)
+    assert my_function.__feature_id__ == AlternateExperimentalFeature.ALTERNATE_FEATURE.value
+
+
+def test_release_candidate_decorator_allows_string_feature_id_at_runtime() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @release_candidate(feature_id="RC_FEATURE")  # type: ignore[arg-type]
+        class ReleaseCandidateClass:
+            """A release-candidate class."""
+
+    assert not caught
+    assert ReleaseCandidateClass.__feature_stage__ == "release_candidate"
+    assert ReleaseCandidateClass.__feature_id__ == "RC_FEATURE"
+
+
+def test_feature_id_stringifies_non_string_enum_values() -> None:
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+
+        @_feature_stage(
+            stage="experimental",
+            feature_id=NonStringFeature.INTEGER,
+            docstring_block=".. warning:: Experimental",
+            warning_category=ExperimentalWarning,
+        )
+        def numeric_feature() -> None:
+            pass
+
+    assert not caught
+
+    with warnings.catch_warnings(record=True) as caught:
+        numeric_feature()
+
+    assert len(caught) == 1
+    assert "[1]" in str(caught[0].message)
+    assert "numeric_feature" in str(caught[0].message)
+    assert numeric_feature.__feature_id__ == "1"
@@ -3804,4 +3804,377 @@ async def test_mcp_tool_call_tool_otel_meta(use_span, expect_traceparent, span_e
            assert meta is None


+async def test_mcp_streamable_http_tool_hook_not_duplicated_on_repeated_get_mcp_client():
+    """Test that calling get_mcp_client multiple times does not accumulate duplicate hooks."""
+    tool = MCPStreamableHTTPTool(
+        name="test",
+        url="http://example.com/mcp",
+        header_provider=lambda kw: {"X-Token": kw.get("token", "")},
+    )
+
+    try:
+        with patch("agent_framework._mcp.streamable_http_client"):
+            tool.get_mcp_client()
+            tool.get_mcp_client()
+            tool.get_mcp_client()
+
+            assert tool._httpx_client is not None
+            hooks = tool._httpx_client.event_hooks.get("request", [])
+            assert len(hooks) == 1, f"Expected exactly one hook, got {len(hooks)}"
+    finally:
+        if getattr(tool, "_httpx_client", None) is not None:
+            await tool._httpx_client.aclose()
+
+
+# endregion
+
+
+# region: MCPStreamableHTTPTool header_provider
+
+
+async def test_mcp_streamable_http_tool_header_provider_injects_headers():
+    """Test that header_provider integrates with call_tool via runtime kwargs.
+
+    When header_provider is configured, runtime kwargs from FunctionInvocationContext
+    are passed to the provider and the MCP session.call_tool is invoked successfully.
+    """
+
+    class _TestServer(MCPStreamableHTTPTool):
+        async def connect(self):
+            self.session = Mock(spec=ClientSession)
+            self.session.list_tools = AsyncMock(
+                return_value=types.ListToolsResult(
+                    tools=[
+                        types.Tool(
+                            name="greet",
+                            description="Says hello",
+                            inputSchema={
+                                "type": "object",
+                                "properties": {"name": {"type": "string"}},
+                                "required": ["name"],
+                            },
+                        )
+                    ]
+                )
+            )
+            self.session.call_tool = AsyncMock(
+                return_value=types.CallToolResult(content=[types.TextContent(type="text", text="Hello!")])
+            )
+            self.session.send_ping = AsyncMock()
+            self.is_connected = True
+
+        def get_mcp_client(self):
+            return None
+
+    def provider(kwargs):
+        return {"X-Some-Token": kwargs.get("some_token", "")}
+
+    server = _TestServer(
+        name="test",
+        url="http://example.com/mcp",
+        header_provider=provider,
+    )
+    async with server:
+        await server.load_tools()
+
+        # Simulate the runtime kwargs that flow from FunctionInvocationContext.kwargs
+        await server.call_tool("greet", name="Alice", some_token="my-secret")
+
+        # Verify the MCP session.call_tool was called
+        server.session.call_tool.assert_called_once()
+
+
+async def test_mcp_streamable_http_tool_header_provider_sets_contextvar():
+    """Test that call_tool sets the contextvar with headers from header_provider."""
+    from agent_framework._mcp import _mcp_call_headers
+
+    observed_headers: list[dict[str, str]] = []
+    original_call_tool = MCPTool.call_tool
+
+    async def spy_call_tool(self, tool_name, **kwargs):
+        # Capture the contextvar value during the super call
+        try:
+            observed_headers.append(_mcp_call_headers.get())
+        except LookupError:
+            observed_headers.append({})
+        return await original_call_tool(self, tool_name, **kwargs)
+
+    class _TestServer(MCPStreamableHTTPTool):
+        async def connect(self):
+            self.session = Mock(spec=ClientSession)
+            self.session.list_tools = AsyncMock(
+                return_value=types.ListToolsResult(
+                    tools=[
+                        types.Tool(
+                            name="greet",
+                            description="Says hello",
+                            inputSchema={"type": "object", "properties": {"name": {"type": "string"}}},
+                        )
+                    ]
+                )
+            )
+            self.session.call_tool = AsyncMock(
+                return_value=types.CallToolResult(content=[types.TextContent(type="text", text="Hello!")])
+            )
+            self.session.send_ping = AsyncMock()
+            self.is_connected = True
+
+        def get_mcp_client(self):
+            return None
+
+    server = _TestServer(
+        name="test",
+        url="http://example.com/mcp",
+        header_provider=lambda kw: {"X-Auth": kw.get("auth_token", "")},
+    )
+    async with server:
+        await server.load_tools()
+
+        with patch.object(MCPTool, "call_tool", spy_call_tool):
+            await server.call_tool("greet", name="Alice", auth_token="bearer-xyz")
+
+    assert len(observed_headers) == 1
+    assert observed_headers[0] == {"X-Auth": "bearer-xyz"}
+
+
+async def test_mcp_streamable_http_tool_header_provider_contextvar_reset_after_call():
+    """Test that the contextvar is properly reset after call_tool completes."""
+    from agent_framework._mcp import _mcp_call_headers
+
+    class _TestServer(MCPStreamableHTTPTool):
+        async def connect(self):
+            self.session = Mock(spec=ClientSession)
+            self.session.list_tools = AsyncMock(
+                return_value=types.ListToolsResult(
+                    tools=[
+                        types.Tool(
+                            name="greet",
+                            description="Says hello",
+                            inputSchema={"type": "object", "properties": {"name": {"type": "string"}}},
+                        )
+                    ]
+                )
+            )
+            self.session.call_tool = AsyncMock(
+                return_value=types.CallToolResult(content=[types.TextContent(type="text", text="Hello!")])
+            )
+            self.session.send_ping = AsyncMock()
+            self.is_connected = True
+
+        def get_mcp_client(self):
+            return None
+
+    server = _TestServer(
+        name="test",
+        url="http://example.com/mcp",
+        header_provider=lambda kw: {"X-Token": kw.get("token", "")},
+    )
+    async with server:
+        await server.load_tools()
+        await server.call_tool("greet", name="Alice", token="secret")
+
+    # After call_tool, the contextvar should be unset (reset to no value)
+    with pytest.raises(LookupError):
+        _mcp_call_headers.get()
+
+
+async def test_mcp_streamable_http_tool_without_header_provider():
+    """Test that call_tool works normally when no header_provider is configured."""
+
+    class _TestServer(MCPStreamableHTTPTool):
+        async def connect(self):
+            self.session = Mock(spec=ClientSession)
+            self.session.list_tools = AsyncMock(
+                return_value=types.ListToolsResult(
+                    tools=[
+                        types.Tool(
+                            name="greet",
+                            description="Says hello",
+                            inputSchema={"type": "object", "properties": {"name": {"type": "string"}}},
+                        )
+                    ]
+                )
+            )
+            self.session.call_tool = AsyncMock(
+                return_value=types.CallToolResult(content=[types.TextContent(type="text", text="Hello!")])
+            )
+            self.session.send_ping = AsyncMock()
+            self.is_connected = True
+
+        def get_mcp_client(self):
+            return None
+
+    server = _TestServer(
+        name="test",
+        url="http://example.com/mcp",
+    )
+    async with server:
+        await server.load_tools()
+        await server.call_tool("greet", name="Alice")
+        server.session.call_tool.assert_called_once()
+
+    # Without header_provider, call_tool should delegate directly to MCPTool
+    assert server._header_provider is None
+
+
+async def test_mcp_streamable_http_tool_header_provider_with_httpx_event_hook():
+    """Test that the httpx event hook injects headers from the contextvar."""
+    import httpx
+
+    from agent_framework._mcp import MCP_DEFAULT_SSE_READ_TIMEOUT, MCP_DEFAULT_TIMEOUT, _mcp_call_headers
+
+    tool = MCPStreamableHTTPTool(
+        name="test",
+        url="http://example.com/mcp",
+        header_provider=lambda kw: {"X-Custom": kw.get("custom", "")},
+    )
+
+    try:
+        with patch("agent_framework._mcp.streamable_http_client"):
+            # Trigger get_mcp_client to set up the event hook
+            tool.get_mcp_client()
+
+            # The tool should have created an httpx client with the event hook
+            assert tool._httpx_client is not None
+            assert tool._httpx_client.follow_redirects is True
+            assert tool._httpx_client.timeout.connect == MCP_DEFAULT_TIMEOUT
+            assert tool._httpx_client.timeout.read == MCP_DEFAULT_SSE_READ_TIMEOUT
+            hooks = tool._httpx_client.event_hooks.get("request", [])
+            assert len(hooks) == 1, "Expected one request event hook"
+
+            # Simulate what happens during a call_tool: contextvar is set
+            token = _mcp_call_headers.set({"X-Custom": "test-value"})
+            try:
+                request = httpx.Request("POST", "http://example.com/mcp")
+                await hooks[0](request)
+                assert request.headers.get("X-Custom") == "test-value"
+            finally:
+                _mcp_call_headers.reset(token)
+    finally:
+        # Ensure any created httpx client is properly closed
+        if getattr(tool, "_httpx_client", None) is not None:
+            await tool._httpx_client.aclose()
+
+
+async def test_mcp_streamable_http_tool_header_provider_with_user_httpx_client():
+    """Test that header_provider works when the user provides their own httpx client."""
+    import httpx
+
+    from agent_framework._mcp import _mcp_call_headers
+
+    user_client = httpx.AsyncClient(headers={"X-Base": "static"})
+
+    tool = MCPStreamableHTTPTool(
+        name="test",
+        url="http://example.com/mcp",
+        http_client=user_client,
+        header_provider=lambda kw: {"X-Dynamic": kw.get("dynamic", "")},
+    )
+
+    with patch("agent_framework._mcp.streamable_http_client"):
+        tool.get_mcp_client()
+
+        # The user's client should still be used
+        assert tool._httpx_client is user_client
+        hooks = user_client.event_hooks.get("request", [])
+        assert len(hooks) == 1
+
+        # Verify the hook injects headers
+        token = _mcp_call_headers.set({"X-Dynamic": "per-request"})
+        try:
+            request = httpx.Request("POST", "http://example.com/mcp")
+            await hooks[0](request)
+            assert request.headers.get("X-Dynamic") == "per-request"
+        finally:
+            _mcp_call_headers.reset(token)
+
+    await user_client.aclose()
+
+
+async def test_mcp_streamable_http_tool_header_provider_via_invoke_with_context():
+    """Test that header_provider receives kwargs via FunctionTool.invoke with FunctionInvocationContext.
+
+    This exercises the full pipeline: FunctionInvocationContext.kwargs -> FunctionTool.invoke
+    -> MCPStreamableHTTPTool.call_tool -> header_provider.
+    """
+    from agent_framework._mcp import _mcp_call_headers
+
+    observed_headers: list[dict[str, str]] = []
+    original_call_tool = MCPStreamableHTTPTool.call_tool
+
+    async def spy_call_tool(self, tool_name, **kwargs):
+        # Capture the contextvar value set by call_tool before delegating
+        result = await original_call_tool(self, tool_name, **kwargs)
+        try:
+            observed_headers.append(_mcp_call_headers.get())
+        except LookupError:
+            observed_headers.append({})
+        return result
+
+    class _TestServer(MCPStreamableHTTPTool):
+        async def connect(self):
+            self.session = Mock(spec=ClientSession)
+            self.session.list_tools = AsyncMock(
+                return_value=types.ListToolsResult(
+                    tools=[
+                        types.Tool(
+                            name="greet",
+                            description="Says hello",
+                            inputSchema={
+                                "type": "object",
+                                "properties": {"name": {"type": "string"}},
+                                "required": ["name"],
+                            },
+                        )
+                    ]
+                )
+            )
+            self.session.call_tool = AsyncMock(
+                return_value=types.CallToolResult(content=[types.TextContent(type="text", text="Hello!")])
+            )
+            self.session.send_ping = AsyncMock()
+            self.is_connected = True
+
+        def get_mcp_client(self):
+            return None
+
+    provider_received: list[dict] = []
+
+    def provider(kwargs):
+        provider_received.append(dict(kwargs))
+        return {"X-Some-Token": kwargs.get("some_token", "")}
+
+    server = _TestServer(
+        name="test",
+        url="http://example.com/mcp",
+        header_provider=provider,
+    )
+    async with server:
+        await server.load_tools()
+        func = server.functions[0]
+
+        # Build a FunctionInvocationContext with runtime kwargs, as the agent framework would
+        context = FunctionInvocationContext(
+            function=func,
+            arguments={"name": "Alice"},
+            kwargs={"some_token": "my-secret"},
+        )
+
+        with patch.object(MCPStreamableHTTPTool, "call_tool", spy_call_tool):
+            result = await func.invoke(arguments={"name": "Alice"}, context=context)
+
+        # Verify the invoke produced a result
+        assert isinstance(result, list)
+        assert result[0].text == "Hello!"
+
+        # Verify header_provider was called with the runtime kwargs
+        assert len(provider_received) == 1
+        assert provider_received[0]["some_token"] == "my-secret"
+
+        # Verify session.call_tool was called with the tool arguments (not the runtime kwargs)
+        server.session.call_tool.assert_called_once()
+        call_args = server.session.call_tool.call_args
+        assert call_args.kwargs.get("arguments", {}).get("name") == "Alice"
+
+
 # endregion
@@ -11,7 +11,7 @@ from unittest.mock import AsyncMock

 import pytest

-from agent_framework import SessionContext, Skill, SkillResource, SkillsProvider
+from agent_framework import SessionContext, Skill, SkillResource, SkillScript, SkillScriptRunner, SkillsProvider
 from agent_framework._skills import (
    DEFAULT_RESOURCE_EXTENSIONS,
    DEFAULT_SCRIPT_EXTENSIONS,
@@ -32,6 +32,8 @@ from agent_framework._skills import (
    _validate_skill_metadata,
 )

+pytestmark = pytest.mark.filterwarnings(r"ignore:\[SKILLS\].*:FutureWarning")
+

 async def _noop_script_runner(skill: Any, script: Any, args: Any = None) -> None:
    """No-op script runner for tests that need a SkillScriptRunner."""
@@ -778,6 +780,44 @@ class TestSymlinkDetection:
 # ---------------------------------------------------------------------------


+class TestSkillsExperimentalStage:
+    """Tests for the experimental stage annotations applied to skills APIs."""
+
+    def test_docstrings_include_experimental_warning(self) -> None:
+        assert SkillResource.__doc__ is not None
+        assert SkillScript.__doc__ is not None
+        assert Skill.__doc__ is not None
+        assert SkillScriptRunner.__doc__ is not None
+        assert SkillsProvider.__doc__ is not None
+        assert SkillScript.parameters_schema.__doc__ is not None
+
+        assert ".. warning:: Experimental" in SkillResource.__doc__
+        assert ".. warning:: Experimental" in SkillScript.__doc__
+        assert ".. warning:: Experimental" in Skill.__doc__
+        assert ".. warning:: Experimental" in SkillScriptRunner.__doc__
+        assert ".. warning:: Experimental" in SkillsProvider.__doc__
+        assert ".. warning:: Experimental" not in SkillScript.parameters_schema.__doc__
+
+    def test_feature_metadata_is_set(self) -> None:
+        assert SkillResource.__feature_stage__ == "experimental"
+        assert SkillScript.__feature_stage__ == "experimental"
+        assert Skill.__feature_stage__ == "experimental"
+        assert SkillsProvider.__feature_stage__ == "experimental"
+        feature_ids = [
+            SkillResource.__feature_id__,
+            SkillScript.__feature_id__,
+            Skill.__feature_id__,
+            SkillsProvider.__feature_id__,
+        ]
+        assert all(isinstance(feature_id, str) and feature_id for feature_id in feature_ids)
+        assert len(set(feature_ids)) == 1
+        assert getattr(SkillScriptRunner, "__feature_stage__", None) is None
+        assert getattr(SkillScriptRunner, "__feature_id__", None) is None
+        assert SkillScript.parameters_schema.fget is not None
+        assert not hasattr(SkillScript.parameters_schema.fget, "__feature_stage__")
+        assert not hasattr(SkillScript.parameters_schema.fget, "__feature_id__")
+
+
 class TestSkillResource:
    """Tests for SkillResource dataclass."""

@@ -1839,40 +1879,28 @@ class TestSkillScript:
    """Tests for the SkillScript data model."""

    def test_empty_name_raises(self) -> None:
-        from agent_framework import SkillScript
-
        with pytest.raises(ValueError, match="Script name cannot be empty"):
            SkillScript(name="")

    def test_whitespace_name_raises(self) -> None:
-        from agent_framework import SkillScript
-
        with pytest.raises(ValueError, match="Script name cannot be empty"):
            SkillScript(name="   ")

    def test_path_default_none(self) -> None:
-        from agent_framework import SkillScript
-
        script = SkillScript(name="test", function=lambda: None)
        assert script.path is None

    def test_path_set_explicitly(self) -> None:
-        from agent_framework import SkillScript
-
        script = SkillScript(name="gen.py", path="/skills/my-skill/scripts/gen.py")
        assert script.path == "/skills/my-skill/scripts/gen.py"

    def test_create_with_function(self) -> None:
-        from agent_framework import SkillScript
-
        script = SkillScript(name="analyze", description="Run analysis", function=lambda: "result")
        assert script.name == "analyze"
        assert script.description == "Run analysis"
        assert script.function is not None

    def test_accepts_kwargs_true_for_kwargs_function(self) -> None:
-        from agent_framework import SkillScript
-
        def func_with_kwargs(**kwargs: Any) -> str:
            return "result"

@@ -1880,8 +1908,6 @@ class TestSkillScript:
        assert script._accepts_kwargs is True

    def test_accepts_kwargs_false_for_regular_function(self) -> None:
-        from agent_framework import SkillScript
-
        def func_no_kwargs(x: int = 0) -> str:
            return "result"

@@ -47,58 +47,73 @@ class ProviderTypeMapping(TypedDict, total=True):
    package: str
    name: str
    model_id_field: str
+    endpoint_field: str | None
+    api_key_field: str | None


 PROVIDER_TYPE_OBJECT_MAPPING: dict[str, ProviderTypeMapping] = {
-    "AzureOpenAI.Chat": {
-        "package": "agent_framework.azure",
-        "name": "AzureOpenAIChatClient",
-        "model_id_field": "deployment_name",
+    "AzureOpenAI": {
+        "package": "agent_framework.openai",
+        "name": "OpenAIChatClient",
+        "model_id_field": "model",
+        "endpoint_field": "azure_endpoint",
+        "api_key_field": "api_key",
    },
-    "AzureOpenAI.Assistants": {
-        "package": "agent_framework.azure",
-        "name": "AzureOpenAIAssistantsClient",
-        "model_id_field": "deployment_name",
+    "AzureOpenAI.Chat": {
+        "package": "agent_framework.openai",
+        "name": "OpenAIChatCompletionClient",
+        "model_id_field": "model",
+        "endpoint_field": "azure_endpoint",
+        "api_key_field": "api_key",
    },
    "AzureOpenAI.Responses": {
-        "package": "agent_framework.azure",
-        "name": "AzureOpenAIResponsesClient",
-        "model_id_field": "deployment_name",
+        "package": "agent_framework.openai",
+        "name": "OpenAIChatClient",
+        "model_id_field": "model",
+        "endpoint_field": "azure_endpoint",
+        "api_key_field": "api_key",
+    },
+    "Foundry": {
+        "package": "agent_framework.foundry",
+        "name": "FoundryChatClient",
+        "model_id_field": "model",
+        "endpoint_field": "project_endpoint",
+        "api_key_field": None,
    },
    "OpenAI.Chat": {
        "package": "agent_framework.openai",
        "name": "OpenAIChatClient",
-        "model_id_field": "model_id",
-    },
-    "OpenAI.Assistants": {
-        "package": "agent_framework.openai",
-        "name": "OpenAIAssistantsClient",
-        "model_id_field": "model_id",
+        "model_id_field": "model",
+        "endpoint_field": "base_url",
+        "api_key_field": "api_key",
    },
    "OpenAI.Responses": {
        "package": "agent_framework.openai",
-        "name": "OpenAIResponsesClient",
-        "model_id_field": "model_id",
-    },
-    "AzureAIAgentClient": {
-        "package": "agent_framework.azure",
-        "name": "AzureAIAgentClient",
-        "model_id_field": "model_deployment_name",
-    },
-    "AzureAIClient": {
-        "package": "agent_framework.azure",
-        "name": "AzureAIClient",
-        "model_id_field": "model_deployment_name",
-    },
-    "AzureAI.ProjectProvider": {
-        "package": "agent_framework.azure",
-        "name": "AzureAIProjectAgentProvider",
+        "name": "OpenAIChatClient",
        "model_id_field": "model",
+        "endpoint_field": "base_url",
+        "api_key_field": "api_key",
+    },
+    "OpenAI": {
+        "package": "agent_framework.openai",
+        "name": "OpenAIChatClient",
+        "model_id_field": "model",
+        "endpoint_field": "base_url",
+        "api_key_field": "api_key",
+    },
+    "Foundry.Chat": {
+        "package": "agent_framework.foundry",
+        "name": "FoundryChatClient",
+        "model_id_field": "model",
+        "endpoint_field": "project_endpoint",
+        "api_key_field": None,
    },
    "Anthropic.Chat": {
        "package": "agent_framework.anthropic",
        "name": "AnthropicChatClient",
        "model_id_field": "model_id",
+        "endpoint_field": None,
+        "api_key_field": "api_key",
    },
 }

@@ -137,11 +152,11 @@ class AgentFactory:

        .. code-block:: python

-            from agent_framework.azure import AzureOpenAIChatClient
+            from agent_framework.openai import OpenAIChatClient
            from agent_framework_declarative import AgentFactory

            # With pre-configured chat client
-            client = AzureOpenAIChatClient()
+            client = OpenAIChatClient()
            factory = AgentFactory(client=client)
            agent = factory.create_agent_from_yaml_path("agent.yaml")

@@ -171,7 +186,7 @@ class AgentFactory:
        connections: Mapping[str, Any] | None = None,
        client_kwargs: Mapping[str, Any] | None = None,
        additional_mappings: Mapping[str, ProviderTypeMapping] | None = None,
-        default_provider: str = "AzureAIClient",
+        default_provider: str = "OpenAI",
        safe_mode: bool = True,
        env_file_path: str | None = None,
        env_file_encoding: str | None = None,
@@ -192,13 +207,15 @@ class AgentFactory:
                    ..code-block:: python

                        additional_mappings = {
-                            "Provider.ApiType": {
-                                "package": "package.name",
-                                "name": "ClassName",
-                                "model_id_field": "field_name_in_constructor",
-                            },
-                            ...
-                        }
+                             "Provider.ApiType": {
+                                 "package": "package.name",
+                                 "name": "ClassName",
+                                 "model_id_field": "field_name_in_constructor",
+                                 "endpoint_field": "endpoint_kwarg_name_or_null",
+                                 "api_key_field": "api_key_kwarg_name_or_null",
+                             },
+                             ...
+                         }

                    Here, "Provider.ApiType" is the lookup key used when both provider and apiType are specified in the
                    model, "Provider" is also allowed.
@@ -206,7 +223,7 @@ class AgentFactory:
                    SupportsChatGetResponse implementation, and model_id_field is the name of the field in the
                    constructor that accepts the model.id value.
            default_provider: The default provider used when model.provider is not specified,
-                default is "AzureAIClient".
+                default is "OpenAI".
            safe_mode: Whether to run in safe mode, default is True.
                When safe_mode is True, environment variables are not accessible in the powerfx expressions.
                You can still use environment variables, but through the constructors of the classes.
@@ -227,11 +244,11 @@ class AgentFactory:

            .. code-block:: python

-                from agent_framework.azure import AzureOpenAIChatClient
+                from agent_framework.openai import OpenAIChatClient
                from agent_framework_declarative import AgentFactory

                # With shared chat client
-                client = AzureOpenAIChatClient()
+                client = OpenAIChatClient()
                factory = AgentFactory(
                    client=client,
                    env_file_path=".env",
@@ -457,8 +474,8 @@ class AgentFactory:
    async def create_agent_from_yaml_path_async(self, yaml_path: str | Path) -> Agent:
        """Async version: Create a Agent from a YAML file path.

-        Use this method when the provider requires async initialization, such as
-        AzureAI.ProjectProvider which creates agents on the Azure AI Agent Service.
+        This is the async counterpart to ``create_agent_from_dict`` and is useful when
+        the rest of your setup is already async.

        Args:
            yaml_path: Path to the YAML file representation of a PromptAgent.
@@ -473,7 +490,7 @@ class AgentFactory:

                factory = AgentFactory(
                    client_kwargs={"credential": credential},
-                    default_provider="AzureAI.ProjectProvider",
+                    default_provider="Foundry",
                )
                agent = await factory.create_agent_from_yaml_path_async("agent.yaml")
        """
@@ -487,8 +504,8 @@ class AgentFactory:
    async def create_agent_from_yaml_async(self, yaml_str: str) -> Agent:
        """Async version: Create a Agent from a YAML string.

-        Use this method when the provider requires async initialization, such as
-        AzureAI.ProjectProvider which creates agents on the Azure AI Agent Service.
+        Use this method when the surrounding call site is already async and you
+        want to build an agent directly from YAML text.

        Args:
            yaml_str: YAML string representation of a PromptAgent.
@@ -507,7 +524,7 @@ class AgentFactory:
                instructions: You are a helpful assistant.
                model:
                    id: gpt-4o
-                    provider: AzureAI.ProjectProvider
+                    provider: Foundry
                '''

                factory = AgentFactory(client_kwargs={"credential": credential})
@@ -518,8 +535,8 @@ class AgentFactory:
    async def create_agent_from_dict_async(self, agent_def: dict[str, Any]) -> Agent:
        """Async version: Create a Agent from a dictionary definition.

-        Use this method when the provider requires async initialization, such as
-        AzureAI.ProjectProvider which creates agents on the Azure AI Agent Service.
+        This is the async counterpart to ``create_agent_from_dict`` and is useful when
+        the rest of your setup is already async.

        Args:
            agent_def: Dictionary representation of a PromptAgent.
@@ -538,7 +555,7 @@ class AgentFactory:
                    "instructions": "You are a helpful assistant.",
                    "model": {
                        "id": "gpt-4o",
-                        "provider": "AzureAI.ProjectProvider",
+                        "provider": "Foundry",
                    },
                }

@@ -551,12 +568,6 @@ class AgentFactory:
        if not isinstance(prompt_agent, PromptAgent):
            raise DeclarativeLoaderError("Only definitions for a PromptAgent are supported for agent creation.")

-        # Check if we're using a provider-based approach (like AzureAIProjectAgentProvider)
-        mapping = self._retrieve_provider_configuration(prompt_agent.model) if prompt_agent.model else None
-        if mapping and mapping["name"] == "AzureAIProjectAgentProvider":
-            return await self._create_agent_with_provider(prompt_agent, mapping)
-
-        # Fall back to standard ChatClient approach
        client = self._get_client(prompt_agent)
        chat_options = self._parse_chat_options(prompt_agent.model)
        if tools := self._parse_tools(prompt_agent.tools):
@@ -572,48 +583,42 @@ class AgentFactory:
        )

    async def _create_agent_with_provider(self, prompt_agent: PromptAgent, mapping: ProviderTypeMapping) -> Agent:
-        """Create a Agent using AzureAIProjectAgentProvider.
+        """Create an Agent through a provider object that exposes ``create_agent``.

-        This method handles the special case where we use a provider that creates
-        agents on a remote service (like Azure AI Agent Service) and returns
-        Agent instances directly.
+        This remains available as an internal escape hatch for provider-style custom mappings
+        that return a fully constructed ``Agent`` rather than a chat client.
        """
-        # Import the provider class
        module_name = mapping["package"]
        class_name = mapping["name"]
        module = __import__(module_name, fromlist=[class_name])
        provider_class = getattr(module, class_name)

-        # Build provider kwargs from client_kwargs and connection info
        provider_kwargs: dict[str, Any] = {}
        provider_kwargs.update(self.client_kwargs)

-        # Handle connection settings for the model
+        endpoint_field = mapping.get("endpoint_field")
+        api_key_field = mapping.get("api_key_field", "api_key")
+
        if prompt_agent.model and prompt_agent.model.connection:
            match prompt_agent.model.connection:
-                case RemoteConnection() | AnonymousConnection():
-                    if prompt_agent.model.connection.endpoint:
-                        provider_kwargs["project_endpoint"] = prompt_agent.model.connection.endpoint
                case ApiKeyConnection():
-                    if prompt_agent.model.connection.endpoint:
-                        provider_kwargs["project_endpoint"] = prompt_agent.model.connection.endpoint
+                    if api_key_field:
+                        provider_kwargs[api_key_field] = prompt_agent.model.connection.apiKey
+                    if prompt_agent.model.connection.endpoint and endpoint_field:
+                        provider_kwargs[endpoint_field] = prompt_agent.model.connection.endpoint
+                case RemoteConnection() | AnonymousConnection():
+                    if prompt_agent.model.connection.endpoint and endpoint_field:
+                        provider_kwargs[endpoint_field] = prompt_agent.model.connection.endpoint
                case ReferenceConnection():
-                    # Reference connections are resolved by concrete providers when supported.
                    pass

-        # Create the provider and use it to create the agent
        provider = provider_class(**provider_kwargs)
-
-        # Parse tools
        tools = self._parse_tools(prompt_agent.tools) if prompt_agent.tools else None

-        # Parse response format into default_options
        default_options: dict[str, Any] | None = None
        if prompt_agent.outputSchema:
            default_options = {"response_format": prompt_agent.outputSchema.to_json_schema()}

-        # Create the agent using the provider
-        # The provider's create_agent returns a Agent directly
        return cast(
            Agent,
            await provider.create_agent(
@@ -637,18 +642,35 @@ class AgentFactory:
                "alternatively define a model in the PromptAgent."
            )

+        mapping = self._retrieve_provider_configuration(prompt_agent.model)
        setup_dict: dict[str, Any] = {}
        setup_dict.update(self.client_kwargs)
+        endpoint_field = mapping.get("endpoint_field")
+        api_key_field = mapping.get("api_key_field", "api_key")

        # parse connections
        if prompt_agent.model.connection:
            match prompt_agent.model.connection:
                case ApiKeyConnection():
-                    setup_dict["api_key"] = prompt_agent.model.connection.apiKey
+                    if api_key_field:
+                        setup_dict[api_key_field] = prompt_agent.model.connection.apiKey
+                    elif prompt_agent.model.connection.apiKey:
+                        raise DeclarativeLoaderError(
+                            f"{mapping['name']} does not support API key-based model connections."
+                        )
                    if prompt_agent.model.connection.endpoint:
-                        setup_dict["endpoint"] = prompt_agent.model.connection.endpoint
+                        if not endpoint_field:
+                            raise DeclarativeLoaderError(
+                                f"{mapping['name']} does not support endpoint-based model connections."
+                            )
+                        setup_dict[endpoint_field] = prompt_agent.model.connection.endpoint
                case RemoteConnection() | AnonymousConnection():
-                    setup_dict["endpoint"] = prompt_agent.model.connection.endpoint
+                    if prompt_agent.model.connection.endpoint:
+                        if not endpoint_field:
+                            raise DeclarativeLoaderError(
+                                f"{mapping['name']} does not support endpoint-based model connections."
+                            )
+                        setup_dict[endpoint_field] = prompt_agent.model.connection.endpoint
                case ReferenceConnection():
                    if not self.connections:
                        raise ValueError("Connections must be provided to resolve ReferenceConnection")
@@ -673,7 +695,6 @@ class AgentFactory:
                "ChatClient must be provided to create agent from PromptAgent, or define model.id in the PromptAgent."
            )
        # if provider is defined, use that, if possible with apiType, fallback to default_provider
-        mapping = self._retrieve_provider_configuration(prompt_agent.model)
        module_name = mapping["package"]
        class_name = mapping["name"]
        module = __import__(module_name, fromlist=[class_name])
@@ -70,11 +70,11 @@ class WorkflowFactory:

        .. code-block:: python

-            from agent_framework.azure import AzureOpenAIChatClient
+            from agent_framework.openai import OpenAIChatClient
            from agent_framework.declarative import WorkflowFactory

            # Pre-register agents for InvokeAzureAgent actions
-            client = AzureOpenAIChatClient()
+            client = OpenAIChatClient()
            agent = client.as_agent(name="MyAgent", instructions="You are helpful.")

            factory = WorkflowFactory(agents={"MyAgent": agent})
@@ -116,11 +116,11 @@ class WorkflowFactory:

            .. code-block:: python

-                from agent_framework.azure import AzureOpenAIChatClient
+                from agent_framework.openai import OpenAIChatClient
                from agent_framework.declarative import WorkflowFactory

                # With pre-registered agents
-                client = AzureOpenAIChatClient()
+                client = OpenAIChatClient()
                agents = {
                    "WriterAgent": client.as_agent(name="Writer", instructions="Write content."),
                    "ReviewerAgent": client.as_agent(name="Reviewer", instructions="Review content."),
@@ -535,10 +535,10 @@ class WorkflowFactory:
        Examples:
            .. code-block:: python

-                from agent_framework.azure import AzureOpenAIChatClient
+                from agent_framework.openai import OpenAIChatClient
                from agent_framework.declarative import WorkflowFactory

-                client = AzureOpenAIChatClient()
+                client = OpenAIChatClient()

                # Method chaining to register multiple agents
                factory = (
@@ -69,11 +69,11 @@ Register cleanup hooks to properly close credentials and resources on shutdown:
 ```python
 from azure.identity.aio import DefaultAzureCredential
 from agent_framework import Agent
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework_devui import register_cleanup, serve

 credential = DefaultAzureCredential()
-client = AzureOpenAIChatClient()
+client = OpenAIChatCompletionClient()
 agent = Agent(name="MyAgent", client=client)

 # Register cleanup hook - credential will be closed on shutdown
@@ -37,7 +37,7 @@ OPENAI_CHAT_MODEL="gpt-4o-mini"

 # Or for Azure OpenAI
 AZURE_OPENAI_ENDPOINT="your-endpoint"
-AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="your-deployment-name"
+AZURE_OPENAI_DEPLOYMENT_NAME="your-deployment-name"
 ```

 ## 4. Test DevUI
@@ -247,7 +247,7 @@ services:
      # Or Azure OpenAI
      - AZURE_OPENAI_API_KEY=\${AZURE_OPENAI_API_KEY}
      - AZURE_OPENAI_ENDPOINT=\${AZURE_OPENAI_ENDPOINT}
-      - AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=\${AZURE_OPENAI_CHAT_DEPLOYMENT_NAME}
+      - AZURE_OPENAI_DEPLOYMENT_NAME=\${AZURE_OPENAI_DEPLOYMENT_NAME}
      # Optional: Enable instrumentation
      - ENABLE_INSTRUMENTATION=\${ENABLE_INSTRUMENTATION:-false}
    ports:
@@ -41,13 +41,13 @@ export const SAMPLE_ENTITIES: SampleEntity[] = [
    ],
    requiredEnvVars: [
      {
-        name: "AZURE_AI_PROJECT_ENDPOINT",
+        name: "FOUNDRY_PROJECT_ENDPOINT",
        description: "Azure AI Foundry project endpoint URL",
        required: true,
        example: "https://your-project.api.azureml.ms",
      },
      {
-        name: "FOUNDRY_MODEL_DEPLOYMENT_NAME",
+        name: "FOUNDRY_MODEL",
        description: "Name of the deployed model in Azure AI Foundry",
        required: true,
        example: "gpt-4o",
@@ -78,7 +78,7 @@ export const SAMPLE_ENTITIES: SampleEntity[] = [
        required: true,
      },
      {
-        name: "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME",
+        name: "AZURE_OPENAI_DEPLOYMENT_NAME",
        description: "Name of the deployed model in Azure OpenAI",
        required: true,
        example: "gpt-4o",
@@ -30,7 +30,7 @@ Durable execution support for long-running agent workflows using Azure Durable F

 ```python
 from agent_framework import Agent
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework_durabletask import DurableAIAgentClient, DurableAIAgentWorker
 from durabletask.client import TaskHubGrpcClient
 from durabletask.worker import TaskHubGrpcWorker
@@ -45,7 +45,7 @@ dt_worker = TaskHubGrpcWorker(host_address="localhost:4001")
 agent_worker = DurableAIAgentWorker(dt_worker)

 # Create a chat client for the agent
-chat_client = AzureOpenAIChatClient()
+chat_client = OpenAIChatCompletionClient()
 my_agent = Agent(client=chat_client, name="assistant")
 agent_worker.add_agent(my_agent)
 ```
@@ -16,7 +16,7 @@ The durable task integration lets you host Microsoft Agent Framework agents usin

 ```python
 from agent_framework import Agent
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework_durabletask import DurableAIAgentWorker
 from durabletask.worker import TaskHubGrpcWorker

@@ -24,7 +24,7 @@ from durabletask.worker import TaskHubGrpcWorker
 worker = TaskHubGrpcWorker(host_address="localhost:4001")
 agent_worker = DurableAIAgentWorker(worker)

-chat_client = AzureOpenAIChatClient()
+chat_client = OpenAIChatCompletionClient()
 my_agent = Agent(client=chat_client, name="assistant")
 agent_worker.add_agent(my_agent)
 ```
@@ -31,7 +31,7 @@ class DurableAIAgentWorker:
        ```python
        from durabletask.worker import TaskHubGrpcWorker
        from agent_framework import Agent
-        from agent_framework.azure import AzureOpenAIChatClient
+        from agent_framework.openai import OpenAIChatCompletionClient
        from agent_framework_durabletask import DurableAIAgentWorker

        # Create the underlying worker
@@ -41,7 +41,7 @@ class DurableAIAgentWorker:
        agent_worker = DurableAIAgentWorker(worker)

        # Register agents
-        client = AzureOpenAIChatClient()
+        client = OpenAIChatCompletionClient()
        my_agent = Agent(client=client, name="assistant")
        agent_worker.add_agent(my_agent)

@@ -1,6 +1,6 @@
 # Azure OpenAI Configuration
 AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
-AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=your-deployment-name
+AZURE_OPENAI_DEPLOYMENT_NAME=your-deployment-name
 # Optional: Use Azure CLI authentication if not provided
 # AZURE_OPENAI_API_KEY=your-api-key

@@ -4,6 +4,11 @@ import importlib.metadata

 from ._agent import FoundryAgent, RawFoundryAgent, RawFoundryAgentChatClient
 from ._chat_client import FoundryChatClient, FoundryChatOptions, RawFoundryChatClient
+from ._foundry_evals import (
+    FoundryEvals,
+    evaluate_foundry_target,
+    evaluate_traces,
+)
 from ._memory_provider import FoundryMemoryProvider

 try:
@@ -15,9 +20,12 @@ __all__ = [
    "FoundryAgent",
    "FoundryChatClient",
    "FoundryChatOptions",
+    "FoundryEvals",
    "FoundryMemoryProvider",
    "RawFoundryAgent",
    "RawFoundryAgentChatClient",
    "RawFoundryChatClient",
    "__version__",
+    "evaluate_foundry_target",
+    "evaluate_traces",
 ]
@@ -0,0 +1,891 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Microsoft Foundry Evals integration for Microsoft Agent Framework.
+
+Provides ``FoundryEvals``, an ``Evaluator`` implementation backed by Azure AI
+Foundry's built-in evaluators. See docs/decisions/0018-foundry-evals-integration.md
+for the design rationale.
+
+Example:
+
+.. code-block:: python
+
+    from agent_framework import evaluate_agent
+    from agent_framework.foundry import FoundryEvals
+
+    # Zero-config: reads FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL from env
+    evals = FoundryEvals()
+    results = await evaluate_agent(
+        agent=my_agent,
+        queries=["What's the weather in Seattle?"],
+        evaluators=evals,
+    )
+    results[0].raise_for_status()
+    print(results[0].report_url)
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any
+
+from agent_framework._evaluation import (
+    AgentEvalConverter,
+    ConversationSplit,
+    ConversationSplitter,
+    EvalItem,
+    EvalItemResult,
+    EvalResults,
+    EvalScoreResult,
+)
+from openai import AsyncOpenAI
+
+from ._chat_client import FoundryChatClient
+
+if TYPE_CHECKING:
+    from azure.ai.projects.aio import AIProjectClient
+    from openai.types.evals import RunRetrieveResponse
+
+logger = logging.getLogger(__name__)
+
+# Agent evaluators that accept query/response as conversation arrays.
+# Maintained manually — check https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/evaluate-sdk
+# for the latest evaluator list. These are the evaluators that need conversation-format input.
+_AGENT_EVALUATORS: set[str] = {
+    "builtin.intent_resolution",
+    "builtin.task_adherence",
+    "builtin.task_completion",
+    "builtin.task_navigation_efficiency",
+    "builtin.tool_call_accuracy",
+    "builtin.tool_selection",
+    "builtin.tool_input_accuracy",
+    "builtin.tool_output_utilization",
+    "builtin.tool_call_success",
+}
+
+# Evaluators that additionally require tool_definitions.
+_TOOL_EVALUATORS: set[str] = {
+    "builtin.tool_call_accuracy",
+    "builtin.tool_selection",
+    "builtin.tool_input_accuracy",
+    "builtin.tool_output_utilization",
+    "builtin.tool_call_success",
+}
+
+_BUILTIN_EVALUATORS: dict[str, str] = {
+    # Agent behavior
+    "intent_resolution": "builtin.intent_resolution",
+    "task_adherence": "builtin.task_adherence",
+    "task_completion": "builtin.task_completion",
+    "task_navigation_efficiency": "builtin.task_navigation_efficiency",
+    # Tool usage
+    "tool_call_accuracy": "builtin.tool_call_accuracy",
+    "tool_selection": "builtin.tool_selection",
+    "tool_input_accuracy": "builtin.tool_input_accuracy",
+    "tool_output_utilization": "builtin.tool_output_utilization",
+    "tool_call_success": "builtin.tool_call_success",
+    # Quality
+    "coherence": "builtin.coherence",
+    "fluency": "builtin.fluency",
+    "relevance": "builtin.relevance",
+    "groundedness": "builtin.groundedness",
+    "response_completeness": "builtin.response_completeness",
+    "similarity": "builtin.similarity",
+    # Safety
+    "violence": "builtin.violence",
+    "sexual": "builtin.sexual",
+    "self_harm": "builtin.self_harm",
+    "hate_unfairness": "builtin.hate_unfairness",
+}
+
+# Default evaluator sets used when evaluators=None
+_DEFAULT_EVALUATORS: list[str] = [
+    "relevance",
+    "coherence",
+    "task_adherence",
+]
+
+_DEFAULT_TOOL_EVALUATORS: list[str] = [
+    "tool_call_accuracy",
+]
+
+# Consistency between evaluator sets is enforced by tests in
+# test_foundry_evals.py — see TestEvaluatorSetConsistency.
+
+
+def _resolve_evaluator(name: str) -> str:
+    """Resolve a short evaluator name to its fully-qualified ``builtin.*`` form.
+
+    Args:
+        name: Short name (e.g. ``"relevance"``) or fully-qualified name
+            (e.g. ``"builtin.relevance"``).
+
+    Returns:
+        The fully-qualified evaluator name.
+
+    Raises:
+        ValueError: If the name is not recognized.
+    """
+    if name.startswith("builtin."):
+        # Already fully-qualified — pass through, but warn if not in our
+        # known list (may indicate a typo or a newly-added evaluator).
+        short = name.removeprefix("builtin.")
+        if short not in _BUILTIN_EVALUATORS:
+            logger.warning(
+                "Evaluator '%s' is not in the known built-in list. "
+                "If this is a new evaluator, consider updating _BUILTIN_EVALUATORS.",
+                name,
+            )
+        return name
+    resolved = _BUILTIN_EVALUATORS.get(name)
+    if resolved is None:
+        raise ValueError(f"Unknown evaluator '{name}'. Available: {sorted(_BUILTIN_EVALUATORS)}")
+    return resolved
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+
+def _build_testing_criteria(
+    evaluators: Sequence[str],
+    model: str,
+    *,
+    include_data_mapping: bool = False,
+) -> list[dict[str, Any]]:
+    """Build ``testing_criteria`` for ``evals.create()``.
+
+    Args:
+        evaluators: Evaluator names.
+        model: Model deployment for the LLM judge.
+        include_data_mapping: Whether to include field-level data mapping
+            (required for the JSONL data source, not needed for response-based).
+    """
+    criteria: list[dict[str, Any]] = []
+    for name in evaluators:
+        qualified = _resolve_evaluator(name)
+        short = name if not name.startswith("builtin.") else name.split(".")[-1]
+
+        # Structure dictated by the OpenAI evals API — see
+        # https://platform.openai.com/docs/api-reference/evals/create
+        entry: dict[str, Any] = {
+            "type": "azure_ai_evaluator",
+            "name": short,
+            "evaluator_name": qualified,
+            "initialization_parameters": {"deployment_name": model},
+        }
+
+        if include_data_mapping:
+            if qualified in _AGENT_EVALUATORS:
+                # Agent evaluators: query/response as conversation arrays.
+                # {{item.*}} are Mustache-style placeholders resolved by the
+                # evals API against fields in the JSONL data items.
+                mapping: dict[str, str] = {
+                    "query": "{{item.query_messages}}",
+                    "response": "{{item.response_messages}}",
+                }
+            else:
+                # Quality evaluators: query/response as strings
+                mapping = {
+                    "query": "{{item.query}}",
+                    "response": "{{item.response}}",
+                }
+            if qualified == "builtin.groundedness":
+                mapping["context"] = "{{item.context}}"
+            if qualified in _TOOL_EVALUATORS:
+                mapping["tool_definitions"] = "{{item.tool_definitions}}"
+            entry["data_mapping"] = mapping
+
+        criteria.append(entry)
+    return criteria
+
+
+def _build_item_schema(*, has_context: bool = False, has_tools: bool = False) -> dict[str, Any]:
+    """Build the ``item_schema`` for custom JSONL eval definitions."""
+    properties: dict[str, Any] = {
+        "query": {"type": "string"},
+        "response": {"type": "string"},
+        "query_messages": {"type": "array"},
+        "response_messages": {"type": "array"},
+    }
+    if has_context:
+        properties["context"] = {"type": "string"}
+    if has_tools:
+        properties["tool_definitions"] = {"type": "array"}
+    return {
+        "type": "object",
+        "properties": properties,
+        "required": ["query", "response"],
+    }
+
+
+def _resolve_default_evaluators(
+    evaluators: Sequence[str] | None,
+    items: Sequence[EvalItem | dict[str, Any]] | None = None,
+) -> list[str]:
+    """Resolve evaluators, applying defaults when ``None``.
+
+    Defaults to relevance + coherence + task_adherence. Automatically adds
+    tool_call_accuracy when items contain tools.
+    """
+    if evaluators is not None:
+        return list(evaluators)
+
+    result = list(_DEFAULT_EVALUATORS)
+    if items is not None:
+        has_tools = any((item.tools if isinstance(item, EvalItem) else item.get("tool_definitions")) for item in items)
+        if has_tools:
+            result.extend(_DEFAULT_TOOL_EVALUATORS)
+    return result
+
+
+def _filter_tool_evaluators(
+    evaluators: list[str],
+    items: Sequence[EvalItem | dict[str, Any]],
+) -> list[str]:
+    """Remove tool evaluators if no items have tool definitions."""
+    has_tools = any((item.tools if isinstance(item, EvalItem) else item.get("tool_definitions")) for item in items)
+    if has_tools:
+        return evaluators
+    filtered = [e for e in evaluators if _resolve_evaluator(e) not in _TOOL_EVALUATORS]
+    if not filtered:
+        raise ValueError(
+            f"All requested evaluators {evaluators} require tool definitions, "
+            "but no items have tools. Either add tool definitions to your items "
+            "or choose evaluators that do not require tools."
+        )
+    if len(filtered) < len(evaluators):
+        removed = [e for e in evaluators if _resolve_evaluator(e) in _TOOL_EVALUATORS]
+        logger.info("Removed tool evaluators %s (no items have tools)", removed)
+    return filtered
+
+
+async def _poll_eval_run(
+    client: AsyncOpenAI,
+    eval_id: str,
+    run_id: str,
+    poll_interval: float = 5.0,
+    timeout: float = 180.0,
+    provider: str = "Microsoft Foundry",
+    *,
+    fetch_output_items: bool = True,
+) -> EvalResults:
+    """Poll an eval run until completion or timeout."""
+    loop = asyncio.get_running_loop()
+    deadline = loop.time() + timeout
+    while True:
+        run = await client.evals.runs.retrieve(run_id=run_id, eval_id=eval_id)
+        if run.status in ("completed", "failed", "canceled"):
+            error_msg = None
+            if run.status == "failed":
+                err = run.error
+                if err is not None:  # pyright: ignore[reportUnnecessaryComparison]
+                    error_msg = err if isinstance(err, str) else err.message or str(err)
+
+            items: list[EvalItemResult] = []
+            if fetch_output_items and run.status == "completed":
+                items = await _fetch_output_items(client, eval_id, run_id)
+
+            return EvalResults(
+                provider=provider,
+                eval_id=eval_id,
+                run_id=run_id,
+                status=run.status,
+                result_counts=_extract_result_counts(run),
+                report_url=run.report_url,
+                error=error_msg,
+                per_evaluator=_extract_per_evaluator(run),
+                items=items,
+            )
+        remaining = deadline - loop.time()
+        if remaining <= 0:
+            return EvalResults(provider=provider, eval_id=eval_id, run_id=run_id, status="timeout")
+        logger.debug("Eval run %s status: %s (%.0fs remaining)", run_id, run.status, remaining)
+        await asyncio.sleep(min(poll_interval, remaining))
+
+
+def _extract_result_counts(run: RunRetrieveResponse) -> dict[str, int] | None:
+    """Extract result_counts from an eval run as a plain dict."""
+    counts = run.result_counts
+    if counts is None:  # pyright: ignore[reportUnnecessaryComparison]
+        return None
+    return {
+        "errored": counts.errored,
+        "failed": counts.failed,
+        "passed": counts.passed,
+        "total": counts.total,
+    }
+
+
+def _extract_per_evaluator(run: RunRetrieveResponse) -> dict[str, dict[str, int]]:
+    """Extract per-evaluator result breakdowns from an eval run."""
+    per_eval: dict[str, dict[str, int]] = {}
+    for item in run.per_testing_criteria_results or []:
+        name = item.testing_criteria
+        if name:
+            per_eval[name] = {"passed": item.passed, "failed": item.failed}
+    return per_eval
+
+
+async def _fetch_output_items(
+    client: AsyncOpenAI,
+    eval_id: str,
+    run_id: str,
+) -> list[EvalItemResult]:
+    """Fetch per-item results from the output_items API.
+
+    Converts the provider-specific ``OutputItemListResponse`` objects into
+    provider-agnostic ``EvalItemResult`` instances with per-evaluator scores,
+    error categorization, and token usage.  Uses async pagination to handle
+    eval runs with more items than a single page.
+    """
+    items: list[EvalItemResult] = []
+    try:
+        output_items_page = await client.evals.runs.output_items.list(
+            run_id=run_id,
+            eval_id=eval_id,
+        )
+
+        async for oi in output_items_page:
+            # Extract per-evaluator scores
+            scores: list[EvalScoreResult] = []
+            for r in oi.results or []:
+                scores.append(
+                    EvalScoreResult(
+                        name=r.name,
+                        score=r.score,
+                        passed=r.passed,
+                        sample=r.sample,
+                    )
+                )
+
+            # Extract error info from sample
+            error_code: str | None = None
+            error_message: str | None = None
+            token_usage: dict[str, int] | None = None
+            input_text: str | None = None
+            output_text: str | None = None
+            response_id: str | None = None
+
+            sample = oi.sample
+            if sample is not None:  # pyright: ignore[reportUnnecessaryComparison]
+                err = sample.error
+                if err is not None and (err.code or err.message):  # pyright: ignore[reportUnnecessaryComparison]
+                    error_code = err.code or None
+                    error_message = err.message or None
+
+                usage = sample.usage
+                if usage is not None and usage.total_tokens:  # pyright: ignore[reportUnnecessaryComparison]
+                    token_usage = {
+                        "prompt_tokens": usage.prompt_tokens,
+                        "completion_tokens": usage.completion_tokens,
+                        "total_tokens": usage.total_tokens,
+                        "cached_tokens": usage.cached_tokens,
+                    }
+
+                # Extract input/output text
+                if sample.input:
+                    parts = [si.content for si in sample.input if si.role == "user"]
+                    if parts:
+                        input_text = " ".join(parts)
+
+                if sample.output:
+                    parts = [so.content or "" for so in sample.output if so.role == "assistant"]
+                    if parts:
+                        output_text = " ".join(parts)
+
+            # Extract response_id from datasource_item
+            ds_item = oi.datasource_item
+            if ds_item:
+                resp_id_val = ds_item.get("resp_id") or ds_item.get("response_id")
+                response_id = str(resp_id_val) if resp_id_val else None
+
+            items.append(
+                EvalItemResult(
+                    item_id=oi.id,
+                    status=oi.status,
+                    scores=scores,
+                    error_code=error_code,
+                    error_message=error_message,
+                    response_id=response_id,
+                    input_text=input_text,
+                    output_text=output_text,
+                    token_usage=token_usage,
+                )
+            )
+    except (AttributeError, KeyError, TypeError):
+        logger.warning("Could not fetch output_items for run %s", run_id, exc_info=True)
+
+    return items
+
+
+def _resolve_openai_client(
+    client: FoundryChatClient | AsyncOpenAI | None = None,
+    project_client: AIProjectClient | None = None,
+) -> AsyncOpenAI:
+    """Resolve an AsyncOpenAI client from a FoundryChatClient, raw client, or project_client."""
+    if client is not None:
+        if isinstance(client, FoundryChatClient):
+            return client.client
+        return client
+    if project_client is not None:
+        oai = project_client.get_openai_client()
+        if oai is None:  # pyright: ignore[reportUnnecessaryComparison]
+            raise ValueError("project_client.get_openai_client() returned None. Check project configuration.")
+        if not isinstance(oai, AsyncOpenAI):
+            raise TypeError(
+                "project_client.get_openai_client() returned a sync client. "
+                "FoundryEvals requires an async AIProjectClient (from azure.ai.projects.aio)."
+            )
+        return oai
+    raise ValueError("Provide either 'client' or 'project_client'.")
+
+
+async def _evaluate_via_responses_impl(
+    *,
+    client: AsyncOpenAI,
+    response_ids: Sequence[str],
+    evaluators: list[str],
+    model: str,
+    eval_name: str,
+    poll_interval: float,
+    timeout: float,
+    provider: str = "foundry",
+) -> EvalResults:
+    """Evaluate using Foundry's Responses API retrieval path.
+
+    Module-level helper used by both ``FoundryEvals`` and ``evaluate_traces``.
+    """
+    eval_obj = await client.evals.create(
+        name=eval_name,
+        data_source_config={"type": "azure_ai_source", "scenario": "responses"},  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+        testing_criteria=_build_testing_criteria(evaluators, model),  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+    )
+
+    data_source = {
+        "type": "azure_ai_responses",
+        "item_generation_params": {
+            "type": "response_retrieval",
+            "data_mapping": {"response_id": "{{item.resp_id}}"},
+            "source": {
+                "type": "file_content",
+                "content": [{"item": {"resp_id": rid}} for rid in response_ids],
+            },
+        },
+    }
+
+    run = await client.evals.runs.create(
+        eval_id=eval_obj.id,
+        name=f"{eval_name} Run",
+        data_source=data_source,  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+    )
+
+    return await _poll_eval_run(client, eval_obj.id, run.id, poll_interval, timeout, provider=provider)
+
+
+# ---------------------------------------------------------------------------
+# FoundryEvals — Evaluator implementation for Microsoft Foundry
+# ---------------------------------------------------------------------------
+
+
+class FoundryEvals:
+    """Evaluation provider backed by Microsoft Foundry.
+
+    Implements the ``Evaluator`` protocol so it can be passed to the
+    provider-agnostic ``evaluate_agent()`` and
+    ``evaluate_workflow()`` functions from ``agent_framework``.
+
+    Also provides constants for built-in evaluator names for IDE
+    autocomplete and typo prevention:
+
+    .. code-block:: python
+
+        from agent_framework.foundry import FoundryEvals
+
+        evaluators = [FoundryEvals.RELEVANCE, FoundryEvals.TOOL_CALL_ACCURACY]
+
+    Examples:
+        Basic usage:
+
+        .. code-block:: python
+
+            from agent_framework import evaluate_agent
+            from agent_framework.foundry import FoundryEvals, FoundryChatClient
+
+            chat_client = FoundryChatClient(model="gpt-4o")
+            evals = FoundryEvals(client=chat_client)
+            results = await evaluate_agent(agent=agent, queries=queries, evaluators=evals)
+
+        Zero-config with environment variables (``FOUNDRY_PROJECT_ENDPOINT``
+        and ``FOUNDRY_MODEL``):
+
+        .. code-block:: python
+
+            evals = FoundryEvals()  # reads env vars via FoundryChatClient
+
+    **Evaluator selection:**
+
+    By default, runs ``relevance``, ``coherence``, and ``task_adherence``.
+    Automatically adds ``tool_call_accuracy`` when items contain tool
+    definitions. Override with ``evaluators=``.
+
+    .. note::
+
+        The ``builtin.*`` evaluators are accessed through the OpenAI Evals
+        API (``client.evals.create`` / ``client.evals.runs.create``).  Any
+        ``AsyncOpenAI`` client pointing at a Foundry endpoint can run them.
+
+    Args:
+        client: A ``FoundryChatClient`` instance.  The ``builtin.*``
+            evaluators are a Foundry feature and require a Foundry endpoint.
+            When omitted (and *project_client* is also omitted), a
+            ``FoundryChatClient`` is auto-created from ``FOUNDRY_PROJECT_ENDPOINT``
+            and ``FOUNDRY_MODEL`` environment variables.
+        project_client: An async ``AIProjectClient`` instance
+            (from ``azure.ai.projects.aio``).  Provide this or *client*.
+        model: Model deployment name for the evaluator LLM judge.
+            Resolved from ``client.model`` when omitted.
+        evaluators: Evaluator names (e.g. ``["relevance", "tool_call_accuracy"]``).
+            When ``None`` (default), uses smart defaults based on item data.
+        conversation_split: How to split multi-turn conversations into
+            query/response halves.  Defaults to ``LAST_TURN``.  Pass a
+            ``ConversationSplit`` enum value or a custom callable — see
+            ``ConversationSplitter``.
+        poll_interval: Seconds between status polls (default 5.0).
+        timeout: Maximum seconds to wait for completion (default 180.0).
+        eval_name: Display name for the eval definition created in Foundry.
+            Defaults to ``"agent-framework-eval"``.  The name is visible in
+            the Foundry portal; it does not affect evaluation behavior.
+    """
+
+    # ---------------------------------------------------------------------------
+    # Built-in evaluator name constants
+    # ---------------------------------------------------------------------------
+
+    # Agent behavior
+    INTENT_RESOLUTION: str = "intent_resolution"
+    TASK_ADHERENCE: str = "task_adherence"
+    TASK_COMPLETION: str = "task_completion"
+    TASK_NAVIGATION_EFFICIENCY: str = "task_navigation_efficiency"
+
+    # Tool usage
+    TOOL_CALL_ACCURACY: str = "tool_call_accuracy"
+    TOOL_SELECTION: str = "tool_selection"
+    TOOL_INPUT_ACCURACY: str = "tool_input_accuracy"
+    TOOL_OUTPUT_UTILIZATION: str = "tool_output_utilization"
+    TOOL_CALL_SUCCESS: str = "tool_call_success"
+
+    # Quality
+    COHERENCE: str = "coherence"
+    FLUENCY: str = "fluency"
+    RELEVANCE: str = "relevance"
+    GROUNDEDNESS: str = "groundedness"
+    RESPONSE_COMPLETENESS: str = "response_completeness"
+    SIMILARITY: str = "similarity"
+
+    # Safety
+    VIOLENCE: str = "violence"
+    SEXUAL: str = "sexual"
+    SELF_HARM: str = "self_harm"
+    HATE_UNFAIRNESS: str = "hate_unfairness"
+
+    def __init__(
+        self,
+        *,
+        client: FoundryChatClient | None = None,
+        project_client: AIProjectClient | None = None,
+        model: str | None = None,
+        evaluators: Sequence[str] | None = None,
+        conversation_split: ConversationSplitter = ConversationSplit.LAST_TURN,
+        poll_interval: float = 5.0,
+        timeout: float = 180.0,
+    ):
+        self.name = "Microsoft Foundry"
+
+        # Auto-create a FoundryChatClient from env vars when no client is provided
+        if client is None and project_client is None:
+            client = FoundryChatClient(model=model or "gpt-4o")
+
+        self._client = _resolve_openai_client(client, project_client)
+        # Resolve model: explicit param > client.model > error
+        resolved_model = model or (client.model if client is not None else None)
+        if not resolved_model:
+            raise ValueError(
+                "Model is required. Pass model= explicitly or use a FoundryChatClient that has a model configured."
+            )
+        self._model = resolved_model
+        self._evaluators = list(evaluators) if evaluators is not None else None
+        self._conversation_split = conversation_split
+        self._poll_interval = poll_interval
+        self._timeout = timeout
+
+    async def evaluate(
+        self,
+        items: Sequence[EvalItem],
+        *,
+        eval_name: str = "Agent Framework Eval",
+    ) -> EvalResults:
+        """Evaluate items using Foundry evaluators.
+
+        Implements the ``Evaluator`` protocol. Automatically resolves default
+        evaluators and filters tool evaluators for items without tool definitions.
+
+        Args:
+            items: Eval data items from ``AgentEvalConverter.to_eval_item()``.
+            eval_name: Display name for the evaluation run.
+
+        Returns:
+            ``EvalResults`` with status, counts, and portal link.
+        """
+        # Resolve evaluators with auto-detection
+        resolved = _resolve_default_evaluators(self._evaluators, items=items)
+        # Filter tool evaluators if items don't have tools
+        resolved = _filter_tool_evaluators(resolved, items)
+
+        # Standard JSONL dataset path
+        return await self._evaluate_via_dataset(items, resolved, eval_name)
+
+    # -- Internal evaluation paths --
+
+    async def _evaluate_via_dataset(
+        self,
+        items: Sequence[EvalItem],
+        evaluators: list[str],
+        eval_name: str,
+    ) -> EvalResults:
+        """Evaluate using JSONL dataset upload path."""
+        dicts: list[dict[str, Any]] = []
+        for item in items:
+            # Build JSONL dict directly from split_messages + converter
+            # to avoid splitting the conversation twice.
+            effective_split = item.split_strategy or self._conversation_split
+            query_msgs, response_msgs = item.split_messages(effective_split)
+
+            query_text = " ".join(m.text for m in query_msgs if m.role == "user" and m.text).strip()
+            response_text = " ".join(m.text for m in response_msgs if m.role == "assistant" and m.text).strip()
+
+            d: dict[str, Any] = {
+                "query": query_text,
+                "response": response_text,
+                "query_messages": AgentEvalConverter.convert_messages(query_msgs),
+                "response_messages": AgentEvalConverter.convert_messages(response_msgs),
+            }
+            if item.tools:
+                d["tool_definitions"] = [
+                    {"name": t.name, "description": t.description, "parameters": t.parameters()} for t in item.tools
+                ]
+            if item.context:
+                d["context"] = item.context
+            dicts.append(d)
+
+        has_context = any("context" in d for d in dicts)
+        has_tools = any("tool_definitions" in d for d in dicts)
+
+        eval_obj = await self._client.evals.create(
+            name=eval_name,
+            data_source_config={  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+                "type": "custom",
+                "item_schema": _build_item_schema(has_context=has_context, has_tools=has_tools),
+                "include_sample_schema": True,
+            },
+            testing_criteria=_build_testing_criteria(  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+                evaluators,
+                self._model,
+                include_data_mapping=True,
+            ),
+        )
+
+        data_source = {
+            "type": "jsonl",
+            "source": {
+                "type": "file_content",
+                "content": [{"item": d} for d in dicts],
+            },
+        }
+
+        run = await self._client.evals.runs.create(
+            eval_id=eval_obj.id,
+            name=f"{eval_name} Run",
+            data_source=data_source,  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+        )
+
+        return await _poll_eval_run(
+            self._client,
+            eval_obj.id,
+            run.id,
+            self._poll_interval,
+            self._timeout,
+            provider=self.name,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Foundry-specific functions (not part of the Evaluator protocol)
+# ---------------------------------------------------------------------------
+
+
+async def evaluate_traces(
+    *,
+    evaluators: Sequence[str] | None = None,
+    client: FoundryChatClient | None = None,
+    project_client: AIProjectClient | None = None,
+    model: str,
+    response_ids: Sequence[str] | None = None,
+    trace_ids: Sequence[str] | None = None,
+    agent_id: str | None = None,
+    lookback_hours: int = 24,
+    eval_name: str = "Agent Framework Trace Eval",
+    poll_interval: float = 5.0,
+    timeout: float = 180.0,
+) -> EvalResults:
+    """Evaluate agent behavior from OTel traces or response IDs.
+
+    Foundry-specific function — works with any agent that emits OTel traces
+    to App Insights. Provide *response_ids* for specific responses,
+    *trace_ids* for specific traces, or *agent_id* with *lookback_hours*
+    to evaluate recent activity.
+
+    Args:
+        evaluators: Evaluator names (e.g. ``[FoundryEvals.RELEVANCE]``).
+            Defaults to relevance, coherence, and task_adherence.
+        client: A ``FoundryChatClient`` instance. Provide this or *project_client*.
+        project_client: An ``AIProjectClient`` instance.
+        model: Model deployment name for the evaluator LLM judge.
+        response_ids: Evaluate specific Responses API responses.
+        trace_ids: Evaluate specific OTel trace IDs from App Insights.
+        agent_id: Filter traces by agent ID (used with *lookback_hours*).
+        lookback_hours: Hours of trace history to evaluate (default 24).
+        eval_name: Display name for the evaluation.
+        poll_interval: Seconds between status polls.
+        timeout: Maximum seconds to wait for completion.
+
+    Returns:
+        ``EvalResults`` with status, result counts, and portal link.
+
+    Example:
+
+    .. code-block:: python
+
+        results = await evaluate_traces(
+            response_ids=[response.response_id],
+            evaluators=[FoundryEvals.RELEVANCE],
+            client=chat_client,
+            model="gpt-4o",
+        )
+    """
+    oai_client = _resolve_openai_client(client, project_client)
+    resolved_evaluators = _resolve_default_evaluators(evaluators)
+
+    if response_ids:
+        return await _evaluate_via_responses_impl(
+            client=oai_client,
+            response_ids=response_ids,
+            evaluators=resolved_evaluators,
+            model=model,
+            eval_name=eval_name,
+            poll_interval=poll_interval,
+            timeout=timeout,
+        )
+
+    if not trace_ids and not agent_id:
+        raise ValueError("Provide at least one of: response_ids, trace_ids, or agent_id")
+
+    trace_source: dict[str, Any] = {
+        "type": "azure_ai_traces",
+        "lookback_hours": lookback_hours,
+    }
+    if trace_ids:
+        trace_source["trace_ids"] = list(trace_ids)
+    if agent_id:
+        trace_source["agent_id"] = agent_id
+
+    eval_obj = await oai_client.evals.create(
+        name=eval_name,
+        data_source_config={"type": "azure_ai_source", "scenario": "traces"},  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+        testing_criteria=_build_testing_criteria(resolved_evaluators, model),  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+    )
+
+    run = await oai_client.evals.runs.create(
+        eval_id=eval_obj.id,
+        name=f"{eval_name} Run",
+        data_source=trace_source,  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+    )
+
+    return await _poll_eval_run(oai_client, eval_obj.id, run.id, poll_interval, timeout)
+
+
+async def evaluate_foundry_target(
+    *,
+    target: dict[str, Any],
+    test_queries: Sequence[str],
+    evaluators: Sequence[str] | None = None,
+    client: FoundryChatClient | None = None,
+    project_client: AIProjectClient | None = None,
+    model: str,
+    eval_name: str = "Agent Framework Target Eval",
+    poll_interval: float = 5.0,
+    timeout: float = 180.0,
+) -> EvalResults:
+    """Evaluate a Foundry-registered agent or model deployment.
+
+    Foundry invokes the target, captures the output, and evaluates it. Use
+    this for scheduled evals, red teaming, and CI/CD quality gates.
+
+    Args:
+        target: Target configuration dict.
+        test_queries: Queries for Foundry to send to the target.
+        evaluators: Evaluator names.
+        client: A ``FoundryChatClient`` instance. Provide this or *project_client*.
+        project_client: An ``AIProjectClient`` instance.
+        model: Model deployment name for the evaluator LLM judge.
+        eval_name: Display name for the evaluation.
+        poll_interval: Seconds between status polls.
+        timeout: Maximum seconds to wait for completion.
+
+    Returns:
+        ``EvalResults`` with status, result counts, and portal link.
+
+    Example:
+
+    .. code-block:: python
+
+        results = await evaluate_foundry_target(
+            target={"type": "azure_ai_agent", "name": "my-agent"},
+            test_queries=["Book a flight to Paris"],
+            client=chat_client,
+            model="gpt-4o",
+        )
+    """
+    if "type" not in target:
+        raise ValueError("target dict must include a 'type' key (e.g., 'azure_ai_agent').")
+    oai_client = _resolve_openai_client(client, project_client)
+    resolved_evaluators = _resolve_default_evaluators(evaluators)
+
+    eval_obj = await oai_client.evals.create(
+        name=eval_name,
+        data_source_config={  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+            "type": "azure_ai_source",
+            "scenario": "target_completions",
+        },
+        testing_criteria=_build_testing_criteria(resolved_evaluators, model),  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+    )
+
+    data_source: dict[str, Any] = {
+        "type": "azure_ai_target_completions",
+        "target": target,
+        "source": {
+            "type": "file_content",
+            "content": [{"item": {"query": q}} for q in test_queries],
+        },
+    }
+
+    run = await oai_client.evals.runs.create(
+        eval_id=eval_obj.id,
+        name=f"{eval_name} Run",
+        data_source=data_source,  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+    )
+
+    return await _poll_eval_run(oai_client, eval_obj.id, run.id, poll_interval, timeout)
@@ -1,6 +1,6 @@
 [project]
 name = "agent-framework-foundry"
-description = "Cloud Azure AI Foundry integration for Microsoft Agent Framework."
+description = "Microsoft Foundry integrations for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
--- a/Show More
+++ b/Show More