* WIP * big update to new ResponseStream model * fixed tests and typing * fixed tests and typing * fixed tools typevar import * fix * mypy fix * mypy fixes and some cleanup * fix missing quoted names * and client * fix imports agui * fix anthropic override * fix agui * fix ag ui * fix import * fix anthropic types * fix mypy * refactoring * updated typing * fix 3.11 * fixes * redid layering of chat clients and agents * redid layering of chat clients and agents * Fix lint, type, and test issues after rebase - Add @overload decorators to AgentProtocol.run() for type compatibility - Add missing docstring params (middleware, function_invocation_configuration) - Fix TODO format (TD002) by adding author tags - Fix broken observability tests from upstream: - Replace non-existent use_instrumentation with direct instantiation - Replace non-existent use_agent_instrumentation with AgentTelemetryLayer mixin - Fix get_streaming_response to use get_response(stream=True) - Add AgentInitializationError import - Update streaming exception tests to match actual behavior * Fix AgentExecutionException import error in test_agents.py - Replace non-existent AgentExecutionException with AgentRunException * Fix test import and asyncio deprecation issues - Add 'tests' to pythonpath in ag-ui pyproject.toml for utils_test_ag_ui import - Replace deprecated asyncio.get_event_loop().run_until_complete with asyncio.run * Fix azure-ai test failures - Update _prepare_options patching to use correct class path - Fix test_to_azure_ai_agent_tools_web_search_missing_connection to clear env vars * Convert ag-ui utils_test_ag_ui.py to conftest.py - Move test utilities to conftest.py for proper pytest discovery - Update all test imports to use conftest instead of utils_test_ag_ui - Remove old utils_test_ag_ui.py file - Revert pythonpath change in pyproject.toml * fix: use relative imports for ag-ui test utilities * fix agui * Rename Bare*Client to Raw*Client and BaseChatClient - Renamed BareChatClient to BaseChatClient (abstract base class) - Renamed BareOpenAIChatClient to RawOpenAIChatClient - Renamed BareOpenAIResponsesClient to RawOpenAIResponsesClient - Renamed BareAzureAIClient to RawAzureAIClient - Added warning docstrings to Raw* classes about layer ordering - Updated README in samples/getting_started/agents/custom with layer docs - Added test for span ordering with function calling * Fix layer ordering: FunctionInvocationLayer before ChatTelemetryLayer This ensures each inner LLM call gets its own telemetry span, resulting in the correct span sequence: chat -> execute_tool -> chat Updated all production clients and test mocks to use correct ordering: - ChatMiddlewareLayer (first) - FunctionInvocationLayer (second) - ChatTelemetryLayer (third) - BaseChatClient/Raw...Client (fourth) * Remove run_stream usage * Fix conversation_id propagation * Python: Add BaseAgent implementation for Claude Agent SDK (#3509) * Added ClaudeAgent implementation * Updated streaming logic * Small updates * Small update * Fixes * Small fix * Naming improvements * Updated imports * Addressed comments * Updated package versions * Update Claude agent connector layering * fix test and plugin * Store function middleware in invocation layer * Fix telemetry streaming and ag-ui tests * Remove legacy ag-ui tests folder * updates * Remove terminate flag from FunctionInvocationContext, use MiddlewareTermination instead - Remove terminate attribute from FunctionInvocationContext - Add result attribute to MiddlewareTermination to carry function results - FunctionMiddlewarePipeline.execute() now lets MiddlewareTermination propagate - _auto_invoke_function captures context.result in exception before re-raising - _try_execute_function_calls catches MiddlewareTermination and sets should_terminate - Fix handoff middleware to append to chat_client.function_middleware directly - Update tests to use raise MiddlewareTermination instead of context.terminate - Add middleware flow documentation in samples/concepts/tools/README.md - Fix ag-ui to use FunctionMiddlewarePipeline instead of removed create_function_middleware_pipeline * fix: remove references to removed terminate flag in purview tests, add type ignore * fix: move _test_utils.py from package to test folder * fix: call get_final_response() to trigger context provider notification in streaming test * fix: correct broken links in tools README * docs: clarify default middleware behavior in summary table * fix: ensure inner stream result hooks are called when using map()/from_awaitable() * Fix mypy type errors * Address PR review comments on observability.py - Remove TODO comment about unconsumed streams, add explanatory note instead - Remove redundant _close_span cleanup hook (already called in _finalize_stream) - Clarify behavior: cleanup hooks run after stream iteration, if stream is not consumed the span remains open until garbage collected * Remove gen_ai.client.operation.duration from span attributes Duration is a metrics-only attribute per OpenTelemetry semantic conventions. It should be recorded to the histogram but not set as a span attribute. * Remove duration from _get_response_attributes, pass directly to _capture_response Duration is a metrics-only attribute. It's now passed directly to _capture_response instead of being included in the attributes dict that gets set on the span. * Remove redundant _close_span cleanup hook in AgentTelemetryLayer _finalize_stream already calls _close_span() in its finally block, so adding it as a separate cleanup hook is redundant. * Use weakref.finalize to close span when stream is garbage collected If a user creates a streaming response but never consumes it, the cleanup hooks won't run. Now we register a weak reference finalizer that will close the span when the stream object is garbage collected, ensuring spans don't leak in this scenario. * Fix _get_finalizers_from_stream to use _result_hooks attribute Renamed function to _get_result_hooks_from_stream and fixed it to look for the _result_hooks attribute which is the correct name in ResponseStream class. * Add missing asyncio import in test_request_info_mixin.py * Fix leftover merge conflict marker in image_generation sample * Update integration tests * Fix integration tests: increase max_iterations from 1 to 2 Tests with tool_choice options require at least 2 iterations: 1. First iteration to get function call and execute the tool 2. Second iteration to get the final text response With max_iterations=1, streaming tests would return early with only the function call/result but no final text content. * Fix duplicate function call error in conversation-based APIs When using conversation_id (for Responses/Assistants APIs), the server already has the function call message from the previous response. We should only send the new function result message, not all messages including the function call which would cause a duplicate ID error. Fix: When conversation_id is set, only send the last message (the tool result) instead of all response.messages. * Add regression test for conversation_id propagation between tool iterations Port test from PR #3664 with updates for new streaming API pattern. Tests that conversation_id is properly updated in options dict during function invocation loop iterations. * Fix tool_choice=required to return after tool execution When tool_choice is 'required', the user's intent is to force exactly one tool call. After the tool executes, return immediately with the function call and result - don't continue to call the model again. This fixes integration tests that were failing with empty text responses because with tool_choice=required, the model would keep returning function calls instead of text. Also adds regression tests for: - conversation_id propagation between tool iterations (from PR #3664) - tool_choice=required returns after tool execution * Document tool_choice behavior in tools README - Add table explaining tool_choice values (auto, none, required) - Explain why tool_choice=required returns immediately after tool execution - Add code example showing the difference between required and auto - Update flow diagram to show the early return path for tool_choice=required * Fix tool_choice=None behavior - don't default to 'auto' Remove the hardcoded default of 'auto' for tool_choice in ChatAgent init. When tool_choice is not specified (None), it will now not be sent to the API, allowing the API's default behavior to be used. Users who want tool_choice='auto' can still explicitly set it either in default_options or at runtime. Fixes #3585 * Fix tool_choice=none should not remove tools In OpenAI Assistants client, tools were not being sent when tool_choice='none'. This was incorrect - tool_choice='none' means the model won't call tools, but tools should still be available in the request (they may be used later in the conversation). Fixes #3585 * Add test for tool_choice=none preserving tools Adds a regression test to ensure that when tool_choice='none' is set but tools are provided, the tools are still sent to the API. This verifies the fix for #3585. * Fix tool_choice=none should not remove tools in all clients Apply the same fix to OpenAI Responses client and Azure AI client: - OpenAI Responses: Remove else block that popped tool_choice/parallel_tool_calls - Azure AI: Remove tool_choice != 'none' check when adding tools When tool_choice='none', the model won't call tools, but tools should still be sent to the API so they're available for future turns. Also update README to clarify tool_choice=required supports multiple tools. Fixes #3585 * Keep tool_choice even when tools is None Move tool_choice processing outside of the 'if tools' block in OpenAI Responses client so tool_choice is sent to the API even when no tools are provided. * Update test to match new parallel_tool_calls behavior Changed test_prepare_options_removes_parallel_tool_calls_when_no_tools to test_prepare_options_preserves_parallel_tool_calls_when_no_tools to reflect that parallel_tool_calls is now preserved even when no tools are present, consistent with the tool_choice behavior. * Fix ChatMessage API and Role enum usage after rebase - Update ChatMessage instantiation to use keyword args (role=, text=, contents=) - Fix Role enum comparisons to use .value for string comparison - Add created_at to AgentResponse in error handling - Fix AgentResponse.from_updates -> from_agent_run_response_updates - Fix DurableAgentStateMessage.from_chat_message to convert Role enum to string - Add Role import where needed * Fix additional ChatMessage API and method name changes - Fix ChatMessage usage in workflow files (use text= instead of contents= for strings) - Fix AgentResponse.from_updates -> from_agent_run_response_updates in workflow files - Fix test files for ChatMessage and Role enum usage * Fix remaining ChatMessage API usage in test files * Fix more ChatMessage and Role API changes in source and test files - Fix ChatMessage in _magentic.py replan method - Fix Role enum comparison in test assertions - Fix remaining test files with old ChatMessage syntax * Fix ChatMessage and Role API changes across packages - Add Role import where missing - Fix ChatMessage signature: positional args to keyword args (role=, text=, contents=) - Fix Role enum comparisons: .role.value instead of .role string - Fix FinishReason enum usage in ag-ui event converters - Rename AgentResponse.from_updates to from_agent_run_response_updates in ag-ui Fixes API compatibility after Types API Review improvements merge * Fix ChatMessage and Role API changes in github_copilot tests * Fix ChatMessage and Role API changes in redis and github_copilot packages - Fix redis provider: Role enum comparison using .value - Fix redis tests: ChatMessage signature and Role comparisons - Fix github_copilot tests: ChatMessage signature and Role comparisons - Update docstring examples in redis chat message store * Fix ChatMessage and Role API changes in devui package - Fix executor: ChatMessage signature change - Fix conversations: Role enum to string conversion in two places - Fix tests: ChatMessage signatures and Role comparisons * Fix ChatMessage and Role API changes in a2a and lab packages - Fix a2a tests: Role comparisons and ChatMessage signatures - Fix lab tau2 source: Role enum comparison in flip_messages, log_messages, sliding_window - Fix lab tau2 tests: ChatMessage signatures and Role comparisons * Remove duplicate test files from ag-ui/tests (tests are in ag_ui_tests) * Fix ChatMessage and Role API changes across packages After rebasing on upstream/main which merged PR #3647 (Types API Review improvements), fix all packages to use the new API: - ChatMessage: Use keyword args (role=, text=, contents=) instead of positional args - Role: Compare using .value attribute since it's now an enum Packages fixed: - ag-ui: Fixed Role value extraction bugs in _message_adapters.py - anthropic: Fixed ChatMessage and Role comparisons in tests - azure-ai: Fixed Role comparison in _client.py - azure-ai-search: Fixed ChatMessage and Role in source/tests - bedrock: Fixed ChatMessage signatures in tests - chatkit: Fixed ChatMessage and Role in source/tests - copilotstudio: Fixed ChatMessage and Role in tests - declarative: Fixed ChatMessage in _executors_agents.py - mem0: Fixed ChatMessage and Role in source/tests - purview: Fixed ChatMessage in source/tests * Fix mypy errors for ChatMessage and Role API changes - durabletask: Use str() fallback in role value extraction - core: Fix ChatMessage in _orchestrator_helpers.py to use keyword args - core: Add type ignore for _conversation_state.py contents deserialization - ag-ui: Fix type ignore comments (call-overload instead of arg-type) - azure-ai-search: Fix get_role_value type hint to accept Any - lab: Move get_role_value to module level with Any type hint * Improve CI test timeout configuration - Increase job timeout from 10 to 15 minutes - Reduce per-test timeout to 60s (was 900s/300s) - Add --timeout_method thread for better timeout handling - Add --timeout-verbose to see which tests are slow - Reduce retries from 3 to 2 and delay from 10s to 5s This ensures individual test timeouts are shorter than the job timeout, providing better visibility when tests hang. With 60s timeout and 2 retries, worst case per test is ~180s. * Fix ChatMessage API usage in docstrings and source - Fix ChatMessage positional args in docstrings: _serialization.py, _threads.py, _middleware.py - Fix ChatMessage in tau2 runner.py - Fix role comparison in _orchestrator_helpers.py to use .value - Fix role comparison in _group_chat.py docstring example - Fix role assertions in test_durable_entities.py to use .value * Revert tool_choice/parallel_tool_calls changes - must be removed when no tools OpenAI API requires tool_choice and parallel_tool_calls to only be present when tools are specified. Restored the logic that removes these options when there are no tools. - Restored check in _chat_client.py to remove tool_choice and parallel_tool_calls when no tools present - Restored same logic in _responses_client.py - Reverted test to expect the correct behavior * fixed issue in tests * fix: resolve merge conflict markers in ag-ui tests * fix: restructure ag-ui tests and fix Role/FinishReason to use string types * fix: streaming function invocation and middleware termination - Refactor streaming function invocation to use get_final_response() on inner streams - Fix MiddlewareTermination to accept result parameter for passing results - Fix _AutoHandoffMiddleware to use MiddlewareTermination instead of context.terminate - Fix AgentMiddlewareLayer.run() to properly forward function/chat middleware - Remove duplicate middleware registration in AgentMiddlewareLayer.__init__ - Fix exception handling in _auto_invoke_function to properly capture termination - Fix mypy errors in core package - Update tests to use stream=True parameter for unified run API * fix all tests command * Refactor integration tests to use pytest fixtures - Merge testutils.py into conftest.py for azurefunctions integration tests - Merge dt_testutils.py into conftest.py for durabletask integration tests - Convert all integration tests to use fixtures instead of direct imports (fixes ModuleNotFoundError with --import-mode=importlib) - Add sample_helper fixture for azurefunctions tests - Add agent_client_factory and orchestration_helper fixtures for durabletask - Integration tests now skip with descriptive messages when services unavailable - Restructure devui tests into tests/devui/ with proper conftest.py - Add test organization guidelines to CODING_STANDARD.md - Remove __init__.py from test directories per pytest best practices * Fix pytest_collection_modifyitems to only skip integration tests The hook was skipping all tests in the test session, not just integration tests. Now it only skips items in the integration_tests directory. * Fix mem0 tests failing on Python 3.13 Use patch.object on the imported module instead of @patch with string path to ensure the mock takes effect regardless of import timing. * fix mem0 * another attempt for mem0 * fix for mem0 * fix mem0 * Increase worker initialization wait time in durabletask tests Increase from 2 to 8 seconds to allow time for: - Python startup and module imports - Azure OpenAI client creation - Agent registration with DTS worker - Worker connection to DTS This helps prevent test failures in CI where the first tests may run before the worker is fully ready to process requests. * Fix streaming test to use ResponseStream with finalizer The _consume_stream method now expects a ResponseStream that can provide a final AgentResponse via get_final_response(). Update the test to use ResponseStream with AgentResponse.from_updates as the finalizer. * Fix MockToolCallingAgent to use new ResponseStream API and update samples * small updates to run_stream to run * fix sub workflow * temp fix for az func test --------- Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
8.9 KiB
ChatKit Integration Sample with Weather Agent and Image Analysis
This sample demonstrates how to integrate Microsoft Agent Framework with OpenAI ChatKit. It provides a complete implementation of a weather assistant with interactive widget visualization, image analysis, and file upload support.
Features:
- Weather information with interactive widgets
- Image analysis using vision models
- Current time queries
- File upload with attachment storage
- Chat interface with streaming responses
- City selector widget with one-click weather
Architecture
graph TB
subgraph Frontend["React Frontend (ChatKit UI)"]
UI[ChatKit Components]
Upload[File Upload]
end
subgraph Backend["FastAPI Server"]
FastAPI[FastAPI Endpoints]
subgraph ChatKit["WeatherChatKitServer"]
Respond[respond method]
Action[action method]
end
subgraph Stores["Data & Storage Layer"]
SQLite[SQLiteStore<br/>Store Protocol]
AttStore[FileBasedAttachmentStore<br/>AttachmentStore Protocol]
DB[(SQLite DB<br/>chatkit_demo.db)]
Files[/uploads directory/]
end
subgraph Integration["Agent Framework Integration"]
Converter[ThreadItemConverter]
Streamer[stream_agent_response]
Agent[ChatAgent]
end
Widgets[Widget Rendering<br/>render_weather_widget<br/>render_city_selector_widget]
end
subgraph Azure["Azure AI"]
Foundry[GPT-5<br/>with Vision]
end
UI -->|HTTP POST /chatkit| FastAPI
Upload -->|HTTP POST /upload/id| FastAPI
FastAPI --> ChatKit
ChatKit -->|save/load threads| SQLite
ChatKit -->|save/load attachments| AttStore
ChatKit -->|convert messages| Converter
SQLite -.->|persist| DB
AttStore -.->|save files| Files
AttStore -.->|save metadata| SQLite
Converter -->|ChatMessage array| Agent
Agent -->|AgentResponseUpdate| Streamer
Streamer -->|ThreadStreamEvent| ChatKit
ChatKit --> Widgets
Widgets -->|WidgetItem| ChatKit
Agent <-->|Chat Completions API| Foundry
ChatKit -->|ThreadStreamEvent| FastAPI
FastAPI -->|SSE Stream| UI
style ChatKit fill:#e1f5ff
style Stores fill:#fff4e1
style Integration fill:#f0e1ff
style Azure fill:#e1ffe1
Server Implementation
The sample implements a ChatKit server using the ChatKitServer base class from the chatkit package:
Core Components:
-
WeatherChatKitServer: Custom ChatKit server implementation that:- Extends
ChatKitServer[dict[str, Any]] - Uses Agent Framework's
ChatAgentwith Azure OpenAI - Converts ChatKit messages to Agent Framework format using
ThreadItemConverter - Streams responses back to ChatKit using
stream_agent_response - Creates and streams interactive widgets after agent responses
- Extends
-
SQLiteStore: Data persistence layer that:- Implements the
Store[dict[str, Any]]protocol from ChatKit - Persists threads, messages, and attachment metadata in SQLite
- Provides thread management and item history
- Stores attachment metadata for the upload lifecycle
- Implements the
-
FileBasedAttachmentStore: File storage implementation that:- Implements the
AttachmentStore[dict[str, Any]]protocol from ChatKit - Stores uploaded files on the local filesystem (in
./uploadsdirectory) - Generates upload URLs for two-phase file upload
- Saves attachment metadata to the data store for upload tracking
- Provides preview URLs for images
- Implements the
Key Integration Points:
# Converting ChatKit messages to Agent Framework
converter = ThreadItemConverter(
attachment_data_fetcher=self._fetch_attachment_data
)
agent_messages = await converter.to_agent_input(user_message_item)
# Running agent and streaming back to ChatKit
async for event in stream_agent_response(
self.weather_agent.run(agent_messages, stream=True),
thread_id=thread.id,
):
yield event
# Streaming widgets
widget = render_weather_widget(weather_data)
async for event in stream_widget(thread_id=thread.id, widget=widget):
yield event
Installation and Setup
Prerequisites
- Python 3.10+
- Node.js 18.18+ and npm 9+
- Azure OpenAI service configured
- Azure CLI for authentication (
az login)
Network Requirements
Important: This sample uses the OpenAI ChatKit frontend, which requires internet connectivity to OpenAI services.
The frontend makes outbound requests to:
cdn.platform.openai.com- ChatKit UI library (required)chatgpt.com- Configuration endpointapi-js.mixpanel.com- Telemetry
This sample is not suitable for air-gapped or network-restricted environments. The ChatKit frontend library cannot be self-hosted. See Limitations for details.
Domain Key Configuration
For local development, the sample uses a default domain key (domain_pk_localhost_dev).
For production deployment:
-
Register your domain at platform.openai.com
-
Create a
.envfile in thefrontenddirectory:VITE_CHATKIT_API_DOMAIN_KEY=your_domain_key_here
Backend Setup
- Install Python packages:
cd python/samples/demos/chatkit-integration
pip install agent-framework-chatkit fastapi uvicorn azure-identity
- Configure Azure OpenAI:
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
export AZURE_OPENAI_API_VERSION="2024-06-01"
export AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="gpt-4o"
- Authenticate with Azure:
az login
Frontend Setup
Install the Node.js dependencies:
cd frontend
npm install
How to Run
Start the Backend Server
From the chatkit-integration directory:
python app.py
Or with auto-reload for development:
uvicorn app:app --host 127.0.0.1 --port 8001 --reload
The backend will start on http://localhost:8001
Start the Frontend Development Server
In a new terminal, from the frontend directory:
npm run dev
The frontend will start on http://localhost:5171
Access the Application
Open your browser and navigate to:
http://localhost:5171
You can now:
- Ask about weather in any location (weather widgets display automatically)
- Upload images for analysis using the attachment button
- Get the current time
- Ask to see available cities and click city buttons for instant weather
Project Structure
chatkit-integration/
├── app.py # FastAPI backend with ChatKitServer implementation
├── store.py # SQLiteStore implementation
├── attachment_store.py # FileBasedAttachmentStore implementation
├── weather_widget.py # Widget rendering functions
├── chatkit_demo.db # SQLite database (auto-created)
├── uploads/ # Uploaded files directory (auto-created)
└── frontend/
├── package.json
├── vite.config.ts
├── index.html
└── src/
├── main.tsx
└── App.tsx # ChatKit UI integration
Configuration
You can customize the application by editing constants at the top of app.py:
# Server configuration
SERVER_HOST = "127.0.0.1" # Bind to localhost only for security (local dev)
SERVER_PORT = 8001
SERVER_BASE_URL = f"http://localhost:{SERVER_PORT}"
# Database configuration
DATABASE_PATH = "chatkit_demo.db"
# File storage configuration
UPLOADS_DIRECTORY = "./uploads"
# User context
DEFAULT_USER_ID = "demo_user"
Sample Conversations
Try these example queries:
- "What's the weather like in Tokyo?"
- "Show me available cities" (displays interactive city selector)
- "What's the current time?"
- Upload an image and ask "What do you see in this image?"
Limitations
Air-Gapped / Regulated Environments
The ChatKit frontend (chatkit.js) is loaded from OpenAI's CDN and cannot be self-hosted. This means:
- Not suitable for air-gapped environments where
*.openai.comis blocked - Not suitable for regulated environments that prohibit external telemetry
- Requires domain registration with OpenAI for production use
What you CAN self-host:
- The Python backend (FastAPI server,
ChatKitServer, stores) - The
agent-framework-chatkitintegration layer - Your LLM infrastructure (Azure OpenAI, local models, etc.)
What you CANNOT self-host:
- The ChatKit frontend UI library
For more details, see:
- openai/chatkit-js#57 - Self-hosting feature request
- openai/chatkit-js#76 - Domain key requirements