* 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>
Configure agents as both HTTP endpoints and MCP tools for flexible integration patterns.
Durable Task
These samples demonstrate durable agent hosting using the Durable Task Scheduler with a worker-client architecture pattern, enabling distributed agent execution with persistent conversation state.
Demonstrates how to suspend and resume a service-managed thread
Tools
Note: Many tool samples set approval_mode="never_require" to keep the examples concise. For production scenarios,
keep approval_mode="always_require" unless you are confident in the tool behavior and approval flow. See
getting_started/tools/function_tool_with_approval.py and
getting_started/tools/function_tool_with_approval_and_threads.py, plus the workflow approval samples in
getting_started/workflows/tool-approval/, for end-to-end approval handling.
Eduard van Valkenburg
3dc59c83b5