* changed AIFunction to FunctionTool and @ai_function to @tool
* test and mypy fixes
* mypy fix
* switch function tool to always_require
* fix noop
* fix github copilot imports
* test fixes
* fix ollama test
* fixes for tests
* fix tests
* reverted change to always_require and extended timeout
* fix test
* Fix: Add system_instructions to ChatClient LLM span tracing
- Add system_instructions parameter to _capture_messages() calls in
_trace_get_response() and _trace_get_streaming_response()
- Extract instructions from chat_options in kwargs
- Add unit tests to verify system_instructions are captured correctly
When using ChatClient with ChatOptions.instructions, the OpenTelemetry
LLM span was missing system messages in gen_ai.input.messages and the
gen_ai.system_instructions attribute was not being set.
This fix aligns the ChatClient-level tracing with the Agent-level
tracing which already correctly passes system_instructions.
Fixes#3163
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Add edge case tests for system_instructions
- Add test for empty string instructions (should not set attribute)
- Add test for list-type instructions (verify multiple items captured)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Simplify: use options.get('instructions') directly instead of kwargs.get('chat_options')
Addresses reviewer feedback:
- Removed unnecessary chat_options variable from kwargs
- Directly access instructions from the options parameter
- Updated tests to use dict syntax for options (TypedDict convention)
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* Group chat refactoring Part 1; Next: HIL and handoff
* Add agent approval flow; next samples
* WIP: samples
* WIP: HIL samples
* Group chat HIL working; next: handoff
* Fix group chat tool approval sample
* WIP: refactor handoff; next handoff handling
* Handoff done; next handoff samples and concurrent and sequential
* Handoff samples, concurrent, and sequential done; next Magentic
* WIP: magentic; next test with samples + HIL
* Magentic Working; next fix all samples and tests
* Fix handoff samples; next tests
* WIP: fixing tests; some orchestration as agent samples are failing
* Group chat unit tests done
* Handoff unit tests done
* Remove old orchestration_request_info and fix related tests
* Magentic unit tests done
* Fix samples
* Fix test
* Fix test 2
* mypy
* Address comments
* Update readme
* Address comments
* Address comments 2
* Replace display name
* removed display_name, renamed context_providers, middleware and AggregateContextProvider
* fixes
* fixed test
* testfix
* removed mistakenly put back test
* updated new test
* rename middlewares to middleware
* middleware fixes
* Fix MCP tool result serialization for list[TextContent]
When MCP tools return results containing list[TextContent], they were
incorrectly serialized to object repr strings like:
'[<agent_framework._types.TextContent object at 0x...>]'
This fix properly extracts text content from list items by:
1. Checking if items have a 'text' attribute (TextContent)
2. Using model_dump() for items that support it
3. Falling back to str() for other types
4. Joining single items as plain text, multiple items as JSON array
Fixes#2509
* Address PR review feedback for MCP tool result serialization
- Extract serialize_content_result() to shared _utils.py
- Fix logic: use texts[0] instead of join for single item
- Add type annotation: texts: list[str] = []
- Return empty string for empty list instead of '[]'
- Move import json to file top level
- Add comprehensive unit tests for serialization
* Address PR review feedback: fix type checking and double serialization
- Add isinstance(item.text, str) check to ensure text attribute is a string
- Fix double-serialization issue by keeping model_dump results as dicts
until final json.dumps (removes escaped JSON strings in arrays)
- Improve docstring with detailed return value documentation
- Add test for non-string text attribute handling
- Add tests for list type tool results in _events.py path
* Simplify PR: minimal changes to fix MCP tool result serialization
Addresses reviewer feedback about excessive refactoring:
- Reset _events.py to original structure
- Only add import and use serialize_content_result in one location
- All review comments addressed in serialize_content_result():
- Added isinstance(item.text, str) check
- Use model_dump(mode="json") to avoid double-serialization
- Improved docstring with explicit return value documentation
- Empty list returns "" instead of "[]"
* Refactor: Move MCP TextContent serialization to core prepare_function_call_results
Per reviewer feedback, moved the TextContent serialization logic from
ag-ui's serialize_content_result to the core package's
prepare_function_call_results function.
Changes:
- Added handling for objects with 'text' attribute (like MCP TextContent)
in _prepare_function_call_results_as_dumpable
- Removed serialize_content_result from ag-ui/_utils.py
- Updated _events.py and _message_adapters.py to use
prepare_function_call_results from core package
- Updated tests to match the core function's behavior
* Fix failing tests for prepare_function_call_results behavior
- test_tool_result_with_none: Update expected value to 'null' (JSON serialization of None)
- test_tool_result_with_model_dump_objects: Use Pydantic BaseModel instead of plain class
* Fix B903 linter error: Convert MockTextContent to dataclass
The ruff linter was reporting B903 (class could be dataclass or namedtuple)
for the MockTextContent test helper classes. This commit converts them to
dataclasses to satisfy the linter check.
* refactoring and unifying naming schemes of internal methods of chat clients
* set tool_choice to auto
* fix for mypy
* added note on naming and fix#2951
* fix responses
* fixes in azure ai agents client
* Correction of MCP image type conversion in _mcp.py
* Added a new overload to the init function of the DataContent() type of the Agent Framework, edited the test case to correctly test the usage of the data and uri fields while using DataContent()
* Fixed tests related to the changes of the DataContent type, added testing for both string and byte representations
* Added an example of using kwargs in ai_function
* Added thread object to ai_function kwargs
* Updated docs
* Small fix
* Added thread parameter filtering
* added more complete parsing for mcp tool arguments
* fixed mypy
* added nonlocal model counter, and some fixes
* fixes in naming logic
* extracted json parsing function, added parametrized test and checked coverage
Tao ChenandGitHub
Eduard van ValkenburgandGitHub
Giles OdigweandGitHub
Evan MattsonandGitHub
Dmytro StrukandGitHub
Hao LuoandGitHub
Ege Ozan ÖzyedekandGitHub