* Added an example of using kwargs in ai_function
* Added thread object to ai_function kwargs
* Updated docs
* Small fix
* Added thread parameter filtering
* Add factory pattern to sequential orchestration builder
* Use temp list to avoid override
* Add sample and some other fixes
* Fix comments
* Small fix
* Update readme
* Support HITL for orchestration patterns
* Cleanup around naming
* Fix typing issues
* Clean up
* Naming clean up
* Updates to HITL to make it cleaner
* Rename human input hook to orchestration request info
* Clean up per PR feedback
- Replace OPENAI_APIKEY with OPENAI_API_KEY across all samples
- Replace AZURE_FOUNDRY_OPENAI_APIKEY with AZURE_FOUNDRY_OPENAI_API_KEY
- Ensures consistency with OpenAI's standard naming convention
- Applies to .NET and Python samples
Fixes#1001
Co-authored-by: Alexander Zarei <alzarei@users.noreply.github.com>
* Provide way for HITL with magentic
* support tool call approvals and hitl stall replan
* human plan intervention sample
* Clean up
* Improve loging
* updates
* Add sample to show handoff as agent with HITL
* Update uv.lock with latest pkg versions. Fix lint error.
* Upgrade grpcio to 1.76.0
* Handle grpcio versions
* Case insensitive compare for declarative
* Standardize orchestration outputs as list of chatmessage. Add chat options to group chat prompt manager
* refactor group chat
* Improve group chat manager
* README Update
* Cleanup
* Add comment
* More cleanup
* Standardize termination condition for group chat
* Improvements on termination logic
* Fix tests
* Fix new line
* PR feedback
* Update ChatKit based on OpenAI type change
* Raise error if response format is not expected type
* Only one starting executor required. Add tests.
* Add magentic start executor test
* feat(mcp): add full _meta field support for CallToolResult objects
- Extract and preserve complete _meta field from MCP CallToolResult responses
- Merge metadata into additional_properties of converted content items
- Handle isError field for proper error state integration
- Support arbitrary metadata like token usage, costs, and performance metrics
- Maintain backward compatibility with existing tool execution workflows
- Add comprehensive test coverage for all metadata scenarios including edge cases
- Update documentation with metadata handling examples and patterns
Fixes protocol compliance violation where _meta fields were being dropped,
enables proper monitoring and cost tracking of MCP tool usage.
* Update python/packages/core/agent_framework/_mcp.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Clarify MCP _meta field test to use generic example metadata
- Updated test_mcp_call_tool_result_with_meta_arbitrary_data to use arbitrary metadata fields
- Added comments to emphasize that _meta structure is server-specific and not standardized
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Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Python: Fix pyright errors and move search provider to core (#1546)
* address pablo coments
* update azure ai search pypi version to latest prev
* init update
* Fix MyPy type annotation errors in search provider
- Add type annotation to DEFAULT_CONTEXT_PROMPT
- Add type annotation to vectorizable_fields
- Add union type annotation to vector_queries
* Fix DEFAULT_CONTEXT_PROMPT MyPy error and update test
- Rename DEFAULT_CONTEXT_PROMPT to _DEFAULT_SEARCH_CONTEXT_PROMPT to avoid conflict with base class Final variable
- Update test to use new constant name
- All core package tests passing (1123 passed)
* Python: Move Azure AI Search to separate package per PR feedback
Addresses reviewer feedback from PR #1546 by isolating the beta dependency
(azure-search-documents==11.7.0b2) into a new agent-framework-aisearch package.
Changes:
- Created new agent-framework-aisearch package with complete structure
- Moved AzureAISearchContextProvider from core to aisearch package
- Added AzureAISearchSettings class for environment variable auto-loading
- Added support for direct API key string (auto-converts to AzureKeyCredential)
- Added azure_openai_api_key parameter for Knowledge Base authentication
- Updated embedding_function type to Callable[[str], Awaitable[list[float]]]
- Moved Role import to top-level imports
- Maintained lazy loading through agent_framework.azure module
- Removed beta dependency from core package
- Updated all tests to use new package location
- All quality checks pass: ruff format/lint, pyright, mypy (0 errors)
- All 21 unit tests pass with 59% coverage
Semantic search mode verified working with both API key and managed identity authentication.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Python: Clarify top_k parameter only applies to semantic mode
Updated documentation to clarify that the top_k parameter only affects
semantic search mode. In agentic mode, the server-side Knowledge Base
determines retrieval based on query complexity and reasoning effort.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Python: Add Knowledge Base output mode and retrieval reasoning effort parameters
Added support for configurable Knowledge Base behavior in agentic mode:
- knowledge_base_output_mode: "extractive_data" (default) or "answer_synthesis"
Some knowledge sources require answer_synthesis mode for proper functionality.
- retrieval_reasoning_effort: "minimal" (default), "medium", or "low"
Controls query planning complexity and multi-hop reasoning depth.
These parameters give users fine-grained control over Knowledge Base behavior
and enable support for knowledge sources that require answer synthesis.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* effort and outputmode query params
* Address PR review feedback for Azure AI Search context provider
* comments eduward
* ed latest comments
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Co-authored-by: Farzad Sunavala <farzad.sunavala.enovate.ai>
Co-authored-by: farzad528 <farzad528@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
* First working version
* Simplify the implementations
* Remove unused env var
* Update Python syntax
* Address feedbacks
* Fix a typo
* Update names as review suggestions
* Citation for self-reflection
* Move to independent folder
* Update python/samples/getting_started/evaluation/azure_ai_foundry/evaluation/README.md
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Updated from parquet to JSONL and hide the default environment variables
* As review feedback, remove the purpose of using `run_self_reflection_batch` as a library, only use it as sample code
* Update python/samples/getting_started/evaluation/azure_ai_foundry/evaluation/self_reflection.py
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
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Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
Dmytro StrukandGitHub
Evan MattsonandGitHub
Tao ChenandGitHub
Giles OdigweandGitHub
Laveesh RohraandGitHub
David WuandGitHub
