* ADR for simplified get response
* updated some language, added agent option and code comparison
* small update in sample
* added workflows and expanded some points
* changed decision and number
* updated with stream=False default
* Make ChatMessageStore and AIContextProvider context props setable
* Add validation to preserve non-null requirement of certain properties.
* Fix broken tests.
* 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
* feat(ag-ui): Add Pydantic request model and OpenAPI tags support
- Add AGUIRequest Pydantic model in _types.py with field descriptions
- Update add_agent_framework_fastapi_endpoint() to accept tags parameter
- Use AGUIRequest model for automatic validation and OpenAPI schema generation
- Export AGUIRequest and DEFAULT_TAGS in __init__.py
- Update test_endpoint.py to expect 422 for invalid requests
- Add tests for OpenAPI schema, default tags, custom tags, and validation
Benefits:
- Better API documentation with complete request schema in Swagger UI
- Automatic request validation with Pydantic
- Organized endpoints under 'AG-UI' tag instead of 'default'
- Improved developer experience and type safety
Fixes #<issue-number>
* test(ag-ui): Add test for internal error handling to achieve 100% coverage
- Add test_endpoint_internal_error_handling() to cover exception handling code
- Mock copy.deepcopy to simulate internal error during default_state processing
- Add type: ignore for FastAPI tags parameter (known pyright compatibility issue)
- Achieves 100% test coverage for _endpoint.py (previously missing lines 103-105)
When processing `input_json_delta` events, the Anthropic client was
passing the tool name from the previous `tool_use` event. This caused
ag-ui's `_handle_function_call_content` to emit a `ToolCallStartEvent`
for every streaming chunk (since it triggers on `if content.name:`).
This fix changes the behavior to pass an empty string for `name` in
`input_json_delta` events, matching OpenAI's behavior where streaming
argument chunks have `name=""`. The initial `tool_use` event still
provides the tool name, so only one `ToolCallStartEvent` is emitted.
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* fix Python: kwargs are not passed to _prepare_thread_and_messages in ChatAgent.run
Fixes#3118
* fix Python: [Bug]: model_id versus model_deployment_name is confusing in Azure AI Agents
Fixes#3147
* add types
* fixed type and docstring
* fix(ag-ui): execute tools after approval in human-in-the-loop flow
* Fix shared state bug
* Bug fix finalized
* Refactoring to clean up code
* Code cleanup
* More fixes
* More code cleanup
* Add version detection in __init__.py to ruff ignore list
* fix: Expose WorkflowErrorEvent as ErrorContent
When hosted using .AsAgent(), Workflows were not exposing inner errors coming as Exceptions (through the WorkflowErrorEvent)
The fix is to convert their message to an ErrorContent on the way out, rather than rely on the default "empty update" to collect the raw event.
* feat: Add a way to show/suppress exception information
* save input messages and stream updates to the continuation token to be able to use them in the last successful stream resumption call.
* Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgentContinuationToken.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgentContinuationToken.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update dotnet/tests/Microsoft.Agents.AI.UnitTests/ChatClient/ChatClientAgent_BackgroundResponsesTests.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgentContinuationToken.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgentContinuationToken.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* fix typo
* init continuation token from chat response
* remove unnecessary types for source generation
* remove check for continuation token passed at initial run
* remove check for continuation token pass at initial run
* centralize continuation token parsing
* update xml comments
* use readonly collection instead of enumerable
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* map additional props from agent run options to a2a request metadata
* small touches
* add unit tests for new extension methods
* Sort using
* add unit test
* add additiona unit tests
* special case json element to avoid unnecessary serialization
* Improve DevUI, add Context Inspector view as new tab under traces
* fix mypy errors
* fix: Handle stale MCP connections in DevUI executor
MCP tools can become stale when HTTP streaming responses end - the underlying
stdio streams close but `is_connected` remains True. This causes subsequent
requests to fail with `ClosedResourceError`.
Add `_ensure_mcp_connections()` to detect and reconnect stale MCP tools before
agent execution. This is a workaround for an upstream Agent Framework issue
where connection state isn't properly tracked.
Fixes MCP tools failing on second HTTP request in DevUI.
fixes #1476#1515#2865
* fix#1572 report import dependency errors more clearly
* Ensure there is streaming toggle where users can select streaming vs non streaming mode in devui . Fixes .NET: [Python] DevUI tool call rendering in non-streaming mode?
* remove unused dead code
* improve ux - workflows with agents show a chat component in execution timelien, also ensure magentic final output shows correctly
* update ui build
* update devui to use instrumentation instead of tracing, other instrumentation and type/instance check 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.
* Refactor ChatMessageStore methods to be similar to AIContextProvider
* Fix file encoding
* Ensure that AIContextProvider messages area also persisted.
* Update formatting and seal context classes
* Improve formatting
* Remove optional messages from constructor and add unit test
* Add ChatMessageStore filtering via a decorator
* Update sample and cosmos message store to store AIContextProvider messages in right order. Fix unit tests.
* Update Workflowmessage store to use aicontext provider messages.
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com>
* Improve xml docs messaging
* Address code review comments.
* Also notify message store on failure
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com>
* Pushing the bedrock related changes to the new branch after addressing the review comments
* 2524 Addressed the second round review comments
* 2524 Addressed few more minor comments on the PR
* resolving the merge conflict
* 2524 resolved the uv.lock conflicts
* 2524 addressed more comments
* 2524 removed the print statement to fix the checks failure
* 2524 resolved the CI failure issues
* 2524 fixing the CI breaks
* 2524 Addressed the review comment
* 2524 resolved conflict
---------
Co-authored-by: Sunil Dutta <sunil.dutta@penske.com>
Co-authored-by: budgetboardingai <apurva.sharma31@gmail.com>
* Use GrpcEntityRunner instead of TaskEntityDispatcher
* Pin to Durable worker 1.11.0
* Set the invocation result
* Update all Durable packages
* Update changelog, rename dispatcher to encondedEntityRequest
* Add workflow cancellation sample
Add sample demonstrating how to cancel a running workflow using asyncio
tasks. Shows both cancellation mid-execution and normal completion paths.
Useful for implementing timeouts, graceful shutdown, or A2A executors.
* update docstring
* 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
* fix: correct BadRequestError when using Pydantic model in response_format
* Fix lint
---------
Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
* 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
* Switch to new "RunAgent" method name.
* Try to disable false positive naming warning.
* Add comment about disabled warnings.
* Rename `RunAgent` to just `Run`.
* Update CHANGELOG.
* Cosmos DB UT Fast Skip (Non-Configured Local envs) + Long running UT skip in pipeline when no CosmosDB changes happened
* Force a CosmosDB source code change to trigger the pipeline
* Address possible string boolean mismatch
* Add debug
* Enabling emulator always when running IT
* Update to latest Azure.AI.*, OpenAI, and M.E.AI*
Absorb breaking changes in Responses surface area
* Update dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Utilities/ChatClientExtensions.cs
* Update dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Utilities/ChatClientExtensions.cs
* Update dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Utilities/ChatClientExtensions.cs
* Update dotnet/samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient/Program.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Using patch to remove the model is necessary, updated the response client to actually use the the ForAgent
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
## Summary
Enhanced `HandoffBuilder._apply_auto_tools` to use the target agent's
description when creating handoff tools, providing more informative tool
descriptions for LLMs.
## Changes
- Modified `_apply_auto_tools` to extract `description` from
`AgentExecutor._agent` when available
- Updated iteration to use `.items()` for more efficient dict traversal
- Handoff tools now use agent descriptions instead of generic placeholders
## Example
Before: "Handoff to the refund_agent agent."
After: "You handle refund requests. Ask for order details and process refunds."
## Testing
- All handoff tests pass (20/20)
- No breaking changes to existing API
Fixes#2713
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Added an example of using kwargs in ai_function
* Added thread object to ai_function kwargs
* Updated docs
* Small fix
* Added thread parameter filtering
* adds support for labels in edges, fixes rendering of labels in dot and mermaid, adds rendering of labels in edges
* Update dotnet/src/Microsoft.Agents.AI.Workflows/Visualization/WorkflowVisualizer.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* escaping edge labels, adding tests for labels containing strange characters that would break the diagram and enabling the previous signature so the API has backwards compatibility.
* Unify label in EdgeData
* Edge API adjustments, removed useless "sanitizer"
* fixed test
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Jacob Alber <jaalber@microsoft.com>
Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
* 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
* 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>
* .NET: [Durable Agents] Update CHANGELOG with release notes for past releases
Backfills the CHANGELOG.md files with the last several updates.
* Update dotnet/src/Microsoft.Agents.AI.DurableTask/CHANGELOG.md
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update the Azure Functions changelog and add GHCP changelog instructions for these projects
* Tweak instructions
* Remove the timestamp requirement
* Rename instructions file
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* change namespaces for agents and extension methods of the Microsoft.Agents.AI.OpenAI package
* remove unnecessary namespace
* remove unused namespaces
* fix compilation issues and rrolled back removed run methods
* sort usings
* add extension methods for AIAgent to work with OpenAI Responses primitives
* Move OpenAIChatClientAgent and OpenAIResponseClientAgent to samples
* sort usings
* sort usings
* Title: Fix WorkflowFailedEvent error extraction to use details instead of error Body:
Summary
Fixed WorkflowFailedEvent mapping to extract error message from details.message instead of non-existent error attribute
Added support for including details.extra context in error messages when present
Problem
The WorkflowFailedEvent handler in _mapper.py was reading event.error, but WorkflowFailedEvent uses a details attribute (of type WorkflowErrorDetails), not error. This caused all workflow failures to display "Unknown error" in the UI instead of the actual error message.
Fix
Updated the handler to match the pattern already used by ExecutorFailedEvent:
Read from event.details instead of event.error
Extract details.message for the error text
Include details.extra context when available
* improve error handling consistency
* Provide way for HITL with magentic
* support tool call approvals and hitl stall replan
* human plan intervention sample
* Clean up
* Improve loging
* updates
* Add type annotations for AgentRunEvent and AgentRunUpdateEvent data attributes
* Fix unnecessary cast after typing improvement
* Mcp pkg update introduced type change. Fix it.
* update opentelemetry deps to 1.39.0 and use LogRecordExporter for type compatibility
* Added unit test for RetrieveConversationMessagesExecutor
* Unit test for declarative object model AddConversationMessageExecutor
* Remove unnecessary test.
* Fix test.
* show app version in devui .NET: Python: Improved Versioning for DevUI
Fixes#2059
* feat: Add multimodal input support for workflows and refactor chat input
This PR adds support for multimodal content (images, files) in workflow
inputs and refactors the chat input into a reusable component.
## Multimodal Workflow Support
- Add `isChatMessageSchema()` to detect ChatMessage input schemas
- Update `RunWorkflowButton` to use `ChatMessageInput` for ChatMessage workflows
- Wrap multimodal content in OpenAI message format for backend processing
- Add `_is_openai_multimodal_format()` to detect OpenAI ResponseInputParam
- Update `_parse_workflow_input()` to route multimodal input through
existing `_convert_input_to_chat_message()` converter
## Reusable ChatMessageInput Component
- Extract chat input logic from agent-view into `ChatMessageInput` component
- Support file upload, drag & drop, paste handling, and attachments
- Add `useDragDrop` hook for parent-level drag handling with full-area
drop zones
- Refactor agent-view to use the new shared component
## Other Improvements
- Add `isStreaming` prop to executor nodes for animation control
- Clean up unused imports and state variables in agent-view
- Add tests for multimodal workflow input handling
Fixes workflow input not receiving images when using AgentExecutor nodes.
* add self loop edge, fix#2470
* fix test
* Fix AG-UI forwardedProps JSON property name
The RunAgentInput.ForwardedProperties property was using the wrong JSON property
name 'forwardedProperties' instead of 'forwardedProps' per the AG-UI protocol
specification.
This fix:
- Changes JsonPropertyName from 'forwardedProperties' to 'forwardedProps'
- Adds comprehensive integration tests for forwarded properties
Fixes#2468
* Fix formatting
* update .net sdk and runtime images and update nuget packages to the latests versions
* align version of roslyn analyzers
* unlock roslyn analyzer packages
* 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
* Update the declarative agent samples
* Add the Microsoft.Agents.AI.Declarative project
* Make the package non packable
* Use the RecalcEngine when creating the ChatOptions
* Ignore VSTHRD200
* Add geting started samples
* Address code review feedback
* prevent stremed updates loss when resuming streaming with non-agent managed store or/and context provider
* Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgent.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* throw not supported exception instead invalid operation
* Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgent.cs
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgent.cs
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* use conversation id to check if chat history is managed by agent service or not
* extract background responses tests into a separate file
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* docs: Update Python orchestration documentation
Remove outdated 'coming soon' statements for GroupChat, Sequential,
and Concurrent orchestrations in core package README and transparency FAQ.
Add links to existing samples in python/samples/getting_started/workflows/orchestration/.
Note: python/README.md was already updated in PR #1914 (2499262f).
Fixes documentation inconsistency found in Issue #1899.
* Address review feedback: make TRANSPARENCY_FAQ language-neutral
Remove Python-specific sample links from TRANSPARENCY_FAQ.md as it should
pertain to all MAF languages (Python and .NET), not strictly Python.
The python/packages/core/README.md retains the specific sample links as
that is Python-specific documentation.
* Update Learn documentation link in README
---------
Co-authored-by: kishikawa-hayato <84244732+HerBest-max@users.noreply.github.com>
Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>
* Propagate orchestration ID (if any).
* Add integration test for orchestration ID in entity state.
* Update schema.
* Fixup formatting issues.
* Fix more formatting issues.
* draft commit
* Added Cosmos agent thread and tests
* revert unnecessary changes and fix tests
* add multi-tenant support with hierarchical partition keys (and tests).
* enhance transactional batch
* address review comments
* Address PR review comments from @westey-m
* Merge upstream/main - resolve slnx conflicts
* use param validation helpers
* Replace useManagedIdentity boolean with TokenCredential parameter
* Remove redundant suppressions and fix tests
* Rename project from Microsoft.Agents.AI.Abstractions.CosmosNoSql to Microsoft.Agents.AI.CosmosNoSql
* Refactor constructors to use chaining pattern
* Reorder deserialization constructor parameters for consistency
* Remove database/container IDs from serialized state
* Remove auto-generation of MessageId
* Optimize AddMessagesAsync to avoid enumeration when possible
* Add MaxMessagesToRetrieve to limit context window
* Make Role nullable instead of defaulting
* Fix net472 build without rebasing 19 commits
* Add Cosmos DB emulator to CI workflow
* Fix Cosmos DB emulator tests: use Skip.If instead of Assert.Fail and start emulator before unit tests
* Replace Skip.If() with conditional return to fix compilation
* Use env var to skip Cosmos tests on non-Windows CI
* Add Xunit.SkippableFact package to properly skip Cosmos tests on Linux
* Change [Fact] to [SkippableFact] for proper test skipping behavior
* Remove stale Microsoft.Agents.AI.Abstractions.CosmosNoSql directory
* Fix code formatting: add braces, this. qualifications, and final newlines
* Fix file encoding to UTF-8 with BOM, fix import ordering, and remove unnecessary using directives
* Convert backing fields to auto-properties and remove Azure.Identity using directive
* Fix CosmosChatMessageStore.cs encoding back to UTF-8 with BOM
* Fix test file formatting: indentation, encoding, imports, this. qualifications, naming conventions, and simplify new expressions
* Fix const field naming violations: Remove s_ prefix from const fields and add this. qualification to Dispose call
* Add local .editorconfig for Cosmos DB tests to suppress IDE0005 false positives from multi-targeting
* Fix IDE1006 naming violations: Rename TestDatabaseId to s_testDatabaseId and add final newlines
* Address PR review comments
Address Wesley's review comments:
- Remove Cosmos DB package references from core projects
- Delete duplicate test files from old package structure
- Remove redundant parameter validation from extension methods
Address Kiran's review comments:
- Remove redundant 429 retry logic (SDK handles automatically)
- Add explicit RequestEntityTooLarge error handling
- Remove dead code in GetMessageCountAsync
- Add defensive partition key validation comments
* Fix IDE0001 formatting error in AgentProviderExtensions.cs. Use type alias to resolve namespace conflict between Azure.AI.Agents.Persistent.RunStatus and Microsoft.Agents.AI.Workflows.RunStatus. This eliminates the need for global:: qualifier which triggered the formatter warning.
* Update package versions for Aspire 13.0.0 compatibility
* Fix TargetFrameworks in Cosmos DB projects
- Replace with which is defined in Directory.Build.props
- Fix package reference from System.Linq.Async to System.Linq.AsyncEnumerable to match Directory.Packages.props
* Remove redundant counter, add partition key validation, use factory pattern for deserialization
* 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
* WIP
* WIP
* Simple call working
* Update Thinking sample
* Non-Streaming Function calling working
* Update Anthropic Impl
* Public Preps
* UT + IT working
* Update documentation + samples
* Update variable
* Revert nuget.config
* Add IT for BetaService implementation
* Remove polyfill + enable IT to run for netstandard 2.0
* Skipping Anthropic IT's for manual execution and avoid pipeline execution
* Fix compilation error
* Address error in UT
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Fix warning
* Net 10 update
* Update for NET 10, remove Anthropic.Foundry due to vulnerability
* Final missing adjustments for NET 10
* Address PR comments
* Remove unused code
* Address feedback
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* make tool call view optional in devui + other link fixes
* fix#2310, ensure correct port is shown in command
* fix dialog bug
* ensure executor ids are tracked per items, fix bug where data from concurrent executors where not seperated properly fix#2351
* fix: Enable multi-round human-in-the-loop (HIL) in DevUI workflows
- Backend: Enrich RequestInfoEvents with response schemas in send_responses_streaming path
- Frontend: Replace old HIL requests with new ones instead of accumulating them
- Frontend: Fix HIL response state management to prevent sending stale request responses
This allows workflows to properly handle sequential HIL requests, showing only the
current request to users and progressing through multiple input rounds correctly.
fixes#2334
* fix bug to ensure in memory entities cannot be reloaded in ui
* Upgrade to .NET 10
- Require .NET 10 SDK
- Include net10.0 assets in all assemblies
- Move net9.0-only targets to net10.0
- Update LangVersion to latest
- Remove complicated distinctions between debug target TFMs and release target TFMs
- Remove unnecessary package dependencies when built into netcoreapp
- Clean up some ifdefs
- Clean up some analyzer warnings
* Fix CI
* 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
---------
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
---------
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>
* Add unit tests for create conversation executor
* Update indentation and comment typo.
* Added unit tests for declarative executor SetMultipleVariablesExecutor
* Updated comments and syntactic sugar
* Add unit test for declarative executor RetrieveConversationMessageExecutor
* Removed irrelevant code statements
* Updated based on copilot feedback.
Fixes#2219
Adds default=str to json.dumps() calls to handle non-JSON-serializable
types like datetime objects in tool function results.
Co-authored-by: kishikawa-hayato <84244732+HerBest-max@users.noreply.github.com>
* Deep copy the agent chat options to avoid mutations
* avoiding _thread.RLock pickling errors
2025-11-20 08:06:14 +00:00
Evan MattsonGitHubCopilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* Potential fix for code scanning alert no. 18: Information exposure through an exception
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* Fix test
---------
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.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>
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* first work on declarative
* initial version of the declarative support
* fix tests and mypy
* fix parameters of functiontool
* slight logic improvement
* remove path until merge
* updates from comments
* create dispatcher and spec type, json_schema method
* fix mypy, skipping model
* updated lock
* fixed declarative tests and renamed some other test files
* refined loader
* updated lock
* fix mypy
* added readme to samples folder
* fixes from review
* undid test file rename
* fix: resolve string annotations in FunctionExecutor
Enhance type hint validation in FunctionExecutor by importing `typing` and
using `get_type_hints` to correctly resolve annotations.
This fixes validation failures when `from __future__ import annotations`
is enabled, which stores annotations as strings.
Fixes#1808
* Update python/packages/core/tests/workflow/test_function_executor_future.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* ran pre commit
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Fix bug where ChatAgent system instructions were not captured in Langfuse
traces due to incorrect attribute access.
The observability code was attempting to retrieve instructions using
getattr(self, "instructions", None), but ChatAgent stores instructions
in self.chat_options.instructions. This caused system_instructions to
always be None in Langfuse traces.
Changed both _trace_agent_run and _trace_agent_run_stream functions
to correctly retrieve instructions from chat_options.instructions.
Fixes affect:
- Line 1123: _trace_agent_run (non-streaming)
- Line 1192: _trace_agent_run_stream (streaming)
2025-11-19 07:08:27 +00:00
Eduard van ValkenburgGitHubCopilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>eavanvalkenburgcopilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>CopilotVictor Dibia
Update XML documentation to clarify exception behavior.
See `ChatClientAgentThreadTests.SetConversationIdThrowsWhenMessageStoreIsSet` which already verifies this is the actual behavior.
Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
* Fix: Prevent duplicate MCP tools and prompts (#1876)
- Added deduplication logic in MCPTool.load_tools() method
- Added deduplication logic in MCPTool.load_prompts() method
- Track existing function names before loading from MCP server
- Skip tools/prompts that are already registered in _functions list
- Prevents 400 error from Azure AI Foundry caused by duplicate tool names
The issue occurred because load_tools() was being called multiple times
(during connect() and by notification handlers), causing tools to be
appended without duplicate checking.
Changes made:
1. In load_tools(): Added existing_names set to track registered functions
2. In load_tools(): Added check to skip tools already in existing_names
3. In load_prompts(): Applied same deduplication pattern
Testing:
- Created unit test verifying deduplication logic
- Confirmed duplicates are skipped correctly
- Confirmed new functions are added correctly
- Prevents duplicate tool names being sent to LLM
Fixes#1876
* Address review feedback: Prevent multiple calls to load_tools and load_prompts
- Added _tools_loaded and _prompts_loaded flags to MCPTool class
- Modified load_tools() to check if already loaded and return early
- Modified load_prompts() to check if already loaded and return early
- Moved test cases from test_mcp_fix.py to test_mcp.py
- Added tests for multiple call prevention
- Deleted separate test_mcp_fix.py file
Addresses review feedback from @eavanvalkenburg:
- Prevents accidental multiple calls to load_tools()
- Prevents accidental multiple calls to load_prompts()
- Test file now in proper location (test_mcp.py)
* Address review feedback: Move flag checks to connect() and remove comments
- Removed verbose comments from code
- Moved _tools_loaded and _prompts_loaded checks to connect() method
- Allows manual calls to load_tools() and load_prompts() for updates
- Updated tests to reflect new behavior
- connect() now prevents duplicate loading during connection
- Users can still manually call load_tools()/load_prompts() to refresh
Addresses feedback from @eavanvalkenburg
* Fix: Code quality and formatting issues
- Applied black formatting
- Fixed ruff linting issues
- All tests passing locally
* chore: Re-run uv lock per review request
* Apply pre-commit formatting: consolidate type annotations
- Consolidate multi-line type annotations to single line
- Remove unnecessary parentheses
- Apply ruff format and security checks
* Move Purview integration logic into middleware
* Improve error handling and user id management
* Rename purview package
* Handle 402s more explicitly; add Middleware generation methods; don't ignore exceptions
* Use DI container; pass scope id to PC
* Add protection scope caching
* Wrap more exceptions in PurviewClient
* Remove block check dedup; add tests
* Refactor PurviewWrapper intialization; Add unit tests
* Use different .Use method and add IDisposable stub
* Add background job processing for Purview
* Misc comment cleanup
* Apply copilot comments
* Fix formatting
* Formatting other files to fix pipeline
* Small updates to settings and exceptions
* Add README
* Move Purview sample
* Address review comments and update XML comments
* Newline after namespace
* Move public Purview classes to single namespace; Clean up csproj and slnx
* Commit the renames
* Remove unused openAI dependency
---------
Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
* fix devui regression from #2021 where all input is stringified but devui HIL input does not handle stringified json strings correctly.
* update incorrect test
* add devui hil input tests
This commit fixes three issues in the security_filter_middleware:
1. Missing context.terminate flag - Without this, middleware continues processing after setting blocked response
2. No streaming support - When context.is_streaming is True, middleware now returns async generator with ChatResponseUpdate
3. Checks all messages - Changed to check only context.messages[-1] (most recent user message) instead of iterating through conversation history
Changes:
- Added AsyncIterable import
- Added ChatResponseUpdate and TextContent imports
- Modified security_filter_middleware to handle both streaming and non-streaming modes
- Added context.terminate = True to properly stop execution
- Changed message checking logic to only inspect the last user message
Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Added changes (#1909)
* Python: [Feature Branch] Renamed Azure AI agent and small fixes (#1919)
* Renaming
* Small fixes
* Update python/packages/core/agent_framework/openai/_shared.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Small fix
* Python: [Feature Branch] Added use_latest_version parameter to AzureAIClient (#1959)
* Added use_latest_version parameter to AzureAIClient
* Added unit tests
* Update python/samples/getting_started/agents/azure_ai/azure_ai_use_latest_version.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update python/packages/azure-ai/agent_framework_azure_ai/_client.py
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Python: [Feature Branch] Structured Outputs and more examples for AzureAIClient (#1987)
* Small updates
* Added support for structured outputs
* Added code interpreter example
* More examples and fixes
* Added more examples and README
* Small fix
* Addressed PR feedback
* Removed optional ID from FunctionResultContent (#2011)
* Added hosted MCP support (#2018)
* Python: [Feature Branch] Fixed "store" parameter handling (#2069)
* Fixed store parameter handling
* Small fix
* Python: [Feature Branch] Added more examples and fixes for Azure AI agent (#2077)
* Updated azure-ai-projects package version
* Added an example of hosted MCP with approval required
* Updated code interpreter example
* Added file search example
* Update python/samples/getting_started/agents/azure_ai/azure_ai_with_file_search.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update python/samples/getting_started/agents/azure_ai/azure_ai_with_file_search.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Small fix
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Added handling for conversation_id (#2098)
* Merge from main
* Revert "Merge from main"
This reverts commit b8206a85d7.
* Python: [Feature Branch] Merge from main to Azure AI branch (#2111)
* Do not build DevUI assets during .NET project build (#2010)
* .NET: Add unit tests for declarative executor SetMultipleVariables (#2016)
* Add unit tests for create conversation executor
* Update indentation and comment typo.
* Added unit tests for declarative executor SetMultipleVariablesExecutor
* Updated comments and syntactic sugar
* Python: DevUI: Use metadata.entity_id instead of model field (#1984)
* DevUI: Use metadata.entity_id for agent/workflow name instead of model field
* OpenAI Responses: add explicit request validation
* Review feedback
* .NET: DevUI - Do not automatically add/map OpenAI services/endpoints (#2014)
* Don't add OpenAIResponses as part of Dev UI
You should be able to add and remove Dev UI without impacting your other production endpoints.
* Remove `AddDevUI()` and do not map OpenAI endpoints from `MapDevUI()`
* Fix comment wording
* Revise documentation
---------
Co-authored-by: Daniel Roth <daroth@microsoft.com>
* Python: DevUI: Add OpenAI Responses API proxy support + HIL for Workflows (#1737)
* DevUI: Add OpenAI Responses API proxy support with enhanced UI features
This commit adds support for proxying requests to OpenAI's Responses API,
allowing DevUI to route conversations to OpenAI models when configured to enable testing.
Backend changes:
- Add OpenAI proxy executor with conversation routing logic
- Enhance event mapper to support OpenAI Responses API format
- Extend server endpoints to handle OpenAI proxy mode
- Update models with OpenAI-specific response types
- Remove emojis from logging and CLI output for cleaner text
Frontend changes:
- Add settings modal with OpenAI proxy configuration UI
- Enhance agent and workflow views with improved state management
- Add new UI components (separator, switch) for settings
- Update debug panel with better event filtering
- Improve message renderers for OpenAI content types
- Update types and API client for OpenAI integration
* update ui, settings modal and workflow input form, add register cleanup hooks.
* add workflow HIL support, user mode, other fixes
* feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas
Implement HIL workflow support allowing workflows to pause for user input
with dynamically generated JSON schemas based on response handler type hints.
Key Features:
- Automatic response schema extraction from @response_handler decorators
- Dynamic form generation in UI based on Pydantic/dataclass response types
- Checkpoint-based conversation storage for HIL requests/responses
- Resume workflow execution after user provides HIL response
Backend Changes:
- Add extract_response_type_from_executor() to introspect response handlers
- Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema()
- Map RequestInfoEvent to response.input.requested OpenAI event format
- Store HIL responses in conversation history and restore checkpoints
Frontend Changes:
- Add HILInputModal component with SchemaFormRenderer for dynamic forms
- Support Pydantic BaseModel and dataclass response types
- Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects
- Display original request context alongside response form
Testing:
- Add tests for checkpoint storage (test_checkpoints.py)
- Add schema generation tests for all input types (test_schema_generation.py)
- Validate end-to-end HIL flow with spam workflow sample
This enables workflows to seamlessly pause execution and request structured user input
with type-safe, validated forms generated automatically from response type annotations.
* improve HIL support, improve workflow execution view
* ui updates
* ui updates
* improve HIL for workflows, add auth and view modes
* update workflow
* security improvements , ui fixes
* fix mypy error
* update loading spinner in ui
---------
Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
* .NET: Remove launchSettings.json from .gitignore in dotnet/samples (#2006)
* Remove launchSettings.json from .gitignore in dotnet/samples
* Update dotnet/samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/Properties/launchSettings.json
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update dotnet/samples/AGUIClientServer/AGUIServer/Properties/launchSettings.json
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format (#2021)
Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>
* Add Microsoft Agent Framework logo to assets (#2007)
* Updated package versions (#2027)
* DevUI: Prevent line breaks within words in the agent view (#2024)
Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>
* .NET [AG-UI]: Adds support for shared state. (#1996)
* Product changes
* Tests
* Dojo project
* Cleanups
* Python: Fix underlying tool choice bug and all for return to previous Handoff subagent (#2037)
* Fix tool_choice override bug and add enable_return_to_previous support
* Add unit test for handoff checkpointing
* Handle tools when we have them
* added missing chatAgent params (#2044)
* .NET: fix ChatCompletions Tools serialization (#2043)
* fix serialization in chat completions on tools
* nit
* .NET: assign AgentCard's URL to mapped-endpoint if not defined explicitly (#2047)
* fix serialization in chat completions on tools
* nit
* write e2e test for agent card resolve + adjust behavior
* nit
* Version 1.0.0-preview.251110.1 (#2048)
* .NET: Remove moved OpenAPI sample and point to SK one. (#1997)
* Remove moved OpenAPI sample and point to SK one.
* Update dotnet/samples/GettingStarted/Agents/README.md
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Bump AWSSDK.Extensions.Bedrock.MEAI from 4.0.4.2 to 4.0.4.6 (#2031)
---
updated-dependencies:
- dependency-name: AWSSDK.Extensions.Bedrock.MEAI
dependency-version: 4.0.4.6
dependency-type: direct:production
update-type: version-update:semver-patch
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
* .NET: Separate all memory and rag samples into their own folders (#2000)
* Separate all memory and rag samples into their own folders
* Fix broken link.
* Python: .Net: Dotnet devui compatibility fixes (#2026)
* DevUI: Add OpenAI Responses API proxy support with enhanced UI features
This commit adds support for proxying requests to OpenAI's Responses API,
allowing DevUI to route conversations to OpenAI models when configured to enable testing.
Backend changes:
- Add OpenAI proxy executor with conversation routing logic
- Enhance event mapper to support OpenAI Responses API format
- Extend server endpoints to handle OpenAI proxy mode
- Update models with OpenAI-specific response types
- Remove emojis from logging and CLI output for cleaner text
Frontend changes:
- Add settings modal with OpenAI proxy configuration UI
- Enhance agent and workflow views with improved state management
- Add new UI components (separator, switch) for settings
- Update debug panel with better event filtering
- Improve message renderers for OpenAI content types
- Update types and API client for OpenAI integration
* update ui, settings modal and workflow input form, add register cleanup hooks.
* add workflow HIL support, user mode, other fixes
* feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas
Implement HIL workflow support allowing workflows to pause for user input
with dynamically generated JSON schemas based on response handler type hints.
Key Features:
- Automatic response schema extraction from @response_handler decorators
- Dynamic form generation in UI based on Pydantic/dataclass response types
- Checkpoint-based conversation storage for HIL requests/responses
- Resume workflow execution after user provides HIL response
Backend Changes:
- Add extract_response_type_from_executor() to introspect response handlers
- Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema()
- Map RequestInfoEvent to response.input.requested OpenAI event format
- Store HIL responses in conversation history and restore checkpoints
Frontend Changes:
- Add HILInputModal component with SchemaFormRenderer for dynamic forms
- Support Pydantic BaseModel and dataclass response types
- Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects
- Display original request context alongside response form
Testing:
- Add tests for checkpoint storage (test_checkpoints.py)
- Add schema generation tests for all input types (test_schema_generation.py)
- Validate end-to-end HIL flow with spam workflow sample
This enables workflows to seamlessly pause execution and request structured user input
with type-safe, validated forms generated automatically from response type annotations.
* improve HIL support, improve workflow execution view
* ui updates
* ui updates
* improve HIL for workflows, add auth and view modes
* update workflow
* security improvements , ui fixes
* fix mypy error
* update loading spinner in ui
* DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format
* Phase 1: Add /meta endpoint and fix workflow event naming for .NET DevUI compatibility
* additional fixes for .NET DevUI workflow visualization item ID tracking
**Problem:**
.NET DevUI was generating different item IDs for ExecutorInvokedEvent and
ExecutorCompletedEvent, causing only the first executor to highlight in the
workflow graph. Long executor names and error messages also broke UI layout.
**Changes:**
- Add ExecutorActionItemResource to match Python DevUI implementation
- Track item IDs per executor using dictionary in AgentRunResponseUpdateExtensions
- Reuse same item ID across invoked/completed/failed events for proper pairing
- Add truncateText() utility to workflow-utils.ts
- Truncate executor names to 35 chars in execution timeline
- Truncate error messages to 150 chars in workflow graph nodes
** Details:**
- ExecutorActionItemResource registered with JSON source generation context
- Dictionary cleaned up after executor completion/failure to prevent memory leaks
- Frontend item tracking by unique item.id supports multiple executor runs
- All changes follow existing codebase patterns and conventions
Tested with review-workflow showing correct executor highlighting and state
transitions for sequential and concurrent executors.
* format fixes, remove cors tests
* remove unecessary attributes
---------
Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
Co-authored-by: Reuben Bond <reuben.bond@gmail.com>
* DevUI: support having both an agent and a workflow with the same id in discovery (#2023)
* Python: Fix Model ID attribute not showing up in `invoke_agent` span (#2061)
* Best effort to surface the model id to invoke agent span
* Fix tests
* Fix tests
* Version 1.0.0-preview.251107.2 (#2065)
* Version 1.0.0-preview.251110.2 (#2067)
* Update README.md to change Grafana links to Azure portal links for dashboard access (#1983)
* .NET - Enable build & test on branch `feature-foundry-agents` (#2068)
* Tests good, mkay
* Update .github/workflows/dotnet-build-and-test.yml
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Enable feature build pipelines
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
* Python: Add concrete AGUIChatClient (#2072)
* Add concrete AGUIChatClient
* Update logging docstrings and conventions
* PR feedback
* Updates to support client-side tool calls
* .NET: Move catalog samples to the HostedAgents folder (#2090)
* move catalog samples to the HostedAgents folder
* move the catalog samples' projects to the HostedAgents folder
* Bump OpenTelemetry.Instrumentation.Runtime from 1.12.0 to 1.13.0 (#1856)
---
updated-dependencies:
- dependency-name: OpenTelemetry.Instrumentation.Runtime
dependency-version: 1.13.0
dependency-type: direct:production
update-type: version-update:semver-minor
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
* .NET: Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0 (#1962)
* Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0
---
updated-dependencies:
- dependency-name: Microsoft.SemanticKernel.Agents.Abstractions
dependency-version: 1.67.0
dependency-type: direct:production
update-type: version-update:semver-minor
...
Signed-off-by: dependabot[bot] <support@github.com>
* .NET: Bump all Microsoft.SemanticKernel packages from 1.66.* to 1.67.* (#1969)
* Initial plan
* Update all Microsoft.SemanticKernel packages to 1.67.*
Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com>
* Remove unrelated changes to package-lock.json and yarn.lock
Co-authored-by: markwallace-microsoft <127216156+markwallace-microsoft@users.noreply.github.com>
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com>
Co-authored-by: markwallace-microsoft <127216156+markwallace-microsoft@users.noreply.github.com>
---------
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com>
Co-authored-by: markwallace-microsoft <127216156+markwallace-microsoft@users.noreply.github.com>
* .NET: fix: WorkflowAsAgent Sample (#1787)
* fix: WorkflowAsAgent Sample
* Also makes ChatForwardingExecutor public
* feat: Expand ChatForwardingExecutor handled types
Make ChatForwardingExecutor match the input types of ChatProtocolExecutor.
* fix: Update for the new AgentRunResponseUpdate merge logic
AIAgent always sends out List<ChatMessage> now.
* Updated (#2076)
* Bump vite in /python/samples/demos/chatkit-integration/frontend (#1918)
Bumps [vite](https://github.com/vitejs/vite/tree/HEAD/packages/vite) from 7.1.9 to 7.1.12.
- [Release notes](https://github.com/vitejs/vite/releases)
- [Changelog](https://github.com/vitejs/vite/blob/v7.1.12/packages/vite/CHANGELOG.md)
- [Commits](https://github.com/vitejs/vite/commits/v7.1.12/packages/vite)
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* Bump Roslynator.Analyzers from 4.14.0 to 4.14.1 (#1857)
---
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* Bump MishaKav/pytest-coverage-comment from 1.1.57 to 1.1.59 (#2034)
Bumps [MishaKav/pytest-coverage-comment](https://github.com/mishakav/pytest-coverage-comment) from 1.1.57 to 1.1.59.
- [Release notes](https://github.com/mishakav/pytest-coverage-comment/releases)
- [Changelog](https://github.com/MishaKav/pytest-coverage-comment/blob/main/CHANGELOG.md)
- [Commits](https://github.com/mishakav/pytest-coverage-comment/compare/v1.1.57...v1.1.59)
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* Python: Handle agent user input request in AgentExecutor (#2022)
* Handle agent user input request in AgentExecutor
* fix test
* Address comments
* Fix tests
* Fix tests
* Address comments
* Address comments
* Python: OpenAI Responses Image Generation Stream Support, Sample and Unit Tests (#1853)
* support for image gen streaming
* small fixes
* fixes
* added comment
* Python: Fix MCP Tool Parameter Descriptions Not Propagated to LLMs (#1978)
* mcp tool description fix
* small fix
* .NET: Allow extending agent run options via additional properties (#1872)
* Allow extending agent run options via additional properties
This mirrors the M.E.AI model in ChatOptions.AdditionalProperties which is very useful when building functionality pipelines.
Fixes https://github.com/microsoft/agent-framework/issues/1815
* Expand XML documentation
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* Add AdditionalProperties tests to AgentRunOptions
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* Python: Use the last entry in the task history to avoid empty responses (#2101)
* Use the last entry in the task history to avoid empty responses
* History only contains Messages
* Updated package versions (#2104)
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* Updated azure-ai-projects package version and small fixes (#2139)
* Python: [Feature Branch] Resolve CI issues (#2143)
* Small documentation and code fixes
* Small fix in documentation
* Addressed PR feedback
* Added AI Search example
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- Each pull request that modifies code should add just one bulleted entry to the `CHANGELOG.md` file containing a change title (usually the PR title) and a link to the PR itself.
- New PRs should be added to the top of the `CHANGELOG.md` file under a "## [Unreleased]" heading.
- If the PR is the first since the last release, the existing "## [Unreleased]" heading should be replaced with a "## v[X.Y.Z]" heading and the PRs since the last release should be added to the new "## [Unreleased]" heading.
- The style of new `CHANGELOG.md` entries should match the style of the other entries in the file.
- If the PR introduces a breaking change, the changelog entry should be prefixed with "[BREAKING]".
@@ -980,6 +1016,8 @@ AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential(
### 3. OpenAI Assistants Migration
> ⚠️ **DEPRECATION WARNING**: The OpenAI Assistants API has been deprecated. The Agent Framework extension methods for Assistants are marked as `[Obsolete]`. **Please use the Responses API instead** (see Section 6: OpenAI Responses Migration).
<configuration_changes>
**Remove Semantic Kernel Packages:**
```xml
@@ -1291,52 +1329,7 @@ var result = await agent.RunAsync(userInput, thread);
- **[Migration from Semantic Kernel](https://learn.microsoft.com/en-us/agent-framework/migration-guide/from-semantic-kernel)** - Guide to migrate from Semantic Kernel
- **[Migration from AutoGen](https://learn.microsoft.com/en-us/agent-framework/migration-guide/from-autogen)** - Guide to migrate from AutoGen
Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-community-office-hours) or ask questions in our [Discord channel](https://discord.gg/b5zjErwbQM) to get help from the team and other users.
### ✨ **Highlights**
- **Graph-based Workflows**: Connect agents and deterministic functions using data flows with streaming, checkpointing, human-in-the-loop, and time-travel capabilities
- **API Dependencies**: Requires proper configuration of LLM provider keys and endpoints
- **Orchestration Features**: Advanced orchestration patterns like GroupChat, Sequential, and Concurrent orchestrations are "coming soon" for Python implementation
- **Orchestration Features**: Advanced orchestration patterns including GroupChat, Sequential, and Concurrent workflows are now available in both Python and .NET implementations. See the respective language documentation for examples.
- **Privacy and Data Protection**: The framework allows for human participation in conversations between agents. It is important to ensure that user data and conversations are protected and that developers use appropriate measures to safeguard privacy.
- **Accountability and Transparency**: The framework involves multiple agents conversing and collaborating, it is important to establish clear accountability and transparency mechanisms. Users should be able to understand and trace the decision-making process of the agents involved in order to ensure accountability and address any potential issues or biases.
- **Security & unintended consequences**: The use of multi-agent conversations and automation in complex tasks may have unintended consequences. Especially, allowing agents to make changes in external environments through tool calls or function execution could pose significant risks. Developers should carefully consider the potential risks and ensure that appropriate safeguards are in place to prevent harm or negative outcomes, including keeping a human in the loop for decision making.
This folder contains sample agent definitions that can be run using the declarative agent support, for python see the [declarative agent python sample folder](../python/samples/getting_started/declarative/).
instructions:You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response.
instructions:You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Assistants as the type in your response.
instructions:You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response.
instructions:You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Responses as the type in your response.
instructions:You are a helpful assistant. You answer questions is the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response.
instructions:You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Assistants as the type in your response.
instructions:You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Chat as the type in your response.
instructions:You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format. You must include Responses as the type in your response.
@@ -64,7 +64,7 @@ Approaches observed from the compared SDKs:
| AutoGen | **Approach 1** Separates messages into Agent-Agent (maps to Primary) and Internal (maps to Secondary) and these are returned as separate properties on the agent response object. See [types of messages](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/messages.html#types-of-messages) and [Response](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.Response) | **Approach 2** Returns a stream of internal events and the last item is a Response object. See [ChatAgent.on_messages_stream](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.ChatAgent.on_messages_stream) |
| OpenAI Agent SDK | **Approach 1** Separates new_items (Primary+Secondary) from final output (Primary) as separate properties on the [RunResult](https://github.com/openai/openai-agents-python/blob/main/src/agents/result.py#L39) | **Approach 1** Similar to non-streaming, has a way of streaming updates via a method on the response object which includes all data, and then a separate final output property on the response object which is populated only when the run is complete. See [RunResultStreaming](https://github.com/openai/openai-agents-python/blob/main/src/agents/result.py#L136) |
| Google ADK | **Approach 2** [Emits events](https://google.github.io/adk-docs/runtime/#step-by-step-breakdown) with [FinalResponse](https://github.com/google/adk-java/blob/main/core/src/main/java/com/google/adk/events/Event.java#L232) true (Primary) / false (Secondary) and callers have to filter out those with false to get just the final response message | **Approach 2** Similar to non-streaming except [events](https://google.github.io/adk-docs/runtime/#streaming-vs-non-streaming-output-partialtrue) are emitted with [Partial](https://github.com/google/adk-java/blob/main/core/src/main/java/com/google/adk/events/Event.java#L133) true to indicate that they are streaming messages. A final non partial event is also emitted. |
| AWS (Strands) | **Approach 3** Returns an [AgentResult](https://strandsagents.com/latest/api-reference/agent/#strands.agent.agent_result.AgentResult) (Primary) with messages and a reason for the run's completion. | **Approach 2** [Streams events](https://strandsagents.com/latest/api-reference/agent/#strands.agent.agent.Agent.stream_async) (Primary+Secondary) including, response text, current_tool_use, even data from "callbacks" (strands plugins) |
| AWS (Strands) | **Approach 3** Returns an [AgentResult](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent_result/) (Primary) with messages and a reason for the run's completion. | **Approach 2** [Streams events](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.stream_async) (Primary+Secondary) including, response text, current_tool_use, even data from "callbacks" (strands plugins) |
| LangGraph | **Approach 2** A mixed list of all [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) | **Approach 2** A mixed list of all [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) |
| Agno | **Combination of various approaches** Returns a [RunResponse](https://docs.agno.com/reference/agents/run-response) object with text content, messages (essentially chat history including inputs and instructions), reasoning and thinking text properties. Secondary events could potentially be extracted from messages. | **Approach 2** Returns [RunResponseEvent](https://docs.agno.com/reference/agents/run-response#runresponseevent-types-and-attributes) objects including tool call, memory update, etc, information, where the [RunResponseCompletedEvent](https://docs.agno.com/reference/agents/run-response#runresponsecompletedevent) has similar properties to RunResponse|
| A2A | **Approach 3** Returns a [Task or Message](https://a2aproject.github.io/A2A/latest/specification/#71-messagesend) where the message is the final result (Primary) and task is a reference to a long running process. | **Approach 2** Returns a [stream](https://a2aproject.github.io/A2A/latest/specification/#72-messagestream) that contains task updates (Secondary) and a final message (Primary) |
@@ -163,8 +163,8 @@ foreach (var update in response.Messages)
### Option 2 Run: Container with Primary and Secondary Properties, RunStreaming: Stream of Primary + Secondary
Run returns a new response type that has separate properties for the Primary Content and the Secondary Updates leading up to it.
The Primary content is available in the `AgentRunResponse.Messages` property while Secondary updates are in a new `AgentRunResponse.Updates` property.
`AgentRunResponse.Text` returns the Primary content text.
The Primary content is available in the `AgentResponse.Messages` property while Secondary updates are in a new `AgentResponse.Updates` property.
`AgentResponse.Text` returns the Primary content text.
Since streaming would still need to return an `IAsyncEnumerable` of updates, the design would differ from non-streaming.
With non-streaming Primary and Secondary content is split into separate lists, while with streaming it's combined in one stream.
@@ -232,24 +232,24 @@ await foreach (var update in responses)
@@ -463,7 +463,7 @@ Option 2 chosen so that we can vary Agent responses independently of Chat Client
### StructuredOutputs Decision
We will not support structured output per run request, but individual agents are free to allow this on the concrete implementation or at construction time.
We will however add support for easily extracting a structured output type from the `AgentRunResponse`.
We will however add support for easily extracting a structured output type from the `AgentResponse`.
## Addendum 1: AIContext Derived Types for different response types / Gap Analysis (Work in progress)
@@ -495,8 +495,8 @@ We need to decide what AIContent types, each agent response type will be mapped
| SDK | Structured Outputs support |
|-|-|
| AutoGen | **Approach 1** Supports [configuring an agent](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/agents.html#structured-output) at agent creation. |
| Google ADK | **Approach 1** Both [input and output shemas can be specified for LLM Agents](https://google.github.io/adk-docs/agents/llm-agents/#structuring-data-input_schema-output_schema-output_key) at construction time. This option is specific to this agent type and other agent types do not necessarily support |
| AWS (Strands) | **Approach 2** Supports a special invocation method called [structured_output](https://strandsagents.com/latest/api-reference/agent/#strands.agent.agent.Agent.structured_output) |
| Google ADK | **Approach 1** Both [input and output schemas can be specified for LLM Agents](https://google.github.io/adk-docs/agents/llm-agents/#structuring-data-input_schema-output_schema-output_key) at construction time. This option is specific to this agent type and other agent types do not necessarily support |
| AWS (Strands) | **Approach 2** Supports a special invocation method called [structured_output](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.structured_output) |
| LangGraph | **Approach 1** Supports [configuring an agent](https://langchain-ai.github.io/langgraph/agents/agents/?h=structured#6-configure-structured-output) at agent construction time, and a [structured response](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) can be retrieved as a special property on the agent response |
| Agno | **Approach 1** Supports [configuring an agent](https://docs.agno.com/examples/getting-started/structured-output) at agent construction time |
| A2A | **Informal Approach 2** Doesn't formally support schema negotiation, but [hints can be provided via metadata](https://a2a-protocol.org/latest/specification/#97-structured-data-exchange-requesting-and-providing-json) at invocation time |
@@ -508,7 +508,7 @@ We need to decide what AIContent types, each agent response type will be mapped
|-|-|
| AutoGen | Supports a [stop reason](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.TaskResult.stop_reason) which is a freeform text string |
| Google ADK | [No equivalent present](https://github.com/google/adk-python/blob/main/src/google/adk/events/event.py) |
| AWS (Strands) | Exposes a [stop_reason](https://strandsagents.com/latest/api-reference/types/#strands.types.event_loop.StopReason) property on the [AgentResult](https://strandsagents.com/latest/api-reference/agent/#strands.agent.agent_result.AgentResult) class with options that are tied closely to LLM operations. |
| AWS (Strands) | Exposes a [stop_reason](https://strandsagents.com/latest/documentation/docs/api-reference/python/types/event_loop/#strands.types.event_loop.StopReason) property on the [AgentResult](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent_result/) class with options that are tied closely to LLM operations. |
| LangGraph | No equivalent present, output contains only [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) |
| A2A | No equivalent present, response only contains a [message](https://a2a-protocol.org/latest/specification/#64-message-object) or [task](https://a2a-protocol.org/latest/specification/#61-task-object). |
@@ -54,7 +54,7 @@ The table below represents the majority of the naming changes discussed in issue
| *Mcp* & *Http* | *MCP* & *HTTP* | accepted | Acronyms should be uppercased in class names, according to PEP 8. | None |
| `agent.run_streaming` | `agent.run_stream` | accepted | Shorter and more closely aligns with AutoGen and Semantic Kernel names for the same methods. | None |
| `workflow.run_streaming` | `workflow.run_stream` | accepted | In sync with `agent.run_stream` and shorter and more closely aligns with AutoGen and Semantic Kernel names for the same methods. | None |
| AgentRunResponse & AgentRunResponseUpdate | AgentResponse & AgentResponseUpdate | rejected | Rejected, because it is the response to a run invocation and AgentResponse is too generic. | None |
| AgentResponse & AgentResponseUpdate | AgentResponse & AgentResponseUpdate | rejected | Rejected, because it is the response to a run invocation and AgentResponse is too generic. | None |
| *Content | * | rejected | Rejected other content type renames (removing `Content` suffix) because it would reduce clarity and discoverability. | Item was also considered, but rejected as it is very similar to Content, but would be inconsistent with dotnet. |
| ChatResponse & ChatResponseUpdate | Response & ResponseUpdate | rejected | Rejected, because Response is too generic. | None |
@@ -1279,7 +1279,7 @@ Below are the details of the option selected for chat clients that is also selec
#### 3.1 Continuation Token of a Custom Type
This option suggests using `ContinuationToken` to encapsulate all properties representing a long-running operation. The continuation token will be returned by agents in the
`ContinuationToken` property of the `AgentRunResponse` and `AgentRunResponseUpdate` responses to indicate that the response is part of a long-running operation. A null value
`ContinuationToken` property of the `AgentResponse` and `AgentResponseUpdate` responses to indicate that the response is part of a long-running operation. A null value
of the property will indicate that the response is not part of a long-running operation or the long-running operation has been completed. Callers will set the token in the
`ContinuationToken` property of the `AgentRunOptions` class in follow-up calls to the `Run{Streaming}Async` methods to indicate that they want to "continue" the long-running
operation identified by the token.
@@ -1313,18 +1313,18 @@ public class AgentRunOptions
- Provides bidirectional client and server support
@@ -69,7 +69,7 @@ Chosen option: "Current approach with internal event types and framework-native
3.**Agent Factory Pattern** - `MapAGUIAgent` uses factory function `(messages) => AIAgent` to allow request-specific agent configuration supporting multi-tenancy
4.**Bidirectional Conversion Architecture** - Symmetric conversion logic in shared namespace compiled into both packages for server (`AgentRunResponseUpdate` → AG-UI events) and client (AG-UI events → `AgentRunResponseUpdate`)
4.**Bidirectional Conversion Architecture** - Symmetric conversion logic in shared namespace compiled into both packages for server (`AgentResponseUpdate` → AG-UI events) and client (AG-UI events → `AgentResponseUpdate`)
5.**Thread Management** - `AGUIAgentThread` stores only `ThreadId` with thread ID communicated via `ConversationId`; applications manage persistence for parity with other implementations and to be compliant with the protocol. Future extensions will support having the server manage the conversation.
There is a misalignment between the create/get agent API in the .NET and Python implementations.
In .NET, the `CreateAIAgent` method can create either a local instance of an agent or a remote instance if the backend provider supports it. For remote agents, once the agent is created, you can retrieve an existing remote agent by using the `GetAIAgent` method. If a backend provider doesn't support remote agents, `CreateAIAgent` just initializes a new local agent instance and `GetAIAgent` is not available. There is also a `BuildAIAgent` method, which is an extension for the `ChatClientBuilder` class from `Microsoft.Extensions.AI`. It builds pipelines of `IChatClient` instances with an `IServiceProvider`. This functionality does not exist in Python, so `BuildAIAgent` is out of scope.
In Python, there is only one `create_agent` method, which always creates a local instance of the agent. If the backend provider supports remote agents, the remote agent is created only on the first `agent.run()` invocation.
Below is a short summary of different providers and their APIs in .NET:
| Package | Method | Behavior | Python support |
|---|---|---|---|
| Microsoft.Agents.AI | `CreateAIAgent` (based on `IChatClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`create_agent` in `BaseChatClient`). |
| Microsoft.Agents.AI.Anthropic | `CreateAIAgent` (based on `IBetaService` and `IAnthropicClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`AnthropicClient` inherits `BaseChatClient`, which exposes `create_agent`). |
| Microsoft.Agents.AI.AzureAI (V2) | `GetAIAgent` (based on `AIProjectClient` with `AgentReference`) | Creates a local instance of `ChatClientAgent`. | Partial (Python uses `create_agent` from `BaseChatClient`). |
| Microsoft.Agents.AI.AzureAI (V2) | `GetAIAgent`/`GetAIAgentAsync` (with `Name`/`ChatClientAgentOptions`) | Fetches `AgentRecord` via HTTP, then creates a local `ChatClientAgent` instance. | No |
| Microsoft.Agents.AI.AzureAI (V2) | `CreateAIAgent`/`CreateAIAgentAsync` (based on `AIProjectClient`) | Creates a remote agent first, then wraps it into a local `ChatClientAgent` instance. | No |
| Microsoft.Agents.AI.AzureAI.Persistent (V1) | `GetAIAgent` (based on `PersistentAgentsClient` with `PersistentAgent`) | Creates a local instance of `ChatClientAgent`. | Partial (Python uses `create_agent` from `BaseChatClient`). |
| Microsoft.Agents.AI.AzureAI.Persistent (V1) | `GetAIAgent`/`GetAIAgentAsync` (with `AgentId`) | Fetches `PersistentAgent` via HTTP, then creates a local `ChatClientAgent` instance. | No |
| Microsoft.Agents.AI.AzureAI.Persistent (V1) | `CreateAIAgent`/`CreateAIAgentAsync` | Creates a remote agent first, then wraps it into a local `ChatClientAgent` instance. | No |
| Microsoft.Agents.AI.OpenAI | `GetAIAgent` (based on `AssistantClient` with `Assistant`) | Creates a local instance of `ChatClientAgent`. | Partial (Python uses `create_agent` from `BaseChatClient`). |
| Microsoft.Agents.AI.OpenAI | `GetAIAgent`/`GetAIAgentAsync` (with `AgentId`) | Fetches `Assistant` via HTTP, then creates a local `ChatClientAgent` instance. | No |
| Microsoft.Agents.AI.OpenAI | `CreateAIAgent`/`CreateAIAgentAsync` (based on `AssistantClient`) | Creates a remote agent first, then wraps it into a local `ChatClientAgent` instance. | No |
| Microsoft.Agents.AI.OpenAI | `CreateAIAgent` (based on `ChatClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`create_agent` in `BaseChatClient`). |
| Microsoft.Agents.AI.OpenAI | `CreateAIAgent` (based on `OpenAIResponseClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`create_agent` in `BaseChatClient`). |
Another difference between Python and .NET implementation is that in .NET `CreateAIAgent`/`GetAIAgent` methods are implemented as extension methods based on underlying SDK client, like `AIProjectClient` from Azure AI or `AssistantClient` from OpenAI:
```csharp
// Definition
publicstaticChatClientAgentCreateAIAgent(
thisAIProjectClientaiProjectClient,
stringname,
stringmodel,
stringinstructions,
string?description=null,
IList<AITool>?tools=null,
Func<IChatClient,IChatClient>?clientFactory=null,
IServiceProvider?services=null,
CancellationTokencancellationToken=default)
{}
// Usage
AIProjectClientaiProjectClient=new(newUri(endpoint),newAzureCliCredential());// Initialization of underlying SDK client
varnewAgent=awaitaiProjectClient.CreateAIAgentAsync(name:AgentName,model:deploymentName,instructions:AgentInstructions,tools:[tool]);// ChatClientAgent creation from underlying SDK client
// Alternative usage (same as extension method, just explicit syntax)
Python doesn't support extension methods. Currently `create_agent` method is defined on `BaseChatClient`, but this method only creates a local instance of `ChatAgent` and it can't create remote agents for providers that support it for a couple of reasons:
- It's defined as non-async.
-`BaseChatClient` implementation is stateful for providers like Azure AI or OpenAI Assistants. The implementation stores agent/assistant metadata like `AgentId` and `AgentName`, so currently it's not possible to create different instances of `ChatAgent` from a single `BaseChatClient` in case if the implementation is stateful.
## Decision Drivers
- API should be aligned between .NET and Python.
- API should be intuitive and consistent between backend providers in .NET and Python.
## Considered Options
Add missing implementations on the Python side. This should include the following:
### agent-framework-azure-ai (both V1 and V2)
- Add a `get_agent` method that accepts an underlying SDK agent instance and creates a local instance of `ChatAgent`.
- Add a `get_agent` method that accepts an agent identifier, performs an additional HTTP request to fetch agent data, and then creates a local instance of `ChatAgent`.
- Override the `create_agent` method from `BaseChatClient` to create a remote agent instance and wrap it into a local `ChatAgent`.
.NET:
```csharp
varagent1=newAIProjectClient(...).GetAIAgent(agentInstanceFromSdkType);// Creates a local ChatClientAgent instance from Azure.AI.Projects.OpenAI.AgentReference
varagent2=newAIProjectClient(...).GetAIAgent(agentName);// Fetches agent data, creates a local ChatClientAgent instance
varagent3=newAIProjectClient(...).CreateAIAgent(...);// Creates a remote agent, returns a local ChatClientAgent instance
```
### agent-framework-core (OpenAI Assistants)
- Add a `get_agent` method that accepts an underlying SDK agent instance and creates a local instance of `ChatAgent`.
- Add a `get_agent` method that accepts an agent name, performs an additional HTTP request to fetch agent data, and then creates a local instance of `ChatAgent`.
- Override the `create_agent` method from `BaseChatClient` to create a remote agent instance and wrap it into a local `ChatAgent`.
.NET:
```csharp
varagent1=newAssistantClient(...).GetAIAgent(agentInstanceFromSdkType);// Creates a local ChatClientAgent instance from OpenAI.Assistants.Assistant
varagent2=newAssistantClient(...).GetAIAgent(agentId);// Fetches agent data, creates a local ChatClientAgent instance
varagent3=newAssistantClient(...).CreateAIAgent(...);// Creates a remote agent, returns a local ChatClientAgent instance
```
### Possible Python implementations
Methods like `create_agent` and `get_agent` should be implemented separately or defined on some stateless component that will allow to create multiple agents from the same instance/place.
Possible options:
#### Option 1: Module-level functions
Implement free functions in the provider package that accept the underlying SDK client as the first argument (similar to .NET extension methods, but expressed in Python).
| Multiple implementations | One package may contain V1, V2, and other agent types. Function names like `create_agent` become ambiguous - which agent type does it create? | Each provider class is explicit: `AzureAIAgentsProvider` vs `AzureAIProjectAgentProvider` |
| Discoverability | Users must know to import specific functions from the package | IDE autocomplete on provider instance shows all available methods |
| Client reuse | SDK client must be passed to every function call: `create_agent(client, ...)`, `get_agent(client, ...)` | SDK client passed once at construction: `provider = Provider(client)` |
**Option 1 example:**
```python
from agent_framework.azure import create_agent, get_agent
agent1 = await create_agent(client, name="Agent1", ...) # Which agent type, V1 or V2?
The method names (`create_agent`, `get_agent`) do not explicitly mention "service" or "remote" because:
- In Python, the provider class name explicitly identifies the service (`AzureAIAgentsProvider`, `OpenAIAssistantProvider`), making additional qualifiers in method names redundant.
- In .NET, these are extension methods on `AIProjectClient` or `AssistantClient`, which already imply service operations.
### Provider Class Naming
| Package | Provider Class | SDK Client | Service |
Current method `create_agent` (python) / `CreateAIAgent` (.NET) can be renamed to `as_agent` (python) / `AsAIAgent` (.NET) to emphasize the conversion logic rather than creation/initialization logic and to avoid collision with `create_agent` method for remote calls.
```python
from agent_framework import ChatAgent
from agent_framework.openai import OpenAIChatClient
# Convert chat client to ChatAgent (no remote service involved)
client = OpenAIChatClient(model="gpt-4")
agent = client.as_agent(name="LocalAgent", instructions="...") # instead of create_agent
```
### Adding New Agent Types
Python:
1. Create provider class in appropriate package.
2. Implement `create_agent`, `get_agent`, `as_agent` as applicable.
.NET:
1. Create static class for extension methods.
2. Implement `CreateAIAgentAsync`, `GetAIAgentAsync`, `AsAIAgent` as applicable.
# Leveraging TypedDict and Generic Options in Python Chat Clients
## Context and Problem Statement
The Agent Framework Python SDK provides multiple chat client implementations for different providers (OpenAI, Anthropic, Azure AI, Bedrock, Ollama, etc.). Each provider has unique configuration options beyond the common parameters defined in `ChatOptions`. Currently, developers using these clients lack type safety and IDE autocompletion for provider-specific options, leading to runtime errors and a poor developer experience.
How can we provide type-safe, discoverable options for each chat client while maintaining a consistent API across all implementations?
## Decision Drivers
- **Type Safety**: Developers should get compile-time/static analysis errors when using invalid options
- **IDE Support**: Full autocompletion and inline documentation for all available options
- **Extensibility**: Users should be able to define custom options that extend provider-specific options
- **Consistency**: All chat clients should follow the same pattern for options handling
- **Provider Flexibility**: Each provider can expose its unique options without affecting the common interface
## Considered Options
- **Option 1: Status Quo - Class `ChatOptions` with `**kwargs`**
- **Option 2: TypedDict with Generic Type Parameters**
### Option 1: Status Quo - Class `ChatOptions` with `**kwargs`
The current approach uses a base `ChatOptions` Class with common parameters, and provider-specific options are passed via `**kwargs` or loosely typed dictionaries.
```python
# Current usage - no type safety for provider-specific options
response=awaitclient.get_response(
messages=messages,
temperature=0.7,
top_k=40,
random=42,# No validation
)
```
**Pros:**
- Simple implementation
- Maximum flexibility
**Cons:**
- No type checking for provider-specific options
- No IDE autocompletion for available options
- Runtime errors for typos or invalid options
- Documentation must be consulted for each provider
### Option 2: TypedDict with Generic Type Parameters (Chosen)
Each chat client is parameterized with a TypeVar bound to a provider-specific `TypedDict` that extends `ChatOptions`. This enables full type safety and IDE support.
- Users can extend options for their specific needs or advances in models
**Cons:**
- More complex implementation
- Some type: ignore comments needed for TypedDict field overrides
- Minor: Requires TypeVar with default (Python 3.13+ or typing_extensions)
> [NOTE!]
> In .NET this is already achieved through overloads on the `GetResponseAsync` method for each provider-specific options class, e.g., `AnthropicChatOptions`, `OpenAIChatOptions`, etc. So this does not apply to .NET.
### Implementation Details
1.**Base Protocol**: `ChatClientProtocol[TOptions]` is generic over options type, with default set to `ChatOptions` (the new TypedDict)
2.**Provider TypedDicts**: Each provider defines its options extending `ChatOptions`
They can even override fields with type=None to indicate they are not supported.
4.**Option Translation**: Common options are kept in place,and explicitly documented in the Options class how they are used. (e.g., `user` → `metadata.user_id`) in `_prepare_options` (for Anthropic) to preserve easy use of common options.
## Decision Outcome
Chosen option: **"Option 2: TypedDict with Generic Type Parameters"**, because it provides full type safety, excellent IDE support with autocompletion, and allows users to extend provider-specific options for their use cases. Extended this Generic to ChatAgents in order to also properly type the options used in agent construction and run methods.
See [typed_options.py](../../python/samples/getting_started/chat_client/typed_options.py) for a complete example demonstrating the usage of typed options with custom extensions.
# Simplify Python Get Response API into a single method
## Context and Problem Statement
Currently chat clients must implement two separate methods to get responses, one for streaming and one for non-streaming. This adds complexity to the client implementations and increases the maintenance burden. This was likely done because the .NET version cannot do proper typing with a single method, in Python this is possible and this for instance is also how the OpenAI python client works, this would then also make it simpler to work with the Python version because there is only one method to learn about instead of two.
## Implications of this change
### Current Architecture Overview
The current design has **two separate methods** at each layer:
These are parallel methods on the agent, so consolidating the client methods would **not break** the agent API. You could keep `agent.run()` and `agent.run_stream()` unchanged while internally calling `get_response(stream=True/False)`.
All subclasses implement both `_inner_*` methods, except:
- OpenAI Assistants Client (and similar clients, such as Foundry Agents V1) - it implements `_inner_get_response` by calling `_inner_get_streaming_response`
### Implications of Consolidation
| Aspect | Impact |
|--------|--------|
| **Type Safety** | Overloads work well: `@overload` with `Literal[True]` → `AsyncIterable`, `Literal[False]` → `ChatResponse`. Runtime return type based on `stream` param. |
| **Breaking Change** | **Major breaking change** for anyone implementing custom chat clients. They'd need to update from 2 methods to 1 (or 2 inner methods to 1). |
| **Decorator Complexity** | All 3 decorator systems (function invocation, middleware, observability) would need refactoring to handle both paths in one wrapper. |
| **Code Reduction** | Significant reduction in _tools.py (~200 lines of near-duplicate code) and other decorators. |
| **Samples/Tests** | Many samples call `get_streaming_response()` directly - would need updates. |
| **Protocol Simplification** | `ChatClientProtocol` goes from 2 methods + 1 property to 1 method + 1 property. |
### Recommendation
The consolidation makes sense architecturally, but consider:
1.**The overload pattern with `stream: bool`** works well in Python typing:
2. **The decorator complexity** is the biggest concern. The current approach of separate decorators for separate methods is cleaner than conditional logic inside one wrapper.
## Decision Drivers
- Reduce code needed to implement a Chat Client, simplify the public API for chat clients
- Reduce code duplication in decorators and middleware
- Maintain type safety and clarity in method signatures
## Considered Options
1. Status quo: Keep separate methods for streaming and non-streaming
2. Consolidate into a single `get_response` method with a `stream` parameter
3. Option 2 plus merging `agent.run` and `agent.run_stream` into a single method with a `stream` parameter as well
## Option 1: Status Quo
- Good: Clear separation of streaming vs non-streaming logic
- Good: Aligned with .NET design, although it is already `run` for Python and `RunAsync` for .NET
- Bad: Code duplication in decorators and middleware
- Bad: More complex client implementations
## Option 2: Consolidate into Single Method
- Good: Simplified public API for chat clients
- Good: Reduced code duplication in decorators
- Good: Smaller API footprint for users to get familiar with
- Good: People using OpenAI directly already expect this pattern
- Bad: Increased complexity in decorators and middleware
- Bad: Less alignment with .NET design (`get_response(stream=True)` vs `GetStreamingResponseAsync`)
## Option 3: Consolidate + Merge Agent and Workflow Methods
- Good: Further simplifies agent and workflow implementation
- Good: Single method for all chat interactions
- Good: Smaller API footprint for users to get familiar with
- Good: People using OpenAI directly already expect this pattern
- Good: Workflows internally already use a single method (_run_workflow_with_tracing), so would eliminate public API duplication as well, with hardly any code changes
- Bad: More breaking changes for agent users
- Bad: Increased complexity in agent implementation
- Bad: More extensive misalignment with .NET design (`run(stream=True)` vs `RunStreamingAsync` in addition to `get_response` change)
## Misc
Smaller questions to consider:
- Should default be `stream=False` or `stream=True`? (Current is False)
- Default to `False` makes it simpler for new users, as non-streaming is easier to handle.
- Default to `False` aligns with existing behavior.
- Streaming tends to be faster, so defaulting to `True` could improve performance for common use cases.
- Should this differ between ChatClient, Agent and Workflows? (e.g., Agent and Workflow defaults to streaming, ChatClient to non-streaming)
The durable agents automatically maintain conversation history and state for each session. Without automatic cleanup, this state can accumulate indefinitely, consuming storage resources and increasing costs. The Time-To-Live (TTL) feature provides automatic cleanup of idle agent sessions, ensuring that sessions are automatically deleted after a period of inactivity.
## What is TTL?
Time-To-Live (TTL) is a configurable duration that determines how long an agent session state will be retained after its last interaction. When an agent session is idle (no messages sent to it) for longer than the TTL period, the session state is automatically deleted. Each new interaction with an agent resets the TTL timer, extending the session's lifetime.
## Benefits
- **Automatic cleanup**: No manual intervention required to clean up idle agent sessions
- **Cost optimization**: Reduces storage costs by automatically removing unused session state
- **Resource management**: Prevents unbounded growth of agent session state in storage
- **Configurable**: Set TTL globally or per-agent type to match your application's needs
## Configuration
TTL can be configured at two levels:
1.**Global default TTL**: Applies to all agent sessions unless overridden
2.**Per-agent type TTL**: Overrides the global default for specific agent types
Additionally, you can configure a **minimum deletion delay** that controls how frequently deletion operations are scheduled. The default value is 5 minutes, and the maximum allowed value is also 5 minutes.
> [!NOTE]
> Reducing the minimum deletion delay below 5 minutes can be useful for testing or for ensuring rapid cleanup of short-lived agent sessions. However, this can also increase the load on the system and should be used with caution.
### Default values
- **Default TTL**: 14 days
- **Minimum TTL deletion delay**: 5 minutes (maximum allowed value, subject to change in future releases)
### Configuration examples
#### .NET
```csharp
// Configure global default TTL and minimum signal delay
services.ConfigureDurableAgents(
options=>
{
// Set global default TTL to 7 days
options.DefaultTimeToLive=TimeSpan.FromDays(7);
// Add agents (will use global default TTL)
options.AddAIAgent(myAgent);
});
// Configure per-agent TTL
services.ConfigureDurableAgents(
options=>
{
options.DefaultTimeToLive=TimeSpan.FromDays(14);// Global default
The following sections describe how TTL works in detail.
### Expiration tracking
Each agent session maintains an expiration timestamp in its internally managed state that is updated whenever the session processes a message:
1. When a message is sent to an agent session, the expiration time is set to `current time + TTL`
2. The runtime schedules a delete operation for the expiration time (subject to minimum delay constraints)
3. When the delete operation runs, if the current time is past the expiration time, the session state is deleted. Otherwise, the delete operation is rescheduled for the next expiration time.
### State deletion
When an agent session expires, its entire state is deleted, including:
- Conversation history
- Any custom state data
- Expiration timestamps
After deletion, if a message is sent to the same agent session, a new session is created with a fresh conversation history.
## Behavior examples
The following examples illustrate how TTL works in different scenarios.
### Example 1: Agent session expires after TTL
1. Agent configured with 30-day TTL
2. User sends message at Day 0 → agent session created, expiration set to Day 30
3. No further messages sent
4. At Day 30 → Agent session is deleted
5. User sends message at Day 31 → New agent session created with fresh conversation history
### Example 2: TTL reset on interaction
1. Agent configured with 30-day TTL
2. User sends message at Day 0 → agent session created, expiration set to Day 30
3. User sends message at Day 15 → Expiration reset to Day 45
4. User sends message at Day 40 → Expiration reset to Day 70
5. Agent session remains active as long as there are regular interactions
## Logging
The TTL feature includes comprehensive logging to track state changes:
- **Expiration time updated**: Logged when TTL expiration time is set or updated
- **Deletion scheduled**: Logged when a deletion check signal is scheduled
- **Deletion check**: Logged when a deletion check operation runs
- **Session expired**: Logged when an agent session is deleted due to expiration
- **TTL rescheduled**: Logged when a deletion signal is rescheduled
These logs help monitor TTL behavior and troubleshoot any issues.
## Best practices
1.**Choose appropriate TTL values**: Balance between storage costs and user experience. Too short TTLs may delete active sessions, while too long TTLs may accumulate unnecessary state.
2.**Use per-agent TTLs**: Different agents may have different usage patterns. Configure TTLs per-agent based on expected session lifetimes.
3.**Monitor expiration logs**: Review logs to understand TTL behavior and adjust configuration as needed.
4.**Test with short TTLs**: During development, use short TTLs (e.g., minutes) to verify TTL behavior without waiting for long periods.
## Limitations
- TTL is based on wall-clock time, not activity time. The expiration timer starts from the last message timestamp.
- Deletion checks are durably scheduled operations and may have slight delays depending on system load.
- Once an agent session is deleted, its conversation history cannot be recovered.
- TTL deletion requires at least one worker to be available to process the deletion operation message.
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