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>
---------
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* 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>
---------
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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>
---------
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* 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
...
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* .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
...
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* .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
...
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* .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>
---------
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* .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)
---
updated-dependencies:
- dependency-name: vite
dependency-version: 7.1.12
dependency-type: direct:development
...
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* Bump Roslynator.Analyzers from 4.14.0 to 4.14.1 (#1857)
---
updated-dependencies:
- dependency-name: Roslynator.Analyzers
dependency-version: 4.14.1
dependency-type: direct:production
update-type: version-update:semver-patch
...
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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)
---
updated-dependencies:
- dependency-name: MishaKav/pytest-coverage-comment
dependency-version: 1.1.59
dependency-type: direct:production
update-type: version-update:semver-patch
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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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Co-authored-by: Peter Ibekwe <109177538+peibekwe@users.noreply.github.com>
Co-authored-by: Jeff Handley <jeffhandley@users.noreply.github.com>
Co-authored-by: Daniel Roth <daroth@microsoft.com>
Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>
Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
Co-authored-by: Shawn Henry <sphenry@gmail.com>
Co-authored-by: Javier Calvarro Nelson <jacalvar@microsoft.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
Co-authored-by: Korolev Dmitry <deagle.gross@gmail.com>
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Reuben Bond <reuben.bond@gmail.com>
Co-authored-by: Tao Chen <taochen@microsoft.com>
Co-authored-by: wuweng <wuweng@microsoft.com>
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com>
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Jacob Alber <jaalber@microsoft.com>
Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com>
Co-authored-by: Daniel Cazzulino <daniel@cazzulino.com>
Co-authored-by: kzu <169707+kzu@users.noreply.github.com>
* move catalog samples to the HostedAgents folder
* move the catalog samples' projects to the HostedAgents folder
* move deep research sample out of the HostedAgents folder
* 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.
* 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: 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>
* 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>
* 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 ChatHistoryMemoryProvider with unit tests
* Set new project to not packable.
* Fix bugs
* Add serialization support.
* Update dotnet/src/Microsoft.Agents.AI.VectorDataMemory/ChatHistoryMemoryProvider.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Remove unnecessary line
* Convert ChatHistoryMemoryProvider to use Dynamic collections.
* Sealing options and scope classes.
* Add sample, add scope to logs and improve scope validation
* Move ChatHistoryMemoryProvider to MAAI project.
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Fix the ordering of chained resolvers in JsonSerializerOptions
We want the resolvers from AIJsonUtilities to be used before the ones from the source generator, in case the source generator emits its own copy in that assembly for the M.E.AI types.
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update dotnet/src/Microsoft.Agents.AI/AgentJsonUtilities.cs
* Update dotnet/src/Microsoft.Agents.AI.Mem0/Mem0JsonUtilities.cs
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* Remove unused using directive in Mem0JsonUtilities
Removed unused using directive for Microsoft.Extensions.AI.
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* fix: Workflow Validation
* adds orphan validation to workflow builder
* adds tests for workflow validation
* expands on the underlying reasoning why type validation is not supported
* fixup: CodeGen template
* [BREAKING] refactor: Normalize WorkflowBuilder APIs
* "partitioner" => "assigner"
* normalize ordering so sources always to the left of targets for edges
* normalize parameter ordering so sources and targets are always first arguments
* remove `params` (users should use collection expressions instead)
* refactor: Align name with Python
In #1551 we added a mechanism to open a Streaming workflow run without providing any input. This caused unintuitive behaviour when passing a string as input without providing a runId, resulting in the input being misinterpreted as the runId and the workflow not executing.
As well, the name caused confusion about why the Workflow was not running when using the input-less StreamAsync (since the Workflow cannot run without any messages to drive its execution).
* Removed automatic agent cleanup in AzureAIAgentClient
* Revert "Removed automatic agent cleanup in AzureAIAgentClient"
This reverts commit 89846c7212.
* Exposed boolean flag to control deletion behavior
* Update sample
* Initial plan
* Infrastructure setup
* Plan for minimal client
* Plan update
* Basic agentic chat
* cleanup
* Cleanups
* More cleanups
* Cleanups
* More cleanups
* Test plan
* Sample
* Fix streaming and error handling
* Fix notifications
* Cleanups
* cleanup sample
* Additional tests
* Additional tests
* Run dotnet format
* Remove unnecessary files
* Mark packages as non packable
* Fix build
* Address feedback
* Fix build
* Fix remaining warnings
* Feedback
* Feedback and cleanup
* Cleanup
* Cleanups
* Cleanups
* Cleanups
* Retrieve existing messages from the store to send them along the way and update the sample client
* Run dotnet format
* Add ADR for AG-UI
* Switch to use the SG and use a convention for run ids
* Cleanup MapAGUI API
* Fix formatting
* Fix solution
* Fix solution
* feat: Add ChatKit integration with a new frontend application
- Created a new frontend application using React and Vite for the ChatKit integration.
- Added essential files including package.json, vite.config.ts, and Tailwind CSS configuration.
- Implemented core components: App, Home, ChatKitPanel, ThemeToggle, and hooks for color scheme management.
- Established SQLite-based store implementation for ChatKit data persistence in store.py.
- Integrated theme toggling functionality for light and dark modes.
- Set up ESLint and TypeScript configurations for better development experience.
* git ignore
* fix mypy
* add mising file
* minimal frontend for chatkit sample
* update ignore files
* version
* set python version lowerbound on chatkit
* update project settings for chatkit
* update setup
* update setup
* update setup
* update setup
* weather widget
* add select city widget sample
* remove widget helper
* update chatkit to include file attachments and cover more thread item types
* update readme with mermaid diagram
* update diagram
* update instructions
* update chatkit dependency
* fix converter imports
* move to demos/
* move to demos/ -- rename references
* support multiple session instead of using global variable in sample
* support chunk streaming
* fix tests
* Update python/samples/demos/chatkit-integration/store.py
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* use local host
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Adding Sample for writer-critic workflow implemented using Worfklow, custom executors, agents, switch, custom states, different entry points for the executors.
* Update dotnet/samples/GettingStarted/Workflows/_Foundational/08_WriterCriticWorkflow/Program.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update dotnet/samples/GettingStarted/Workflows/_Foundational/08_WriterCriticWorkflow/Program.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* using now structured output, with streaming for UX responsiveness.
* improved comments and order, so comments directly precede what they're describing
* fixing issue with internal class that the analyzer doesn't recognize that CriticDecision is instantiated, just indirectly via JSON deserialization
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
* Port store for adding text to a vector store to AF
* Fix typo.
* Change TextSearchStore to sample, and add sample to use it and do rag with a custom schema
* Add more tests and fix broken ones
* Fix merge issue
* Fix sample after merge.
* Convert TextSearchStore to use Dynamic mode to be AOT compatible.
* Add some more clarification on when to use assistant messages in rag searches.
* Adding sample demonstrating hosted MCP with Responses
* Add mcp readme.md to slnx
* Update FoundryAgent sample to use MCP types from abstraction and to show how to do approval
* Fix param name after package update.
* Fix environment variable name for consistency
* Apply suggestion from @Copilot
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Add workflow as an agent with observability sample
* Address comment
* Fix formatting
* enable sensitive data
* enable sensitive data for sub agents
* adjust aggregator handlers
* initial version of anthropic connector
* updated implementation and added tests
* fix type and readme
* mypy fix and int tests enabled
* add integration test setup
* updated based on comments
* improved function result handling
* added extra unordered test
* updated from review
* fix tool choice handling
* same fix for chat client
* refactor: Unify ExecutorIsh and ExecutorRegistration => ExecutorBinding
* Switch to more modern Record type-tree for Sum Types
* Unify APIs for getting ExecutorBinding
* Fix an issue where workflows consisting entirely of cross-run shareable executors which are not instance-resettable do not properly clear state when running non-concurrently.
* feat: Simplify function-to-executor pattern
* refactor: Normalize API naming
* Propagate cancellation token down the stack
* Added unit tests to cover workflow cancellation scenarios
* Updated tests based on feedback to simplify assert.
* Create custom AsyncEnumrable to gracefully handle cancellation for Channel reader. Tailor cancellation tests to declarative scenarios.
* Update comment and naming for readability.
* Fixing minor stylistic recommendation.
---------
Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
* ensure agent thread is part of checkpoint
* Update python/packages/core/agent_framework/_workflows/_agent_executor.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* remove data copying for server side thread.
* refine warning check
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Fix issue where AIContextProvider messages were not added to MessageStores
* Fix typos
* Update XML docs to reduce ambiguity.
* Update AIContext XML docs
* Fix merge issue
* Prototype: Add request_info API and @response_handler
* Add original_request as a parameter to the response handler
* Prototype: request interception in sub workflows
* Prototype: request interception in sub workflows 2
* WIP: Make checkpointing work
* checkpointing with sub workflow
* Fix function executor
* Allow sub-workflow to output directly
* Remove ReqeustInfoExecutor and related classes; Debugging checkpoint_with_human_in_the_loop
* Fix Handoff and sample
* fix pending requests in checkpoint
* Fix unit tests
* Fix formatting
* Resolve comments
* Address comment
* Add checkpoint tests
* Add tests
* misc
* fix mypy
* fix mypy
* Use request type as part of the key
* Log warning if there is not response handler for a request
* Update Internal edge group comments
* REcord message type in executor processing span
* Update sample
* Improve tests
* Add Rag AIContext Provider
* Fix issues
* Improve options naming based on PR feedback.
* Move Rag Provider to Data namespace
* Add Raw Representation to RagSearchResult
* Renaming RagProvider to TextSearchProvider
* Porting Mem0Provider to AF from SK
* Switch integration tests to manual
* Address issues
* Move Mem0Provider to separate project.
* Move integration tests to new project
* Address PR comments.
* Intro group chat and refactor magentic. Fix as_agent()
* Cleanup and improvements
* Add as_agent docstring clarification
* Standardize orchestration messages to use agent-style inputs.
* Simplify group chat constructs
* Further cleanup
* Add sk to af group chat migration sample. Update README.
* Improvements and simplifications
* consolidating shared orchestration logic
* Further clean up
* Add group chat sample
* Improve typing
* Fix test imports
* Fix readme links
* Cleanup per PR Feedback
Concurrent run support was recently added to workflows, but Orchestrations did not fully update to support it. A few executors were missing Cross-Run Shareable annotations, and the ConcurrentEnd executor needed to be factory-instantiated.
This also ports the fix for #1613 from #1637, to avoid waiting on that PR.
Checkpointing is used by the WorkflowHostAgent to be able to support resume from a provided thread. When a CheckpointManager is not specified, we use the InMemoryCheckpointManager and serialize its state into the thread's Serialize()ed JsonElement.
At some point InMemoryCheckpointManager became not serializable, breaking this behaviour. This change restores serializability, and adds a test.
* Improve conformance of OpenAI Responses API serving
* Update dotnet/src/Microsoft.Agents.AI.Hosting.OpenAI/Responses/AgentRunResponseExtensions.cs
Co-authored-by: Stephen Toub <stoub@microsoft.com>
* Update dotnet/src/Microsoft.Agents.AI.Hosting.OpenAI/Responses/AgentRunResponseExtensions.cs
Co-authored-by: Stephen Toub <stoub@microsoft.com>
* Sort packages
* Relax adherence where acceptable
* nit
* PromptCacheKey is not obsolete
* format
---------
Co-authored-by: Stephen Toub <stoub@microsoft.com>
* Add Handoff orchestration pattern support
* PR feedback
* Use AOAI client in samples
* Adjust to tool
* Handoff to sub-agent via ai function
* PR feedback
* More cleanup
* Improvements
* PR feedback cleanup
* Add handoff migration sample.
* Remove type ignore
* fix markdown link formatting
* Remove readme link for non-existent sample
* use extension methods from A2A package for converting between MEAI and A2A model classes.
* Update dotnet/tests/Microsoft.Agents.AI.A2A.UnitTests/A2AAgentTests.cs
Co-authored-by: Stephen Toub <stoub@microsoft.com>
* remove unused using
---------
Co-authored-by: Stephen Toub <stoub@microsoft.com>
* Fix handoff function naming
We don't need the agent's name or a guid in the handoff name... we can just use simple numbering. There's a possibility that someone built an agent with a built-in function tool named "handoff_to_x"; if that turns out to be an issue, we could add back some longer bit of randomness.
* Update dotnet/src/Microsoft.Agents.AI.Workflows/Specialized/HandoffAgentExecutor.cs
---------
Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
Remove input type checking in favour of explicit `.DescribeProtocolAsync()` flow. Also removes `.AsAgentAsync()` as the validation happens at workflow run time. This makes it easier to use Workflows with DI without resorting to async-over-sync.
* Add support for getting and creating Assistant and Foundry agents with ChatClientAgentOptions
* Fix options cloning and agent creation
* Fix inconsistency
* Add support for mapping more tools and integration tests for ensuring CreateAIAgent works with those tools.
* Add support for additional openai tools with tests.
* Remove special casing for function tools, since it's either not supported yet, or requires a lot of code duplication.
* Removed unused using.
* Fix broken unit tests
* Change integration test to reduce flakiness.
* Add copilot instructions for c#
* Add some further improvements based on copilot suggestions.
* Update .github/copilot-instructions.md
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update C# sample code guidelines
Added a comment guideline for sample code in C#.
* Fix casing of Program.cs in instructions
* Update guidelines for coding standards and testing
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* unit test for using create agent option by constructor
* remove this for prevent duplicate when ChatClientAgentOption and ChatOption has same Instruction
* update unit test for ChatClientAgentOptions
* ensure function aproval is parsed correctly
* udpate ui, add deployment guide button, other debug panel fixes
* feat(devui): Implement lazy loading architecture with enhanced security and state management
Major architectural improvements to DevUI for better performance, security, and developer experience:
Performance & Architecture:
- Implement lazy loading for entity discovery - entities loaded on-demand instead of at startup
- Add hot reload capability for development workflow via new reload endpoint
- Reduce startup time and memory footprint by deferring module imports
Security Enhancements:
- Remove remote entity loading capabilities (POST /v1/entities/add, DELETE endpoints)
- DevUI now strictly local development tool - no remote code execution
- Add explicit security documentation and best practices in README
Frontend Improvements:
- Migrate to Zustand for centralized state management (replacing prop drilling)
- Add lightweight zero-dependency markdown renderer with code block copy support
- Improve gallery UX with setup instructions modal instead of direct URL loading
- Enhanced message UI with copy functionality and better token usage display
Testing & Quality:
- Expand test coverage for lazy loading, type detection, and cache invalidation
- Add comprehensive tests for new behaviors (+231 lines of test code)
- Improve type safety and documentation throughout
Breaking Changes:
- Remote entity loading via URLs is no longer supported
- Entities must be loaded from local filesystem only
* update ui issues, uupdate test descripion
* refactor: remove unused internals
* feat: Execution Mode for sharing a workflow among concurrent runs
* feat: Update WorkflowHostAgent to support concurrent execution
* Also update AsAgent APIs to support injecting a CheckpointManager and an IWorkflowExecutionEnvironment
* fix: Make Read logic consistent in DeclarativeWorkflowContext
WorkflowHostAgent was initially implemented before Checkpointing was available. This meant that in order to support resuming, the WorkflowHostAgent needed to keep the runs around, which broke it when stricter rules about concurrent sharing of workflows during execution were introduced.
This change updates the hosting logic to release the underlying StreamingRun when the RunStreamingAsync or RunAsync are invoked, in favour of keeping the checkpointing information in the WorkflowThread to enable resumption.
* Python: Fix AI Search Tool Sample and improve AI Search Exceptions
* Python: Fix AI Search Tool Test
---------
Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
* Python: set role=tool when processing approval responses
* Python: set role=tool when processing approval responses
* Fix approval mode with OpenAIChatClient and threads: add approval requests to assistant message, fix deduplication/rejection call_id, filter approval content, add tests and example
* update test tools after change
* Rename _collect_approval_todos to _collect_approval_responses and filter empty call_ids
* sanitize agent name
* simplify
* Update dotnet/src/Microsoft.Agents.AI/AgentExtensions.cs
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update dotnet/src/Microsoft.Agents.AI/AgentExtensions.cs
Co-authored-by: Stephen Toub <stoub@microsoft.com>
* Update dotnet/src/Microsoft.Agents.AI/AgentExtensions.cs
Co-authored-by: Stephen Toub <stoub@microsoft.com>
* Update dotnet/tests/Microsoft.Agents.AI.UnitTests/AgentExtensionsTests.cs
Co-authored-by: Stephen Toub <stoub@microsoft.com>
* change regex to flag underscores as well so their sequence can be replaced with a single one.
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Stephen Toub <stoub@microsoft.com>
- Added ChatMessage[] handler to ConfigureRoutes to support array dispatch
- Workflow runtime can dispatch messages as either List<ChatMessage> or ChatMessage[]
- Added 10 comprehensive unit tests validating message routing behavior
- Tests ensure functionality and protect against future refactoring (issue #782)
- Updated code comments to reflect exact-type-matching requirement
Fixes issue where raw WorkflowBuilder with AIAgent nodes failed to receive
initial messages due to missing array type handler.
* add workflow edge data properties to the workflow.definition tag.
* use workflow info classes for workflow.definition tag value
* add unit test
* fix test
* fix formatting issue
* remove flaky unit test
* remove unused package dependency
* Python: DevUI - Internal Refactor, Conversations API support, and performance improvements
Comprehensive refactor of DevUI package including samples relocation,
frontend reorganization, OpenAI Conversations API support, and critical
performance and code quality improvements.
Key Changes:
Architecture & Organization
- Moved DevUI samples to python/samples/getting_started/devui/
- Consolidated with other framework samples for better discoverability
- Added .env.example files and comprehensive README
- Restructured frontend components into feature-based folders (agent, workflow, gallery, layout)
- Created new OpenAI-compliant message renderers (devui should render oai responses types primarily)
New Features
- Added _conversations.py (467 lines) - Full conversation storage abstraction, replaces the /threads endpoint to better match oai conversations api
- Implements OpenAI Conversations API for thread management, Supports in-memory and extensible storage backends
API Simplification
- Use 'model' field as entity_id (agent/workflow name) instead of extra_body
- Use standard OpenAI 'conversation' field for conversation context.
Performance & Quality Improvements
- Improved context management in MessageMapper with bounded memory (~500KB max)
- Implemented hybrid LRU + cleanup approach to prevent unbounded memory growth
- General QOL improvement - Eliminated ~150 lines of dead/duplicate code, Consolidated helper functions into _utils.py, Extracted magic numbers to module-level constants, Optimized conversation item lookups with index-based approach
Testing
- Added test_conversations.py (13 tests)
- Added test_performance_fixes.py (9 tests)
- Updated existing tests for code consolidation
- 53 tests passing
Impact: 76 files changed: +4,106 insertions, -2,373 deletions
All linting and formatting checks passing. No breaking changes - backward compatible.
Migration: Samples moved to python/samples/getting_started/devui/
* readme lint fixes
* initial support for function approval and minor ui fixes
* Update readme.md sample to show sample parameter values and make azure sdk pre-release
* Add clarifying comment about params.
* Switch sample to use the OpenAI SDK
All python code resides under the `python/` directory.
All C# code resides under the `dotnet/` directory.
The purpose of the code is to provide a framework for building AI agents.
When contributing to this repository, please follow these guidelines:
## C# Code Guidelines
Here are some general guidelines that apply to all code.
- The top of all *.cs files should have a copyright notice: `// Copyright (c) Microsoft. All rights reserved.`
- All public methods and classes should have XML documentation comments.
### C# Sample Code Guidelines
Sample code is located in the `dotnet/samples` directory.
When adding a new sample, follow these steps:
- The sample should be a standalone .net project in one of the subdirectories of the samples directory.
- The directory name should be the same as the project name.
- The directory should contain a README.md file that explains what the sample does and how to run it.
- The README.md file should follow the same format as other samples.
- The csproj file should match the directory name.
- The csproj file should be configured in the same way as other samples.
- The project should preferably contain a single Program.cs file that contains all the sample code.
- The sample should be added to the solution file in the samples directory.
- The sample should be tested to ensure it works as expected.
- A reference to the new samples should be added to the README.md file in the parent directory of the new sample.
The sample code should follow these guidelines:
- Configuration settings should be read from environment variables, e.g. `var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");`.
- Environment variables should use upper snake_case naming convention.
- Secrets should not be hardcoded in the code or committed to the repository.
- The code should be well-documented with comments explaining the purpose of each step.
- The code should be simple and to the point, avoiding unnecessary complexity.
- Prefer inline literals over constants for values that are not reused. For example, use `new ChatClientAgent(chatClient, instructions: "You are a helpful assistant.")` instead of defining a constant for "instructions".
- Ensure that all private classes are sealed
- Use the Async suffix on the name of all async methods that return a Task or ValueTask.
- Prefer defining variables using types rather than var, to help users understand the types involved.
- Follow the patterns in the samples in the same directories where new samples are being added.
- The structure of the sample should be as follows:
- The top of the Program.cs should have a copyright notice: `// Copyright (c) Microsoft. All rights reserved.`
- Then add a comment describing what the sample is demonstrating.
- Then add the necessary using statements.
- Then add the main code logic.
- Finally, add any helper methods or classes at the bottom of the file.
### C# Unit Test Guidelines
Unit tests are located in the `dotnet/tests` directory in projects with a `.UnitTests.csproj` suffix.
Unit tests should follow these guidelines:
- Use `this.` for accessing class members
- Add Arrange, Act and Assert comments for each test
- Ensure that all private classes, that are not subclassed, are sealed
- Use the Async suffix on the name of all async methods
- Use the Moq library for mocking objects where possible
- Validate that each test actually tests the target behavior, e.g. we should not have tests that creates a mock, calls the mock and then verifies that the mock was called, without the target code being involved. We also shouldn't have tests that test language features, e.g. something that the compiler would catch anyway.
- Avoid adding excessive comments to tests. Instead favour clear easy to understand code.
- Follow the patterns in the unit tests in the same project or classes to which new tests are being added
Below are some ways that you can get involved in the Agent Framework Community.
## Engage on GitHub
- [Discussions](https://github.com/microsoft/agent-framework/discussions): Ask questions, provide feedback and ideas to what you'd like to see from the Agent Framework.
- [Issues](https://github.com/microsoft/agent-framework/issues) - If you find a bug, unexpected behavior or have a feature request, please open an issue.
- [Pull Requests](https://github.com/microsoft/agent-framework/pulls) - We welcome contributions! Please see our [Contributing Guide](https://github.com/microsoft/agent-framework/blob/main/CONTRIBUTING.md)
We do our best to respond to each submission.
## Public Community Office Hours
We regularly have Community Office Hours that are open to the **public** to join.
Add Agent Framework events to your calendar. We are running two community calls to accommodate different time zones for Q&A Office Hours:
- **Americas & EMEA timezone:** Every Wednesday at 8:00 AM Pacific Time/17:00 CET. Adjusted for daylight savings. Join here: [AF-AG-SK-Americas-Europe-OfficeHours](https://aka.ms/sk-officehours).
- **Asia Pacific timezone:** The second Wednesday of every month at 4:00 PM Pacific Time Wednesday. In much of Asia this occurs on Thursday local time. Adjusted for daylight savings. Join here: [AF-AG-SK-APAC-OfficeHours](https://aka.ms/sk-apac-officehours).
If you are unable to make it live, all meetings will be recorded and posted online.
For help and questions about using this project, please create a GitHub issue.
AI Support team will support Microsoft Agent Framework issues for customers under a **Unified support agreement when the issue arises from usage of Azure AI services** (Foundry Models, Foundry Agents etc.) in conjunction with the SDK. Conversely, if customer has any other / non unified support agreement and/or Agent Framework SDK is used in a way **not involving an Azure service**, it is treated as a purely open-source tool – Microsoft’s support organization will not handle it, and users should use GitHub or forums for assistance
For Copilot Studio SDK implementation issues, customers should use GitHub Issues for assistance, as outlined above. Conversely, for prerequisites managed within the Copilot Studio portal, customers can rely on the standard Microsoft Copilot Studio support channels.
## Microsoft Support Policy
Support for this **PROJECT or PRODUCT** is limited to the resources listed above.
@@ -499,7 +499,7 @@ We need to decide what AIContent types, each agent response type will be mapped
| 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) |
| 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://a2aproject.github.io/A2A/v0.2.5/specification/#97-structured-data-exchange-requesting-and-providing-json) at invocation 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 |
| Protocol Activity | Supports returning [Complex types](https://github.com/microsoft/Agents/blob/main/specs/activity/protocol-activity.md#complex-types) but no support for requesting a type |
### Response Reason Support
@@ -511,5 +511,5 @@ We need to decide what AIContent types, each agent response type will be mapped
| 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. |
| 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://a2aproject.github.io/A2A/v0.2.5/specification/#64-message-object) or [task](https://a2aproject.github.io/A2A/v0.2.5/specification/#61-task-object). |
| 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). |
@@ -22,7 +22,6 @@ This document aims to provide options and capture the decision on how to model t
See various features that would need to be supported via this type of mechanism, plus how various other frameworks support this:
- Also see [dotnet issue 6492](https://github.com/dotnet/extensions/issues/6492), which discusses the need for a similar pattern in the context of MCP approvals.
- Also see [the openai RunToolApprovalItem](https://openai.github.io/openai-agents-js/openai/agents/classes/runtoolapprovalitem/).
- Also see [the openai human-in-the-loop guide](https://openai.github.io/openai-agents-js/guides/human-in-the-loop/#approval-requests).
- Also see [the openai MCP guide](https://openai.github.io/openai-agents-js/guides/mcp/#optional-approval-flow).
- Also see [MCP Approval Requests from OpenAI](https://platform.openai.com/docs/guides/tools-remote-mcp#approvals).
The .NET Agent Framework needed a standardized way to enable communication between AI agents and user-facing applications with support for streaming, real-time updates, and bidirectional communication. Without AG-UI protocol support, .NET agents could not interoperate with the growing ecosystem of AG-UI-compatible frontends and agent frameworks (LangGraph, CrewAI, Pydantic AI, etc.), limiting the framework's adoption and utility.
The AG-UI (Agent-User Interaction) protocol is an open, lightweight, event-based protocol that addresses key challenges in agentic applications including streaming support for long-running agents, event-driven architecture for nondeterministic behavior, and protocol interoperability that complements MCP (tool/context) and A2A (agent-to-agent) protocols.
## Decision Drivers
- Need for streaming communication between agents and client applications
- Requirement for protocol interoperability with other AI frameworks
- Support for long-running, multi-turn conversation sessions
- Real-time UI updates for nondeterministic agent behavior
- Standardized approach to agent-to-UI communication
- Framework abstraction to protect consumers from protocol changes
## Considered Options
1.**Implement AG-UI event types as public API surface** - Expose AG-UI event models directly to consumers
2.**Use custom AIContent types for lifecycle events** - Create new content types (RunStartedContent, RunFinishedContent, RunErrorContent)
3.**Current approach** - Internal event types with framework-native abstractions
## Decision Outcome
Chosen option: "Current approach with internal event types and framework-native abstractions", because it:
- Protects consumers from protocol changes by keeping AG-UI events internal
- Maintains framework abstractions through conversion at boundaries
- Uses existing framework types (AgentRunResponseUpdate, ChatMessage) for public API
- Text message events: `TextMessageStart`, `TextMessageContent`, `TextMessageEnd`
- Thread and run ID management via `ConversationId` and `ResponseId`
### Key Design Decisions
1.**Event Models as Internal Types** - AG-UI event types are internal with conversion via extension methods; public API uses the existing types in Microsoft.Extensions.AI as those are the abstractions people are familiar with
2.**No Custom Content Types** - Run lifecycle communicated through existing `ChatResponseUpdate` properties (`ConversationId`, `ResponseId`) and standard `ErrorContent` type
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`)
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.
6.**Custom JSON Converter** - Uses custom polymorphic deserialization via `BaseEventJsonConverter` instead of built-in System.Text.Json support to handle AG-UI protocol's flexible discriminator positioning
### Consequences
**Positive:**
- .NET developers can consume AG-UI servers from any framework
- .NET agents accessible from any AG-UI-compatible client
- Standardized streaming communication patterns
- Protected from protocol changes through internal implementation
- Symmetric conversion logic between client and server
@@ -175,7 +175,7 @@ Sub-packages are comprised of two parts, the code itself and the dependencies, t
- Subpackage naming should also follow this, so in principle a package name is `<vendor/folder>-<feature/brand>`, so `google-gemini`, `azure-purview`, `microsoft-copilotstudio`, etc. For smaller vendors, where it's less likely to have a multitude of connectors, we can skip the feature/brand part, so `mem0`, `redis`, etc.
- For Microsoft services we will have two vendor folders, `azure` and `microsoft`, where `azure` contains all Azure services, while `microsoft` contains other Microsoft services, such as Copilot Studio Agents.
This setup was discussed at length and the decision is captured in [ADR-0007](../decisions/0007-python-subpackages.md).
This setup was discussed at length and the decision is captured in [ADR-0008](../decisions/0008-python-subpackages.md).
#### Evolving the package structure
For each of the advanced components, we have two reason why we may split them into a folder, with an `__init__.py` and optionally a `_files.py`:
stringendpoint=builder.Configuration["AZURE_OPENAI_ENDPOINT"]??thrownewInvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
stringdeploymentName=builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"]??thrownewInvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
This sample demonstrates how to use the AG-UI (Agent UI) protocol to enable communication between a client application and a remote agent server. The AG-UI protocol provides a standardized way for clients to interact with AI agents.
## Overview
The demonstration has two components:
1.**AGUIServer** - An ASP.NET Core web server that hosts an AI agent and exposes it via the AG-UI protocol
2.**AGUIClient** - A console application that connects to the AG-UI server and displays streaming updates
> **Warning**
> The AG-UI protocol is still under development and changing.
> We will try to keep these samples updated as the protocol evolves.
## Configuring Environment Variables
Configure the required Azure OpenAI environment variables:
> **Note:** This sample uses `DefaultAzureCredential` for authentication. Make sure you're authenticated with Azure (e.g., via `az login`, Visual Studio, or environment variables).
## Running the Sample
### Step 1: Start the AG-UI Server
```bash
cd AGUIServer
dotnet build
dotnet run --urls "http://localhost:5100"
```
The server will start and listen on `http://localhost:5100`.
### Step 2: Testing with the REST Client (Optional)
Before running the client, you can test the server using the included `.http` file:
1. Open [./AGUIServer/AGUIServer.http](./AGUIServer/AGUIServer.http) in Visual Studio or VS Code with the REST Client extension
2. Send a test request to verify the server is working
3. Observe the server-sent events stream in the response
Sample request:
```http
POSThttp://localhost:5100/
Content-Type:application/json
{
"threadId":"thread_123",
"runId":"run_456",
"messages":[
{
"role":"user",
"content":"WhatisthecapitalofFrance?"
}
],
"context":{}
}
```
### Step 3: Run the AG-UI Client
In a new terminal window:
```bash
cd AGUIClient
dotnet run
```
Optionally, configure a different server URL:
```powershell
$env:AGUI_SERVER_URL="http://localhost:5100"
```
### Step 4: Interact with the Agent
1. The client will connect to the AG-UI server
2. Enter your message at the prompt
3. Observe the streaming updates with color-coded output:
- **Yellow**: Run started notification showing thread and run IDs
- **Cyan**: Agent's text response (streamed character by character)
- **Green**: Run finished notification
- **Red**: Error messages (if any occur)
4. Type `:q` or `quit` to exit
## Sample Output
```
AGUIClient> dotnet run
info: AGUIClient[0]
Connecting to AG-UI server at: http://localhost:5100
User (:q or quit to exit): What is the capital of France?
[Run Started - Thread: thread_abc123, Run: run_xyz789]
The capital of France is Paris. It is known for its rich history, culture, and iconic landmarks such as the Eiffel Tower and the Louvre Museum.
User (:q or quit to exit): Tell me a fun fact about space
[Run Started - Thread: thread_abc123, Run: run_def456]
Here's a fun fact: A day on Venus is longer than its year! Venus takes about 243 Earth days to rotate once on its axis, but only about 225 Earth days to orbit the Sun.
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that hosts a single AI agent and provides direct HTTP API access for interactive conversations.
## Key Concepts Demonstrated
- Using the Microsoft Agent Framework to define a simple AI agent with a name and instructions.
- Registering agents with the Function app and running them using HTTP.
- Conversation management (via session IDs) for isolated interactions.
## Environment Setup
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
## Running the Sample
With the environment setup and function app running, you can test the sample by sending an HTTP request to the agent endpoint.
You can use the `demo.http` file to send a message to the agent, or a command line tool like `curl` as shown below:
Bash (Linux/macOS/WSL):
```bash
curl -X POST http://localhost:7071/api/agents/Joker/run \
-H "Content-Type: text/plain"\
-d "Tell me a joke about a pirate."
```
PowerShell:
```powershell
Invoke-RestMethod-MethodPost`
-Urihttp://localhost:7071/api/agents/Joker/run`
-ContentTypetext/plain`
-Body"Tell me a joke about a pirate."
```
You can also send JSON requests:
```bash
curl -X POST http://localhost:7071/api/agents/Joker/run \
-H "Content-Type: application/json"\
-H "Accept: application/json"\
-d '{"message": "Tell me a joke about a pirate."}'
```
To continue a conversation, include the `thread_id` in the query string or JSON body:
```bash
curl -X POST "http://localhost:7071/api/agents/Joker/run?thread_id=your-thread-id"\
-H "Content-Type: application/json"\
-H "Accept: application/json"\
-d '{"message": "Tell me another one."}'
```
The response from the agent will be displayed in the terminal where you ran `func start`. The expected `text/plain` output will look something like:
```text
Why don't pirates ever learn the alphabet? Because they always get stuck at "C"!
```
The expected `application/json` output will look something like:
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that orchestrates sequential calls to a single AI agent using the same conversation thread for context continuity.
## Key Concepts Demonstrated
- Orchestrating multiple interactions with the same agent in a deterministic order
- Using the same `AgentThread` across multiple calls to maintain conversational context
- Durable orchestration with automatic checkpointing and resumption from failures
- HTTP API integration for starting and monitoring orchestrations
## Environment Setup
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
## Running the Sample
With the environment setup and function app running, you can test the sample by sending an HTTP request to start the orchestration.
You can use the `demo.http` file to start the orchestration, or a command line tool like `curl` as shown below:
Bash (Linux/macOS/WSL):
```bash
curl -X POST http://localhost:7071/api/singleagent/run
The orchestration will proceed to run the WriterAgent twice in sequence:
1. First, it writes an inspirational sentence about learning
2. Then, it refines the initial output using the same conversation thread
Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response will be a JSON object that looks something like the following:
```json
{
"failureDetails":null,
"input":null,
"instanceId":"86313f1d45fb42eeb50b1852626bf3ff",
"output":"Learning serves as the key, opening doors to boundless opportunities and a brighter future.",
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create an Azure Functions app that orchestrates concurrent execution of multiple AI agents, each with specialized expertise, to provide comprehensive answers to complex questions.
## Key Concepts Demonstrated
- Multi-agent orchestration with specialized AI agents (physics and chemistry)
- Concurrent execution using the fan-out/fan-in pattern for improved performance and distributed processing
- Response aggregation from multiple agents into a unified result
- Durable orchestration with automatic checkpointing and resumption from failures
## Environment Setup
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
## Running the Sample
With the environment setup and function app running, you can test the sample by sending an HTTP request with a custom prompt to the orchestration.
You can use the `demo.http` file to send a message to the agents, or a command line tool like `curl` as shown below:
Bash (Linux/macOS/WSL):
```bash
curl -X POST http://localhost:7071/api/multiagent/run \
-H "Content-Type: text/plain"\
-d "What is temperature?"
```
PowerShell:
```powershell
Invoke-RestMethod-MethodPost`
-Urihttp://localhost:7071/api/multiagent/run`
-ContentTypetext/plain`
-Body"What is temperature?"
```
The response will be a JSON object that looks something like the following, which indicates that the orchestration has started.
The orchestration will run both the PhysicistAgent and ChemistAgent concurrently, asking them the same question. Their responses will be combined to provide a comprehensive answer covering both physical and chemical aspects.
Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response will be a JSON object that looks something like the following:
```json
{
"failureDetails":null,
"input":"What is temperature?",
"instanceId":"e7e29999b6b8424682b3539292afc9ed",
"output":{
"physicist":"Temperature is a measure of the average kinetic energy of particles in a system. From a physics perspective, it represents the thermal energy and determines the direction of heat flow between objects.",
"chemist":"From a chemistry perspective, temperature is crucial for chemical reactions as it affects reaction rates through the Arrhenius equation. It influences the equilibrium position of reversible reactions and determines the physical state of substances."
# Multi-Agent Orchestration with Conditionals Sample
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a multi-agent orchestration workflow that includes conditional logic. The workflow implements a spam detection system that processes emails and takes different actions based on whether the email is identified as spam or legitimate.
## Key Concepts Demonstrated
- Multi-agent orchestration with conditional logic and different processing paths
- Spam detection using AI agent analysis
- Structured output from agents for reliable processing
- Activity functions for integrating non-agentic workflow actions
## Environment Setup
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
## Running the Sample
With the environment setup and function app running, you can test the sample by sending an HTTP request with email data to the orchestration.
You can use the `demo.http` file to send email data to the agents, or a command line tool like `curl` as shown below:
Bash (Linux/macOS/WSL):
```bash
# Test with a legitimate email
curl -X POST http://localhost:7071/api/spamdetection/run \
-H "Content-Type: application/json"\
-d '{
"email_id": "email-001",
"email_content": "Hi John, I hope you are doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!"
}'
# Test with a spam email
curl -X POST http://localhost:7071/api/spamdetection/run \
-H "Content-Type: application/json"\
-d '{
"email_id": "email-002",
"email_content": "URGENT! You have won $1,000,000! Click here now to claim your prize! Limited time offer! Do not miss out!"
}'
```
PowerShell:
```powershell
# Test with a legitimate email
$body=@{
email_id="email-001"
email_content="Hi John, I hope you are doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!"
}|ConvertTo-Json
Invoke-RestMethod-MethodPost`
-Urihttp://localhost:7071/api/spamdetection/run`
-ContentTypeapplication/json`
-Body$body
# Test with a spam email
$body=@{
email_id="email-002"
email_content="URGENT! You have won $1,000,000! Click here now to claim your prize! Limited time offer! Do not miss out!"
}|ConvertTo-Json
Invoke-RestMethod-MethodPost`
-Urihttp://localhost:7071/api/spamdetection/run`
-ContentTypeapplication/json`
-Body$body
```
The response from either input will be a JSON object that looks something like the following, which indicates that the orchestration has started.
1. Analyze the email content using the SpamDetectionAgent
2. If spam: Mark the email as spam with a reason
3. If legitimate: Use the EmailAssistantAgent to draft a professional response and "send" it
Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response for the legitimate email will be a JSON object that looks something like the following:
```json
{
"failureDetails":null,
"input":{
"email_content":"Hi John, I hope you're doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!",
"email_id":"email-001"
},
"instanceId":"555dbbb63f75406db2edf9f1f092de95",
"output":"Email sent: Subject: Re: Follow-Up on Quarterly Report\n\nHi [Recipient's Name],\n\nI hope this message finds you well. Thank you for your patience. I will ensure the updated figures for the quarterly report are sent to you by Friday.\n\nIf you have any further questions or need additional information, please feel free to reach out.\n\nBest regards,\n\nJohn",
"runtimeStatus":"Completed"
}
```
The response for the spam email will be a JSON object that looks something like the following, which indicates that the email was marked as spam:
```json
{
"failureDetails":null,
"input":{
"email_content":"URGENT! You have won $1,000,000! Click here now to claim your prize! Limited time offer! Do not miss out!",
"email_id":"email-002"
},
"instanceId":"555dbbb63f75406db2edf9f1f092de95",
"output":"Email marked as spam: The email contains misleading claims of winning a large sum of money and encourages immediate action, which are common characteristics of spam.",
"runtimeStatus":"Completed"
}
```
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