* 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>
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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.*
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* Remove unrelated changes to package-lock.json and yarn.lock
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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
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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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* 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.
* AgentFactory abstractions and ChatClient implementation
* Add a getitng started sample
* Update to latest M.B.OM
* Add some additional samples
* Work in progress
* Merge latest from main
* Start to add support for using different kinds of connections
* Remove IsSupported
* Remove IsSupported
* Refactor code to create clients to support DI
* Add some unit tests
* Update based on the latest code review feedback
* Add support for OOB tools when using persistent agent sdk
* Fix sample naming
* Fix error based on latest MEAI
* Update M.B.OM package to latest
* Update to the latest M.B.OM release
* Remove some obsolete helper methods
* Update to the latest M.B.OM version
* Fix broken unit test
* Update MCP sample
* Bump to latest M.B.OM release
* Update to latest M.B.OM release
* Update to latest M.B.OM release
* Switch to using ExternalModel
* Update to latest M.B.OM
* Resolve merge conflicts
* All tests pass
* All tests pass
* Start to clean up the code
* Start to clean up the code
* More clean up
* More clean up
* More clean up
* Fix apiType checks
* Run dotnet format
* Fix typo
* Address code review feedback
* Add all properties for MCP tool
* Address code review feedback
* Address code review feedback
* Fix merge
* Undo warnings
* Undo test change
* More copilot feedback
* Make class sealed
* Address additional core review feedback
---------
Co-authored-by: Mark Wallace <markwallace@microsoft.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
* 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.
---------
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* 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
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* 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.
---------
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* Update extensions methods that accepts AgentDefinition type to not be restrictive
* Update Unit Tests
* Revert yarn/package-lock
* Revert yarn/package-lock
* Address copilot feedback
* Improve reusability of extension code and additional option to losen the strictiness of in-proc tools
* Add missing UT scenarios
* Add missing UT test scenarios
* Move packages
* Update nuget.config
* Address Xmldoc
* Remove format from branches checks
* Address Xmldocs
* Add more details to the implementation
* Moving Agent logic to ChatClient
* Adding Name and Id overrides to AzureAIAgent
* Updating extensions
* Add GetAiAgent extensions
* Adding support for version as name can conflict 409 using the Agents API with same name
* Addressing more updates to the extensions
* More improvements
* Remove debugging code from sample
* Address copilot feedback
* Apply suggestions from co-pilot code review
- **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
- **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.
This folder contains sample agent definitions than be ran 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.
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 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.",
"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!"
}
### Test spam detection with a spam email
POST http://localhost:7071/api/spamdetection/run
Content-Type: application/json
{
"email_id": "email-002",
"email_content": "URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!"
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