* 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>
* 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>
* 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.
Thecontentgenerationworkflowforthetopic"The Future of Artificial Intelligence"hasbeensuccessfullystarted,andtheinstanceIDis**6a04276e8d824d8d941e1dc4142cc254**.Ifyouneedanyfurtherassistanceorupdatesontheworkflow,feelfreetoask!
@@ -18,7 +18,7 @@ These samples are designed to be run locally in a cloned repository.
The following prerequisites are required to run the samples:
- [.NET 9.0 SDK or later](https://dotnet.microsoft.com/download/dotnet)
- [.NET 10.0 SDK or later](https://dotnet.microsoft.com/download/dotnet)
- [Azure Functions Core Tools](https://learn.microsoft.com/azure/azure-functions/functions-run-local) (version 4.x or later)
- [Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli) installed and authenticated (`az login`) or an API key for the Azure OpenAI service
- [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource) with a deployed model (gpt-4o-mini or better is recommended)
Before you begin, ensure you have the following prerequisites:
- .NET 8.0 SDK or later
- .NET 10 SDK or later
- Access to the A2A agent host service
**Note**: These samples need to be run against a valid A2A server. If no A2A server is available, they can be run against the echo-agent that can be spun up locally by following the guidelines at: https://github.com/a2aproject/a2a-dotnet/blob/main/samples/AgentServer/README.md
// This sample shows how to create and use a AI agents with Azure Foundry Agents as the backend.
usingAzure.AI.Agents;
usingAzure.Identity;
usingMicrosoft.Agents.AI;
varendpoint=Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT")??thrownewInvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
Set the following environment variables:
```powershell
$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"# Replace with your Azure Foundry resource endpoint
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini"# Optional, defaults to gpt-4o-mini
// This sample shows how to create and use a AI agents with Azure Foundry Agents as the backend.
usingAzure.AI.Projects;
usingAzure.AI.Projects.OpenAI;
usingAzure.Identity;
usingMicrosoft.Agents.AI;
varendpoint=Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT")??thrownewInvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
Set the following environment variables:
```powershell
$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"# Replace with your Azure Foundry resource endpoint
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini"# Optional, defaults to gpt-4o-mini
@@ -15,7 +15,8 @@ See the README.md for each sample for the prerequisites for that sample.
|Sample|Description|
|---|---|
|[Creating an AIAgent with A2A](./Agent_With_A2A/)|This sample demonstrates how to create AIAgent for an existing A2A agent.|
|[Creating an AIAgent with AzureFoundry Agent](./Agent_With_AzureFoundryAgent/)|This sample demonstrates how to create an Azure Foundry agent and expose it as an AIAgent|
|[Creating an AIAgent with Foundry Agents using Azure.AI.Agents.Persistent](./Agent_With_AzureAIAgentsPersistent/)|This sample demonstrates how to create a Foundry Persistent agent and expose it as an AIAgent using the Azure.AI.Agents.Persistent SDK|
|[Creating an AIAgent with Foundry Agents using Azure.AI.Project](./Agent_With_AzureAIProject/)|This sample demonstrates how to create an Foundry Project agent and expose it as an AIAgent using the Azure.AI.Project SDK|
|[Creating an AIAgent with AzureFoundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Azure Foundry to create an AIAgent|
|[Creating an AIAgent with Azure OpenAI ChatCompletion](./Agent_With_AzureOpenAIChatCompletion/)|This sample demonstrates how to create an AIAgent using Azure OpenAI ChatCompletion as the underlying inference service|
|[Creating an AIAgent with Azure OpenAI Responses](./Agent_With_AzureOpenAIResponses/)|This sample demonstrates how to create an AIAgent using Azure OpenAI Responses as the underlying inference service|
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