* fix: handle thread.message.completed event in Assistants API streaming Previously, `thread.message.completed` events fell through to the catch-all `else` branch and yielded empty `ChatResponseUpdate` objects, silently discarding fully-resolved annotation data (file citations, file paths, and their character-offset regions). This commit adds a dedicated handler for `thread.message.completed` that: - Walks the completed ThreadMessage.content array - Extracts text blocks with their fully-resolved annotations - Maps FileCitationAnnotation and FilePathAnnotation to the framework's Annotation type with proper TextSpanRegion data - Yields a ChatResponseUpdate containing the complete text and annotations Fixes #4322 * test: add tests for thread.message.completed annotation handling Tests cover: - File citation annotation extraction - File path annotation extraction - Multiple annotations on a single text block - Text-only messages (no annotations) - Non-text blocks are skipped - Mixed content blocks (text + image) - Conversation ID propagation * fix: address Copilot review - add quote field and log unrecognized annotations - Include `quote` from `annotation.file_citation.quote` in `additional_properties` for FileCitationAnnotation, preserving the exact cited text snippet from the source file - Add `else` clause to log unrecognized annotation types at debug level, consistent with the pattern in `_responses_client.py` - Add `import logging` and module-level logger * test: add coverage for quote field and unrecognized annotation logging - test_message_completed_with_file_citation_quote: verifies quote is included in additional_properties - test_message_completed_with_file_citation_no_quote: verifies quote is omitted when None - test_message_completed_unrecognized_annotation_logged: verifies unknown annotation types are logged at debug level and skipped * fix: address reviewer nits — logger name convention + annotation type string Per @giles17's review: - Use logging.getLogger('agent_framework.openai') to match module convention - Simplify debug message to use annotation.type instead of type().__name__ * refactor: move message.completed tests into consolidated test file Per @giles17's review: moved all tests from test_assistants_message_completed.py into test_openai_assistants_client.py and deleted the standalone file. * fix: resolve mypy no-redef and ruff RET504 lint errors - Remove duplicate type annotation for 'ann' variable (no-redef) - Return directly from fixture instead of unnecessary assignment (RET504) * fix: rename annotation variable in completed block to fix mypy type conflict The 'annotation' loop variable in thread.message.completed has type FileCitationAnnotation | FilePathAnnotation, which conflicts with the delta block's 'annotation' of type FileCitationDeltaAnnotation | FilePathDeltaAnnotation. Renamed to 'completed_annotation' to avoid mypy 'Incompatible types in assignment' error. * fix: remove quote field from FileCitationAnnotation handling --------- Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com>
Welcome to Microsoft Agent Framework!
Welcome to Microsoft's comprehensive multi-language framework for building, orchestrating, and deploying AI agents with support for both .NET and Python implementations. This framework provides everything from simple chat agents to complex multi-agent workflows with graph-based orchestration.
Watch the full Agent Framework introduction (30 min)
📋 Getting Started
📦 Installation
Python
pip install agent-framework --pre
# This will install all sub-packages, see `python/packages` for individual packages.
# It may take a minute on first install on Windows.
.NET
dotnet add package Microsoft.Agents.AI
📚 Documentation
- Overview - High level overview of the framework
- Quick Start - Get started with a simple agent
- Tutorials - Step by step tutorials
- User Guide - In-depth user guide for building agents and workflows
- Migration from Semantic Kernel - Guide to migrate from Semantic Kernel
- Migration from AutoGen - Guide to migrate from AutoGen
Still have questions? Join our weekly office hours or ask questions in our Discord channel to get help from the team and other users.
✨ Highlights
- Graph-based Workflows: Connect agents and deterministic functions using data flows with streaming, checkpointing, human-in-the-loop, and time-travel capabilities
- AF Labs: Experimental packages for cutting-edge features including benchmarking, reinforcement learning, and research initiatives
- DevUI: Interactive developer UI for agent development, testing, and debugging workflows
See the DevUI in action (1 min)
- Python and C#/.NET Support: Full framework support for both Python and C#/.NET implementations with consistent APIs
- Observability: Built-in OpenTelemetry integration for distributed tracing, monitoring, and debugging
- Multiple Agent Provider Support: Support for various LLM providers with more being added continuously
- Middleware: Flexible middleware system for request/response processing, exception handling, and custom pipelines
💬 We want your feedback!
- For bugs, please file a GitHub issue.
Quickstart
Basic Agent - Python
Create a simple Azure Responses Agent that writes a haiku about the Microsoft Agent Framework
# pip install agent-framework --pre
# Use `az login` to authenticate with Azure CLI
import os
import asyncio
from agent_framework.azure import AzureOpenAIResponsesClient
from azure.identity import AzureCliCredential
async def main():
# Initialize a chat agent with Azure OpenAI Responses
# the endpoint, deployment name, and api version can be set via environment variables
# or they can be passed in directly to the AzureOpenAIResponsesClient constructor
agent = AzureOpenAIResponsesClient(
# endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
# deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
# api_version=os.environ["AZURE_OPENAI_API_VERSION"],
# api_key=os.environ["AZURE_OPENAI_API_KEY"], # Optional if using AzureCliCredential
credential=AzureCliCredential(), # Optional, if using api_key
).as_agent(
name="HaikuBot",
instructions="You are an upbeat assistant that writes beautifully.",
)
print(await agent.run("Write a haiku about Microsoft Agent Framework."))
if __name__ == "__main__":
asyncio.run(main())
Basic Agent - .NET
Create a simple Agent, using OpenAI Responses, that writes a haiku about the Microsoft Agent Framework
// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
using Microsoft.Agents.AI;
using OpenAI;
using OpenAI.Responses;
// Replace the <apikey> with your OpenAI API key.
var agent = new OpenAIClient("<apikey>")
.GetResponsesClient("gpt-4o-mini")
.AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
Create a simple Agent, using Azure OpenAI Responses with token based auth, that writes a haiku about the Microsoft Agent Framework
// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
// dotnet add package Azure.Identity
// Use `az login` to authenticate with Azure CLI
using System.ClientModel.Primitives;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI;
using OpenAI.Responses;
// Replace <resource> and gpt-4o-mini with your Azure OpenAI resource name and deployment name.
var agent = new OpenAIClient(
new BearerTokenPolicy(new AzureCliCredential(), "https://ai.azure.com/.default"),
new OpenAIClientOptions() { Endpoint = new Uri("https://<resource>.openai.azure.com/openai/v1") })
.GetResponsesClient("gpt-4o-mini")
.AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
More Examples & Samples
Python
- Getting Started with Agents: progressive tutorial from hello-world to hosting
- Agent Concepts: deep-dive samples by topic (tools, middleware, providers, etc.)
- Getting Started with Workflows: workflow creation and integration with agents
.NET
- Getting Started with Agents: basic agent creation and tool usage
- Agent Provider Samples: samples showing different agent providers
- Workflow Samples: advanced multi-agent patterns and workflow orchestration
Contributor Resources
Important Notes
If you use the Microsoft Agent Framework to build applications that operate with third-party servers or agents, you do so at your own risk. We recommend reviewing all data being shared with third-party servers or agents and being cognizant of third-party practices for retention and location of data. It is your responsibility to manage whether your data will flow outside of your organization's Azure compliance and geographic boundaries and any related implications.
