* Changes
* Fix ChatClientAgent streaming responses missing MessageId
Generate fallback MessageId in ChatClientAgent.RunCoreStreamingAsync when
the underlying LLM provider does not set ChatResponseUpdate.MessageId.
Without a MessageId the AGUI converter's null==null check silently drops
all text content, causing CopilotKit Zod validation errors.
Changes:
- ChatClientAgent: generate msg_{Guid} fallback via ??= in streaming loop
- AgentResponseExtensions: sync wrapper MessageId back to RawRepresentation
in AsChatResponseUpdate() so downstream consumers see the value
- Add unit tests for both fixes and AGUI streaming MessageId scenarios
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR #4615 review comments
- Fix MessageId seeding: use first-seen provider MessageId (or generate
fallback) and apply consistently to all chunks in the stream, preventing
message splitting when providers set MessageId only on the first chunk
- Add test for mixed MessageId scenario (first chunk only)
- Fix skipped TextStreaming test: assert Empty (not NotEmpty) to match
actual null==null behavior
- Fix skipped ToolCalls test: assert empty ParentMessageId to match
actual empty-string passthrough behavior
* Handle empty MessageId in AsChatResponseUpdate sync
Treat empty/whitespace MessageId the same as null when syncing from
the AgentResponseUpdate wrapper back to RawRepresentation. Providers
that return empty string MessageId (e.g. tool call responses) now get
the wrapper value recovered correctly.
Add test for empty string MessageId recovery scenario.
* Move MessageId fallback generation to AGUI layer
Move fallback MessageId generation from ChatClientAgent to
AsAGUIEventStreamAsync, addressing the architectural concern that
MessageId is nullable in the AIAgent abstraction and the requirement
for non-null values is specific to the AGUI protocol.
The AGUI layer now generates a fallback MessageId for null or
empty/whitespace values, covering all agent types (not just
ChatClientAgent) including external implementations.
Changes:
- Revert MessageId generation from ChatClientAgent.RunCoreStreamingAsync
- Add fallback MessageId generation in AsAGUIEventStreamAsync for
null/empty MessageId values (handles both null and whitespace)
- Unskip and update AGUI tests to verify fallback generation
- Update ChatClientAgent tests to reflect passthrough behavior
* Revert AsChatResponseUpdate MessageId sync-back
Remove the MessageId sync-back logic from AsChatResponseUpdate() as it
is no longer needed. With fallback generation moved to the AGUI layer,
the abstraction layer should not mutate the RawRepresentation object.
Revert to the original passthrough behavior for AsChatResponseUpdate()
and update tests accordingly.
---------
Co-authored-by: Copilot <223556219+Copilot@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.
