Jacob Alber 7ebe00ec3d [BREAKING] .NET: Workflow Off-Thread Execution Mode (#1233)
* Updates to async run loop.

* fix: Workflow Onwership can be release by nonowner

* fix: Incorrect handling of blockOnPending in StreamingRun

Depending on whether we are running in streaming on non-streaming mode, we may be using the StreamingRun in different ways. Unfortunately, the only place we can really know what is the actual state of execution is in the RunEventStream implementations.

This resulted in blocking where blocking was unneeded and occasionally not-blocking when blocking was needed.

The fix is to move the logic of handling this blocking into RunEventStream implementations.

* fix: Fix cleanup on error and end run

This ensures we clean up the background resources correctly.

* fix: Ensure we let the run loop proceed when shutting down

* fix: Add timeout for Input Waiting

* fix: Make the samples properly clean up `Run`s and `StreamingRun`s

* fix: Simplify Declarative Workflow Run disposal pattern

* Also fixes missing .Disposal() in Integration tests

---------

Co-authored-by: Ben Thomas <ben.thomas@microsoft.com>
7ebe00ec3d · 2025-10-07 01:07:38 +00:00
570 Commits
2025-10-02 18:50:47 +00:00
2025-04-28 12:54:43 -07:00
2025-04-28 12:54:42 -07:00
2025-09-02 12:18:12 +00:00

Microsoft Agent Framework

Welcome to Microsoft Agent Framework!

Microsoft Azure AI Foundry Discord MS Learn Documentation PyPI NuGet

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)

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

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

See the DevUI in action (1 min)

💬 We want your feedback!

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
    ).create_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

// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
// dotnet add package Azure.AI.OpenAI
// dotnet add package Azure.Identity
// Use `az login` to authenticate with Azure CLI
using System;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI;

var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!;
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME")!;

var agent = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
    .GetOpenAIResponseClient(deploymentName)
    .CreateAIAgent(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

.NET

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.

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