Microsoft Agent Framework

Agent Framework offers two primary categories of capabilities:

The framework also provides foundational building blocks, including model clients (chat completions and responses), an agent session for state management, context providers for agent memory, middleware for intercepting agent actions, and MCP clients for tool integration. Together, these components give you the flexibility and power to build interactive, robust, and safe AI applications.

Get started

dotnet add package Azure.AI.OpenAI --prerelease
dotnet add package Azure.Identity
dotnet add package Microsoft.Agents.AI.OpenAI --prerelease

using System;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;

AIAgent agent = new AzureOpenAIClient(
        new Uri(Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!),
        new AzureCliCredential())
    .GetChatClient("gpt-4o-mini")
    .AsAIAgent(instructions: "You are a friendly assistant. Keep your answers brief.");

Console.WriteLine(await agent.RunAsync("What is the largest city in France?"));

pip install agent-framework --pre

    credential = AzureCliCredential()
    client = AzureOpenAIResponsesClient(
        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
        deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
        credential=credential,
    )

    agent = client.as_agent(
        name="HelloAgent",
        instructions="You are a friendly assistant. Keep your answers brief.",
    )

    # Non-streaming: get the complete response at once
    result = await agent.run("What is the largest city in France?")
    print(f"Agent: {result}")

That's it — an agent that calls an LLM and returns a response. From here you can add tools, multi-turn conversations, middleware, and workflows to build production applications.

When to use agents vs workflows

If you can write a function to handle the task, do that instead of using an AI agent.

Why Agent Framework?

Agent Framework combines AutoGen's simple agent abstractions with Semantic Kernel's enterprise features — session-based state management, type safety, middleware, telemetry — and adds graph-based workflows for explicit multi-agent orchestration.

Semantic Kernel and AutoGen pioneered the concepts of AI agents and multi-agent orchestration. The Agent Framework is the direct successor, created by the same teams. It combines AutoGen's simple abstractions for single- and multi-agent patterns with Semantic Kernel's enterprise-grade features such as session-based state management, type safety, filters, telemetry, and extensive model and embedding support. Beyond merging the two, Agent Framework introduces workflows that give developers explicit control over multi-agent execution paths, plus a robust state management system for long-running and human-in-the-loop scenarios. In short, Agent Framework is the next generation of both Semantic Kernel and AutoGen.

To learn more about migrating from either Semantic Kernel or AutoGen, see the Migration Guide from Semantic Kernel and Migration Guide from AutoGen.

Both Semantic Kernel and AutoGen have benefited significantly from the open-source community, and the same is expected for Agent Framework. Microsoft Agent Framework welcomes contributions and will keep improving with new features and capabilities.

Next steps

Go deeper:

• Agents overview — architecture, providers, tools
• Workflows overview — sequential, concurrent, branching
• Integrations — A2A, AG-UI, Azure Functions, M365