AutoGen

Microsoft's framework for multi-agent conversations and tool use

AI Agents & AutomationFree planOverseasβ˜…β˜…β˜…β˜…β˜† 4.0

What is AutoGen?

AutoGen is a Microsoft research framework for building applications where several AI agents talk to each other to solve a task. You define agents with roles, give them tools or code execution, and let a conversation protocol drive the work - one agent plans, another critiques, a third runs the resulting code. It handles the orchestration concerns that ad-hoc scripts miss: message passing, termination conditions, human input, and local or distributed runtimes. It is a developer library rather than a product, best suited to teams comfortable writing Python and debugging agent behaviour.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • Multi-agent conversation patterns with configurable roles
  • Code execution inside Docker or a restricted local runtime
  • Tool and function calling shared across agent teams
  • Human-in-the-loop intervention points in a conversation
  • Group chat and nested conversation orchestration
  • Model-agnostic support for hosted and local LLMs

Pros & cons

Strengths

  • Purpose-built for multi-agent orchestration
  • Active research backing and a large example library
  • Runs models locally or through hosted APIs

Watch out for

  • Requires solid Python and debugging skills
  • Costs and token use can grow fast in group chats
  • APIs have shifted between versions, so older tutorials age badly

Best for & use cases

developer teams, multi-agent research, code generation pipelines and prototypes

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FAQ

Do I need to know Python well?

Yes. AutoGen is a library, not an application. Comfort with Python, async patterns and reading tracebacks matters more here than with a no-code agent builder.

How does it handle running generated code?

It can execute code inside a Docker container or a restricted local runtime, which is the usual way to keep agent-generated code away from your main environment.

Is it production ready?

Parts of it are used in production, but the framework moves quickly. Pin versions, keep an eye on token spend, and expect to write your own evaluation and guardrails.