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AI Agent2023
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AutoGen

Coordinates multiple LLM agents that converse to solve a task, splitting work across customizable roles that call tools, run code, and loop in humans. The v0.4 redesign adds async messaging and Python/.NET interoperability across distributed networks.

Introduction

Most agent frameworks treat "multi-agent" as a config flag bolted onto a single chat loop. AutoGen instead makes conversation the primitive: agents are independent participants that exchange asynchronous messages, so a planner, a coder, an executor, and a human reviewer can interleave turns the same way a real team does. That framing is why the v0.4 rewrite tore the library down to an event-driven message bus rather than patching the old request/response core.

What Sets It Apart
  • Agents are conversable peers, not sub-functions — work is divided by who is in the conversation, so swapping a role means swapping a participant, not rewriting orchestration.
  • A human can be dropped into any conversation as just another agent, making human-in-the-loop a first-class mode instead of an escape hatch.
  • Built-in code execution lets an agent write, run, and debug code mid-conversation, closing the loop without a separate tool layer.
  • v0.4's async, event-driven core plus Python/.NET interop targets distributed networks that span teams and languages, not just a single process.
Who It's For

Great fit if you are prototyping or researching multi-agent patterns and want conversation, tool use, and code execution to compose cleanly — AutoGen Studio's low-code canvas helps here too. Look elsewhere if you need a single deterministic agent with a fixed pipeline, or if the churn between v0.2 and v0.4 APIs is a problem; the redesign is powerful but the ecosystem and your own code may still be catching up.

Information

  • Websitemicrosoft.github.io
  • OrganizationsMicrosoft Research
  • AuthorsMicrosoft
  • Published date2023/08/18

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