AutoGen
Microsoft's framework for building multi-agent systems where AI agents collaborate through conversation.
Technical explanation
AutoGen takes a different approach to AI applications. Instead of chains or single agents, it creates systems where multiple AI agents talk to each other. One agent might be a coder, another a reviewer, another a planner. They collaborate through conversation, iterating until they solve a problem. It's inspired by how human teams work.
The framework provides agent types out of the box. AssistantAgent wraps an LLM and follows instructions. UserProxyAgent can execute code and interact with humans. You define the agents, their capabilities, and how they're allowed to interact. Then you kick off a conversation and let them work through the task.
Where AutoGen shines is complex tasks that benefit from iteration. Code generation with review and refinement. Research that needs multiple perspectives. Planning that requires checking and double-checking. The back-and-forth between agents catches mistakes and improves outputs in ways a single agent often can't.
The downside is token consumption. Every message in the agent conversation uses tokens. Complex tasks can burn through API credits quickly. There's also a learning curve in designing effective agent topologies. How many agents? What are their roles? When should they stop? These design decisions significantly impact results. Microsoft continues developing AutoGen actively, and the community has grown around creative multi-agent architectures.