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AutoGen: Build Multi-Agent Systems That Actually Coordinate
Microsoft's framework handles the hard part of agentic AI: getting multiple agents to talk, remember context, and solve problems together. Purpose-built for production.
microsoft/autogen ↗AutoGen solves a real problem: when you give AI agents a task, they need to collaborate, not just run in parallel. Think of it as a conversation framework where agents can talk to each other, remember what was said, and hand off work intelligently.
You define agents with specific roles (researcher, coder, validator), wire up their conversation rules, and AutoGen manages the back-and-forth. It keeps conversation history, so agents build on prior context instead of starting fresh each time. This matters because most toy agent frameworks skip the coordination layer entirely.
Built by Microsoft, battle-tested in production systems. If you're orchestrating multiple AI models or specialized agents toward a single goal, this handles the plumbing.
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AutoGen: Multi-agent orchestration that actually works
Microsoft's framework for building AI agents that coordinate with each other. Conversation-based, context-aware, and production-ready. Worth evaluating if you're shipping multi-agent workflows.
Most agent frameworks treat multi-agent systems like a parlor trick. AutoGen treats them like a real problem to solve: agents need to talk, remember context, and know when to hand off work. Conversation-based coordination + persistent history. Built for production. https://github.com/microsoft/autogen
The hard part of agentic AI isn't building one agent. It's building multiple agents that actually coordinate. AutoGen (Microsoft) is a framework purpose-built for multi-agent orchestration. Agents communicate via structured conversations, maintain context across interactions, and hand off work intelligently. If you're shipping systems with specialized agents working toward a shared goal, this deserves a look. https://github.com/microsoft/autogen
been shipping multi-agent systems for a while now, and the conversation orchestration problem keeps hitting the same wall: how do you get agents to actually collaborate without losing context or turning into spaghetti code. AutoGen solves this by treating agent interaction as first-class. conversation history stays intact, agent roles are explicit, and you can chain complex workflows without reinventing the state machine each time. if you're scaling beyond single-agent demos, this is the framework that actually ships.
AutoGen is doing something real: multi-agent orchestration where conversation history is the spine. not a toy. used in production by teams building actual workflows. if you're past the single-LLM phase and hitting the coordination problem, spend an hour on the repo.