AutoGen

Microsoft's open-source framework for multi-agent AI applications

Free· Free and open-source (MIT/CC-BY-4.0). You pay only for the underlying model providers (OpenAI, Azure OpenAI, Anthropic, local models, etc.) you plug into it.AgentsModel-agnostic: OpenAI (GPT-4o, GPT-4.1, o-series), Azure OpenAI, Anthropic Claude, Google Gemini, Ollama, and any OpenAI-compatible local model (vLLM, llama.cpp)

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Best for

Python teams and researchers building multi-agent systems where several specialized LLM agents need to collaborate, debate, or hand off work — especially coder+critic loops, planner+executor teams, and Magentic-One-style generalist assistants.

Skip if

Developers who only need a single chatbot or a simple RAG pipeline, JavaScript/TypeScript-only stacks, or teams that want a hosted managed-agent product rather than a framework to self-host.

AutoGen is Microsoft Research's open-source Python framework for building multi-agent AI systems — applications where several LLM-powered agents (and optionally humans and tools) converse, plan, and hand tasks off to each other to solve a problem. The v0.4+ codebase is split into three layers: a low-level 'Core' event-driven runtime for scalable, distributed agent networks (including a cross-language gRPC runtime so Python and .NET agents can talk); 'AgentChat', an opinionated higher-level API for the common single-agent and group-chat patterns (round-robin, selector, magentic-one, swarm); and 'Extensions' that wire in model clients (OpenAI, Azure OpenAI, Anthropic, Ollama, Gemini, local llama.cpp/vLLM endpoints), tool executors (Docker sandbox, Python code exec), memory stores, and MCP servers. On top of that sits AutoGen Studio, a browser UI for prototyping agent teams by dragging components together without writing code, and Magentic-One, a reference generalist multi-agent team with a web surfer, file surfer, coder, and terminal agent. Typical workflows include a coder-and-critic pair that iteratively writes and reviews code in a sandbox, a research team that plans, browses, and drafts a report, RAG assistants that call retrieval tools, and back-office automations where an orchestrator routes work to specialist agents. It is one of the most-cited academic references for multi-agent LLM orchestration and is broadly used as a research testbed as well as a production framework.

Editor's take

AutoGen is still the reference implementation for 'agents talking to agents' — the v0.4 rewrite finally made it feel like a real framework instead of a research demo, and the Core/AgentChat split is the right shape. It rewards teams willing to think in terms of message passing; use LangGraph if you want graphs and CrewAI if you want opinionated roles, but pick AutoGen when you actually need scalable multi-agent orchestration.

— The AI Tool Bible editorial team

Pros

✅ Genuinely multi-agent by design — group chat, selector, swarm, and Magentic-One patterns are first-class, not bolted on
✅ Clean layered architecture (Core / AgentChat / Extensions) lets you start high-level and drop down when you need custom orchestration
✅ Distributed, event-driven runtime with gRPC means agents can run across processes, machines, and even .NET/Python boundaries
✅ AutoGen Studio provides a no-code UI for prototyping and demoing agent teams to non-engineers
✅ Model-agnostic client layer covers OpenAI, Azure OpenAI, Anthropic, Gemini, Ollama, and any OpenAI-compatible endpoint
✅ Strong human-in-the-loop and code-execution primitives (Docker sandbox) for safe agent-written code
✅ Backed by Microsoft Research with an active community, frequent releases, and extensive documentation and samples

Cons

⚠️ The v0.2 to v0.4 rewrite broke APIs and much of the older tutorial content on the internet is now out of date
⚠️ Steeper learning curve than single-agent frameworks — you must think about message routing, termination conditions, and turn selection
⚠️ Multi-agent loops can be expensive and slow; without careful termination logic, agents happily burn tokens talking to each other
⚠️ Python-first (with a partial .NET port); no first-class TypeScript/JS SDK
⚠️ Observability and eval tooling are thinner than in competitors like LangGraph or CrewAI unless you bolt on external tracing

Use cases

Coder and critic pair programming loopsMulti-agent research and report writingMagentic-One style generalist web+file+code agent teamsRAG assistants with tool-callingBack-office task routing across specialist agentsDistributed cross-language agent systems (Python + .NET)No-code agent prototyping in AutoGen StudioAcademic benchmarks for multi-agent LLM research