multi-agent
AutoGen
AutoGen is a multi-agent framework for conversational collaboration between specialized agents, often used for coding and research workflows.
- Python
- multi-agent
Website: microsoft.github.io/autogen/
GitHub: github.com/microsoft/autogen
Languages: Python
License: CC-BY-4.0 / project licenses vary by package—verify upstream
Best for
- Conversational multi-agent experiments
- Coding agents with reviewer/user-proxy patterns
- Research and Microsoft-centric stacks
Pros and cons
Pros
- Flexible multi-agent conversation patterns
- Strong history in coding agent demos
- Active open-source community
Cons / trade-offs
- Conversational control can be harder to constrain
- Production guardrails still application-owned
Overview
AutoGen focuses on multi-agent conversations: agents message each other (and optional human proxies) to solve tasks such as coding, analysis, or research.
Fit signals
Choose AutoGen when the natural interface between specialists is dialogue and you want that protocol out of the box.
Production note
Constrain tools, add termination conditions, and evaluate total token spend. Debates are expensive.
Related reading
LangGraph vs CrewAI vs AutoGen
Vendor-neutral comparison of LangGraph, CrewAI, and AutoGen for building multi-step and multi-agent systems.
Multi-Agent Pattern
Multi-agent systems split work across specialized agents with defined roles, handoffs, and shared state.
Building Coding Agents: Architecture and Guardrails
How coding agents work: repo tools, planning, tests as rewards, sandboxes, and evaluation for software engineering agents.