AutoGen

AutoGen is Microsoft's multi-agent framework built around the metaphor of a conversation: define agents, they send messages to each other, problems get solved through the exchange. It started research-flavored and is increasingly production-ready. It fits particularly well when your problem genuinely looks like "agents talking," and less well when it doesn't.

Core concepts

Strengths

• Elegant for conversation-shaped problems. Debate, negotiation, multi-character simulations.
• Strong code execution support. UserProxyAgent runs Python natively, very good for coding and data-analysis setups.
• Built-in multi-agent patterns. Group chat, two-agent, hierarchical.
• Research-friendly. Good for experimenting with how agents coordinate.

Weaknesses

• Steeper learning curve than CrewAI.
• Chat-message paradigm is inefficient for simple sequential workflows.
• Less opinionated; you make more decisions up front.
• Production observability requires glue to external tools.

When AutoGen is the right pick

• Research settings exploring multi-agent dynamics.
• Problems genuinely structured as agent-to-agent conversation (debates, negotiations, narrative generation).
• Code-execution heavy agents (UserProxyAgent is genuinely excellent at running Python).
• Teams comfortable with Python + Microsoft ecosystem tooling.

When to pick something else

• Single-agent tasks. Overhead without benefit.
• Strict orchestration flows. LangGraph's state machines are cleaner.
• Production customer-facing agents where observability and SLAs matter. You'll wind up building a lot of instrumentation.

AutoGen Studio

Microsoft offers AutoGen Studio, a UI for building and debugging agent configurations. Useful for non-programmers prototyping agent workflows, and helpful even for engineers when visualizing agent conversations in a group chat.

A good fit: a research + coding duo

Two agents: a "researcher" who searches and reads documents, and a "coder" (UserProxyAgent) who writes and runs analysis code. They pass findings back and forth until the user's question is answered. AutoGen's chat paradigm fits this naturally: each agent's message is the other's input. Minimal scaffolding.

Pitfalls

• Forcing chat where it doesn't fit. Simple sequential tasks become harder, not easier.
• Runaway group chat. Manager picks the wrong speaker; the conversation loops. Set explicit turn limits.
• Code execution surface. UserProxyAgent can execute arbitrary code. Sandbox it.
• Prototype-to-prod gap. Less mature tracing than LangGraph; plan instrumentation early.

What to do with this

• If your problem naturally looks like a conversation between roles, AutoGen will feel natural.
• If it doesn't, try CrewAI or LangGraph instead.
• Read picking a framework for the full comparison.