AutoGen (Microsoft)
Microsoft's open-source framework for building multi-agent AI systems — agents can converse, write and execute code, collaborate in groups, and use tools in configurable chat patterns.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Code execution by default uses local subprocess — major security risk in production. Always use Docker-sandboxed code executor. Secrets managed via environment variables. MIT licensed with active Microsoft security practices for dependencies.
⚡ Reliability
Best When
You need complex multi-agent collaboration patterns, especially code-generating agents that execute and iterate on their output.
Avoid When
Your use case is straightforward — CrewAI or PydanticAI have simpler APIs for standard agent patterns.
Use Cases
- • Code generation workflows where an agent writes and another executes and tests
- • Multi-agent research tasks with specialized agents (critic, planner, executor)
- • Automated software engineering with code-executing agents in sandboxes
- • Group chat agent patterns where multiple agents debate and refine solutions
- • Building custom agent topologies for complex problem solving
Not For
- • Simple single-agent tasks (significant framework overhead for basic use cases)
- • Real-time latency-sensitive applications (multi-agent conversation adds significant latency)
- • Production systems requiring strict output determinism
Interface
Authentication
Passes through to underlying LLM providers. Multi-provider support (OpenAI, Anthropic, Azure OpenAI, Gemini, local models via Ollama). Config-driven model selection.
Pricing
Framework is free. Primary costs are LLM API calls — multi-agent systems can use significant token budgets due to cross-agent communication.
Agent Metadata
Known Gotchas
- ⚠ AutoGen v0.2 vs v0.4 (AgentChat) have very different APIs — breaking changes between major versions
- ⚠ Code execution requires Docker or a sandboxed environment — default local execution is a security risk
- ⚠ Group chat agent selection can be non-deterministic — conversations may not converge predictably
- ⚠ Token costs grow super-linearly with number of agents (each sees full conversation history)
- ⚠ Infinite loops are possible if termination conditions are not carefully set
Alternatives
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Scores are editorial opinions as of 2026-03-07.