AutoGen vs LangChain
| Feature | AutoGen | LangChain |
|---|---|---|
| Category | LLMs & AI Infra | LLMs & AI Infra |
| Sub-category | AI Agent Framework | AI Agent Framework |
| Maturity | stable | stable |
| Complexity | intermediate | intermediate |
| Performance tier | medium | medium |
| License | MIT | MIT |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 38.0K | 100.0K |
| Contributors | 400 | 3.0K |
| Commit frequency | daily | daily |
| Plugin ecosystem | none | massive |
| Docs quality | good | good |
| Backing org | Microsoft | LangChain Inc |
| Funding model | corporate | vc_backed |
| Min RAM | 512 MB | 512 MB |
| Min CPU cores | 1 | 1 |
| Scaling pattern | single_node | single_node |
| Self-hostable | Yes | Yes |
| K8s native | No | No |
| Offline capable | No | No |
| Vendor lock-in | none | none |
| Languages | Python | Python, TypeScript |
| API type | SDK | SDK |
| Protocols | HTTP | HTTP |
| Deployment | pip | pip, npm |
| SDK languages | python | python, javascript |
| Team size fit | solo, small, medium | solo, small, medium, enterprise |
| First release | 2023 | 2022 |
| Latest version | — | — |
When to use AutoGen
- ✓ Multi-agent coding assistants that debug each other
- ✓ Group chat between specialized AI agents
- ✓ Human-in-the-loop approval for agent actions
- ✓ Automated research with web browsing agents
When to use LangChain
- ✓ Build RAG systems for document Q&A
- ✓ Create AI agents with tool access
- ✓ Chatbot with memory and context
- ✓ Multi-step reasoning workflows
- ✓ Document processing and extraction pipelines
AutoGen anti-patterns
- ✕ Can generate very long conversations (token-heavy)
- ✕ Debugging agent interactions is complex
- ✕ Less opinionated than CrewAI — more setup needed
LangChain anti-patterns
- ✕ Abstractions can hide important details
- ✕ Rapid API changes cause version friction
- ✕ Can be overkill for simple LLM calls
- ✕ Performance overhead for high-throughput