LangGraph vs MetaGPT
Build stateful, multi-actor LLM apps as graphs — durable execution, human-in-the-loop, streaming. — versus — The original "AI software company" multi-agent framework — role-assigned agents (PM, architect, engineer) turn a one-line requirement into PRD, design, and code.
Trade-off runs the other way: LangGraph is low-level graph control you assemble; MetaGPT is a pre-built company you configure. Outgrowing MetaGPT's opinions usually lands you here.
| LangGraph | MetaGPT | |
|---|---|---|
| Stars | 37k | 69k |
| Forks | 6.3k | 8.8k |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | 2 days ago | 5 months ago |
| Topics | agents, orchestration | agents, orchestration |
| Curated connections | 16 | 3 |
LangGraph — the curator's take
You reach for LangGraph the moment a simple agent loop stops being enough — when you need state that survives a crash, a human approving a step mid-run, or a flow that can loop back on itself. Most teams arrive here from plain LangChain and don't leave. If all you want is a quick tool-calling agent, this is more machinery than you need — start lighter and come back when you hit the wall.
MetaGPT — the curator's take
MetaGPT is the canonical demonstration that multi-agent choreography works: encode a software company's SOPs as roles and watch one prompt become a PRD, a design doc, and running code. Study it for that — the role/SOP pattern shows up everywhere now. But be honest about 2026: agentic coding tools (Claude Code, Codex) have eaten the "write my app from a prompt" job, and the repo's cadence has slowed noticeably (last push Jan 2026) while the team focuses on its commercial MGX product. For production pipelines you control, reach for LangGraph; for lightweight role crews, CrewAI. Reach for MetaGPT to learn from the most complete SOP-driven design in the wild.