CrewAI vs deepagents
Orchestrate role-playing, autonomous AI agents that collaborate on tasks. — versus — LangChain's batteries-included agent harness on LangGraph — planning, sub-agents with isolated context, filesystem, shell, skills, human-in-the-loop and persistent memory out of the box.
Same job — a framework for multi-agent systems — different philosophy: CrewAI models role-playing crews; deepagents is one deep agent that plans and delegates to sub-agents with isolated contexts.
| CrewAI | deepagents | |
|---|---|---|
| Stars | 58k | 29k |
| Forks | 8.3k | 4.0k |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | today | today |
| Topics | agents, orchestration | agents, orchestration |
| Curated connections | 12 | 9 |
CrewAI — the curator's take
Orchestrate role-playing, autonomous AI agents that collaborate on tasks.
deepagents — the curator's take
The fastest route to a serious long-horizon agent if you accept LangChain's stack: planning, sub-agents, context offloading and HITL gates work out of the box, and any LangGraph graph plugs in as a sub-agent, so custom orchestration composes instead of forking. NOT for simple tool-calling loops — LangChain's create_agent is lighter — and the opinions run deep: if you're fighting the harness, you wanted LangGraph directly. Model-agnostic in theory; tuned around frontier tool-callers in practice.