README.md
CrewAI is a framework for orchestrating role-playing, autonomous agents. You define agents with roles, goals and tools, then compose them into a "crew" that collaborates to complete tasks.
pip install crewai
Explore / CrewAI
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CrewAI
Orchestrate role-playing, autonomous AI agents that collaborate on tasks.
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Continue your stack
What teams reach for next — and why each earns a place beside CrewAI. Ranked by curator confidence.
Pairs well with
MemMachine
Long-term memory layer for AI agents — episodic (graph), profile (SQL) and working memory behind Python/TS SDKs, REST and MCP; ships LangChain, LangGraph, CrewAI and LlamaIndex integrations.
Why it fitsShips a CrewAI integration: crews get shared persistent memory across sessions instead of CrewAI's per-run state.
litellm
Open-source AI gateway: call 100+ LLM providers in OpenAI format via a Python SDK or self-hosted proxy — with cost tracking, virtual keys, guardrails, load balancing and logging.
Why it fitsAgent framework relies on a unified model layer; LiteLLM supplies the 100+-provider abstraction beneath the agents.
Alternative to See all + compare →
MetaGPT
The original "AI software company" multi-agent framework — role-assigned agents (PM, architect, engineer) turn a one-line requirement into PRD, design, and code.
Why it fitsBoth bet on role-playing agent teams. CrewAI gives you general-purpose crews you compose; MetaGPT ships one opinionated, SOP-encoded software company end to end.
AutoGen
Multi-agent conversation framework for building LLM applications with cooperating agents.
Why it fitsBoth do multi-agent collaboration; CrewAI is more opinionated about roles, AutoGen about conversation.
LangGraph
Build stateful, multi-actor LLM apps as graphs — durable execution, human-in-the-loop, streaming.
Why it fitsHigher-level, opinionated multi-agent API vs. LangGraph's low-level control. Trade control for speed.
Built with / on
Ollama
Run Llama, Mistral and other open models locally with a single command and a clean API.
Why it fitsRun your crew against local models for cost-free iteration.
ai-engineering-hub
36k-star hub of runnable AI engineering tutorials — LLMs, RAG and agent apps as self-contained projects, including build-code-harness: a Claude-Code-style coding harness rebuilt on CrewAI + E2B.
Why it fitsThe flagship walkthroughs — including build-code-harness's hierarchical agent workflow — are built on CrewAI; the hub doubles as its largest applied-tutorial corpus.