CrewAI vs LangGraph
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Python framework for building teams of role-playing AI agents that work together on tasks.
Open source · MIT
A Python framework for building long-running agents that keep state and can resume after failures.
The short answer
Choose CrewAI if you need…
- Python developers prototyping multi-agent workflows
- Teams that want role-based agents collaborating on a task
- Developers who want both autonomous agent teams and tighter step-by-step control via Flows
- Builders who want low-level control over prompts and execution logic
Choose LangGraph if you need…
- Python developers building multi-step AI agents that must survive failures or long waits
- Teams that want fine-grained control over agent state and execution flow
- Projects that need a human to review or edit an agent's state partway through a run
- Developers who want a lower-level base instead of a higher-level agent package
Side-by-side
| CrewAI | LangGraph | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 60.4k | 44.3k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 4 days ago |
What each one is
CrewAI
CrewAI is an open-source Python framework for orchestrating multiple AI agents. You give each agent a role and goal, and they collaborate through what the project calls Crews, while Flows provide event-driven, more precisely controlled workflows. It is aimed at developers building multi-step automations with LLMs.
LangGraph
LangGraph is a low-level framework for building workflows and AI agents that keep track of their state over time. It is designed for tasks that run for a long stretch, can pause for human review, and need to pick up where they left off after an interruption. It is produced by LangChain Inc, but the README says it can be used without LangChain.
Best for
CrewAI
- Python developers prototyping multi-agent workflows
- Teams that want role-based agents collaborating on a task
- Developers who want both autonomous agent teams and tighter step-by-step control via Flows
- Builders who want low-level control over prompts and execution logic
LangGraph
- Python developers building multi-step AI agents that must survive failures or long waits
- Teams that want fine-grained control over agent state and execution flow
- Projects that need a human to review or edit an agent's state partway through a run
- Developers who want a lower-level base instead of a higher-level agent package
Strengths
CrewAI
- Open source under the MIT license with an active repository
- Offers both autonomous Crews and deterministic event-driven Flows
- Includes documentation, courses, and an official skills package for coding agents
LangGraph
- Built-in durable execution that resumes workflows from where they stopped, per the README
- Supports human-in-the-loop checkpoints and both short-term and long-term memory
- Open source under the MIT license with extensive documentation and a free course
Not ideal for
CrewAI
- Non-programmers, since the project is a Python framework and requires writing code
- Projects needing a fully managed hosted product without setup; the README's commercial AMP Suite is a separate paid offering
- Simple single-prompt tasks that do not need multiple agents or workflow control
LangGraph
- Beginners who just want a ready-made agent: the README points to Deep Agents, a higher-level package built on LangGraph, for quicker starts
- Non-programmers, since it is a Python library that requires writing code
- JavaScript/TypeScript-only teams, who are pointed to the separate LangGraph.js project
Trade-offs
CrewAI
- The README's performance and 'production-ready' claims are promotional and not backed by benchmarks in the excerpt
- Core concepts (Crews, Flows, Agents) add a learning curve for newcomers
- Some advanced features and a control plane are part of a commercial suite
LangGraph
- It is a low-level framework, so it has a steeper learning curve than higher-level tools
- Several of the most useful features (debugging, deployment) point to LangSmith, a separate product
- The README is truncated and does not show full API details, so the depth of specific features is hard to verify from it alone
Getting started
CrewAI
Install CrewAI (from PyPI, per the project's PyPI badge)
pip install crewaiFrequently asked questions
What is the difference between CrewAI and LangGraph?
CrewAI: Python framework for building teams of role-playing AI agents that work together on tasks. LangGraph: A Python framework for building long-running agents that keep state and can resume after failures. CrewAI suits python developers prototyping multi-agent workflows, while LangGraph suits python developers building multi-step ai agents that must survive failures or long waits.
Are CrewAI and LangGraph free?
CrewAI: Free and open source. LangGraph: Free and open source.
Is CrewAI or LangGraph more popular?
CrewAI has more GitHub stars (60.4k) than LangGraph (44.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to CrewAI and LangGraph?
Other options people compare them with include LlamaIndex, Semantic Kernel, PageIndex, Stagehand, OpenAI Agents SDK.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.