CrewAI vs Deep Agents
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
An open-source Python agent harness for multi-step AI work, built on LangGraph.
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 Deep Agents if you need…
- Developers building AI agents that run multi-step tasks such as research and writing
- Teams that want a working agent harness they can customize instead of building one from scratch
- People who want to use either hosted or self-hosted language models with the same code
- Developers already using LangChain or LangGraph who want a more opinionated setup
Side-by-side
| CrewAI | Deep Agents | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 60.4k | 30.9k |
| 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.
Deep Agents
Deep Agents is a Python (and JavaScript/TypeScript) library that gives you a ready-made AI agent with planning, file access, sub-agents, and context management already included. You can swap in any tool-calling language model and override or replace parts of it. It is built on LangGraph, a framework for agent workflows.
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
Deep Agents
- Developers building AI agents that run multi-step tasks such as research and writing
- Teams that want a working agent harness they can customize instead of building one from scratch
- People who want to use either hosted or self-hosted language models with the same code
- Developers already using LangChain or LangGraph who want a more opinionated setup
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
Deep Agents
- Ships with planning, filesystem, sub-agent, and context management features out of the box
- Works with any LLM that supports tool calling, including local and open-weight models
- Built on LangGraph, which the README describes as providing streaming, persistence, and checkpointing
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
Deep Agents
- Non-programmers looking for a finished app: the README centers on writing Python code, and the terminal coding agent is a separate feature
- Anyone who needs strict guarantees the agent will not misuse tools, since the README says it follows a "trust the LLM" model and boundaries must be enforced at the tool or sandbox level
- Projects that only need a simple single-call LLM request, since the harness adds more structure than that requires
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
Deep Agents
- The README is truncated and does not document every option, so details of configuration depend on the external docs
- Its security model places responsibility on the developer to restrict what tools and sandboxes can do
- It is tied to the LangChain ecosystem, so adopting it means adopting those dependencies
Getting started
CrewAI
Install CrewAI (from PyPI, per the project's PyPI badge)
pip install crewaiDeep Agents
Add deepagents to a uv project
uv add deepagentsFrequently asked questions
What is the difference between CrewAI and Deep Agents?
CrewAI: Python framework for building teams of role-playing AI agents that work together on tasks. Deep Agents: An open-source Python agent harness for multi-step AI work, built on LangGraph. CrewAI suits python developers prototyping multi-agent workflows, while Deep Agents suits developers building ai agents that run multi-step tasks such as research and writing.
Are CrewAI and Deep Agents free?
CrewAI: Free and open source. Deep Agents: Free and open source.
Is CrewAI or Deep Agents more popular?
CrewAI has more GitHub stars (60.4k) than Deep Agents (30.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to CrewAI and Deep Agents?
Other options people compare them with include LangGraph, LlamaIndex, Semantic Kernel, PageIndex, Stagehand.
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.