CrewAI vs LlamaIndex
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
Python framework for building LLM apps that query your own documents and data.
Our verdict
CrewAI is built for orchestrating teams of AI agents that each have a role and collaborate on multi-step tasks, with Flows for tighter step-by-step control. LlamaIndex is built for connecting an LLM to your own documents and data, mainly through retrieval for question answering. Both are free, MIT-licensed Python libraries that need coding, so the choice depends on whether your core problem is agent coordination or working with private data.
Pick CrewAI if you want several role-based AI agents working together on a task, with step-by-step workflow control when you need it.
Pick LlamaIndex if you want an LLM to answer questions over your own PDFs, documents, APIs or databases.
| Key difference | CrewAI | LlamaIndex |
|---|---|---|
| Core purpose | Orchestrating teams of role-playing AI agents and workflows | Connecting LLMs to your own documents and data |
| Best at | Multi-agent collaboration plus event-driven Flows | Retrieval over private data and swappable components |
| Ecosystem | Docs, courses and an official skills package | Over 300 integration packages per the README |
| Company focus | Commercial AMP Suite is a separate paid offering | Main company focus has shifted to the LlamaParse platform |
| Community size | About 60.3k GitHub stars, updated in the last day | About 52.7k GitHub stars, last code update 3 days ago |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
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 LlamaIndex if you need…
- Python developers building retrieval-augmented generation (RAG) apps over private documents
- Teams wanting a modular toolkit where they can swap LLM, embedding and vector store providers
- Developers building agent workflows that query their own data
- Beginners who want a short path to a working document question-answering prototype
Side-by-side
| CrewAI | LlamaIndex | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 60.4k | 52.8k |
| 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 | 10 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.
LlamaIndex
LlamaIndex is an open-source Python framework for connecting large language models to your own data, such as PDFs, documents, APIs and SQL databases. It provides connectors to load data, ways to structure it into indexes, and retrieval tools that feed relevant context to an LLM. The README also notes that the company's main focus has shifted toward its commercial LlamaParse document platform, with the open-source framework still available.
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
LlamaIndex
- Python developers building retrieval-augmented generation (RAG) apps over private documents
- Teams wanting a modular toolkit where they can swap LLM, embedding and vector store providers
- Developers building agent workflows that query their own data
- Beginners who want a short path to a working document question-answering prototype
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
LlamaIndex
- Large ecosystem of over 300 integration packages, per the README
- Modular design lets you customize or replace components like retrievers and query engines
- Offers both a high-level API for quick starts and lower-level APIs for advanced control
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
LlamaIndex
- Non-programmers, since it is a Python library that requires writing code
- Projects that need a fully managed hosted service, because the README points to LlamaParse for the hosted offering and the framework is a toolkit you run yourself
- Users who only need simple one-off document parsing, where a standalone parser may be simpler
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
LlamaIndex
- The README says the company's primary focus has shifted to LlamaParse, so the open-source framework is secondary to its commercial product
- The README is not updated as often as the documentation, so you should check the docs for current details
- Many separate integration packages means you must choose and install the ones you need
Getting started
CrewAI
Install CrewAI (from PyPI, per the project's PyPI badge)
pip install crewaiLlamaIndex
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openaiFrequently asked questions
Should I use CrewAI or LlamaIndex?
CrewAI is built for orchestrating teams of AI agents that each have a role and collaborate on multi-step tasks, with Flows for tighter step-by-step control. LlamaIndex is built for connecting an LLM to your own documents and data, mainly through retrieval for question answering. Both are free, MIT-licensed Python libraries that need coding, so the choice depends on whether your core problem is agent coordination or working with private data. Pick CrewAI if you want several role-based AI agents working together on a task, with step-by-step workflow control when you need it. Pick LlamaIndex if you want an LLM to answer questions over your own PDFs, documents, APIs or databases.
What is the difference between CrewAI and LlamaIndex?
CrewAI: Python framework for building teams of role-playing AI agents that work together on tasks. LlamaIndex: Python framework for building LLM apps that query your own documents and data. CrewAI suits python developers prototyping multi-agent workflows, while LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents.
Are CrewAI and LlamaIndex free?
CrewAI: Free and open source. LlamaIndex: Free and open source.
Is CrewAI or LlamaIndex more popular?
CrewAI has more GitHub stars (60.4k) than LlamaIndex (52.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to CrewAI and LlamaIndex?
Other options people compare them with include LangGraph, 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.