Composio 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
SDK for building AI agents that can take actions in 1000+ apps via pre-authenticated tools.
Open source · MIT
Python framework for building LLM apps that query your own documents and data.
The short answer
Choose Composio if you need…
- Developers building AI agents that need to read or act in third-party apps
- Teams already using a supported agent framework such as OpenAI Agents, LangChain, or Vercel AI SDK
- Projects that want per-user scoping of app connections
- Developers who want to expose tools to MCP clients like Claude or Cursor
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
| Composio | LlamaIndex | |
|---|---|---|
| Type | Dev tool | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 30.9k | 52.8k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | TypeScript | Python |
| Last code update | 4 days ago | 10 days ago |
What each one is
Composio
Composio is a TypeScript and Python SDK, plus a command-line tool, that lets AI agents use external apps on a user's behalf. It provides pre-authenticated toolkits for over 1000 apps, per-user sessions, authentication, triggers, and a sandbox. Developers use it to connect an agent framework to real services such as email.
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
Composio
- Developers building AI agents that need to read or act in third-party apps
- Teams already using a supported agent framework such as OpenAI Agents, LangChain, or Vercel AI SDK
- Projects that want per-user scoping of app connections
- Developers who want to expose tools to MCP clients like Claude or Cursor
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
Composio
- Broad coverage: the README lists 1000+ toolkits and many provider adapters for common agent frameworks
- Sessions load meta tools at runtime instead of hundreds of tool definitions, per the README
- Available in both TypeScript and Python with an MIT license
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
Composio
- Non-developers looking for a finished end-user app, since the README describes an SDK and CLI
- People who need a fully self-hosted setup, because the README's quickstart relies on a Composio API key and dashboard
- Projects needing a framework the README does not list as supported, which would require writing a custom provider
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
Composio
- Depends on a Composio account and API key for the quickstart, so you rely on a hosted service
- The README is truncated and gives limited detail on sandbox, triggers, and pricing
- Some providers are experimental, such as the Pi provider in @composio/experimental
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
Composio
Install TypeScript SDK with OpenAI Agents provider
npm install @composio/core @composio/openai-agents @openai/agentsInstall Python SDK with OpenAI Agents provider
pip install composio composio-openai-agents openai-agentsLlamaIndex
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openaiFrequently asked questions
What is the difference between Composio and LlamaIndex?
Composio: SDK for building AI agents that can take actions in 1000+ apps via pre-authenticated tools. LlamaIndex: Python framework for building LLM apps that query your own documents and data. Composio suits developers building ai agents that need to read or act in third-party apps, while LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents.
Are Composio and LlamaIndex free?
Composio: Free and open source. LlamaIndex: Free and open source.
Is Composio or LlamaIndex more popular?
LlamaIndex has more GitHub stars (52.8k) than Composio (30.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Composio and LlamaIndex?
Other options people compare them with include LangChain, CrewAI, Botpress, promptfoo, Portkey AI Gateway.
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.