LEANN 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
Local semantic search over your own files and chats, using far less storage than typical vector databases.
Open source · MIT
Python framework for building LLM apps that query your own documents and data.
The short answer
Choose LEANN if you need…
- Developers building a private RAG prototype on a personal machine
- People who want to semantically search their own files or notes locally
- Users of coding agents who want semantic code search through an MCP service
- Anyone with a large document collection who is short on disk space
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
| LEANN | LlamaIndex | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 13.8k | 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
LEANN
LEANN is a Python tool that builds a searchable index of your personal documents, such as files, emails, and chat logs, so an AI assistant can find relevant passages before answering. It is a vector database that recomputes embeddings on demand instead of storing them all, which the project says cuts storage by about 97% without accuracy loss. People use it to run private retrieval-augmented generation (RAG) on their own laptop without sending data to a cloud service.
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
LEANN
- Developers building a private RAG prototype on a personal machine
- People who want to semantically search their own files or notes locally
- Users of coding agents who want semantic code search through an MCP service
- Anyone with a large document collection who is short on disk space
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
LEANN
- Keeps data on the local machine, with no cloud dependency described in the README
- Claims large storage savings, with a benchmark example of 60M chunks in 6GB versus 201GB
- Works with common tooling including Ollama, LangChain, and LlamaIndex, per the repository topics
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
LEANN
- Teams needing a hosted, multi-user database, since the README centres on a personal, single-device setup
- Users who need GPU acceleration now, as the README says it is only being considered based on survey votes
- People who want a polished end-user app with no command line, since setup uses uv and Python tooling
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
LEANN
- Setup involves Python version requirements, uv, and optional build steps for DiskANN that can be fiddly on Linux and macOS
- Some README sections are truncated here, so the full feature set and configuration are not verified
- Accuracy and storage claims come from the project and its paper; independent verification was not part of this review
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
LEANN
Install uv
curl -LsSf https://astral.sh/uv/install.sh | shClone the repository
git clone https://github.com/StarTrail-org/LEANN.git leannLlamaIndex
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 LEANN and LlamaIndex?
LEANN: Local semantic search over your own files and chats, using far less storage than typical vector databases. LlamaIndex: Python framework for building LLM apps that query your own documents and data. LEANN suits developers building a private rag prototype on a personal machine, while LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents.
Are LEANN and LlamaIndex free?
LEANN: Free and open source. LlamaIndex: Free and open source.
Is LEANN or LlamaIndex more popular?
LlamaIndex has more GitHub stars (52.8k) than LEANN (13.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LEANN and LlamaIndex?
Other options people compare them with include Faiss, PageIndex, LightRAG, turbovec, LangGraph.
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.