LEANN vs PageIndex
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
Lets an AI find answers in long documents by reading a table-of-contents-style tree, not vector search.
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 PageIndex if you need…
- Developers building question-answering over long PDFs such as annual reports or manuals
- Teams who want retrieved answers to be traceable to explicit sections of a document
- Projects that already have access to an LLM API key and can afford per-query model calls
- Users with a Claude subscription who want to use Claude Code models through the SDK
Side-by-side
| LEANN | PageIndex | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 13.8k | 40k |
| 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 | 8 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.
PageIndex
PageIndex is a Python library that builds a hierarchical tree index of a document, similar to a table of contents, and then uses an LLM to reason through that tree to find the relevant sections. It is aimed at long, complex professional documents such as financial reports, legal filings, and manuals, where the README says vector similarity search often returns the wrong passages. It does not use a vector database or split documents into chunks.
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
PageIndex
- Developers building question-answering over long PDFs such as annual reports or manuals
- Teams who want retrieved answers to be traceable to explicit sections of a document
- Projects that already have access to an LLM API key and can afford per-query model calls
- Users with a Claude subscription who want to use Claude Code models through the SDK
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
PageIndex
- The README describes retrieval with explicit, traceable references rather than opaque similarity scores
- No vector database or chunking step is required, according to the README
- Released under the MIT license with a published PyPI package
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
PageIndex
- People who need a fast, very cheap search over huge collections, since the README says each query relies on LLM reasoning over the tree and cost depends on the chosen model
- Users who want a no-code tool; the README centers on a Python SDK and API
- Short or simple documents, where the README's positioning suggests ordinary retrieval is enough
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
PageIndex
- Each query calls an LLM, so answer cost and speed depend on the model you choose; the README reports benchmark figures but your results will vary
- The benchmarks and cost numbers are published by the project itself and rely on its own test setup
- The README is truncated and does not fully document limits or edge cases
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 leannFrequently asked questions
What is the difference between LEANN and PageIndex?
LEANN: Local semantic search over your own files and chats, using far less storage than typical vector databases. PageIndex: Lets an AI find answers in long documents by reading a table-of-contents-style tree, not vector search. LEANN suits developers building a private rag prototype on a personal machine, while PageIndex suits developers building question-answering over long pdfs such as annual reports or manuals.
Are LEANN and PageIndex free?
LEANN: Free and open source. PageIndex: Free and open source.
Is LEANN or PageIndex more popular?
PageIndex has more GitHub stars (40k) 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 PageIndex?
Other options people compare them with include LlamaIndex, Faiss, 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.