LlamaIndex 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
Python framework for building LLM apps that query your own documents and data.
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 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
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
| LlamaIndex | PageIndex | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 52.8k | 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 | 10 days ago | 7 days ago |
What each one is
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.
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
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
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
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
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
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
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
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
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
LlamaIndex
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 LlamaIndex and PageIndex?
LlamaIndex: Python framework for building LLM apps that query your own documents and data. PageIndex: Lets an AI find answers in long documents by reading a table-of-contents-style tree, not vector search. LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents, while PageIndex suits developers building question-answering over long pdfs such as annual reports or manuals.
Are LlamaIndex and PageIndex free?
LlamaIndex: Free and open source. PageIndex: Free and open source.
Is LlamaIndex or PageIndex more popular?
LlamaIndex has more GitHub stars (52.8k) than PageIndex (40k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LlamaIndex and PageIndex?
Other options people compare them with include LangChain, LangGraph, OpenAI Agents SDK, LightRAG, GraphRAG.
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.