GraphRAG 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 pipeline that uses an LLM to turn raw text into a graph for answering questions over private documents.
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 GraphRAG if you need…
- Researchers exploring graph-based retrieval over narrative or private text
- Developers who want to experiment with LLM-built knowledge graphs from their own documents
- Teams already comfortable with Python and paying for LLM API usage
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
| GraphRAG | PageIndex | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 36.5k | 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 | 7 days ago |
What each one is
GraphRAG
GraphRAG is a data pipeline and transformation suite from Microsoft Research. It uses large language models to pull structured information (a graph of entities and relationships) out of unstructured text, so that an LLM can reason about a collection of documents more effectively than simple keyword or chunk retrieval. It is released as a research project and the README describes it as largely in maintenance mode.
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
GraphRAG
- Researchers exploring graph-based retrieval over narrative or private text
- Developers who want to experiment with LLM-built knowledge graphs from their own documents
- Teams already comfortable with Python and paying for LLM API usage
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
GraphRAG
- Backed by a published Microsoft Research blog post and arXiv paper
- Has an official documentation site with quickstart and prompt tuning guidance
- MIT licensed with an active PyPI package
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
GraphRAG
- Anyone wanting a polished, supported end-user product, since the README says the code is a demonstration and not an officially supported Microsoft offering
- Users with a tight budget, because the README warns that indexing can be an expensive operation
- Teams expecting new features, since the README says the project won't accept new PRs or implement new features
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
GraphRAG
- Maintenance mode: only bug fixes and dependency updates, no new features
- Indexing cost can be high, and the README advises starting small
- Out-of-the-box results may be weak without prompt tuning
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
GraphRAG
Install from PyPI
pip install graphragInitialize a project folder
graphrag init --root ./ragtestFrequently asked questions
What is the difference between GraphRAG and PageIndex?
GraphRAG: Python pipeline that uses an LLM to turn raw text into a graph for answering questions over private documents. PageIndex: Lets an AI find answers in long documents by reading a table-of-contents-style tree, not vector search. GraphRAG suits researchers exploring graph-based retrieval over narrative or private text, while PageIndex suits developers building question-answering over long pdfs such as annual reports or manuals.
Are GraphRAG and PageIndex free?
GraphRAG: Free and open source. PageIndex: Free and open source.
Is GraphRAG or PageIndex more popular?
PageIndex has more GitHub stars (40k) than GraphRAG (36.5k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to GraphRAG and PageIndex?
Other options people compare them with include LightRAG, LlamaIndex, LEANN, ScrapeGraphAI, LangChain.
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.