GraphRAG 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
Python pipeline that uses an LLM to turn raw text into a graph for answering questions over private documents.
Open source · MIT
Python framework for building LLM apps that query your own documents and data.
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 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
| GraphRAG | LlamaIndex | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 36.5k | 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
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.
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
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
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
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
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
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
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
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
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
GraphRAG
Install from PyPI
pip install graphragInitialize a project folder
graphrag init --root ./ragtestLlamaIndex
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 GraphRAG and LlamaIndex?
GraphRAG: Python pipeline that uses an LLM to turn raw text into a graph for answering questions over private documents. LlamaIndex: Python framework for building LLM apps that query your own documents and data. GraphRAG suits researchers exploring graph-based retrieval over narrative or private text, while LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents.
Are GraphRAG and LlamaIndex free?
GraphRAG: Free and open source. LlamaIndex: Free and open source.
Is GraphRAG or LlamaIndex more popular?
LlamaIndex has more GitHub stars (52.8k) 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 LlamaIndex?
Other options people compare them with include LightRAG, PageIndex, 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.