LightRAG 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
A Python library that builds a knowledge graph from your documents to answer questions with an LLM.
Open source · MIT
Python framework for building LLM apps that query your own documents and data.
The short answer
Choose LightRAG if you need…
- Developers building question-answering over their own document collections
- Teams who want a graph-based alternative to plain vector-search RAG
- Researchers experimenting with graph-based retrieval and evaluation (RAGAS and Langfuse integrations are listed)
- People who want a self-hosted RAG setup with a choice of storage backends
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
| LightRAG | LlamaIndex | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 40.5k | 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 | 8 days ago | 10 days ago |
What each one is
LightRAG
LightRAG is a Python library for retrieval-augmented generation, where a language model answers questions using information pulled from your own documents. It extracts entities and relationships from text into a knowledge graph and uses that graph, alongside vector search, to find relevant context for a query. The README also describes a web UI, Docker-based deployment, and support for several storage backends.
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
LightRAG
- Developers building question-answering over their own document collections
- Teams who want a graph-based alternative to plain vector-search RAG
- Researchers experimenting with graph-based retrieval and evaluation (RAGAS and Langfuse integrations are listed)
- People who want a self-hosted RAG setup with a choice of storage backends
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
LightRAG
- Built on a published research paper (EMNLP 2025 per the description) with a linked arXiv preprint
- Supports multiple storage backends and a range of configurable chunking strategies
- MIT license and an active project with recent releases and a large star count
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
LightRAG
- Non-programmers who only want a ready-made chat app; the README centers on a Python library and API, though a WebUI is mentioned
- Anyone needing a fully managed hosted service; this is self-hosted open source
- Projects where you cannot pay for or run an LLM and embedding model, since the README relies on external model services
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
LightRAG
- Setup involves several moving parts (LLM, embedding model, storage), which the README does not simplify for beginners
- Many features are recent and listed in news items, so behavior may change between versions
- The README shown is truncated, so full configuration details were not available to verify
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
LightRAG
Install uv (package manager recommended by the README)
git clone https://github.com/HKUDS/LightRAG.gitLlamaIndex
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 LightRAG and LlamaIndex?
LightRAG: A Python library that builds a knowledge graph from your documents to answer questions with an LLM. LlamaIndex: Python framework for building LLM apps that query your own documents and data. LightRAG suits developers building question-answering over their own document collections, while LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents.
Are LightRAG and LlamaIndex free?
LightRAG: Free and open source. LlamaIndex: Free and open source.
Is LightRAG or LlamaIndex more popular?
LlamaIndex has more GitHub stars (52.8k) than LightRAG (40.5k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LightRAG and LlamaIndex?
Other options people compare them with include GraphRAG, RAG-Anything, PageIndex, LEANN, Semantica.
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.