run-llama/LlamaIndex
LlamaIndex is the leading document agent and OCR platform
LlamaIndex is an open-source data framework for building LLM applications over your own data, from RAG pipelines to document agents. It handles loading, parsing, indexing, and retrieval across hundreds of data sources, and gives developers tools to ground models in private information.
Our Review
LlamaIndex has 49,000 GitHub stars and is, alongside LangChain, one of the two frameworks most teams reach for when connecting an LLM to their own data. Its focus is the data side of AI apps: turning messy documents into something a model can search, reason over, and cite reliably.
What LlamaIndex does:
- •Data connectors load from hundreds of sources (PDFs, Notion, Slack, SQL, APIs) via LlamaHub, so your data is ready for an LLM.
- •Indexing and retrieval build vector, keyword, and graph indexes, then retrieve the right context for a query.
- •RAG pipelines assemble retrieval-augmented generation with rerankers, query engines, and response synthesis.
- •Document agents agents that read, parse, and reason over documents, including OCR for scanned and complex files.
- •LlamaParse and LlamaCloud managed document parsing and a hosted platform for production-scale ingestion.
- •Python and TypeScript core SDKs in both languages, with integrations across models and vector stores.
Getting started:
Install with pip install llama-index, load documents with a reader, build an index, and query it with index.as_query_engine(). Docs at developers.llamaindex.ai.
Limitations:
LlamaIndex is broad and evolves fast, so the API surface is large and changes between versions; pin a version for production. It is a developer framework, not a finished app, so you write code to assemble pipelines. Some managed parsing and cloud features (LlamaParse, LlamaCloud) are paid services. As with any RAG system, output quality depends on your data, chunking, and retrieval tuning.
Our Verdict
LlamaIndex is a default building block in 2026 for any developer connecting an LLM to private data. If you need to load documents, index them, and retrieve the right context for accurate, cited answers, LlamaIndex gives you mature, well-documented tools for exactly that, with 49,000 stars and an MIT license.
For developers, the strength is the data layer: hundreds of connectors through LlamaHub, multiple index types, query engines, rerankers, and document agents, all composable in Python or TypeScript. LlamaParse handles tricky PDFs and scans, which is often the hardest part of a real RAG pipeline.
Skip LlamaIndex if you only need a couple of simple retrievals; a lighter library or a direct vector-store call may be enough. If you want a ready-made app rather than a framework, a tool like AnythingLLM gives you RAG without writing the pipeline yourself.
Frequently Asked Questions
What is LlamaIndex?
LlamaIndex is an open-source data framework, from the run-llama team, for building LLM applications over your own data. It provides connectors to load documents, tools to index and retrieve them, and components for RAG pipelines and document agents. As of 2026 it also emphasizes document parsing and OCR, so models can reason over messy real-world files.
Is LlamaIndex free and open source?
Yes. LlamaIndex is released under the MIT license and is free and open source as of 2026. The Python and TypeScript frameworks cost nothing to use. The run-llama team also offers paid managed services, LlamaParse for document parsing and LlamaCloud for hosted ingestion, but the core framework is free to self-run.
How is LlamaIndex different from LangChain?
Both are popular frameworks for LLM apps. LlamaIndex specializes in the data and retrieval side: ingestion, indexing, RAG, and document agents. LangChain is broader, with general chains, agents, and integrations. Choose LlamaIndex when your core problem is connecting models to your data and getting retrieval right; choose LangChain for general orchestration, and note many teams use both.
What is LlamaParse?
LlamaParse is LlamaIndex's document-parsing service, designed to extract clean, structured text from complex PDFs, tables, and scanned files using OCR. Accurate parsing is often the hardest part of building a reliable RAG pipeline, so LlamaParse handles it as a managed step. It is a paid service as of 2026, while the core LlamaIndex framework remains free.
What languages does LlamaIndex support?
LlamaIndex provides core SDKs in both Python and TypeScript as of 2026, so you can build retrieval and agent pipelines in the language your app already uses. It integrates with a wide range of model providers and vector stores, and its LlamaHub registry offers hundreds of community data connectors and tools.
How do I install LlamaIndex?
Visit the GitHub repository at https://github.com/run-llama/llama_index for installation instructions.
What license does LlamaIndex use?
LlamaIndex uses the MIT license.
What are alternatives to LlamaIndex?
Explore related tools and alternatives on My AI Guide.
Open source & community-verified
MIT licensed: free to use in any project, no strings attached. 49,798 developers have starred this, meaning the community has reviewed and trusted it.
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