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LlamaIndex

Open Source

Data framework for connecting custom data sources to LLMs to build RAG applications.

Visit websiteGitHub

Pricing

Free / Open source

Type

Automation

Languages

Python, TypeScript

// VERDICT

Reach for LlamaIndex when your LLM app is data-and-RAG-centric - ingesting and querying your own documents. Skip it when you want a general agent framework (LangChain), a minimal approach, or you aren't doing retrieval.

Best for

A data framework for LLM apps focused on retrieval-augmented generation - ingesting, indexing and querying your data so models can answer over it.

Avoid when

You want a general agent/chaining framework, a minimal direct-API approach, or you aren't doing RAG.

CI/CD fit

Python/JS library · pairs with eval tools (Ragas) for RAG testing

Languages

Python · TypeScript

Team fit

RAG app developers · Teams building over their own data · Knowledge-base/Q&A features

Setup

Medium

Maintenance

Medium

Learning

Intermediate

Licence

Free / Open source

// BEST FOR

  • Ingesting and indexing your own data for RAG
  • Querying documents so LLMs answer over them
  • Connectors for many data sources
  • A RAG-first developer experience
  • Python and JS/TS support
  • Pairing with Ragas/DeepEval to test retrieval quality

// AVOID WHEN

  • You want a general agent/chaining framework (LangChain)
  • A minimal direct-API approach is enough
  • You aren't building retrieval/RAG
  • No-code is required
  • Stability over a fast-moving API is critical
  • Your data is trivial enough to inline

// QUICK START

pip install llama-index   # or npm i llamaindex
# load + index your data -> build a query engine
# test retrieval/faithfulness with a RAG eval tool

// ALTERNATIVES TO CONSIDER

ToolChoose it when
LangChainYou want general agents/chaining beyond RAG.
RagasYou need to evaluate the RAG pipeline you build.
LangSmithYou want tracing + eval for the app.

// FEATURES

  • Data connectors for files, APIs, databases, and SaaS sources
  • Indexes (vector, summary, knowledge graph) over private data
  • Query engines with retrieval, ranking, and post-processing
  • Built-in evaluation suite for RAG quality
  • Agent and workflow primitives for multi-step reasoning

// PROS

  • Purpose-built for retrieval-augmented generation
  • Strong selection of indexing strategies and rerankers
  • LlamaHub catalogue of community data loaders
  • Detailed RAG evaluation utilities out of the box

// CONS

  • Narrower scope than general LLM frameworks like LangChain
  • Documentation can lag fast-moving API changes
  • Production hosting requires LlamaCloud or self-managed infrastructure

// EXAMPLE QA WORKFLOW

  1. Install LlamaIndex

  2. Load and chunk your data

  3. Build embeddings and an index

  4. Create a query engine

  5. Test retrieval/faithfulness with a RAG eval tool

  6. Re-index as data changes; pin versions

// RELATED QA.CODES RESOURCES

Cheat sheets

Glossary