LlamaIndex
Data framework for building RAG applications with indexing, retrieval, and query engines.
LlamaIndex (formerly GPT Index) is a data framework purpose-built for connecting LLMs to external data sources. While LangChain takes a broad approach to LLM application development, LlamaIndex focuses on making data ingestion, indexing, and retrieval as seamless as possible.
The framework excels at the RAG pipeline: you connect to data sources (PDFs, databases, APIs, websites), parse and chunk the content, generate embeddings, store them in a vector index, and query the index with sophisticated retrieval strategies. LlamaIndex supports 40+ data connectors and 10+ vector store backends out of the box.
What sets LlamaIndex apart is its query engine architecture. You can build simple top-k retrieval, hybrid search (vector + keyword), recursive retrieval (query decomposes into sub-queries), and agent-based retrieval where the LLM decides which tools to query. The framework also includes advanced features like structured data extraction, knowledge graph indexing, and multi-document querying.
Key Features: 40+ data connectors for files, databases, and APIs; Multiple indexing strategies (vector, summary, tree, keyword, knowledge graph); Advanced retrieval modes (hybrid, recursive, agent-based); Structured data extraction from unstructured documents; Query engine with routing and transformation capabilities; Built-in evaluation tools for measuring retrieval quality.
Who Should Use It: Developers building RAG-powered applications; teams implementing document Q&A systems; data scientists who need to connect LLMs to structured and unstructured data.
Pricing: Open-source (MIT license). LlamaCloud (managed service) has a free tier, paid plans start at $50/month.
Pros: Best-in-class RAG capabilities; excellent data connector library; advanced retrieval strategies produce better results than simple vector search; strong evaluation tools.
Cons: Narrower scope than LangChain (less agent/general-purpose support); documentation can be dense; some advanced features require deep understanding of retrieval theory.
Alternatives: LangChain (broader LLM framework), Haystack (opinionated RAG pipeline), txtai (all-in-one embeddings database).
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