LlamaIndex

Sweet spot: any team whose main LLM problem is "get high-quality context into the model." LlamaIndex has the best loaders, the best complex-document parser in open tools, and the cleanest mental model for retrieval. If your RAG quality is poor today, LlamaIndex (and particularly LlamaParse on hard documents) is the highest-leverage intervention. Failure modes. For agent-heavy workloads, LlamaIndex's workflows are functional but trail LangGraph in production polish — pair them or pick one framework per job. LlamaParse and LlamaExtract, while excellent, are managed services that you are paying per document; for very high volume, an in-house parser may be cheaper. The framework has gone through several large refactors; treat any tutorial older than six months as stale. What to pilot. Pick your single worst document type — the one where your current RAG pipeline produces the worst answers. Run it through LlamaParse and rebuild your index. Measure answer quality on ten real questions before and after. If LlamaParse moves the needle, commit; if the bottleneck was elsewhere (chunking, reranking, prompt), at least you have localised the real problem.

LlamaIndex is the Python (and TypeScript) data framework for LLM applications. Where LangChain started as an orchestration framework, LlamaIndex started as a retrieval framework — the pipeline that gets your data into an LLM, as opposed to the logic around the LLM call. That focus has given it the most thorough toolbox in open source for the first half of any RAG stack. It ships 300+ data loaders (LlamaHub), multiple indexing strategies (vector, keyword, knowledge graph, tree, summary), a query engine abstraction that handles routing and hybrid retrieval, and a workflows system for multi-step agent orchestration. LlamaIndex 0.10+ reorganised the codebase into smaller focused packages, making it easier to install only what you need. LlamaCloud is the commercial managed version — specifically strong on LlamaParse, a PDF-parsing endpoint that handles tables, figures, and complex layouts far better than generic parsers. LlamaExtract turns unstructured documents into structured data given a Pydantic schema. Open source (MIT), maintained by LlamaIndex Inc, and powering production RAG at many companies. Pairs with any vector store and any LLM.