Three libraries, three different bets about what you'll need
All three libraries in this comparison can load documents, chunk them, embed them, and retrieve against a vector store -- the baseline RAG loop is table stakes for each. They differ in what they optimize for beyond that baseline, and that difference determines which one fights you least as the system grows past a prototype.
LlamaIndex: data framework first
LlamaIndex's center of gravity is the data layer: a large catalog of data connectors, index structures beyond flat vector search (tree indexes, keyword-table indexes, knowledge-graph indexes), and query engines that can combine multiple retrieval strategies. It treats "get the right context out of heterogeneous data sources" as the primary hard problem, and agent orchestration as secondary, layered on top of a mature retrieval core.
This makes it the strongest default when the retrieval problem itself is the hard part -- many document types, a corpus that benefits from a non-flat index structure, or a need to combine several retrieval strategies (keyword plus vector plus structured query) for one answer. It's a less natural fit when retrieval is simple (one document type, flat vector search is sufficient) and the actual complexity lives in the agent's control flow instead, where LangGraph or another orchestration-first tool is doing more of the real work.
LangChain: broad integration surface
LangChain's defining trait is breadth: the largest catalog of pre-built integrations across models, vector stores, document loaders, and tools, wired together through a common chaining abstraction (and its LangGraph sibling for more structured orchestration). The pitch is "whatever piece of infrastructure you're already using, there's probably an integration for it already," which matters most for teams whose stack is heterogeneous and changes frequently.
The trade-off that comes with that breadth is real: a library covering this much surface area accumulates abstraction layers, and teams report the sharpest learning curve of the three when they need to step outside the common path and understand what a chain is actually doing underneath. It's the strongest fit when integration breadth is the actual constraint -- swapping vector stores or model providers without a rewrite -- and a weaker fit when the team wants to understand and control every step of a narrow, stable pipeline.
Haystack: production pipeline first
Haystack's center of gravity is the deployable pipeline: components (retrievers, generators, rankers) wired into an explicit, inspectable pipeline graph, with a design history rooted in production search systems rather than notebook prototyping. It tends to feel the most like "software engineering" of the three -- explicit component contracts, less magic, a pipeline definition you can reason about the same way you'd reason about any other data pipeline.
This makes it the strongest fit for a team that already knows its retrieval architecture and wants to build and operate it as a stable production system, with less appetite for the rapid rebinding LangChain's integration breadth optimizes for. It's a weaker fit for early prototyping where the architecture is still being discovered, since its explicitness has an upfront cost that a looser tool doesn't charge.