Haystack

Haystack is the AI orchestration framework for teams that want production RAG without LangChain's sprawl. Built by deepset, it gives you modular pipelines with explicit control over retrieval, routing, memory, and generation. Less magic, more engineering.

If you're building a retrieval-augmented generation system — chatbots over your docs, semantic search, knowledge bases — Haystack is the structured choice. LangChain has a broader scope and more integrations but its abstraction layers frustrate production teams. LlamaIndex focuses specifically on data indexing and retrieval. Commercially, cloud AI platforms (Azure AI, AWS Bedrock) offer managed RAG with less control.

Benchmarks show lower framework overhead (~5.9ms) and less token waste than LangChain. The documentation is "drastically better" according to teams that evaluated both. The pipeline model makes debugging transparent.

The catch: Haystack is narrower than LangChain. If you need complex agentic workflows, multi-step reasoning chains, or broad tool integration, LangChain's flexibility wins. The community and tutorial ecosystem is smaller. And if you're prototyping quickly, LangChain's "throw it together" approach ships faster — Haystack's structure is an investment that pays off in production, not in hackathons.