LangChain vs Haystack

LangChain vs Haystack compares two open-source frameworks for building large-language-model (LLM) applications. LangChain is a modular toolkit—loaders, embeddings, vector stores, chains, agents, and memory—that excels at rapid prototyping and fine-grained control. It lets developers swap GPT-4 for Claude, or Chroma for Pinecone, with a one-line change and supports multi-tool agents, Retrieval-Augmented Generation (RAG), and cost-tracing callbacks. Haystack, by deepset, ships as an opinionated end-to-end stack: document stores, retrievers, rankers, evaluators, and a FastAPI server baked in. It targets production search workloads with built-in labeling UIs, Ray scaling, and elastic Kubernetes charts. LangChain shines when you need flexible agent workflows, custom data ingestion, or model agility; Haystack wins when you want a turnkey RAG API, real-time monitoring, and tight MLOps integration. Many teams combine them—Haystack handles indexing and REST, while LangChain orchestrates prompts and agents—leveraging the strengths of both ecosystems.