Haystack

Haystack by deepset is an open-source Python framework for building production-ready LLM applications, RAG pipelines, and neural search systems using a flexible pipeline architecture that treats each processing step, retrieval, ranking, generation, evaluation, as a composable component that can be swapped, configured, or replaced independently. Its Pipeline abstraction connects components into graphs that define complete application workflows: a document store feeds a retriever that feeds a prompt builder that feeds a generator, with any component replaceable without rewriting the surrounding pipeline. This modular design is Haystack's primary differentiator from LangChain and LlamaIndex: the strict component interface enables easier testing, component swapping, and production monitoring than frameworks where steps are more tightly coupled.

Haystack integrates with 30+ LLM providers (OpenAI, Anthropic, Cohere, Hugging Face), 25+ vector databases and document stores (Weaviate, Pinecone, Elasticsearch, OpenSearch), and tools for document conversion, preprocessing, and embedding generation. The Haystacks Evaluation framework measures RAG pipeline quality, retrieval accuracy, answer faithfulness, and response relevance, enabling systematic quality monitoring in production rather than relying on ad hoc testing. deepset Cloud provides hosted pipeline deployment for teams that want Haystack's flexibility without managing infrastructure. Haystack is strongest for ML engineering teams building complex production search and RAG systems who value architectural flexibility, testability, and component-level replaceability over framework breadth.