Haystack

Haystack by deepset is a Python framework for building production-ready NLP and LLM applications, with a particular focus on retrieval-augmented generation (RAG) pipelines. Now in version 2.x, Haystack was fundamentally redesigned around a pipeline-of-components architecture that emphasizes composability, type safety, and production readiness.

The core abstraction is the Pipeline — a directed graph of Components connected by typed input/output sockets. Components are self-contained units that perform specific tasks: retrievers fetch documents, embedders generate vectors, generators call LLMs, rankers reorder results, and converters handle document formats. This design means you build NLP systems by wiring together components rather than writing monolithic code.

Haystack 2.x enforces explicit connections between components using Pipeline.connect(), which validates input/output type compatibility at construction time rather than runtime. This catches integration errors early and makes pipelines self-documenting. The framework also serializes entire pipelines to YAML, enabling versioning, sharing, and deployment of complete RAG configurations.

The document store abstraction supports Elasticsearch, OpenSearch, Pinecone, Weaviate, ChromaDB, Qdrant, pgvector, and in-memory stores through a unified API. Haystack handles document indexing pipelines (ingest, clean, split, embed, store) and query pipelines (embed query, retrieve, rerank, generate) as separate concerns, which is cleaner than frameworks that conflate ingestion and retrieval.

deepset Cloud provides a managed platform for deploying Haystack pipelines with a visual pipeline editor, evaluation tools, annotation interfaces, and production monitoring. It's particularly valuable for teams that need to involve domain experts in pipeline configuration without requiring Python knowledge.

Haystack's honest differentiator is its maturity in production RAG. It was building document retrieval systems before the LLM boom, and that experience shows in thoughtful design decisions: proper document preprocessing, evaluation frameworks for measuring quality, and a component model that makes it easy to swap providers. The tradeoff is that Haystack is more structured than ad-hoc frameworks — there's an upfront learning curve, but it pays off in maintainability and testability.