Haystack Deepset

Haystack is one of the most production-proven open-source frameworks for RAG and semantic search — deepset has been building NLP infrastructure since before the LLM era. The component-based pipeline architecture is clean and testable: DocumentStore, Retriever, Ranker, Generator, and custom components compose into pipelines that are serializable to YAML. Strong for teams building sophisticated retrieval-augmented generation systems. Haystack 2.0 (current) is a clean redesign over 1.x. Confidence is docs-derived.

Python SDK: pip install haystack-ai. Pipeline = orchestration primitive. Components: DocumentSplitter, OpenAIDocumentEmbedder, InMemoryDocumentStore, OpenAITextEmbedder, InMemoryBM25Retriever, OpenAIChatGenerator. Pipeline.add_component(), Pipeline.connect(). pipeline.run({query: ...}). YAML serialization: pipeline.to_yaml(). 30+ vector store integrations (Qdrant, Weaviate, Pinecone, Elasticsearch, etc.). Evaluation: deepeval, RAGAS integration.

No Haystack API key — open-source library. LLM and embedding provider keys via environment variables or component-level init params. Vector store credentials managed per-connector. Open-source MIT license.

Component-level failures surface as Python exceptions during pipeline run. YAML serialization enables reproducible pipeline definitions. Pipeline validation catches connection errors before execution. Retrieval quality is the primary runtime concern — monitor retrieval accuracy independently. Active GitHub (deepset-ai/haystack) with stable 2.x release track.

docs.haystack.deepset.ai covers quickstart, component library reference, vector store integrations, pipeline patterns, and evaluation. Getting started: pip install haystack-ai, build first RAG pipeline in under 20 minutes. Extensive community tutorials and recipes. Open-source (GitHub: deepset-ai/haystack). Community via Haystack Discord.