haystack

It provides a composable pipeline architecture where developers chain components (retrievers, readers, generators, rankers, preprocessors) into end-to-end systems for extracting information from large document corpora, with a focus on the production engineering requirements that make AI applications reliable at scale.

The framework abstracts over LLM providers, vector databases, and document stores through consistent interfaces, enabling teams to swap components without restructuring their pipeline code.

Haystack's document store integrations cover all major vector databases Qdrant, Weaviate, Pinecone, Chroma, Elasticsearch alongside traditional document stores, and its generator integrations span OpenAI, Anthropic, Cohere, Hugging Face, and local models.

The pipeline definition format is serializable to YAML, enabling version control and deployment of pipeline configurations separately from code.

NLP engineers building enterprise search systems, developers creating document Q&A applications over private knowledge bases, AI platform teams deploying RAG infrastructure for multiple business units, and researchers building complex multi-step information extraction systems use Haystack as their orchestration layer.

Its focus on production engineering component-level monitoring, pipeline debugging, evaluation datasets, and CI/CD integration distinguishes it from prototyping frameworks that don't address the operational concerns that determine whether AI applications succeed in production.

Haystack 2.x introduced a fully redesigned pipeline model with stronger type safety, improved component composition, and native support for agentic workflows where language models make decisions about which tools or retrieval steps to invoke next.