Haystack
Production-ready AI pipeline framework by deepset. Build composable RAG, question answering, and agent pipelines with modular components and any LLM backend.
Haystack is a production-grade framework by deepset for building composable AI pipelines that connect LLMs with data. It provides modular, swappable components for document processing, retrieval, generation, and evaluation that snap together into directed acyclic graphs. For engineering teams building production NLP and RAG systems who need a framework with strong typing, testability, and production operational patterns, Haystack offers the most structured and opinionated pipeline architecture available.
Key Features
Pipeline-first architecture. Haystack organizes AI workflows as directed acyclic graphs of typed components. Each component has defined inputs and outputs, making pipelines self-documenting, testable, and easy to debug. Components can be swapped without changing pipeline structure — replace a vector retriever with a keyword retriever by changing one node.
Modular component system. Pre-built components cover the full pipeline lifecycle: document converters, preprocessors, embedders, retrievers, generators, rankers, routers, and evaluators. Custom components are plain Python classes with typed inputs and outputs, requiring minimal boilerplate.
LLM and provider flexibility. Haystack integrates with OpenAI, Anthropic, Hugging Face, Ollama, vLLM, and other providers through dedicated generator components. Switch between local and cloud LLMs without restructuring pipelines.
Document stores. First-class integrations with vector databases including Qdrant, Weaviate, ChromaDB, Pinecone, Elasticsearch, and pgvector. Document stores handle embedding storage, retrieval, and filtering with a unified API.
Evaluation framework. Built-in evaluation pipelines measure retrieval and generation quality using metrics like faithfulness, answer relevance, context precision, and semantic similarity. Run evaluations as part of CI/CD to catch quality regressions.
Serialization and deployment. Pipelines serialize to YAML for version control, sharing, and deployment. The serialization format captures the complete pipeline configuration, making it reproducible across environments.
When to Use Haystack
Choose Haystack when building production AI systems that require rigorous engineering practices. It suits teams that value typed interfaces, testable components, and pipeline reproducibility. It is well-suited for enterprise RAG deployments, multi-step document processing workflows, and any project where pipeline reliability and maintainability are priorities.
Ecosystem Role
Haystack occupies the production-engineering tier alongside LlamaIndex and LangChain. It is more opinionated about pipeline structure than LangChain, and more focused on engineering rigor than LlamaIndex’s data-first approach. For rapid prototyping, LangChain may be faster. For data-heavy RAG, LlamaIndex has more connectors. For production pipeline engineering, Haystack’s typed, testable architecture is its differentiator.