Haystack is deepset's open-source framework for building production RAG pipelines and LLM applications. Unlike most agent frameworks that treat RAG as an add-on, Haystack's pipeline architecture was designed from the ground up for document retrieval at scale — making it the natural choice for knowledge-intensive production deployments.
Haystack v2 models AI applications as pipelines — directed graphs of components (retrievers, generators, routers, converters) connected by typed inputs and outputs. This makes Haystack unusually explicit about data flow, which has direct implications for PSF compliance: every hop between components is inspectable and testable.
deepset maintains both the open-source framework and deepset Cloud, a managed platform for deploying Haystack pipelines at scale. The company has significant enterprise traction particularly in document-heavy industries: legal, financial services, healthcare, and government. This enterprise focus shows in Haystack's production deployment primitives — areas where most Python frameworks are notably weak.
Haystack's Hayhooks provides production REST API serving for pipelines out of the box — no custom FastAPI wrapper required. Combined with Docker-first design and YAML-driven pipeline configuration, Haystack treats production deployment as a first-class concern rather than an afterthought.
Pipeline versioning via YAML means deployment rollback is a configuration change. Blue-green deployment between pipeline versions is supported. This is a material PSF D5 advantage over LangChain, CrewAI, and AutoGen — none of which provide equivalent deployment primitives natively.
Haystack emits OpenTelemetry traces for every pipeline run — each component step is a span, enabling end-to-end visibility from input to output. Token usage, latency, and component errors are captured without additional instrumentation.
Integration with deepeval for quality evaluation and Langfuse for trace storage gives Haystack a complete D4 stack. For teams already using Langfuse (particularly for its self-hosting and data residency properties), Haystack is the most naturally compatible framework.
Haystack's typical use case — ingesting documents and answering questions about them — means user-submitted documents frequently contain PII, commercially sensitive data, or legally privileged content. The RAG retrieval step surfaces this content directly into prompts.
For document-heavy RAG pipelines, add a DocumentCleaner component that runs Microsoft Presidio (or equivalent) on ingested documents before indexing. Redact or pseudonymise PII at index time — not at query time. Once PII is in your vector store, it is retrievable.
Use the PSF, research library, and Lab material to review your own deployment. Credentials are available when a client, employer, or regulator needs public proof.