What Haystack does
Haystack by deepset is an open-source Python framework for composing document processing, retrieval, generation, tools, and agents into testable AI pipelines.
Haystack is strongest when an AI application needs visible, replaceable stages rather than one opaque chain. Its components and integrations cover document conversion, stores, retrievers, rankers, generators, routers, tools, and evaluation-friendly outputs. Pipelines can branch, loop, and run independent paths, which supports both conventional RAG and tool-using agents.
The framework is free under an open-source license. Operating cost depends on the selected model, embedding service, document store, search provider, compute, storage, and deployment platform; optional enterprise support is sales-assisted. Teams should benchmark a representative corpus because ingestion volume, re-embedding, reranking, long prompts, and retries can dominate cost.
Haystack does not make retrieved documents trustworthy or model answers factual. Access controls must carry through ingestion and retrieval, untrusted documents can inject instructions, and agent tools may change external systems. Preserve source metadata, filter by authorization before retrieval, isolate secrets, constrain tools, evaluate difficult queries, and show evidence to a human reviewer where mistakes matter.
How Haystack works
Developers instantiate components for conversion, splitting, embedding, storage, retrieval, prompting, generation, routing, or tools, connect their typed inputs and outputs in a directed graph, and run the pipeline with application data. Indexing pipelines prepare documents; query pipelines retrieve context and ask a configured model to generate or structure a result. Agent components can loop over model-selected tools and state until an exit condition is met. Haystack orchestrates the work but external models and stores process data under their own terms, so teams must evaluate retrieval, citations, actions, and final answers.
How to set up Haystack
Define the answer contract
Specify users, authorized corpus, freshness, citation needs, latency, abstention behavior, risky actions, and an evaluation set before selecting components.
Install a clean Haystack environment
Install haystack-ai separately from legacy farm-haystack, then add only the optional integration packages required by the pipeline.
Build indexing first
Convert, clean, split, enrich, and write a representative document sample while preserving identity, source, timestamps, and access metadata.
Assemble and evaluate queries
Connect retrieval, ranking, prompting, generation, and answer building; measure recall, groundedness, citation accuracy, latency, and cost.
Harden and operate
Apply authorization before retrieval, restrict tools, set timeouts and exit conditions, trace component outputs, monitor drift, and require review for high-impact results.
Haystack FAQs
Is Haystack free?
Yes. The Haystack framework is open source. Model calls, search services, databases, cloud infrastructure, and optional enterprise support are separate costs.
Is Haystack only for RAG?
No. It supports indexing and retrieval, but its components, branches, loops, tools, and Agent component also support search, extraction, multimodal, and agentic workflows.
What is the difference between a component and a pipeline?
A component performs a bounded operation with declared inputs and outputs. A pipeline connects components into the execution graph for a complete workflow.
Does Haystack include a model?
No single model is bundled as the answer engine. Integrations connect the framework to commercial and open models, and the chosen provider's price, license, and data terms apply.
Does RAG eliminate hallucinations?
No. Retrieval can miss, rank, or expose the wrong source, and a model can misstate retrieved evidence. Test retrieval separately and require traceable citations and review.
Listing reviewed 2026-08-02. Product details and pricing can change; verify important terms on the provider's website.
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