Haystack

Haystack is an open-source AI orchestration framework built by deepset that helps Python developers build production-ready RAG (retrieval-augmented generation) systems, AI agents, and semantic search applications. Instead of hiding the mechanics behind a single black-box call, Haystack breaks an application into explicit, modular pipelines made of retrievers, generators, routers, and tools. Each piece can be tested and swapped independently, which is why enterprise teams building mission-critical search and agent systems reach for it when they need visibility into exactly how context moves through their application.

How Haystack works

Haystack organizes an application into a Pipeline, a graph of Components connected in a defined order.

Components: Each component handles one job, such as embedding text, retrieving documents, ranking results, or generating a response. You can use Haystack’s built-in components or write your own.

Document Stores: These hold your data as indexed documents with vector embeddings attached, so a retriever can search by meaning rather than exact keyword match.

Pipelines: You wire components together into a directed graph, including branches and loops, so data flows through retrieval, ranking, and generation in whatever order your use case needs.

Agents and Tools: Haystack’s Agent component lets a language model decide, at runtime, whether to call a tool like a web search or a calculator before answering, without you writing manual if-else routing logic.

Deployment: Once built, a pipeline can be served as a REST API or an MCP (Model Context Protocol) server using deepset’s Hayhooks tool, and monitored with built-in tracing, logging, and evaluation utilities.

This structure means you can change your vector database, swap a generator model, or add an evaluation step without rewriting the rest of the system.

What you can build with Haystack

Enterprise document Q&A: Index thousands of internal PDFs, wikis, and reports, then let employees ask natural-language questions and get answers with the source passage attached.

Tool-calling support agents: Build an agent that reads a customer’s question, decides whether it needs to search a knowledge base or call an order-lookup API, and responds accordingly.

Text-to-SQL pipelines: Convert a plain-English business question into a validated SQL query, run it against a live database, and return the result in natural language.

Multimodal search systems: Combine text and image inputs in one pipeline, useful for applications like searching product catalogs by photo plus description.

Conversational RAG chatbots: Build multi-turn chat interfaces that retrieve relevant context per turn and keep track of prior conversation state.

Compliance-aware enterprise search: Deploy retrieval pipelines with access controls and evaluation tooling that satisfy governance requirements at large organizations like the kind Haystack already serves in production.