haystack
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
Haystack is an open-source Python framework for building AI-powered apps that connect large language models to your own documents, databases, or tools using modular pipelines.
Haystack is an open-source Python framework for building AI-powered applications that use large language models (LLMs — the same technology behind ChatGPT). The core problem it solves is this: connecting an AI model to your own documents, databases, or tools is complex. Haystack gives you a structured way to design those connections as modular "pipelines" — sequences of steps where data flows from retrieval through filtering to generation and back.
A common use case is RAG (Retrieval-Augmented Generation), where the system first searches a knowledge base for relevant documents and then passes those to the AI model so it can answer questions accurately. Haystack also supports agent workflows, where an AI model can loop, make decisions, and call tools autonomously. Beyond that it handles semantic search (finding documents by meaning, not just keywords), multimodal inputs, and conversational systems.
You would reach for Haystack when you want to build something like a customer-support chatbot that reads your company's documentation, a research assistant that can search and summarize papers, or any production-grade AI pipeline where you need transparent control over how context reaches the model. It integrates with OpenAI, Anthropic, Mistral, Hugging Face, AWS Bedrock, and many others, so you are not locked into one provider. The primary language is Python.
Where it fits
- Build a customer-support chatbot that reads and answers questions from your company's documentation.
- Create a research assistant that searches academic papers and summarizes findings.
- Set up a semantic search system that finds documents by meaning rather than keywords.
- Build an AI agent that can loop, make decisions, and call external tools autonomously.