Build a customer-support chatbot that answers questions by searching your company's documentation.
Create a research assistant that retrieves and summarizes academic papers based on user queries.
Set up a question-answering system that grounds AI responses in your proprietary databases.
Deploy an agent workflow where an AI model autonomously decides which tools to call and loops until it solves a problem.
Requires API key from OpenAI, Anthropic, Mistral, Hugging Face, or AWS Bedrock to run meaningful examples.
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.
Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.