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deepset-ai/haystack

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TLDR

Python framework for building AI applications that connect language models to your documents and tools through modular pipelines.

Mindmap

mindmap
  root((Haystack))
    What it does
      Connect LLMs to data
      Build AI pipelines
      Retrieve and generate
    Key features
      RAG workflows
      Agent loops
      Semantic search
      Multimodal support
    Use cases
      Customer support bots
      Research assistants
      Question answering
    Tech stack
      Python
      LLM integrations
      Vector databases
    Audience
      AI developers
      Product teams
      Researchers

Things people build with this

USE CASE 1

Build a customer-support chatbot that answers questions by searching your company's documentation.

USE CASE 2

Create a research assistant that retrieves and summarizes academic papers based on user queries.

USE CASE 3

Set up a question-answering system that grounds AI responses in your proprietary databases.

USE CASE 4

Deploy an agent workflow where an AI model autonomously decides which tools to call and loops until it solves a problem.

Tech stack

PythonOpenAIAnthropicMistralHugging FaceAWS Bedrock

Getting it running

Difficulty · moderate Time to first run · 30min

Requires API key from OpenAI, Anthropic, Mistral, Hugging Face, or AWS Bedrock to run meaningful examples.

Open-source framework available under a permissive license allowing free use for any purpose including commercial applications.

In plain English

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.

Copy-paste prompts

Prompt 1
Show me how to build a RAG pipeline in Haystack that retrieves documents from a vector database and passes them to an LLM for question answering.
Prompt 2
How do I set up a Haystack agent that can call multiple tools and make decisions autonomously?
Prompt 3
Walk me through connecting Haystack to OpenAI and Anthropic models so I can switch between them.
Prompt 4
How do I implement semantic search in Haystack to find documents by meaning rather than keyword matching?
Prompt 5
Show me how to build a conversational chatbot in Haystack that remembers context across multiple turns.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.