# deepset-ai/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.

Repository: https://github.com/deepset-ai/haystack
Canonical: https://ross.abutalabs.com/products/haystack
Homepage: https://haystack.deepset.ai
Language: Python
License: Apache-2.0
License Family: permissive
Topics: semantic-search, information-retrieval, ai, python, large-language-models, generative-ai, llm, rag, retrieval-augmented-generation, agents, orchestration, agent-framework, agentic-ai, agentic-rag, ai-agents, context-engineering, framework, genai, mcp, multi-agent
Last push: 2026-08-26T15:00:01+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 99, longevity 100
- inputs: {"age_days": 2484, "days_push": 7, "days_rel": 9, "gap_med": 12, "n_releases_24m": 56}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 26325, forks 3042 (observed 2026-08-28T04:11:46.787715+00:00)

## What it is
Haystack is an open-source Python AI orchestration framework by deepset for building production-ready LLM applications, agents, and RAG systems using composable pipelines. It offers modular components for retrieval, memory, routing, and generation, with 180+ integrations across model providers, vector databases, and monitoring tools.

## Use cases
- build a RAG pipeline over my own documents
- create AI agents that use tools and memory
- build semantic search over a document collection
- orchestrate multi-step LLM workflows in production
- build a chatbot that answers questions from a knowledge base
- connect OpenAI or Anthropic models to a vector database
- build multimodal LLM applications
- prototype and deploy agentic AI systems

## When to choose
- you need a mature, production-tested framework for RAG or agent pipelines
- you want explicit control and visibility over retrieval, routing, and generation steps
- you need vendor-neutral integrations with many LLM providers and vector stores
- you want serializable, Kubernetes-ready pipelines for enterprise deployment

## When to avoid
- you only need a thin wrapper around one LLM API for simple calls
- you prefer a minimal single-file agent loop over a structured pipeline framework
- you need a no-code or fully managed platform rather than a Python framework

## Facets
- artifact type: framework
- maturity: stable
- function: agent-framework, rag, llm-inference, search-engine, vector-database, chatbot, workflow-automation, mcp, prompt-engineering
- domain: artificial-intelligence, large-language-models, machine-learning, developer-tools
- platform: python, cross-platform, cloud, self-hosted
- tags: llm-orchestration, pipelines, semantic-search, context-engineering, multi-agent, document-stores, generative-ai, production-ai, ai-agents, retrieval-augmented-generation, search, natural-language-processing, docker, kubernetes

## Member repositories
- deepset-ai/haystack (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:46.787715+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:55:53.588355+00:00, confidence not recorded.
  - readme: https://github.com/deepset-ai/haystack (fetched 2026-08-28T04:11:46.787715+00:00, sha 513eac950aea)
  - homepage: https://haystack.deepset.ai (fetched 2026-08-29T07:52:22.336375+00:00, sha 9a84f1bc0bab)
  - site_page: https://docs.haystack.deepset.ai/docs (fetched 2026-08-29T07:52:22.344480+00:00, sha 44136fa355b3)
  - site_page: https://docs.haystack.deepset.ai/docs/intro (fetched 2026-08-29T07:52:22.348844+00:00, sha c65444430e96)
  - site_page: https://www.deepset.ai/about (fetched 2026-08-29T07:52:22.350602+00:00, sha 32b51e5e3c49)
  - site_page: https://haystack.deepset.ai/integrations (fetched 2026-08-29T07:52:22.346568+00:00, sha 20a0ff4e5eaf)
- Data as of 2026-08-30T08:39:29.467469+00:00.