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. observed · 2026-10-12
Health v2 · maintenance only
99/100
- Activity 99
- Release rhythm 98
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 10.5
- age_days: 2532
- days_rel: 19
- days_push: 10
- n_releases_24m: 55
Adoption not part of the score
26720 stars · 3271 forks observed · 2026-10-12
What it is AI-extracted, prompt v1, taxonomy v1, 2026-10-05, confidence 0.99
Haystack is an open‑source Python framework for orchestrating LLM‑powered pipelines, agents and RAG applications. It provides modular components for retrieval, routing, memory, generation and tooling, enabling production‑grade AI systems.
Use cases
- Build a conversational AI assistant that can retrieve documents and answer questions.
- Create a semantic search engine over a large corpus using vector databases.
- Develop a Retrieval‑Augmented Generation (RAG) pipeline for answering queries with up‑to‑date information.
- Implement autonomous agents that can use tools, maintain context, and make decisions.
- Deploy multimodal AI applications that combine text, images, and other data sources.
- Scale AI workloads in Kubernetes or Docker environments with built‑in monitoring.
When to choose
- You need a flexible, production‑ready framework to build LLM‑based agents or RAG systems.
- Your project requires integration with multiple model providers and vector stores without vendor lock‑in.
- You want built‑in observability, logging and deployment guides for cloud or on‑premise environments.
- You prefer a Python‑centric ecosystem with extensive community support and documentation.
When to avoid
- You are looking for a lightweight, single‑function library rather than a full orchestration stack.
- Your stack is not Python‑based or you need native support for non‑Python runtimes.
- You require a highly specialized component (e.g., only a vector database driver) without the extra framework overhead.
Facets
framework · maturity active
api-framework llm-inference rag agent-framework search-engine vector-database monitoring deployment artificial-intelligence large-language-models chatbots ai-agents data-science python windows cloud modular-pipelines production-ready multimodal observability enterprise-scale open-source retrieval-augmented-generation linux macos docker kubernetes
7 sources
- readme: https://github.com/deepset-ai/haystack · fetched 2026-10-12 · 513eac950aea
- homepage: https://haystack.deepset.ai · fetched 2026-09-21 · ffa45709fca3
- site_page: https://docs.haystack.deepset.ai/docs · fetched 2026-09-21 · 44136fa355b3
- site_page: https://docs.haystack.deepset.ai/docs/intro · fetched 2026-09-21 · c65444430e96
- site_page: https://www.deepset.ai/about · fetched 2026-09-21 · 32b51e5e3c49
- registry_pypi: https://pypi.org/pypi/hayhooks/json · fetched 2026-09-21 · a88abe39e198
- site_page: https://haystack.deepset.ai/integrations · fetched 2026-09-21 · b8c32549fe2b
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| deepset-ai/haystack | main | 99 |
| deepset-ai/haystack-cookbook | examples | 71 |
| deepset-ai/haystack-tutorials | docs | 76 |
| deepset-ai/haystack-core-integrations | plugin | 77 |
| deepset-ai/hayhooks | plugin | 93 |
| deepset-ai/haystack-demos | examples | 73 |
| deepset-ai/haystack-rag-app | examples | 34 |
For agents
markdown · JSON · MCP: product_card(name="deepset-ai/haystack")
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem