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.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest 5 days ago, with 452 contributors on GitHub.
Peaked at #28 in the weekly ranking the week of Sep 14, its best in the past year.
Use Cases & Benefits
- Provides an open-source AI orchestration framework to build modular, production-ready LLM applications with control over retrieval, routing, memory, and generation.
- Enables technology-agnostic, flexible, and extensible pipelines that integrate various LLMs, embedding models, and vector databases for scalable NLP solutions.
- Use for building retrieval-augmented generation (RAG) systems that combine vector search with LLMs for enhanced document understanding and answer generation.
- Use for developing semantic search engines that retrieve documents based on meaning rather than keywords, improving information discovery in large corpora.
- Use for creating conversational AI and multi-agent workflows that handle complex queries by orchestrating multiple AI components and knowledge sources.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Haystack is an end-to-end LLM framework that allows you to build applications powered by LLMs, Transformer models, vector search and more. Whether you want to perform retrieval-augmented generation (RAG), document search, question answering or answer generation, Haystack can orchestrate state-of-the-art embedding models and LLMs into pipelines to build end-to-end NLP applications and solve your use case.
The simplest way to get Haystack is via pip:
Install from the
mainbranch to try the newest features:Haystack supports multiple installation methods including Docker images. For a comprehensive guide please refer to the documentation.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
3.4K+404
Open issues
180+76
Compared with peers in AI Agents
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| morluto/rea | 450.0K | +22.9K | 75 | 16 days ago |
| vercel-labs/agent-desktop | 21.4K | +8.7K | 75 | 5 days ago |
| milind-soni/OpenMausBot | 20.6K | +8.4K | 75 | today |
| stablyai/orca | 121.4K | +6.7K | 75 | today |
| thedotmack/claude-mem | 115.8K | +4.7K | 75 | today |
| deepset-ai/haystack | 26.9K | — | 65 | 5 days ago |
| Category median | 115.8K | +8.4K | 75 | — |
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