Haystack
deepset-ai/haystack
Production AI orchestration — pipelines for RAG and agent-style apps. GitHub: deepset-ai/haystack (~26k stars).
GitHub repository
Overview
Haystack (deepset-ai/haystack) is listed in LimeDock Directories as an AI agent resource. Production AI orchestration — pipelines for RAG and agent-style apps. Group: multi-agent. Community size: ~26k GitHub stars. Repo (copy/paste): https://github.com/deepset-ai/haystack This page is a plain-English guide so searches for the GitHub project can land on how teams actually use it.
Work with LimeDock
Using this skill? LimeDock can wire it into a durable automation you own.
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We sell owned automations for SaaS teams — live workflows that plug into Slack, CRM, and your internal platform — not just a skill list.
Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/deepset-ai/haystack GitHub path: deepset-ai/haystack
How to open it: 1. Copy: https://github.com/deepset-ai/haystack 2. Clone or follow the README for your OS/stack. 3. Start with the smallest example before production use.
How to use it
**Simple example** You need production-grade RAG/orchestration with clear pipelines.
1. Install Haystack. 2. Build a retrieve→generate pipeline. 3. Evaluate on your own questions before tools/agents.
Example prompts
- “Build a Haystack RAG pipeline on our docs.”
- “Haystack vs LlamaIndex?”
- “Where agents fit in Haystack pipelines.”
Use cases and examples
- Evaluate deepset-ai/haystack as an AI agent building block
- Pilot Haystack on one staging workflow
- Compare against agents you already pay for
- Document a team playbook after the pilot
Prerequisites
- Python
- Document corpus
Tips
- Evals before fancy agents.
- Production = monitoring + fallbacks.