# Raggy

> Updated 2026-09-11 · type: tool · category: ai-infrastructure · status: active · rev 1

Raggy is an MIT-licensed CLI for RAG over local documents — hybrid vector+BM25 retrieval with reranking, running fully locally via Ollama.

- Open source: yes (MIT)
- Self-hostable: yes
- Pricing model: free
- Best for: Operators who want Q&A over private playbooks, call notes and internal docs without standing up a RAG stack or sending the corpus to a SaaS.
- Last verified: 2026-09-11

- **Canonical:** https://gtmstacker.com/registry/tool/raggy/
- **Source:** [github · paulknysh/raggy](https://github.com/paulknysh/raggy)
- **Tags:** ai-infrastructure, rag, cli, local-first, ollama, self-hostable
- **Repository:** https://github.com/paulknysh/raggy

## Is Raggy open source?

Yes, Raggy is open source under the MIT license.

## How much does Raggy cost?

Raggy is free to use.

## Can I self-host Raggy?

Yes, Raggy can be self-hosted (the source is available under the MIT license).


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Open-source CLI (MIT, Python) for retrieval-augmented Q&A over your local documents: hybrid retrieval (vector + BM25), cross-encoder reranking, and fully local embedding/indexing via Ollama — answer generation can stay local or use a remote API, so private GTM docs never have to leave your machine.

## Provenance

- MIT independently WebFetch-verified 2026-09-11 (21★, 51 commits, active; Python). Surfaced via the 2026-09-11 daily pull (Show HN, 4 points).
- Curated from the GTM Stacker signal registry (2026-09-11 pass: daily pull + viral-posts brief); license independently WebFetch-verified 2026-09-11.

## Why it matters for a GTM stack

The cheapest useful "knowledge layer" for a lean GTM team is RAG over the docs you already have — ICP notes, call transcripts, positioning. This does that as one local CLI with sane retrieval defaults (hybrid + rerank), and the local-first design keeps client and pipeline data on your own hardware.