| name | raglite |
| description | Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword). Use when this capability is needed. |
RAGLite — a local RAG cache (not a memory replacement)
RAGLite is a local-first RAG cache.
It does not replace model memory or chat context. It gives your agent a durable place to store and retrieve information the model wasn’t trained on — especially useful for local/private knowledge (school work, personal notes, medical records, internal runbooks).
Why it’s better than paid RAG / knowledge bases (for many use cases)
- Local-first privacy: keep sensitive data on your machine/network.
- Open-source building blocks: Chroma 🧠 + ripgrep ⚡ — no managed vector DB required.
- Compression-before-embeddings: distill first → less fluff/duplication → cheaper prompts + more reliable retrieval.
- Auditable artifacts: distilled Markdown is human-readable and version-controllable.
Default engine
This skill defaults to OpenClaw 🦞 for condensation unless you pass --engine explicitly.
Install
./scripts/install.sh
Usage
./scripts/raglite.sh run /path/to/docs \
--out ./raglite_out \
--collection my-docs \
--chroma-url http://127.0.0.1:8100 \
--skip-existing \
--skip-indexed \
--nodes
Pitch
RAGLite is a local RAG cache for repeated lookups.
When you (or your agent) keep re-searching for the same non-training data — local notes, school work, medical records, internal docs — RAGLite gives you a private, auditable library:
- Distill to structured Markdown (compression-before-embeddings)
- Index locally into Chroma
- Query with hybrid retrieval (vector + keyword)
It doesn’t replace memory/context — it’s the place to store what you need again.
Converted and distributed by TomeVault — claim your Tome and manage your conversions.
