Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma (vector) + ripgrep (keyword).
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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).
RAGLite treats extracted document text as untrusted data. If you distill content from third parties (web pages, PDFs, vendor docs), assume it may contain prompt injection attempts.
RAGLite’s distillation prompts explicitly instruct the model to:
Hi — I’m Viraj. I built RAGLite to make local-first retrieval practical: distill first, index second, query forever.
If you hit an issue or want an enhancement:
Contributors are welcome — PRs encouraged; maintainers handle merges.
This skill defaults to OpenClaw 🦞 for condensation unless you pass
--engine./scripts/install.sh
This creates a skill-local venv at
skills/raglite/.venvraglite-chromadbraglite# One-command pipeline: distill → index ./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 \ --nodesThen query
./scripts/raglite.sh query "how does X work?"
--out ./raglite_out
--collection my-docs
--chroma-url http://127.0.0.1:8100
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:
It doesn’t replace memory/context — it’s the place to store what you need again.
No automatic installation available. Please visit the source repository for installation instructions.
View Installation Instructions
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