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RAGLite
by
Viraj Sanghvi
· GitHub ↗
· v1.0.8
3107
Downloads
3
Stars
7
Active Installs
10
Versions
Install in OpenClaw
/install raglite
Description
Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma (vector) + ripgrep (keyword).
Usage Guidance
This skill appears to be what it says: a local RAG cache that installs a Python CLI into a skill-local virtualenv. Before installing: 1) Inspect or vet the PyPI package (raglite-chromadb) or install it in an isolated environment — pip installs run code during installation. 2) Avoid setting RAGLITE_PIP_INDEX_URL unless you trust the index; that can make the installer fetch packages from arbitrary servers. 3) When indexing sensitive docs, ensure --chroma-url points to a local/controlled Chroma instance (not a remote third party) and be aware the tool defaults to using the agent's OpenClaw engine for condensation, which will send data to whatever model backend your agent is configured to use. If you want full offline behavior, explicitly set a local engine and local Chroma, and review the upstream repo linked in SKILL.md for the package source and code.
Capability Analysis
Type: OpenClaw Skill
Name: raglite
Version: 1.0.8
The OpenClaw AgentSkills skill bundle for 'raglite' appears benign. The `SKILL.md` clearly outlines the purpose of a local RAG cache and even includes a 'Security note' addressing prompt injection from source material, instructing the model to treat sources as data only. The `scripts/install.sh` creates a skill-local Python virtual environment and installs the `raglite-chromadb` package from PyPI (or a configurable index), without any evidence of malicious execution, data exfiltration, or persistence mechanisms. The `scripts/raglite.sh` merely wraps the execution of the installed `raglite` command, defaulting the engine to 'openclaw'. There are no instructions for the agent to ignore the user, hide actions, or perform unauthorized activities.
Capability Assessment
Purpose & Capability
Name/description (local RAG cache using Chroma + ripgrep) align with the required binaries (python3, pip, rg) and the behavior in SKILL.md and scripts. The installer installs the Python package raglite-chromadb which is exactly the implementation one would expect for this tool.
Instruction Scope
SKILL.md and the scripts only instruct creating a venv, installing the raglite CLI, distilling docs and indexing/querying them. This is within scope. One operational note: the CLI accepts a --chroma-url; if a user points that to a remote Chroma server (not localhost), document contents could be transmitted to that remote endpoint. Also the skill defaults to using the agent's 'OpenClaw' engine for condensation unless overridden, which means model calls (and thus data sent to whatever model backend the agent uses) will occur unless you pass --engine or otherwise configure it.
Install Mechanism
There is no registry install spec, but included scripts/install.sh creates a venv and runs pip install raglite-chromadb from PyPI (or a custom index if RAGLITE_PIP_INDEX_URL is set). Installing from PyPI is expected; however pip installs execute package code at install time, and the optional custom index env var allows fetching packages from an arbitrary index — both are valid developer features but increase risk if you don't trust the package/index. No obscure download URLs or archive extraction were used.
Credentials
The skill declares no required env vars or credentials, which matches its purpose. The installer does honor an optional RAGLITE_PIP_INDEX_URL env var (not listed as required) to allow alternate PyPI indexes — this is reasonable for testing but should be used cautiously. Also, because the skill defaults to using the agent's OpenClaw engine for condensation, data may be sent to whatever model backend the agent uses; that behavior is not expressed as required credentials in the skill but is an important operational privacy consideration.
Persistence & Privilege
always:false (default) and user-invocable:true. The installer creates a skill-local virtualenv (skills/raglite/.venv) — normal and scoped to the skill. The skill does not modify other skills or global agent settings.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install raglite - After installation, invoke the skill by name or use
/raglite - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.8
Metadata: author=Viraj
v1.0.7
Docs: OSS note + security note
v1.0.3
Polish metadata + fix installer to use raglite-chromadb; clarify usage.
v0.1.0
- Streamlined documentation for clarity; installation and usage sections are now simpler.
- Prerequisite and troubleshooting sections removed—setup instructions are more concise.
- Metadata in manifest updated: "os" constraint dropped.
- No changes to functionality—these updates are editorial/documentation only.
v1.0.6
Add manifest + proper file list so registry can index
v1.0.5
Trigger registry indexing
v1.0.4
Re-publish after making listing discoverable
v1.0.2
Tag as latest
v1.0.1
Fix listing visibility + metadata
v1.0.0
Initial release: local-first RAG cache (distill → index → query).
Metadata
Frequently Asked Questions
What is RAGLite?
Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma (vector) + ripgrep (keyword). It is an AI Agent Skill for Claude Code / OpenClaw, with 3107 downloads so far.
How do I install RAGLite?
Run "/install raglite" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is RAGLite free?
Yes, RAGLite is completely free (open-source). You can download, install and use it at no cost.
Which platforms does RAGLite support?
RAGLite is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created RAGLite?
It is built and maintained by Viraj Sanghvi (@virajsanghvi1); the current version is v1.0.8.
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