by Neverdecel · Agent Tool · ★ 211
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🔒 Is CodeRAG safe to install? View the security audit →
🔎 CodeRAG A standalone, local-first semantic code-search engine for large and custom codebases. CodeRAG indexes a whole codebase into a hybrid (vector + keyword) search index and answers questions like "where is retry/backoff handled?" with the exact functions, classes, and files that matter — ranked by meaning, not just string match. It runs entirely on your machine with no API key (a local ONNX embedding model is the default), keeps its index up to date as you edit, and is built to stay fast on large codebases. Use it from the CLI, embed it as a Python library, self-host it as an HTTP service, or browse with the web UI. Built for the cases off-the-shelf IDE assistants don't cover well: a codebase that's too big, too private, or too custom — or a search/RAG capability you want to own and embed in your own tools. ✨ Highlights Local-first, zero-key. Default embeddings run locally via fastembed (ONNX, no PyTorch). OpenAI is optional. Symbol-aware chunking. Indexes functions, classes,
| Stars | 211 |
| Forks | 34 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 65.5205672327872/100 |
| Open Issues | 3 |
| Last Updated | 2026-06-16 |
| Created | 2024-09-08 |
| Platforms | python |
| Est. Tokens | ~15k |
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CodeRAG is CodeRAG is an AI-powered tool for real-time codebase querying and augmentation using OpenAI and vector search.. It is categorized as a Agent Tool with 211 GitHub stars.
CodeRAG is primarily written in Python. It covers topics such as ai, ai-assistant, ai-code.
You can find installation instructions and usage details in the CodeRAG GitHub repository at github.com/Neverdecel/CodeRAG. The project has 211 stars and 34 forks, indicating an active community.
The top alternatives to CodeRAG on Agent Skills Hub include lucid-memory, octocode-mcp, automem. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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