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Analyze large codebases hierarchically and build a queryable knowledge map.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "codebase-rlm" yet — see the docs or source repo.
Use codebase-rlm to analyze this repository hierarchically, build a knowledge map of core modules, call relationships, entry files, and main business flows, then suggest the 10 files a new team member should read first.
A structured codebase knowledge map, module relationship overview, and a beginner-friendly reading path.
Using the codebase-rlm knowledge map, identify which modules, classes, or functions contain the logic for login failure retries and rate limiting, and rank them by relevance.
A ranked list of the most relevant code locations with hierarchical context and relationship notes.
Use codebase-rlm to build a persistent knowledge map for this project and answer: what is the configuration loading flow, how is cache invalidation handled, and which modules are most affected by context window limits?
A persistently queryable project knowledge summary with answers grounded in global codebase context.
Analyze massive codebases beyond context limits with recursive LLM search.
Enable recursive LLM reasoning and code execution for large-context exploration.
Provides a persistent sandbox for AI coding agents to explore codebases efficiently.
Process arbitrarily long contexts with recursive decomposition, without external LLM APIs.
Analyze codebases with semantic search, dependency insights, and natural language Q&A.
Analyze, match, and transform code structures across multiple programming languages.