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Analyze massive codebases beyond context limits with recursive LLM search.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "RLM-Mem MCP Server" yet — see the docs or source repo.
Use the RLM-Mem MCP Server to scan this codebase larger than 200k tokens. Map the system architecture by module, identify key dependencies and service boundaries, and recommend the files I should read first.
A module-based architecture analysis with dependencies, service boundaries, and a prioritized reading list.
Use the RLM-Mem MCP Server to trace the implementation of the user login feature across the codebase. Find the entry points, authentication logic, session handling, error handling, and related configuration.
A clear feature call-chain explanation with involved files, key functions, and configuration locations.
With the RLM-Mem MCP Server, analyze the payment module I plan to refactor. Identify highly coupled files, implicit dependencies, duplicated logic, and upstream or downstream components that may be affected, then summarize the refactoring risks.
A refactoring risk assessment listing risky dependencies, impact scope, and optimization suggestions.
Analyze large codebases hierarchically and build a queryable knowledge map.
Process arbitrarily long contexts with recursive decomposition, without external LLM APIs.
Enable recursive LLM reasoning and code execution for large-context exploration.
Search and read indexed local documents with full-text and fuzzy matching.
Gives AI coding agents persistent memory and semantic file discovery across sessions.
Provides a persistent sandbox for AI coding agents to explore codebases efficiently.