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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.
Gives AI coding agents persistent memory and semantic file discovery across sessions.
Search code semantically and answer questions about a codebase.
Give AI coding agents long-term memory, search, and organization across sessions.
Automatically stores technical knowledge and retrieves relevant context across future AI sessions.
Provide project knowledge search and failure analysis for Claude Code workflows.
Persist Claude Code conversations and retrieve relevant context across sessions.