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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.
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.
Give Claude Code persistent memory and semantic context retrieval across sessions.
Provides a persistent sandbox for AI coding agents to explore codebases efficiently.
Search code semantically and answer questions about a codebase.