Which AI models are compatible with RLM Memory?

RLM Memory supports AI agents compatible with the Model Context Protocol (MCP), including Claude Code, OpenAI Codex, and Gemini CLI, through simple configuration.

How does RLM Memory benefit AI agents?

It empowers AI with intelligent context, semantic file search, and rich memory creation, eliminating the need for direct file system access and streamlining development workflows for evolving projects.

What is bi-directional communication in RLM Memory?

The `rlm_query` tool facilitates bi-directional communication where AI agents request task-specific context and suggestions, and the MCP can query AI for confirmations, enhancing collaborative workflows.

RLM Memory is a Model Context Protocol (MCP) server that provides AI agents with persistent memory and semantic file discovery, acting as their 'eyes and memory' for codebases.

Can RLM Memory keep up with changing codebases?

Yes, `rlm_manage_sitemap` allows AI agents to dynamically update, move, or delete file entries, ensuring the codebase's contextual map remains accurate and relevant as the project evolves.

RLM Memory: AI Agent Persistent Memory & Codebase Context

About

The RLM Memory server acts as the 'eyes and memory' for AI agents, intentionally blinding them to the raw file system. Instead of using traditional file system commands, agents rely on the Model Context Protocol (MCP) to interact with a semantically rich memory system. It tracks project data, manages a smart file map with component types and feature areas, and provides advanced tools for querying relevant information, creating detailed memories, and keeping the codebase sitemap in sync, thereby streamlining development workflows for AI agents and enhancing their ability to understand and navigate complex codebases.

Key Features

Enhanced memory creation (`rlm_smart_memory`) with AI-extracted metadata, component types, feature areas, and edit history.
4 GitHub stars
Dynamic sitemap management (`rlm_manage_sitemap`) allowing AI agents to update, move, or delete file entries as the codebase evolves.
Semantic file discovery (`rlm_find_files_by_intent`) that uses component types, feature areas, and edit history for precise relevance scoring.
Bi-directional AI-MCP communication via `rlm_query` for task-specific context and suggestions.
Codebase indexing (`rlm_index_codebase`) and post-indexing verification (`rlm_verify_index`) for new and existing projects.

Use Cases

Onboarding AI agents to new software projects by initializing memory and context.
Enabling AI agents to efficiently understand and work on existing codebases through semantic indexing and verification.
Facilitating ongoing development tasks for AI agents by providing relevant files, past memories, and AI-generated suggestions for user requests.

About

Key Features

Enhanced memory creation (`rlm_smart_memory`) with AI-extracted metadata, component types, feature areas, and edit history.
4 GitHub stars
Dynamic sitemap management (`rlm_manage_sitemap`) allowing AI agents to update, move, or delete file entries as the codebase evolves.
Semantic file discovery (`rlm_find_files_by_intent`) that uses component types, feature areas, and edit history for precise relevance scoring.
Bi-directional AI-MCP communication via `rlm_query` for task-specific context and suggestions.
Codebase indexing (`rlm_index_codebase`) and post-indexing verification (`rlm_verify_index`) for new and existing projects.

Use Cases

Onboarding AI agents to new software projects by initializing memory and context.
Enabling AI agents to efficiently understand and work on existing codebases through semantic indexing and verification.
Facilitating ongoing development tasks for AI agents by providing relevant files, past memories, and AI-generated suggestions for user requests.