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Build local codebase memory for AI agents with search and architecture insights.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "codebase-memory-mcp" yet — see the docs or source repo.
Use codebase-memory-mcp to index the current repository and summarize the system modules, key dependencies, and main entry files in an architecture overview for new team members.
A clear architecture summary with module breakdowns, dependency relationships, and core file explanations.
Use codebase-memory-mcp to search for code related to user login and permission checks, then tell me which files, core functions, and call paths are involved.
A list of relevant files, key function explanations, and the implementation path from entry points to validation logic.
Before changing the payment module, use codebase-memory-mcp to analyze which components depend on it, what flows may be affected, and which areas need regression testing.
An impact analysis showing dependent components, affected workflows, and recommended regression test scope.
Index repositories into a persistent graph for fast code search and understanding.
Build persistent, semantically searchable memory for codebases via natural language queries.
Search and navigate multiple code repositories with natural language understanding.
Give AI coding assistants local long-term memory with searchable lessons and patterns.
Build semantic memory and structural code indexes for persistent AI project context.
Store, search, and manage long-term engineering memory for projects.