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Build persistent code memory for cross-session analysis, search, and documentation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "cerebro-code-memory" yet — see the docs or source repo.
Scan this repository and build persistent code memory. Summarize the core modules, key dependencies, main data flows, and directory responsibilities so I can continue asking later.
A codebase overview, module relationship summary, and reusable persisted memory of the repository.
Using the cached code memory, find the implementation files, entry points, and call chains for user authentication, authorization checks, and session management, and briefly explain how they relate.
A list of relevant files and functions, call paths, and a concise explanation of where each feature lives.
Based on the current code memory, generate a maintenance document for this project including architecture overview, key components, common change entry points, risk areas, and onboarding advice for new contributors.
A structured maintenance document that helps the team understand the system and reduce future maintenance effort.
Maintain persistent project understanding across chats with code search and memory.
Build persistent, semantically searchable memory for codebases via natural language queries.
Build local codebase memory for AI agents with search and architecture insights.
Index repositories into a persistent graph for fast code search and understanding.
Give AI coding assistants local long-term memory with searchable lessons and patterns.
Search and navigate multiple code repositories with natural language understanding.