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Give AI coding agents persistent memory, project knowledge, and code context.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "CogMemory MCP Server" yet — see the docs or source repo.
Please retrieve this project's coding conventions, key architecture decisions, and recently recorded common errors from CogMemory, then summarize them as a pre-development checklist.
A summary of conventions, prior decisions, and common pitfalls to align work before coding.
Store the technical choices, API constraints, known limitations, and lessons learned from this implementation in CogMemory for future tasks.
Implementation knowledge is saved as persistent memory so later agents can continue with the same context.
Using CogMemory's code graph, explain the main files, dependencies, and core call chain of the authentication module.
A developer-friendly explanation of the code structure to quickly locate module relationships and entry points.
In long-running or staged development, developers can let AI agents retain decisions, conventions, and error records instead of re-explaining project context each time. This helps later tasks continue within the same working context.
Teams can store project knowledge, conventions, and specification-related information in one shared context system for AI coding agents to retrieve and reuse. This is useful for reducing repeated communication when people or task context changes.
When working with an unfamiliar codebase, developers can use the code graph subsystem to have AI explain code structure, relationships, and key modules. This helps accelerate debugging, refactoring, or feature expansion.
It provides a unified context system for AI coding agents, including memory, knowledge graph, specs, and code graph subsystems. It also persistently stores decisions, conventions, errors, project knowledge, and code structure.
The provided description says all subsystems are stored in SQLite. For the exact schema or data organization, see the source repository.
It emphasizes persistent recall rather than only temporary in-session context. AI agents can keep using previously recorded project decisions, conventions, errors, and code structure information.
Build a project knowledge graph for code search, traversal, and Q&A.
Give AI coding agents long-term memory, search, and organization across sessions.
Give AI coding agents persistent code memory while cutting token usage dramatically.
Give AI coding agents persistent memory and codebase context retrieval.
Give AI coding assistants persistent, conflict-aware memory across projects and sessions.
Give AI coding assistants persistent, structured project memory stored as local Markdown.