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Give AI coding agents persistent code memory while cutting token usage dramatically.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ai-mind-map" yet — see the docs or source repo.
Before changing this repository, use ai-mind-map to query files, dependencies, and recent changes related to the authentication module, then propose the smallest patch.
A map of code relationships, change clues, and a token-efficient modification plan for the auth module.
Continue the refactoring task from the previous session. First read prior context, changed files, and unfinished items from ai-mind-map, then plan the next steps.
Recovered development memory, a summary of current status, and a clear next-step task list.
Use ai-mind-map to inspect recent changes to the payment flow and analyze which modules may be affected and what should be regression-checked.
A change summary, impacted code areas, and recommended regression checkpoints.
When using AI agents on large repositories, developers can query the code knowledge graph instead of repeatedly sending huge context blocks, reducing token usage. This fits tasks that require quickly locating module relationships and relevant files.
When a coding task cannot be finished in a single session, this tool provides persistent memory so the agent can continue with prior understanding later. That reduces the time and cost of re-explaining project context.
For actively evolving projects, developers can use change tracking to inspect recent modifications and their likely impact before making the next change. It is useful for tracing edit history and planning minimal patches.
It is an MCP server for AI coding agents that provides a queryable code knowledge graph, change tracking, and persistent memory across sessions. Its core value is reducing token usage by 80% to 99%.
The known prerequisite is an AI coding agent or client that supports MCP. The provided information does not specify runtime, installation steps, or API key requirements; see the source repository.
The description suggests it does more than repeatedly sending raw code context. It supports agents through a queryable knowledge graph, change tracking, and persistent memory, making it better suited to long-running, multi-session coding tasks.
Give AI coding agents persistent semantic memory with embedding-based retrieval.
Give AI coding assistants local long-term memory with searchable lessons and patterns.
Give AI coding assistants memory, code graph insight, and safe multi-agent coordination.
Give AI coding agents long-term memory, search, and organization across sessions.
Give local AI coding assistants persistent memory and semantic code search.
Give AI coding agents structured access to project architecture and decisions.