local-brain-mcp
Related Servers
Alternatives to local-brain-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityDmaintenanceProvides AI coding assistants with persistent, context-rich memory of a codebase, including documentation and git history, enabling recall across sessions.104Apache 2.0
- AlicenseNot gradedqualityAmaintenanceGives AI coding assistants persistent project memory and semantic code search, running fully locally with no API keys required.MIT
- AlicenseNot gradedqualityAmaintenanceProvides persistent long-term memory (semantic RAG) for AI coding assistants, enabling them to store and semantically search code and documentation across chat sessions without token limits.5MIT

Doclea MCPofficial
AlicenseNot gradedqualityCmaintenanceProvides persistent memory for AI coding assistants, storing and retrieving architectural decisions, patterns, and solutions across sessions using semantic search, while also offering git integration for commit messages and code expertise mapping.MIT- AlicenseAqualityBmaintenanceProvides persistent memory and a codebase knowledge graph for AI coding assistants, enabling shared context across multiple tools like Claude, Cursor, and ChatGPT, with significant token reduction.518 npmMIT
- AlicenseNot gradedqualityBmaintenanceFederated, privacy-first shared memory for AI coding assistants that lets you capture, review, and share team knowledge via git without a central server.3 npmApache 2.0
TDQS
Scored across 7 tools
Most tools have clearly distinct purposes: learn, recall, validate, status, and trace are easy to tell apart. However, brain_prune and brain_forget both involve removing memories, and brain_recall and brain_trace both retrieve memories, so an agent could occasionally hesitate between them.
All tool names follow the same brain_ prefix plus a lowercase verb pattern, such as brain_learn, brain_recall, and brain_forget. The naming is highly consistent and predictable.
Seven tools is a well-scoped size for a local memory management server. Each tool covers a meaningful operation without bloat, and the count feels appropriate for the domain.
The tool set covers the core memory lifecycle: create (brain_learn), read (brain_recall, brain_trace, brain_status), validate (brain_validate), and delete/deprecate (brain_forget, brain_prune). There is no direct way to edit an existing memory's content, but forgetting and relearning can work around that gap.