
Hot Memory Mcp
io.github.michael-denyer由 GitHub 账号 michael-denyer 发布v0.8.0更新于 Oct 24, 2026
Two-tier memory: hot cache (0ms) + semantic search. Self-organizing.
发布方自己的服务:Memory.fit
概览
一个面向 AI 助手的本地持久记忆服务器,结合会话开始时注入的热缓存、语义搜索和知识图谱
- 可以做什么
- 以语义嵌入存储记忆,并按含义而非字面措辞检索,知识图谱连接相关概念并支持多跳召回。管理会被自动呈现的已提升记忆,并查看存储内容的统计信息。通过一次引导操作从项目文档中播种记忆。按 git 仓库隔离记忆,并用信任评分降低过时事实的权重。通过本地仪表板浏览、管理和调试记忆数据库。
- 适用场景
- 当你在每个新会话中反复解释相同的项目架构、约定或模式,并希望这些知识持久保存、跨会话自动召回时适用。最适合希望使用本地、按项目隔离记忆且不依赖云服务的 Claude Code 用户。
- 运行要求
- 作为本地进程运行,从 PyPI 安装(uv tool install 或 pip install)。通过 MEMORY_MCP_DB_PATH 设置 SQLite 数据库路径,通过 MEMORY_MCP_EMBEDDING_MODEL 设置嵌入模型(默认 all-MiniLM-L6-v2)。嵌入模型(约 90MB)在首次运行时下载,耗时 30-60 秒。推荐的插件会添加钩子和斜杠命令;仪表板在本地端口打开。
安装
在 SourceWeft 中
- 打开 控制台中的 Hot Memory Mcp,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
🧠 Memory MCP
Give your AI assistant a persistent second brain
[License: MIT] [Python 3.10+] [MCP 1.0] [Claude Code] [PyPI] [CI]
Stop re-explaining your project every session.
Memory MCP learns what matters and keeps it ready — instant recall for the stuff you use most, semantic search for everything else.
The Problem
Every new chat starts from scratch. You explain your architecture again. You paste the same patterns again. Your context window bloats with repetition.
Other memory solutions help, but they still require tool calls for every lookup — adding latency and eating into Claude's thinking budget.
Memory MCP fixes this with a two-tier architecture:
- Hot cache (0ms) — Frequently-used knowledge printed into context by the plugin's
SessionStartandUserPromptSubmithooks before Claude even starts thinking. Claude Code adds a hook's stdout to the conversation, so no tool call is needed. - Cold storage (~50ms) — Everything else, searchable by meaning via semantic similarity.
The system learns what you use and promotes it automatically. Your most valuable knowledge becomes instantly available. No manual curation required.
Before & After
Install
The plugin gives you auto-configured hooks and slash commands. Embeddings run on sentence-transformers on every platform, including Apple Silicon.
Restart Claude Code. Run memory-mcp-cli bootstrap once to seed memories from your project docs; the hooks inject whatever is promoted, but nothing seeds itself.
First run: Embedding model (~90MB) downloads automatically. Takes 30-60 seconds once.
How It Works
The hot cache (~10 items) reaches Claude through two plugin hooks. SessionStart runs memory-mcp-cli hot-cache --force and UserPromptSubmit runs memory-mcp-cli hot-cache, and Claude Code adds each command's stdout to the conversation. The command prints only when the text differs from what the session last saw, which is cheap while the set is stable and reprints in full when it shifts. The set combines recent recalls, predicted next memories, and top promoted items. Promoted memories (~20 items) is the backing store of frequently-used memories. Memories used 3+ times auto-promote; unused ones demote after 14 days.
What Makes It Different
Most memory systems make you pay a tool-call tax on every lookup. Memory MCP's hot cache bypasses this entirely — a hook prints your most-used knowledge into the conversation before Claude starts thinking.
The Engram Insight: Human memory doesn't search — frequently-used patterns are already there. That's what hot cache does for Claude.
Quick Reference
See Reference for all 10 slash commands and full tool API.
Dashboard
Browse memories, hot cache, injection history, sessions, and the knowledge graph.
How to Use
Memory MCP is designed to run as three complementary components:
The plugin is recommended for most users — it auto-configures the MCP server and adds productivity features. Run the dashboard alongside when you want visibility into what's being stored.
Documentation
License
MIT
来源:README.md,提交 9ed7e1f
工具
0版本历史
1- v0.8.0最新Sep 16, 2026
评分
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