
Hot Memory Mcp
io.github.michael-denyer由 GitHub 帳號 michael-denyer 發布v0.8.0更新於 Oct 24, 2026
Two-tier memory: hot cache (0ms) + semantic search. Self-organizing.
發布方自己的服務:Memory.fit
概覽
為 AI 助理提供本機持久記憶的伺服器,結合工作階段開始時注入的熱快取、語意搜尋與知識圖譜
- 可以做什麼
- 以語意嵌入儲存記憶,並依意義而非字面措辭檢索,知識圖譜連結相關概念並支援多跳召回。管理會自動呈現的已提升記憶,並檢視儲存內容的統計資訊。透過一次引導操作從專案文件播種記憶。依 git 儲存庫隔離記憶,並以信任評分降低過時事實的權重。透過本機儀表板瀏覽、管理與偵錯記憶資料庫。
- 適用情境
- 當你在每個新對話中反覆說明相同的專案架構、慣例或模式,並希望這些知識能持久保存、跨工作階段自動召回時適用。最適合希望使用本機、依專案隔離記憶且不依賴雲端服務的 Claude Code 使用者。
- 執行需求
- 以本機程序執行,從 PyPI 安裝(uv tool install 或 pip install)。透過 MEMORY_MCP_DB_PATH 設定 SQLite 資料庫路徑,透過 MEMORY_MCP_EMBEDDING_MODEL 設定嵌入模型(預設 all-MiniLM-L6-v2)。嵌入模型(約 90MB)會在首次執行時下載,耗時 30-60 秒。建議的外掛會加入鉤子與斜線指令;儀表板會在本機連接埠開啟。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Hot Memory Mcp,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
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
來源:README.md,提交 9ed7e1f
工具
0版本歷史
1- v0.8.0最新Sep 16, 2026
評分
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