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

已驗證STDIO僅桌面知識與記憶開發者工具

概覽

AI 產生的概覽

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。
安裝前請注意
記憶會寫入本機 SQLite 資料庫,並由鉤子自動注入助理上下文,因此儲存的內容可能出現在對話中。從專案文件播種可能把敏感內容複製進該資料庫。儀表板會透過本機網路連接埠暴露已儲存的記憶。在分享資料庫或其路徑前,請先檢視其中內容。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Hot Memory Mcp,將其新增到工作區。
  2. 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:

  1. Hot cache (0ms) — Frequently-used knowledge printed into context by the plugin's SessionStart and UserPromptSubmit hooks before Claude even starts thinking. Claude Code adds a hook's stdout to the conversation, so no tool call is needed.
  2. 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

😤 Without Memory MCP🎯 With Memory MCP
"Let me explain our architecture again..."Project facts persist and isolate per repo
Copy-paste the same patterns every sessionPatterns auto-promoted to instant access
500k+ token context windowsHot cache keeps it lean (~20 items)
Tool call latency on every memory lookupHot cache: 0ms — a hook already put it in context
Stale information lingers foreverTrust scoring demotes outdated facts
Flat list of disconnected factsKnowledge graph connects related concepts

Install

bash
# Install packageuv tool install hot-memory-mcp   # or: pip install hot-memory-mcp
# Add plugin (recommended)claude plugins add michael-denyer/memory-mcp

The plugin gives you auto-configured hooks and slash commands. Embeddings run on sentence-transformers on every platform, including Apple Silicon.

Manual config (no plugin)

Add to ~/.claude.json:

json
{  "mcpServers": {    "memory": {      "command": "memory-mcp"    }  }}

See Reference for full configuration options.

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

mermaid
flowchart LR    subgraph LLM["Claude"]        REQ((Request))    end
    subgraph Hot["HOT CACHE · 0ms"]        HC[Session context]        PM[(Promoted memories)]    end
    subgraph Cold["COLD STORAGE · ~50ms"]        VS[(Vector search)]        KG[(Knowledge graph)]    end
    REQ -->|"hook stdout"| HC    HC -.->|"draws from"| PM    REQ -->|"recall()"| VS    VS <-->|"related"| KG

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.

Memory MCPGeneric Memory Servers
Hot cacheInjected by a hook at 0msEvery lookup = tool call
Self-organizingLearns and promotes automaticallyManual curation required
Project-awareAuto-isolates by git repoOne big pile of memories
Knowledge graphMulti-hop recall across conceptsFlat list of facts
Trust scoringOutdated info decays and sinksAll memories equal
SetupOne command, local SQLiteOften needs cloud setup

The Engram Insight: Human memory doesn't search — frequently-used patterns are already there. That's what hot cache does for Claude.

Quick Reference

Slash CommandToolDescription
/memory-mcp:rememberrememberStore a memory with semantic embedding
/memory-mcp:recallrecallSearch memories by meaning
/memory-mcp:hot-cachepromote / demoteManage promoted memories
/memory-mcp:statsmemory_statsShow statistics
/memory-mcp:bootstrapbootstrap_projectSeed from project docs
—link_memoriesKnowledge graph connections

See Reference for all 10 slash commands and full tool API.

Dashboard

bash
memory-mcp-cli dashboard    # Opens at http://localhost:8765

[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:

ComponentPurpose
Claude Code PluginHooks that inject the hot cache, plus the /memory-mcp:* slash commands
MCP ServerCore memory tools available to Claude via Model Context Protocol
DashboardWeb UI to browse, manage, and debug your memory database

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

DocumentDescription
ReferenceFull API, CLI, configuration, MCP resources
TroubleshootingCommon issues and solutions

License

MIT

來源:README.md,提交 9ed7e1f

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v0.8.0最新Sep 16, 2026

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