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

已验证STDIO仅桌面知识与记忆开发者工具

概览

AI 生成的概览

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。
安装前请注意
记忆会写入本地 SQLite 数据库,并由钩子自动注入助手上下文,因此存储的内容可能出现在对话中。从项目文档播种可能把敏感内容复制进该数据库。仪表板通过本地 Web 端口暴露已存储的记忆。在共享数据库或其路径前请先检查其中内容。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Hot Memory Mcp,将其添加到工作区。
  2. 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:

  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

来源:README.md,提交 9ed7e1f

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.8.0最新Sep 16, 2026

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