io.github.michael-denyer/hot-memory-mcp

编码与调试

by michael-denyer

提供双层 memory:0ms 热缓存结合 semantic search,并具备自组织能力以提升检索效率。

什么是 io.github.michael-denyer/hot-memory-mcp

提供双层 memory:0ms 热缓存结合 semantic search,并具备自组织能力以提升检索效率。

README

<!-- mcp-name: io.github.michael-denyer/hot-memory-mcp --> <div align="center">

🧠 Memory MCP

Give your AI assistant a persistent second brain

License: MIT Python 3.10+ MCP 1.0 Claude Code PyPI CI

<br />

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.

</div>

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 auto-injected into context before Claude even starts thinking. No tool call 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 — already in context
Stale information lingers foreverTrust scoring demotes outdated facts
Flat list of disconnected factsKnowledge graph connects related concepts

Install

bash
# Install package
uv 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, slash commands, and the Memory Analyst agent. MLX is auto-detected on Apple Silicon.

<details> <summary>Manual config (no plugin)</summary>

Add to ~/.claude.json:

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

See Reference for full configuration options.

</details>

Restart Claude Code. The hot cache auto-populates from your project docs.

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 -->|"auto-injected"| HC
    HC -.->|"draws from"| PM
    REQ -->|"recall()"| VS
    VS <-->|"related"| KG

The hot cache (~10 items) is injected into every request — it 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 — your most-used knowledge is already in context when Claude starts thinking.

Memory MCPGeneric Memory Servers
Hot cacheAuto-injected 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
Pattern miningLearns from Claude's outputsNot available
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 14 slash commands and full tool API.

Dashboard

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

Dashboard

Browse memories, hot cache, mining candidates, sessions, and knowledge graph.

How to Use

Memory MCP is designed to run as three complementary components:

ComponentPurpose
Claude Code PluginHooks, slash commands, and Memory Analyst agent for seamless integration
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

常见问题

io.github.michael-denyer/hot-memory-mcp 是什么?

提供双层 memory:0ms 热缓存结合 semantic search,并具备自组织能力以提升检索效率。

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