🧠 Memory MCP

Give your AI assistant a persistent second brain

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.

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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 session	Patterns auto-promoted to instant access
500k+ token context windows	Hot cache keeps it lean (~20 items)
Tool call latency on every memory lookup	Hot cache: 0ms — already in context
Stale information lingers forever	Trust scoring demotes outdated facts
Flat list of disconnected facts	Knowledge graph connects related concepts

Install

# 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.

Manual config (no plugin)

Add to ~/.claude.json:

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

See Reference for full configuration options.

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

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

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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 MCP	Generic Memory Servers
Hot cache	Auto-injected at 0ms	Every lookup = tool call
Self-organizing	Learns and promotes automatically	Manual curation required
Project-aware	Auto-isolates by git repo	One big pile of memories
Knowledge graph	Multi-hop recall across concepts	Flat list of facts
Pattern mining	Learns from Claude's outputs	Not available
Trust scoring	Outdated info decays and sinks	All memories equal
Setup	One command, local SQLite	Often 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 Command	Tool	Description
/memory-mcp:remember	remember	Store a memory with semantic embedding
/memory-mcp:recall	recall	Search memories by meaning
/memory-mcp:hot-cache	promote / demote	Manage promoted memories
/memory-mcp:stats	memory_stats	Show statistics
/memory-mcp:bootstrap	bootstrap_project	Seed from project docs
—	link_memories	Knowledge graph connections

See Reference for all 14 slash commands and full tool API.

Dashboard

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

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

How to Use

Memory MCP is designed to run as three complementary components:

Component	Purpose
Claude Code Plugin	Hooks, slash commands, and Memory Analyst agent for seamless integration
MCP Server	Core memory tools available to Claude via Model Context Protocol
Dashboard	Web 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

Document	Description
Reference	Full API, CLI, configuration, MCP resources
Troubleshooting	Common issues and solutions

License

MIT