Hot Memory Mcp

io.github.michael-denyerPublished by GitHub account michael-denyerv0.8.0Updated Oct 24, 2026

Two-tier memory: hot cache (0ms) + semantic search. Self-organizing.

The publisher's own server: Memory.fit

VerifiedSTDIODesktop onlyKnowledge & MemoryDeveloper Tools

Overview

AI-generated overview

A local persistent memory server for AI assistants, combining a hot cache injected at session start with semantic search and a knowledge graph over stored facts

What you can do
Store memories with semantic embeddings and recall them by meaning rather than exact wording, with a knowledge graph linking related concepts and multi-hop recall across them. Manage promoted memories that are surfaced automatically, and view statistics about what is stored. Seed memories from project documentation in one bootstrap step. Isolate memories per git repository and let trust scoring demote outdated facts. Browse, manage and debug the memory database through a local dashboard.
When to use it
Useful when you keep re-explaining the same project architecture, conventions or patterns in every new chat and want that knowledge to persist and be recalled automatically across sessions. Best suited to Claude Code users who want local, project-scoped memory without a cloud service.
Requirements
Runs as a local process installed from PyPI (uv tool install or pip install). A SQLite database path is set via MEMORY_MCP_DB_PATH, and the embedding model via MEMORY_MCP_EMBEDDING_MODEL (default all-MiniLM-L6-v2). The embedding model (~90MB) downloads on first run, taking 30-60 seconds. The recommended plugin adds hooks and slash commands; the dashboard opens on a local port.
Before you install
Memories are written to a local SQLite database and injected into assistant context automatically by hooks, so anything stored may appear in conversations. Seeding from project docs can copy sensitive content into that database. The dashboard exposes stored memories over a local web port. Review what is stored before sharing the database or its path.

Installation

In SourceWeft

  1. Open Hot Memory Mcp in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

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

Source: README.md at commit 9ed7e1f

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.8.0LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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