Memory MCP

Name: Memory MCP
Author: chenxiaofie

by chenxiaofieversion 0.2.1i★ –iRuns on your computer

Registry nameio.github.chenxiaofie/memory-mcp

Install

Overview

Description from the maker (original English)

情景+实体记忆 MCP 服务，为 Claude Code 提供持久化记忆能力

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: CLAUDE_PROJECT_ROOT

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "memory": {
      "command": "uvx",
      "args": [
        "chenxiaofie-memory-mcp"
      ],
      "env": {
        "CLAUDE_PROJECT_ROOT": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add memory --env CLAUDE_PROJECT_ROOT=your-value -- uvx chenxiaofie-memory-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "memory": {
      "command": "uvx",
      "args": [
        "chenxiaofie-memory-mcp"
      ],
      "env": {
        "CLAUDE_PROJECT_ROOT": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "memory": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "chenxiaofie-memory-mcp"
      ],
      "env": {
        "CLAUDE_PROJECT_ROOT": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

CLAUDE_PROJECT_ROOT env
项目根目录路径

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account chenxiaofie, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7247 servers in this category
Published to the registry more recently than 2% of the other 7247 servers in this category
Basic checks equal to or better than 100% of the other 7247 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Memory MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.1) reached the registry 9 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Memory MCP Service

English | 中文

A persistent memory MCP service for Claude Code. Automatically saves conversations and retrieves relevant history across sessions.

What it does: Every time you chat with Claude Code, your conversation context (decisions, preferences, key discussions) is saved and automatically recalled in future sessions — so Claude always has the background it needs.

Quick Start

Prerequisites

Install uv (Python package runner):

# Windows

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Mac/Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

Requires Python 3.10 - 3.13 (chromadb is not compatible with Python 3.14+).

1. Initialize (First Time Only)

Download the vector model (~400MB, one-time):

uvx --from chenxiaofie-memory-mcp memory-mcp-init

2. Add MCP Server to Claude Code

claude mcp add memory-mcp -s user -- uvx --from chenxiaofie-memory-mcp memory-mcp

3. Configure Hooks (Recommended)

Hooks enable fully automatic message saving. Without hooks, you need to manually call memory tools.

Add the following to ~/.claude/settings.json:

"hooks": { "SessionStart": [{ "matcher": ".*", "hooks": [{ "type": "command", "command": "uvx --from chenxiaofie-memory-mcp memory-mcp-session-start" }] }], "UserPromptSubmit": [{ "matcher": ".*", "hooks": [{ "type": "command", "command": "uvx --from chenxiaofie-memory-mcp memory-mcp-auto-save" }] }], "Stop": [{ "matcher": ".*", "hooks": [{ "type": "command", "command": "uvx --from chenxiaofie-memory-mcp memory-mcp-save-response" }] }], "SessionEnd": [{ "matcher": ".*", "hooks": [{ "type": "command", "command": "uvx --from chenxiaofie-memory-mcp memory-mcp-session-end" }] }] } }

4. Verify

claude mcp list

You should see memory-mcp: ... - ✓ Connected.

That's it! Start a new Claude Code session and your conversations will be automatically saved and recalled.

How It Works

Session Start ──► Create Episode ──► Monitor Process (background)

│ User Message ──► Save Message ──► Recall Related Memories ──► Inject Context │ Claude Reply ──► Save Response │ │ Session End ──► Close Signal ──► Archive Episode + Generate Summary

Episodes: Each conversation session is an "episode" with auto-generated summaries
Entities: Key knowledge extracted from conversations (decisions, preferences, concepts)
Dual-layer storage: User-level (shared across projects) + Project-level (isolated per project)
Semantic search: Vector-based retrieval finds relevant past context

Usage

Automatic Mode (With Hooks)

Once hooks are configured, everything is automatic. Claude will see relevant history from past sessions as context.

Manual Mode

You can also call memory tools directly in Claude Code:

# Start a new episode

memory_start_episode("Login Feature Development", ["auth"])

Record a decision

memory_add_entity("Decision", "Use JWT + Redis", "For distributed deployment")

Search history

memory_recall("login implementation")

Close episode

memory_close_episode("Completed JWT login feature")

Hooks Reference

Hook	What it does	Timing
SessionStart	Creates a new episode	~50ms
UserPromptSubmit	Saves user message + retrieves related memories	~1-2s
Stop	Saves assistant response	~1s
SessionEnd	Signals episode closure	~50ms

Tools Reference

Tool	Description
`memory_start_episode`	Start a new episode
`memory_close_episode`	Close and archive current episode
`memory_get_current_episode`	Get current active episode
`memory_add_entity`	Add a knowledge entity
`memory_confirm_entity`	Confirm a detected entity candidate
`memory_reject_candidate`	Reject a false detection
`memory_deprecate_entity`	Mark an entity as outdated
`memory_get_pending`	List pending entity candidates
`memory_recall`	Semantic search across episodes and entities
`memory_search_by_type`	Search entities by type
`memory_get_episode_detail`	Get full episode details
`memory_list_episodes`	List all episodes chronologically
`memory_stats`	Get system statistics
`memory_encoder_status`	Check vector encoder status
`memory_cache_message`	Manually cache a message
`memory_clear_cache`	Clear message cache
`memory_cleanup_messages`	Clean up old cached messages

Entity Types

Type	Level	Description
`Decision`	Project	Technical decisions for this project
`Architecture`	Project	Architecture designs
`File`	Project	Important file descriptions
`Preference`	User	Personal preferences (shared across projects)
`Concept`	User	General concepts
`Habit`	User	Work habits

Storage Locations

User-level: ~/.claude-memory/
Project-level: {project-root}/.claude/memory/

Alternative: Install from source

If you need to run from source (e.g., for development):

git clone https://github.com/chenxiaofie/memory-mcp.git

cd memory-mcp

Windows:

install.bat

Mac/Linux:

chmod +x install.sh && ./install.sh

Then configure MCP server with the venv Python:

# Windows:

claude mcp add memory-mcp -s user -- "C:\path\to\memory-mcp\venv310\Scripts\python.exe" -m memory_mcp.server

Mac/Linux:

claude mcp add memory-mcp -s user -- /path/to/memory-mcp/venv310/bin/python -m memory_mcp.server

Author

陈佳俊 (Jiajun Chen) — front-end engineer based in Hangzhou, China. GitHub @chenxiaofie · feifeichen1999@gmail.com

本项目由陈佳俊（GitHub: chenxiaofie）开发并维护。

License

MIT License - see LICENSE file for details.

Memory MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Memory MCP Service

Quick Start

Prerequisites

Mac/Linux

1. Initialize (First Time Only)

2. Add MCP Server to Claude Code

3. Configure Hooks (Recommended)

4. Verify

How It Works

Usage

Automatic Mode (With Hooks)

Manual Mode

Record a decision

Search history

Close episode

Hooks Reference

Tools Reference

Entity Types

Storage Locations

Windows:

Mac/Linux:

Mac/Linux:

Author

License