chenxiaofie-memory-mcp

Memory MCP Service

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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 · [e-mail]

本项目由陈佳俊（GitHub: chenxiaofie）开发并维护。

License

MIT License - see LICENSE file for details.