AI Memory MCP - Project Local Memory Server

中文版

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Project Overview

AI Memory MCP is an MCP server designed for Claude Code to provide persistent project memory. It enables AI to remember your project development journey across sessions, including feature planning, bug fixes, development notes, and code standards.

Key Features

Feature	Description
Structured Memory	Categorizes project info into Features, Fixes, Notes, and Standards
Associations	Links between entries to build project knowledge graph
Tag System	Tag management for cross-dimensional queries and experience reuse
Three-Tier Architecture	Separated MCP Server, FastAPI Server, and Business Server
Async Design	Asynchronous architecture supporting concurrent access
Docker Deployment	Containerized deployment with persistent data storage

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System Architecture

The project uses a three-tier architecture with clear responsibilities:

┌─────────────────┐     ┌─────────────────┐
│   MCP Clients   │     │   Web Clients   │
│  (Claude Code)  │     │   (Browser)     │
└────────┬────────┘     └────────┬────────┘
         │                       │
         ▼                       ▼
┌─────────────────┐     ┌─────────────────┐
│   MCP Server    │     │  FastAPI Server │
│   (mcp_server)  │     │   (rest_api)    │
│  - SSE/HTTP     │     │  - RESTful API  │
└────────┬────────┘     └────────┬────────┘
         │                       │
         └───────────┬───────────┘
                     ▼
         ┌─────────────────────┐
         │   Business Server   │
         │   (business)        │
         │  - Core business    │
         │  - Data storage     │
         │  - Tag system       │
         └─────────────────────┘

Directory Structure

ai_memory_mcp/
├── src/
│   ├── business/      # Business logic layer (core)
│   ├── mcp_server/    # MCP server layer
│   ├── rest_api/      # FastAPI REST API layer
│   ├── clients/       # Client modules
│   └── common/        # Common modules
├── docker/            # Docker deployment files
├── test/              # Test files (unit/integration/e2e/performance)
├── scripts/           # Utility scripts
├── examples/          # Example code (agents/skills)
├── docs/              # Documentation
├── config/            # Configuration files
├── scripts/           # Utility scripts
│   ├── start_mcp.py         # MCP startup script
│   ├── start_business.py    # Business service startup script
│   └── start_fastapi.py     # FastAPI startup script

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Quick Start

Method 1: Docker Deployment

# 1. Enter Docker directory
cd docker

# 2. Start service (default port 8000)
./manage.sh start

# 3. Check status
./manage.sh status

# 4. Stop service
./manage.sh stop

# 5. View logs
docker logs -f ai-memory-mcp

Method 2: Local Development

# 1. Create Conda environment
conda create -n ai_memory_mcp python=3.12
conda activate ai_memory_mcp

# 2. Install dependencies
pip install -r requirements.txt

# 3. Start business service
python scripts/start_business.py

# 4. Start MCP server (new terminal)
python scripts/start_mcp.py

# 5. Start FastAPI service (optional, new terminal)
python scripts/start_fastapi.py

Claude Code Configuration

Edit Claude Code configuration file and add MCP server:

{
  "mcpServers": {
    "memory": {
      "url": "http://localhost:8000/mcp",
      "type": "http"
    }
  }
}

Verify Installation

Test in Claude Code:

Please use MCP [memory] to list all projects

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Core Features

Memory Content Categories

Category	Purpose	Record Content
Features	Feature planning & tracking	Pending, in-progress, and completed features
Fixes	Bug fix records	Issue descriptions, severity levels, solutions
Notes	Development knowledge	Technical decisions, debugging processes, lessons learned
Standards	Project standards	Code conventions, naming conventions, architectural principles

Memory Association System

┌─────────────────┐
│   Features      │ ─┐
│   (Feature List)│  │ Associated
└─────────────────┘  │
                     ├─→ Notes (Notes)
┌─────────────────┐  │    (Record development process/decision rationale)
│   Fixes         │ ─┘
│   (Bug Fixes)   │
└─────────────────┘

• Feature ↔ Note: Features can link to related notes (design decisions, reference docs)
• Fix ↔ Note: Bug fixes can link to troubleshooting processes
• Fix ↔ Feature: Bug fixes can link to specific feature modules
• Tag System: All entries support multi-tagging for cross-dimensional queries

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MCP Tools List

Project Management

Tool	Function
project_register	Register new project
project_list	List all projects
project_get	Get project info or query entries
project_rename	Rename project
project_archive	Archive project (MCP only)

Entry Management

Tool	Function
project_add	Add new entry (features/fixes/notes/standards)
project_update	Update entry content
project_delete	Delete entry

Tag System

Tool	Function
tag_register	Register new tag (define semantics)
tag_update	Update tag semantics
tag_delete	Delete tag
tag_merge	Merge tags
project_tags_info	Query tag information
project_item_tag_manage	Manage entry tags

Query Features

• Filter by status (pending/in_progress/completed)
• Filter by severity (critical/high/medium/low)
• Filter by tags
• Filter by creation/update time
• Regex match on summary
• Paginated queries

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Use Case Examples

Scenario 1: Continue Development Across Sessions

Context: Implemented API authentication middleware last week, continuing development today.

You: Continue improving API authentication, add refresh token mechanism

Claude: [Query memory] Found project records:
        - feat_20260401: JWT authentication implemented using FastAPI Security
        - note_20260401: Key stored at /secrets/jwt_key.pem
        - standards_20260315: Auth-related functions need @auth_required decorator

        Based on existing implementation, here's the refresh token design...

Scenario 2: Reuse Solved Experience

Context: New project encounters a Docker networking issue you solved before.

You: Services can't communicate after Docker Compose starts

Claude: [Cross-project query] Found similar issue in project web-dashboard:
        - fix_20260310: Docker network communication failure
        - Solution: Use custom network network_mode: bridge
        - note_20260310: Need to explicitly declare networks in docker-compose.yml

        Generated same configuration for new project...

Scenario 3: Follow Project Conventions

Context: Make AI generate code following team standards.

You: Implement user order query endpoint

Claude: [Query standards] According to project standards:
        - All endpoints return wrapped Result<T> format
        - Pagination uses PageRequest(page, size)
        - Async functions must include _async suffix

        Generated code:

        async def get_orders_async(req: PageRequest) -> Result[List[Order]]:
            ...

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Data Storage

Storage Location

Docker Mode: Persistent volume inside container at /var/lib/project_memory_ai/

Local Mode: data/ directory in project root

Data Directory Structure

/var/lib/project_memory_ai/
├── _metadata.json              # Global metadata (project list, statistics)
├── _stats.json                 # API call statistics
│
├── project_a/                  # ProjectA directory (project name)
│   ├── _project.json           # Project metadata (id, name, info, _version)
│   ├── _tags.json              # Tag registry (tag -> description, aliases)
│   ├── _group_configs.json     # Group configurations (optional)
│   │
│   ├── features/               # Features group
│   │   ├── _index.json         # Feature index (id -> summary, status, tags...)
│   │   ├── feat_20260408_001.json  # Feature details
│   │   └── feat_20260408_002.json
│   │
│   ├── fixes/                  # Bug fixes group (same structure as features)
│   ├── notes/                  # Notes group (same structure as features)
│   └── standards/              # Standards group (same structure as features)
│
├── project_b/                  # ProjectB directory
│
└── .archived/                  # Archived projects
    ├── 20260408_123456_project_a.tar.gz
    └── 20260408_123456_project_a.meta.json

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Development Guide

Requirements

• Python 3.12+
• Conda
• Docker (for containerized deployment)

Running Tests

# Use test script
./scripts/run_tests.sh

# Or manually run
pytest test/ -v --cov=src/business --cov-report=html

Code Standards

• Use black for code formatting
• Use ruff for code linting
• Use mypy for type checking

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Project Status

Project	Version	Dev Branch	Main Branch
ai_memory_mcp	v1.0.0	dev	main

Development Statistics

• Features: 63
• Notes: 125
• Fixes: 24
• Standards: 18
• Tags: 59

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Contributing

Contributions welcome! Please see CONTRIBUTING.md for details.

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License

This project is licensed under the MIT License. See LICENSE file for details.

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Related Links

• MCP Protocol Specification
• Claude Code Documentation
• FastMCP Documentation