Claude Code Memory Server

A Neo4j-based Model Context Protocol (MCP) server that provides intelligent memory capabilities for Claude Code, enabling persistent knowledge tracking, relationship mapping, and contextual development assistance.

Overview

This MCP server creates a sophisticated memory system that tracks Claude Code's activities, decisions, and learned patterns to provide contextual memory across sessions and projects. It uses Neo4j as a graph database to capture and analyze complex relationships between development concepts, solutions, and workflows.

Features

Core Memory Operations

• Persistent Memory Storage - Store development tasks, solutions, and patterns

• Intelligent Search - Find relevant memories by context, content, or relationships

• Relationship Mapping - Track how different concepts, files, and solutions relate

• Context Awareness - Project-specific and technology-specific memory retrieval

Advanced Intelligence

• Pattern Recognition - Automatically identify reusable development patterns

• Solution Effectiveness - Track and learn from successful approaches

• Workflow Memory - Remember and suggest optimal development sequences

• Error Prevention - Learn from past mistakes to prevent similar issues

Development Integration

• Task Execution Tracking - Monitor what Claude Code does and how

• Code Pattern Analysis - Identify and store successful code patterns

• Project Context Memory - Understand codebase conventions and dependencies

• Collaborative Learning - Share knowledge across development sessions

Architecture

Memory Types

• Task - Development tasks and their execution patterns

• CodePattern - Reusable code solutions and architectural decisions

• Problem - Issues encountered and their context

• Solution - How problems were resolved and their effectiveness

• Project - Codebase context and project-specific knowledge

• Technology - Framework, language, and tool-specific knowledge

Relationship Types

The system tracks seven categories of relationships:

1. Causal - CAUSES, TRIGGERS, LEADS_TO, PREVENTS, BREAKS

2. Solution - SOLVES, ADDRESSES, ALTERNATIVE_TO, IMPROVES, REPLACES

3. Context - OCCURS_IN, APPLIES_TO, WORKS_WITH, REQUIRES, USED_IN

4. Learning - BUILDS_ON, CONTRADICTS, CONFIRMS, GENERALIZES, SPECIALIZES

5. Similarity - SIMILAR_TO, VARIANT_OF, RELATED_TO, ANALOGY_TO, OPPOSITE_OF

6. Workflow - FOLLOWS, DEPENDS_ON, ENABLES, BLOCKS, PARALLEL_TO

7. Quality - EFFECTIVE_FOR, INEFFECTIVE_FOR, PREFERRED_OVER, DEPRECATED_BY, VALIDATED_BY

Installation

Prerequisites

• Python 3.10 or higher

• Neo4j database (local or cloud)

• Claude Code with MCP support

Setup

1. Clone the repository:

2. Install dependencies:

3. Set up Neo4j connection:

4. Initialize the database schema:

Configuration

Environment Variables

• NEO4J_URI - Neo4j database URI (default: bolt://localhost:7687)

• NEO4J_USER - Database username (default: neo4j)

• NEO4J_PASSWORD - Database password

• MEMORY_LOG_LEVEL - Logging level (default: INFO)

Claude Code Integration

Add to your Claude Code MCP configuration:

Usage

Available MCP Tools

Core Memory Operations

• store_memory - Store new development memories with context

• get_memory - Retrieve specific memory by ID with relationships

• search_memories - Find memories by content, context, or relationships

• update_memory - Modify existing memory content

• delete_memory - Remove memory and cleanup relationships

Relationship Management

• create_relationship - Link memories with specific relationship types

• get_related_memories - Find memories connected to a specific memory

• analyze_relationships - Discover relationship patterns in memory graph

Development Intelligence

• analyze_codebase - Scan project and create contextual memory graph

• track_task_execution - Record development workflow and patterns

• suggest_similar_solutions - Find analogous past solutions

• predict_solution_effectiveness - Estimate success probability of approaches

Advanced Analytics

• get_memory_graph - Visualize knowledge network and relationships

• find_memory_paths - Discover connection chains between concepts

• memory_effectiveness - Track and analyze solution success rates

Development

Project Structure

Development Setup

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Check existing GitHub Issues

2. Fork the repository and create a feature branch

3. Make changes following our coding standards

4. Add tests for new functionality

5. Submit a pull request with a clear description

License

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

Roadmap

Phase 1: Foundation (Current)

• ✅ Project setup and basic MCP server

• 🔄 Core memory operations (CRUD)

• ⏳ Basic relationship management

Phase 2: Intelligence

• ⏳ Advanced relationship system

• ⏳ Pattern recognition

• ⏳ Context awareness

Phase 3: Integration

• ⏳ Claude Code workflow integration

• ⏳ Automatic memory capture

• ⏳ Proactive suggestions

Phase 4: Analytics

• ⏳ Memory effectiveness tracking

• ⏳ Knowledge graph visualization

• ⏳ Performance optimization

Support

• GitHub Issues - Bug reports and feature requests

• Discussions - Questions and community support

• Documentation - Detailed guides and API reference

Acknowledgments

• Model Context Protocol - Protocol specification and examples

• Neo4j - Graph database platform

• Claude Code - AI-powered development environment