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.

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.

- 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

- 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

- 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

- 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

The system tracks seven categories of relationships:
- Causal-CAUSES,TRIGGERS,LEADS_TO,PREVENTS,BREAKS
- Solution-SOLVES,ADDRESSES,ALTERNATIVE_TO,IMPROVES,REPLACES
- Context-OCCURS_IN,APPLIES_TO,WORKS_WITH,REQUIRES,USED_IN
- Learning-BUILDS_ON,CONTRADICTS,CONFIRMS,GENERALIZES,SPECIALIZES
- Similarity-SIMILAR_TO,VARIANT_OF,RELATED_TO,ANALOGY_TO,OPPOSITE_OF
- Workflow-FOLLOWS,DEPENDS_ON,ENABLES,BLOCKS,PARALLEL_TO
- Quality-EFFECTIVE_FOR,INEFFECTIVE_FOR,PREFERRED_OVER,DEPRECATED_BY,VALIDATED_BY

- Python 3.10 or higher
- Neo4j database (local or cloud)
- Claude Code with MCP support

git clone https://github.com/viralvoodoo/claude-code-memory.git cd claude-code-memory

cp .env.example .env # Edit .env with your Neo4j credentials

- 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)

Add to your Claude Code MCP configuration:

{ "mcpServers": { "claude-memory": { "command": "python", "args": ["-m", "claude_memory.server"], "env": { "NEO4J_URI": "bolt://localhost:7687", "NEO4J_USER": "neo4j", "NEO4J_PASSWORD": "your-password" } } } }

- 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

- 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

- 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

- get_memory_graph- Visualize knowledge network and relationships
- find_memory_paths- Discover connection chains between concepts
- memory_effectiveness- Track and analyze solution success rates

claude-code-memory/ ├── src/claude_memory/ # Main source code │ ├── __init__.py │ ├── server.py # MCP server implementation │ ├── models.py # Data models and schemas │ ├── database.py # Neo4j database operations │ ├── memory_store.py # Core memory logic │ ├── relationships.py # Relationship management │ ├── search.py # Search and retrieval │ └── intelligence.py # Pattern recognition and analytics ├── tests/ # Test suite ├── docs/ # Documentation ├── scripts/ # Utility scripts └── pyproject.toml # Project configuration

# Install development dependencies pip install -e ".[dev]" # Install pre-commit hooks pre-commit install # Run tests pytest # Format code black src/ tests/ ruff --fix src/ tests/ # Type checking mypy src/

We welcome contributions! Please see ourContributing Guidefor details.
- Check existingGitHub Issues
- Fork the repository and create a feature branch
- Make changes following our coding standards
- Add tests for new functionality
- Submit a pull request with a clear description

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

- GitHub Issues- Bug reports and feature requests
- Discussions- Questions and community support
- Documentation- Detailed guides and API reference

- Model Context Protocol- Protocol specification and examples
- Neo4j- Graph database platform
- Claude Code- AI-powered development environment

A server for persistent context management in Claude AI coding assistants, using a local SQLite database for storage.