Claude Code Memory Server

# Claude Code Memory Server - Complete Implementation Plan This document outlines the comprehensive 7-phase implementation plan for the Claude Code Neo4j MCP Memory Server with GitHub project management integration. ## Project Overview **Goal**: Create a Neo4j-based MCP memory server for Claude Code with intelligent relationship tracking, enabling persistent knowledge across development sessions. **Repository**: https://github.com/ViralV00d00/claude-code-memory **Timeline**: ~15-20 weeks total **Methodology**: GitHub Issues tracking with milestone-based phases --- ## Phase 0: Project Management Setup ✅ COMPLETED ### 0.1 Git Repository Initialization ✅ - [x] Initialize git repository in `/home/viralvoodoo/projects/mcp/memory` - [x] Create initial README.md with project overview - [x] Set up .gitignore for Python/Node.js projects - [x] Create initial commit with project structure ### 0.2 GitHub Repository & Issues Setup ✅ - [x] Create GitHub repository for the project - [x] Set up GitHub Issues with labels: - `phase-1` through `phase-7` for project phases - `bug`, `enhancement`, `documentation`, `question` - `priority-high`, `priority-medium`, `priority-low` - `mcp-core`, `neo4j`, `relationships`, `claude-integration` - [x] Create milestone for each phase with target dates - [x] Set up GitHub Projects board for kanban-style tracking ### 0.3 Documentation Structure ✅ - [x] Create `/docs` folder with architecture overview - [x] API documentation template - [x] Development workflow guide - [x] Relationship schema documentation --- ## Phase 1: Foundation Setup ✅ COMPLETED **Timeline**: Weeks 1-3 | **Status**: ✅ COMPLETED ### 1.1 Project Structure ✅ (Issues #1-4) - [x] **Issue #1**: Create Python project with pyproject.toml ✅ CLOSED - [x] **Issue #2**: Set up MCP SDK dependencies and Neo4j driver ✅ CLOSED - [x] **Issue #3**: Configure development environment with Docker Neo4j ✅ CLOSED - [x] **Issue #4**: Create basic project structure and documentation ✅ CLOSED ### 1.2 Core Neo4j Schema Design ✅ (Issues #5-7) - [x] **Issue #5**: Design and document node types schema ✅ CLOSED - [x] **Issue #6**: Create Neo4j indexes and constraints ✅ CLOSED - [x] **Issue #7**: Write schema migration scripts ✅ CLOSED ### 1.3 MCP Server Boilerplate ✅ (Issues #8-11) - [x] **Issue #8**: Implement MCP server initialization ✅ CLOSED - [x] **Issue #9**: Add Neo4j connection management ✅ CLOSED - [x] **Issue #10**: Create error handling and logging system ✅ CLOSED - [x] **Issue #11**: Set up configuration management ✅ CLOSED **Deliverables Completed**: - ✅ Complete Python project with pyproject.toml - ✅ MCP server with 8 core tools - ✅ Neo4j schema with 35 relationship types - ✅ Comprehensive documentation - ✅ Test suite foundation --- ## Phase 2: Core Memory Operations 🔄 IN PROGRESS **Timeline**: Weeks 4-6 | **Target**: January 2025 ### 2.1 Basic CRUD Operations (Issues #12-16) - [ ] **Issue #12**: Implement `store_memory` tool - [ ] **Issue #13**: Implement `get_memory` tool with relationships - [ ] **Issue #14**: Implement `update_memory` tool - [ ] **Issue #15**: Implement `delete_memory` with cleanup - [ ] **Issue #16**: Implement `search_memories` with full-text search ### 2.2 Entity Management (Issues #17-20) - [ ] **Issue #17**: Implement `create_entities` tool - [ ] **Issue #18**: Implement entity deletion with relationship cleanup - [ ] **Issue #19**: Implement observation management tools - [ ] **Issue #20**: Add entity validation and error handling ### 2.3 Basic Relationship Operations (Issues #21-25) - [ ] **Issue #21**: Implement `create_relationship` tool - [ ] **Issue #22**: Implement `get_related_memories` tool - [ ] **Issue #23**: Add relationship validation and constraints - [ ] **Issue #24**: Implement relationship deletion and cleanup - [ ] **Issue #25**: Create relationship analytics tools **Deliverables**: - Core memory CRUD operations - Entity management system - Basic relationship functionality - Comprehensive testing suite - Performance optimization --- ## Phase 3: Advanced Relationship System 📋 PLANNED **Timeline**: Weeks 7-8 | **Target**: February 2025 ### 3.1 Relationship Types Implementation (Issues #26-32) - [ ] **Issue #26**: Implement Causal relationships (CAUSES, TRIGGERS, etc.) - [ ] **Issue #27**: Implement Solution relationships (SOLVES, ADDRESSES, etc.) - [ ] **Issue #28**: Implement Context relationships (OCCURS_IN, APPLIES_TO, etc.) - [ ] **Issue #29**: Implement Learning relationships (BUILDS_ON, CONTRADICTS, etc.) - [ ] **Issue #30**: Implement Similarity relationships (SIMILAR_TO, VARIANT_OF, etc.) - [ ] **Issue #31**: Implement Workflow relationships (FOLLOWS, DEPENDS_ON, etc.) - [ ] **Issue #32**: Implement Quality relationships (EFFECTIVE_FOR, PREFERRED_OVER, etc.) ### 3.2 Weighted Relationships (Issues #33-35) - [ ] **Issue #33**: Add relationship properties (strength, confidence, context) - [ ] **Issue #34**: Implement relationship validation and evolution - [ ] **Issue #35**: Create relationship intelligence tools **Deliverables**: - All 35 relationship types implemented - Weighted relationship properties - Relationship evolution algorithms - Advanced graph traversal --- ## Phase 4: Claude Code Integration 📋 PLANNED **Timeline**: Weeks 9-11 | **Target**: February-March 2025 ### 4.1 Development Context Capture (Issues #36-39) - [ ] **Issue #36**: Implement task context capture - [ ] **Issue #37**: Add command execution tracking - [ ] **Issue #38**: Create error pattern analysis - [ ] **Issue #39**: Build solution effectiveness tracking ### 4.2 Project-Aware Memory (Issues #40-43) - [ ] **Issue #40**: Implement codebase analysis tool - [ ] **Issue #41**: Add file change tracking - [ ] **Issue #42**: Create code pattern identification - [ ] **Issue #43**: Build project dependency mapping ### 4.3 Workflow Memory Tools (Issues #44-45) - [ ] **Issue #44**: Implement workflow tracking and suggestions - [ ] **Issue #45**: Add workflow optimization recommendations **Deliverables**: - Claude Code workflow integration - Automatic context capture - Project-aware memory storage - Development pattern recognition --- ## Phase 5: Advanced Intelligence 📋 PLANNED **Timeline**: Weeks 12-15 | **Target**: March-April 2025 ### 5.1 Pattern Recognition (Issues #46-49) - [ ] **Issue #46**: Implement automatic code pattern detection - [ ] **Issue #47**: Add similar problem matching - [ ] **Issue #48**: Create error prediction system - [ ] **Issue #49**: Build preventive measure suggestions ### 5.2 Automatic Relationship Detection (Issues #50-53) - [ ] **Issue #50**: Implement temporal pattern analysis - [ ] **Issue #51**: Add co-occurrence pattern detection - [ ] **Issue #52**: Create success correlation analysis - [ ] **Issue #53**: Build failure causation detection ### 5.3 Memory Evolution (Issues #54-55) - [ ] **Issue #54**: Implement memory consolidation and cleanup - [ ] **Issue #55**: Add memory deprecation and promotion systems **Deliverables**: - Intelligent pattern recognition - Automatic relationship discovery - Memory evolution algorithms - Predictive capabilities --- ## Phase 6: Advanced Query & Analytics 📋 PLANNED **Timeline**: Weeks 16-18 | **Target**: April-May 2025 ### 6.1 Complex Memory Queries (Issues #56-59) - [ ] **Issue #56**: Implement memory graph visualization - [ ] **Issue #57**: Add memory path discovery - [ ] **Issue #58**: Create memory cluster analysis - [ ] **Issue #59**: Build memory statistics dashboard ### 6.2 Contextual Intelligence (Issues #60-63) - [ ] **Issue #60**: Implement solution similarity matching - [ ] **Issue #61**: Add solution effectiveness prediction - [ ] **Issue #62**: Create learning path recommendations - [ ] **Issue #63**: Build knowledge gap identification ### 6.3 Memory Effectiveness Tracking (Issues #64-65) - [ ] **Issue #64**: Implement memory rating and ROI tracking - [ ] **Issue #65**: Add memory optimization algorithms **Deliverables**: - Advanced analytics dashboard - Memory effectiveness metrics - Knowledge gap analysis - Optimization recommendations --- ## Phase 7: Integration & Optimization 📋 PLANNED **Timeline**: Weeks 19-20 | **Target**: May 2025 ### 7.1 Claude Code Deep Integration (Issues #66-69) - [ ] **Issue #66**: Hook into Claude Code task pipeline - [ ] **Issue #67**: Add automatic memory creation - [ ] **Issue #68**: Implement proactive memory suggestions - [ ] **Issue #69**: Create session continuity features ### 7.2 Performance Optimization (Issues #70-73) - [ ] **Issue #70**: Optimize Cypher queries for performance - [ ] **Issue #71**: Implement memory indexing and caching - [ ] **Issue #72**: Add background consolidation processes - [ ] **Issue #73**: Create performance monitoring ### 7.3 Data Export & Import (Issues #74-75) - [ ] **Issue #74**: Implement memory graph export/import - [ ] **Issue #75**: Add collaborative memory sharing features **Deliverables**: - Deep Claude Code integration - Production-ready performance - Data portability features - Monitoring and observability --- ## Git Workflow & Documentation Strategy ### Branching Strategy - `main` - Production-ready code - `develop` - Integration branch for features - `feature/issue-XX` - Individual issue branches - `phase-X` - Phase completion branches ### Commit Strategy - Each issue gets its own feature branch - Commits reference issue numbers: `git commit -m "feat: implement store_memory tool (closes #12)"` - Pull requests for each issue with proper review - Phase completion tagged with semantic versioning ### Progress Tracking - Update GitHub Issues with progress comments - Use GitHub Projects board to track status - Weekly progress reports in repository wiki - Milestone reviews at end of each phase ### Documentation Updates - Update README.md with each major feature - Maintain CHANGELOG.md for version history - Document API changes in /docs folder - Create usage examples and tutorials --- ## Success Metrics ### Technical Metrics - **Memory Retrieval Accuracy** - Relevance of search results - **Development Workflow Acceleration** - Time saved in development tasks - **Pattern Recognition Effectiveness** - Success rate of pattern identification - **Solution Success Rate Improvement** - Better outcomes from memory suggestions - **User Satisfaction** - Feedback on memory system usefulness ### Performance Metrics - **Query Response Time** - Sub-second memory retrieval - **Database Performance** - Efficient Neo4j operations - **Memory Storage Efficiency** - Optimal space utilization - **Relationship Traversal Speed** - Fast graph queries ### Quality Metrics - **Memory Quality Score** - Usefulness and accuracy of stored memories - **Relationship Accuracy** - Correctness of memory connections - **Context Relevance** - Appropriateness of memory suggestions - **Evolution Effectiveness** - Improvement of memory quality over time --- ## Risk Management ### Technical Risks - **Neo4j Performance** - Large graph performance optimization - **MCP Protocol Changes** - Adaptation to protocol updates - **Claude Code Integration** - API changes and compatibility ### Mitigation Strategies - Regular performance testing and optimization - Modular architecture for easy updates - Comprehensive test suite for regression prevention - Documentation for troubleshooting and maintenance --- ## Current Status Summary **✅ Phase 0 & 1 Complete** (Weeks 1-3) - Project setup, GitHub management, core MCP server - Neo4j schema with 35 relationship types - 8 core tools implemented - Comprehensive documentation **🔄 Phase 2 Starting** (Week 4) - Focus: Core memory operations refinement - Next: Create remaining GitHub issues - Target: Full CRUD and relationship functionality **📋 Phases 3-7 Planned** (Weeks 7-20) - Detailed roadmap with specific issues - Clear deliverables and success criteria - GitHub-based project management The implementation plan provides a clear path from basic memory operations to advanced AI-powered development assistance, with comprehensive tracking and documentation throughout the process.