MCP Memory Service

# Memory Awareness Enhancement Roadmap - Issue #14 ## Executive Summary This roadmap outlines the transformation of GitHub issue #14 from a basic external utility to a sophisticated memory-aware Claude Code experience leveraging advanced features like hooks, project awareness, and MCP deep integration. ## Phase 1: Automatic Memory Awareness (Weeks 1-2) ### 1.1 Session Startup Hooks **Goal**: Automatically inject relevant memories when starting a Claude Code session **Implementation**: ```javascript // claude-hooks/session-start.js export async function onSessionStart(context) { const projectContext = await detectProjectContext(context.workingDirectory); const relevantMemories = await queryMemoryService({ tags: [projectContext.name, 'key-decisions', 'recent-insights'], timeRange: 'last-2-weeks', limit: 8 }); if (relevantMemories.length > 0) { await injectSystemMessage(` Recent project context from memory: ${formatMemoriesForContext(relevantMemories)} `); } } ``` **Features**: - Project detection based on git repository and directory structure - Smart memory filtering by project relevance and recency - Automatic context injection without user intervention ### 1.2 Project-Aware Memory Selection **Goal**: Intelligently select memories based on current project context **Implementation**: ```python # Enhanced memory retrieval with project awareness class ProjectAwareMemoryRetrieval: def select_relevant_memories(self, project_context): # Score memories by relevance to current project memories = self.memory_service.search_by_tags([ project_context.name, f"tech:{project_context.language}", "decisions", "architecture", "bugs-fixed" ]) # Apply relevance scoring scored_memories = self.score_by_relevance(memories, project_context) return scored_memories[:10] ``` ### 1.3 Conversation Context Injection **Goal**: Seamlessly inject memory context into conversation flow **Deliverables**: - Session initialization hooks - Project context detection algorithm - Memory relevance scoring system - Context formatting and injection utilities ## Phase 2: Intelligent Context Updates (Weeks 3-4) ### 2.1 Dynamic Memory Loading **Goal**: Update memory context as conversation topics evolve **Implementation**: ```javascript // claude-hooks/topic-change.js export async function onTopicChange(context, newTopics) { const additionalMemories = await queryMemoryService({ semanticSearch: newTopics, excludeAlreadyLoaded: context.loadedMemoryHashes, limit: 5 }); if (additionalMemories.length > 0) { await updateContext(` Additional relevant context: ${formatMemoriesForContext(additionalMemories)} `); } } ``` ### 2.2 Conversation Continuity **Goal**: Link conversations across sessions for seamless continuity **Features**: - Cross-session conversation linking - Session outcome consolidation - Persistent conversation threads ### 2.3 Smart Memory Filtering **Goal**: AI-powered memory selection based on conversation analysis **Implementation**: - Natural language processing for topic extraction - Semantic similarity matching - Relevance decay algorithms - User preference learning ## Phase 3: Advanced Integration Features (Weeks 5-6) ### 3.1 Auto-Tagging Conversations **Goal**: Automatically categorize and tag conversation outcomes **Implementation**: ```javascript // claude-hooks/session-end.js export async function onSessionEnd(context) { const sessionSummary = await analyzeSession(context.conversation); const autoTags = await generateTags(sessionSummary); await storeMemory({ content: sessionSummary, tags: [...autoTags, 'session-outcome', context.project.name], type: 'session-summary' }); } ``` ### 3.2 Memory Consolidation System **Goal**: Intelligent organization of session insights and outcomes **Features**: - Duplicate detection and merging - Insight extraction and categorization - Knowledge graph building - Memory lifecycle management ### 3.3 Cross-Session Intelligence **Goal**: Maintain knowledge continuity across different coding sessions **Implementation**: - Session relationship mapping - Progressive memory building - Context evolution tracking - Learning pattern recognition ## Technical Architecture ### Core Components 1. **Memory Hook System** - Session lifecycle hooks - Project context detection - Dynamic memory injection 2. **Intelligent Memory Selection** - Relevance scoring algorithms - Topic analysis and matching - Context-aware filtering 3. **Context Management** - Dynamic context updates - Memory formatting utilities - Conversation continuity tracking 4. **Integration Layer** - Claude Code hooks interface - MCP Memory Service connector - Project structure analysis ### API Enhancements ```python # New memory service endpoints for Claude Code integration @app.post("/claude-code/session-context") async def get_session_context(project: ProjectContext): """Get relevant memories for Claude Code session initialization.""" @app.post("/claude-code/dynamic-context") async def get_dynamic_context(topics: List[str], exclude: List[str]): """Get additional context based on evolving conversation topics.""" @app.post("/claude-code/consolidate-session") async def consolidate_session(session_data: SessionData): """Store and organize session outcomes with intelligent tagging.""" ``` ## Success Metrics ### Phase 1 Targets - ✅ 100% automatic session context injection - ✅ <2 second session startup time with memory loading - ✅ 90%+ relevant memory selection accuracy ### Phase 2 Targets - ✅ Real-time context updates based on conversation flow - ✅ 95%+ conversation continuity across sessions - ✅ Intelligent topic detection and memory matching ### Phase 3 Targets - ✅ Fully autonomous memory management - ✅ Cross-session knowledge building - ✅ Adaptive learning from user interactions ## Implementation Priority **High Priority (Phase 1)**: 1. Session startup hooks for automatic memory injection 2. Project-aware memory selection algorithms 3. Basic context injection utilities **Medium Priority (Phase 2)**: 1. Dynamic memory loading based on conversation topics 2. Cross-session conversation linking 3. Smart memory filtering enhancements **Enhancement Priority (Phase 3)**: 1. Auto-tagging and session outcome consolidation 2. Advanced memory organization systems 3. Machine learning-based relevance optimization ## Risk Mitigation ### Technical Risks - **Performance Impact**: Implement lazy loading and caching strategies - **Context Overload**: Smart filtering and relevance-based selection - **Memory Service Availability**: Graceful degradation without memory service ### User Experience Risks - **Information Overload**: Configurable memory injection levels - **Privacy Concerns**: Clear memory access controls and user preferences - **Learning Curve**: Seamless integration with minimal configuration required ## Conclusion This enhancement transforms issue #14 from a basic utility into a revolutionary memory-aware Claude Code experience. By leveraging Claude Code's advanced features, we can deliver the original vision of automatic memory context injection while providing intelligent, context-aware assistance that learns and adapts to user patterns. The phased approach ensures incremental value delivery while building towards a sophisticated memory-aware development environment that fundamentally changes how developers interact with their code and project knowledge.