mcp-adr-analysis-server

# Reflexion Framework Implementation Strategy ## Overview This document outlines the detailed implementation strategy for the Reflexion framework, including the Actor-Evaluator-Self-Reflection pattern, memory management systems, and learning workflows for continuous improvement in MCP ADR Analysis Server tools. ## Implementation Architecture ### Core Components Implementation #### 1. Actor Component **Purpose**: Execute tasks with memory-enhanced context and learning integration **Implementation Approach**: ```typescript // Pseudo-implementation structure class ReflexionActor { async executeWithMemory( task: TaskDefinition, context: any, memories: ReflexionMemory[] ): Promise<TaskAttempt> } ``` **Actor Responsibilities**: - **Memory Integration**: Incorporate relevant past experiences into current task execution - **Context Enhancement**: Enrich task context with lessons learned and strategies - **Strategy Selection**: Choose optimal approaches based on past successes and failures - **Trajectory Generation**: Create detailed execution paths for evaluation and learning #### 2. Evaluator Component **Purpose**: Assess performance using multiple criteria and generate actionable feedback **Evaluation Criteria Implementation**: ##### Task Success (Weight: 25%) - **Metric**: Binary success/failure with quality gradations - **Evaluation**: Compare intended vs actual outcomes - **Scoring**: 0-1 scale with partial credit for near-misses ##### Quality Assessment (Weight: 20%) - **Metric**: Multi-dimensional quality evaluation - **Evaluation**: Accuracy, completeness, relevance, clarity - **Scoring**: Weighted average of quality dimensions ##### Efficiency Analysis (Weight: 15%) - **Metric**: Resource utilization and time optimization - **Evaluation**: Compare to baseline and previous attempts - **Scoring**: Relative efficiency improvement ##### Innovation Evaluation (Weight: 10%) - **Metric**: Novelty and creativity in approach - **Evaluation**: Assess unique strategies and solutions - **Scoring**: Creativity and effectiveness balance ##### Learning Integration (Weight: 30%) - **Metric**: How well past lessons were applied - **Evaluation**: Evidence of memory utilization and improvement - **Scoring**: Learning application effectiveness #### 3. Self-Reflection Component **Purpose**: Generate linguistic feedback and extract actionable lessons **Reflection Types Implementation**: ##### Success Analysis - **Focus**: What worked well and why - **Output**: Successful patterns, effective strategies, replicable approaches - **Integration**: Strengthen successful memory patterns ##### Failure Analysis - **Focus**: What went wrong and how to prevent it - **Output**: Error patterns, failure modes, prevention strategies - **Integration**: Create warning memories and avoidance strategies ##### Pattern Recognition - **Focus**: Recurring themes across multiple attempts - **Output**: Meta-patterns, general principles, transferable insights - **Integration**: Build semantic memory from episodic experiences ##### Strategy Refinement - **Focus**: How to improve approaches and methods - **Output**: Enhanced strategies, optimized workflows, better practices - **Integration**: Update procedural memory with refined methods ## Memory Management System ### Memory Types and Storage #### 1. Episodic Memory **Content**: Specific task attempts and their outcomes **Structure**: ```json { "memoryId": "episode_adr_suggestion_2024_001", "taskType": "adr-suggestion", "context": { "project": "microservices-platform" }, "outcome": { "success": true, "userRating": 4.5 }, "lessons": ["Domain knowledge crucial for relevance"], "applicableScenarios": ["microservices", "distributed-systems"] } ``` #### 2. Semantic Memory **Content**: General principles and knowledge extracted from experiences **Structure**: ```json { "memoryId": "semantic_adr_principles_001", "principle": "ADRs should address specific architectural concerns", "evidence": ["episode_001", "episode_015", "episode_032"], "confidence": 0.85, "applicability": ["all-adr-tasks"] } ``` #### 3. Procedural Memory **Content**: Improved methods and step-by-step approaches **Structure**: ```json { "memoryId": "procedure_context_analysis_v2", "procedure": "Enhanced context analysis workflow", "steps": ["1. Technology detection", "2. Pattern analysis", "3. Constraint identification"], "improvements": ["Added constraint analysis step"], "successRate": 0.78 } ``` #### 4. Meta-Memory **Content**: Knowledge about learning patterns and memory effectiveness **Structure**: ```json { "memoryId": "meta_learning_rate_analysis", "insight": "Learning plateaus after 15-20 attempts without new challenges", "evidence": ["learning_progress_adr", "learning_progress_analysis"], "recommendation": "Introduce complexity variations every 20 attempts" } ``` ### Memory Persistence Using File System #### File Organization Strategy ``` ./reflexion-memory/ ├── episodic/ │ ├── adr-suggestion/ │ ├── project-analysis/ │ └── research-integration/ ├── semantic/ │ ├── principles/ │ ├── patterns/ │ └── best-practices/ ├── procedural/ │ ├── workflows/ │ ├── strategies/ │ └── methods/ ├── meta/ │ ├── learning-patterns/ │ ├── memory-effectiveness/ │ └── improvement-trends/ └── indexes/ ├── memory-catalog.json ├── relevance-index.json └── temporal-index.json ``` #### Memory Persistence Implementation ```typescript // Prompt-driven memory persistence export async function persistMemoryWithPrompt(memory: ReflexionMemory) { const persistencePrompt = ` # Memory Persistence Request Please save the following Reflexion memory to the appropriate file location. ## Memory Details - **Memory ID**: ${memory.memoryId} - **Type**: ${memory.memoryType} - **Category**: ${memory.metadata.category} - **Created**: ${memory.createdAt} ## Memory Content ${JSON.stringify(memory.content, null, 2)} ## File Operations Required 1. **Determine File Path**: Based on memory type and category 2. **Create Directory**: If it doesn't exist 3. **Save Memory File**: In JSON format with proper naming 4. **Update Index**: Add entry to memory catalog and relevant indexes 5. **Validate Storage**: Ensure file was saved correctly ## Expected File Structure - Path: ./reflexion-memory/{type}/{category}/{memoryId}.json - Index: ./reflexion-memory/indexes/memory-catalog.json - Backup: Create backup if updating existing memory Please execute these file operations and confirm successful storage. `; return { content: [{ type: 'text', text: persistencePrompt }], metadata: { operation: 'memory_persistence', memoryId: memory.memoryId, memoryType: memory.memoryType } }; } ``` ## Learning Workflows ### Workflow 1: Single Task Reflexion **Duration**: 3-5 minutes per task **Steps**: 1. **Memory Retrieval** (30s): Find relevant past experiences 2. **Task Execution** (60-180s): Execute with memory-enhanced context 3. **Performance Evaluation** (30s): Score outcomes against criteria 4. **Self-Reflection** (60s): Generate lessons and insights 5. **Memory Integration** (30s): Update memory system ### Workflow 2: Continuous Learning Loop **Duration**: Ongoing across multiple tasks **Process**: 1. **Pattern Detection**: Identify recurring themes across attempts 2. **Meta-Learning**: Learn about learning effectiveness 3. **Strategy Evolution**: Refine approaches based on accumulated evidence 4. **Knowledge Consolidation**: Strengthen validated memories, weaken contradicted ones ### Workflow 3: Cross-Task Learning Transfer **Duration**: Variable based on task similarity **Process**: 1. **Similarity Assessment**: Identify related task types and contexts 2. **Knowledge Transfer**: Apply lessons from one domain to another 3. **Adaptation**: Modify strategies for new contexts 4. **Validation**: Test transferred knowledge effectiveness ## Integration with MCP Tools ### Tool-Specific Learning Patterns #### ADR Generation Tools **Learning Focus Areas**: - **Context Analysis Accuracy**: Learn to better understand project requirements - **Stakeholder Alignment**: Improve ADR relevance and clarity - **Decision Quality**: Learn from ADR adoption and feedback outcomes **Reflexion Pattern**: ```typescript export async function generateAdrsWithReflexion(context: any) { // Step 1: Retrieve relevant memories const memories = await retrieveRelevantMemories('adr-generation', context); // Step 2: Create memory-enhanced prompt const enhancedPrompt = await enhancePromptWithMemories( createAdrGenerationPrompt(context), memories ); // Step 3: Execute with reflexion tracking const result = await executeWithReflexion(enhancedPrompt, { taskType: 'adr-generation', evaluationCriteria: ['relevance', 'clarity', 'feasibility', 'completeness'], memoryIntegration: true }); return result; } ``` #### Analysis Tools **Learning Focus Areas**: - **Technology Detection**: Improve accuracy of technology identification - **Pattern Recognition**: Better identify architectural patterns - **Context Understanding**: Enhanced project context analysis #### Research Tools **Learning Focus Areas**: - **Question Quality**: Generate more effective research questions - **Source Evaluation**: Better assess research source quality - **Synthesis Skills**: Improve research integration and synthesis ### Memory-Enhanced Prompt Generation ```typescript export async function enhancePromptWithMemories( basePrompt: PromptObject, memories: ReflexionMemory[] ): Promise<PromptObject> { const memoryContext = memories.map(memory => ({ lesson: memory.content.summary, applicability: memory.content.applicableScenarios, confidence: memory.relevanceScore, evidence: memory.content.evidence })); const enhancedPrompt = ` # Memory-Enhanced Task Execution ## Original Task ${basePrompt.prompt} ## Relevant Past Experiences ${memoryContext.map((mem, index) => ` ### Experience ${index + 1} (Confidence: ${mem.confidence}) **Lesson**: ${mem.lesson} **Applicable to**: ${mem.applicability.join(', ')} **Evidence**: ${mem.evidence.join('; ')} `).join('\n')} ## Memory-Informed Approach Based on past experiences, please: 1. **Apply Relevant Lessons**: Use the lessons learned from similar situations 2. **Avoid Known Pitfalls**: Be aware of common mistakes and failure patterns 3. **Leverage Successful Strategies**: Build on approaches that have worked well 4. **Adapt to Context**: Modify strategies based on current context differences ## Enhanced Instructions ${basePrompt.instructions} ## Success Criteria - Apply at least 2 relevant lessons from past experiences - Demonstrate learning from previous mistakes - Show improvement over baseline approaches - Generate new insights for future learning Execute the task with memory-informed decision making and document how past experiences influenced your approach. `; return { prompt: enhancedPrompt, instructions: basePrompt.instructions, context: { ...basePrompt.context, memoriesUsed: memories.map(m => m.memoryId), memoryEnhanced: true } }; } ``` ## Performance Optimization ### Memory Retrieval Optimization - **Relevance Scoring**: Use context similarity and past success rates - **Temporal Weighting**: Prefer recent memories while preserving valuable old ones - **Category Filtering**: Focus on memories from similar task types - **Quality Thresholding**: Only retrieve high-quality, validated memories ### Learning Efficiency - **Incremental Updates**: Update memories incrementally rather than wholesale replacement - **Batch Processing**: Process multiple related memories together - **Lazy Loading**: Load memories only when needed - **Compression**: Consolidate similar memories to reduce storage and retrieval overhead ### Resource Management - **Memory Limits**: Implement configurable limits on memory storage - **Cleanup Strategies**: Automatic removal of outdated or low-value memories - **Caching**: Cache frequently accessed memories for faster retrieval - **Indexing**: Maintain efficient indexes for fast memory search This implementation strategy provides a comprehensive roadmap for building the Reflexion framework while maintaining the 100% prompt-driven architecture and ensuring effective learning and memory management across MCP tools.