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BMAD Memory-Enhanced Session State

Current Session Metadata

Session ID: {generate_unique_session_id} Started: {session_start_timestamp} Last Updated: {current_timestamp} Active Project: {project_name} Project Type: {mvp|feature-addition|maintenance|research} Phase: {discovery|requirements|architecture|development|refinement} Session Duration: {calculated_active_duration}

Current Context

Active Persona: {current_persona_name} Persona Activation Time: {persona_start_time} Last Activity: {last_completed_action} Activity Timestamp: {last_activity_time} Current Task: {active_task_name} Task Status: {in-progress|completed|blocked}

Memory Integration Status

Memory Provider: {openmemory-mcp|fallback|unavailable} Memory Queries This Session: {count_memory_queries} Memory Insights Applied: {count_applied_insights} New Memories Created: {count_created_memories} Cross-Project Learning Active: {true|false}

Decision Log (Auto-Enhanced with Memory)

Timestamp Persona Decision Rationale Memory Context Impact Status Confidence
2024-01-15 14:30 PM Chose monorepo architecture Team familiarity, simplified deployment Similar success in 3 past projects Affects all components Active High
2024-01-15 15:45 Architect Selected Next.js + FastAPI SSR requirements, team expertise Proven pattern from EcommerceApp project Tech stack locked Active High
2024-01-15 16:20 Design Architect Material-UI component library Design consistency, rapid development Used successfully in 5 similar projects UI architecture set Active Medium

Cross-Persona Handoffs (Memory-Enhanced)

PM → Architect (2024-01-15 15:30)

Context Transferred: PRD completed with 3 epics, emphasis on real-time features Key Requirements: WebSocket support, mobile-first design, performance < 2s load time Memory Insights Provided: Similar real-time projects, proven WebSocket patterns Pending Questions: Database scaling strategy, caching approach Files Modified: docs/prd.md, docs/epic-1.md, docs/epic-2.md Success Indicators: Clear requirements understanding, no back-and-forth clarifications Memory Learning: PM→Architect handoffs most effective with concrete performance requirements

Architect → Design Architect (2024-01-15 16:15)

Context Transferred: Technical architecture complete, component structure defined Key Constraints: React-based, performance budget 2s, mobile-first approach Memory Insights Provided: Successful component architectures for similar apps Collaboration Points: Component API design, state management patterns Files Modified: docs/architecture.md, docs/component-structure.md Success Indicators: Design constraints acknowledged, technical feasibility confirmed Memory Learning: Early collaboration on component APIs prevents later redesign

Active Concerns & Blockers (Memory-Enhanced)

Current Blockers

  • Database Choice Pending (Priority: High)
    • Raised By: Architect (2024-01-15 15:45)
    • Context: PostgreSQL vs MongoDB for real-time features
    • Memory Insights: Similar projects 80% chose PostgreSQL for consistency
    • Suggested Resolution: Technical feasibility consultation with Dev + SM
    • Timeline Impact: Blocks development start (planned 2024-01-16)

Pending Items

  • UI Mockups for Epic 2 (Priority: Medium)
    • Raised By: PM (2024-01-15 14:45)
    • Context: User dashboard wireframes needed for development estimation
    • Memory Insights: Early mockups reduce dev rework by 60% (from memory)
    • Assigned To: Design Architect
    • Dependencies: Component library selection (completed)

Resolved Items

  • Authentication Strategy Defined (2024-01-15 16:00)
    • Resolution: JWT with refresh tokens, OAuth integration
    • Resolved By: Architect collaboration with memory insights
    • Memory Learning: OAuth integration patterns for user convenience
    • Impact: Unblocked Epic 1 story development

Artifact Evolution Tracking

Primary Documents:

  • docs/prd.md: v1.0 → v1.3 (PM created → PM refined → Architect input)
  • docs/architecture.md: v1.0 → v1.1 (Architect created → Design Arch feedback)
  • docs/frontend-architecture.md: v1.0 (Design Architect created)
  • docs/epic-1.md: v1.0 (PM created from PRD)
  • docs/epic-2.md: v1.0 (PM created from PRD)

Secondary Documents:

  • docs/project-brief.md: v1.0 (Analyst created - foundational)
  • docs/technical-preferences.md: v1.0 (User input - referenced by Architect)

Memory Intelligence Summary

Applied Memory Insights This Session

  1. Monorepo Architecture Decision: Influenced by 3 similar successful projects in memory
  2. Next.js Selection: Pattern from EcommerceApp project (95% user satisfaction)
  3. Component Library Choice: Analysis of 5 similar projects favored Material-UI
  4. Authentication Pattern: OAuth integration lessons from 4 past implementations

Generated Memory Entries This Session

  1. Decision Memory: Monorepo choice with team familiarity rationale
  2. Pattern Memory: PM→Architect handoff optimization approach
  3. Implementation Memory: Authentication strategy with OAuth patterns
  4. Consultation Insight: Early Design Architect collaboration value

Cross-Project Learning Applied

  • Real-time Feature Patterns: From messaging app and dashboard projects
  • Performance Optimization: Mobile-first approaches from 3 e-commerce projects
  • Team Workflow: Successful persona sequencing from similar team contexts
  • Risk Mitigation: Database choice considerations from 6 past projects

User Interaction Patterns (Learning)

Preferred Working Style

  • Detail Level: High technical detail preferred (based on session interactions)
  • Decision Making: Collaborative approach with expert consultation requests
  • Pace: Methodical with thorough validation (as opposed to rapid iteration)
  • Communication: Appreciates cross-references and historical context

Effective Interaction Patterns

  • Consultation Requests: Uses multi-persona consultations for complex decisions
  • Context Preference: Values memory insights and historical patterns
  • Validation Style: Requests explicit confirmation before major decisions
  • Learning Orientation: Asks follow-up questions about rationale and alternatives

Session Productivity Indicators

  • Persona Switching Efficiency: 3.2 minutes average context restoration (vs 5.1 baseline)
  • Decision Quality: 90% confidence in major decisions (vs 70% without memory)
  • Context Continuity: Zero context loss incidents this session
  • Memory Integration Value: 85% of memory insights actively applied

Workflow Intelligence

Current Workflow Pattern

Detected Pattern: Standard New Project MVP Flow Stage: Architecture → Design Architecture → Development Preparation Progress: 65% through architecture phase Next Suggested: Design Architect UI/UX specification completion Confidence: 88% based on similar project patterns

Optimization Opportunities

  1. Parallel Design Work: Design Architect could start component design while architecture finalizes
  2. Early Dev Consultation: Include Dev in database decision for implementation reality check
  3. User Testing Prep: Consider early user testing strategy for Epic 1 features

Risk Indicators

  • Timeline Pressure: No current indicators (healthy progress pace)
  • Scope Creep: Low risk (clear MVP boundaries maintained)
  • Technical Risk: Medium (database choice impact on real-time features)
  • Resource Risk: Low (all personas engaged and productive)

Next Session Preparation

Likely Next Actions

  1. Database Decision Resolution (90% probability)

    • Recommended Approach: Technical feasibility consultation
    • Participants: Architect + Dev + SM
    • Memory Context: Database choice patterns for real-time apps

  2. Frontend Component Architecture (75% probability)

    • Recommended Approach: Design Architect detailed component specification
    • Dependencies: Material-UI library integration patterns
    • Memory Context: Successful component architectures from similar projects

Context Preservation for Next Session

Critical Context to Maintain:

  • Database decision rationale and options analysis
  • Real-time feature requirements and constraints
  • Team working style preferences and effective patterns
  • Cross-persona collaboration insights and optimization opportunities

Memory Queries to Prepare:

  • Database scaling patterns for real-time applications
  • Component architecture best practices for Material-UI + Next.js
  • Development estimation accuracy for similar scope projects
  • User testing strategies for MVP feature validation

Session Quality Metrics

Context Continuity Score: 95% (excellent persona handoffs) Memory Integration Score: 85% (high value from historical insights) Decision Quality Score: 90% (confident, well-supported decisions) Workflow Efficiency Score: 88% (smooth progression with minimal backtracking) User Satisfaction Indicators: High engagement, positive feedback on insights Learning Rate: 12 new memory entries created, 8 patterns refined

Last Auto-Update: {current_timestamp} Next Scheduled Update: On next major decision or persona switch Memory Sync Status: ✅ Synchronized with OpenMemory MCP