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Sprint +1: MEMORY CONTEXT Implementation Project Plan

Sprint +1: MEMORY-CONTEXT Implementation - Project Plan

Sprint Name: Sprint +1: MEMORY-CONTEXT Implementation Project: CODITECT Framework - Memory & Context System Repository: CODITECT-core Phase: Phase 1 - Beta Development Priority: P0 (Foundation) Duration: 2 weeks (10 business days) Budget: $45,000 Start Date: 2025-11-18 Target Completion: 2025-11-29

Executive Summary​

Build the MEMORY-CONTEXT system to enable true cross-session continuity, implement NESTED LEARNING for zero catastrophic forgetting, and provide privacy-controlled context management across all CODITECT projects via distributed intelligence architecture.

This is a foundational capability that will be available to all 19 submodules through the .coditect symlink chain, enabling persistent memory and intelligent context reuse across all CODITECT development.

Objectives​

Primary Goals​

  1. Session Export Automation - Automatic capture of session context
  2. NESTED LEARNING Integration - Google Research pattern for preventing catastrophic forgetting
  3. Privacy Control API - 4-level privacy model (public, team, private, ephemeral)
  4. Cross-Session Continuity - Seamless context loading between sessions
  5. Token Optimization - Intelligent context compression and relevance scoring

Success Criteria​

  • ✅ Automated session export on checkpoint creation
  • ✅ NESTED LEARNING pattern extraction working
  • ✅ Privacy controls enforced programmatically
  • ✅ Context loading < 5 seconds for typical session
  • ✅ 40%+ token reduction via intelligent context selection
  • ✅ Available to all 19 submodules via distributed intelligence

Architecture​

Components to Build​

1. Session Export Engine (scripts/core/session_export.py)​

Purpose: Automatically capture session context

Features​

  • Conversation extraction
  • Decision logging
  • Code change tracking
  • File modification mapping
  • Metadata generation (timestamp, participants, objectives)

2. NESTED LEARNING Processor (scripts/core/nested_learning.py)​

Purpose: Extract reusable patterns to prevent catastrophic forgetting

Features​

  • Pattern recognition (common workflows, decision patterns)
  • Knowledge graph construction
  • Similarity scoring
  • Pattern library management
  • Incremental learning support

3. Privacy Control Manager (scripts/core/privacy_control.py)​

Purpose: 4-level privacy enforcement

Privacy Levels​

  • Level 0 (Public): Shareable across all projects and users
  • Level 1 (Team): Project team only
  • Level 2 (Private): User-specific, not shared
  • Level 3 (Ephemeral): Session-only, deleted after session

Features​

  • Automatic PII detection and redaction
  • Privacy level tagging
  • Access control enforcement
  • Audit logging

4. Context Loader (scripts/core/context_loader.py)​

Purpose: Intelligent context retrieval for new sessions

Features​

  • Relevance scoring (recency, similarity, importance)
  • Token budget management
  • Progressive context loading
  • Checkpoint integration
  • Session continuity verification

5. Token Optimizer (scripts/core/token_optimizer.py)​

Purpose: Compress context while preserving quality

Features​

  • Semantic compression
  • Redundancy elimination
  • Priority-based selection
  • Cost tracking and reporting
  • A/B testing for compression strategies

Technology Stack​

Core Technologies​

  • Python 3.11+ - Primary development language
  • SQLite - Local context database (lightweight, fast)
  • Redis (optional) - Distributed caching for team contexts
  • ChromaDB - Vector database for semantic search
  • Anthropic Claude API - Pattern extraction and summarization

AI/ML Libraries​

  • LangChain - Context processing and chain management
  • Sentence-Transformers - Embedding generation
  • spaCy - NLP for PII detection
  • tiktoken - Token counting and optimization

Testing & Quality​

  • pytest - Unit and integration testing
  • black - Code formatting
  • mypy - Type checking
  • coverage.py - Code coverage tracking

Directory Structure​

coditect-core/
├── MEMORY-CONTEXT/                    # Session context storage
│   ├── sessions/                      # Session exports
│   │   ├── 2025-11-16-session.md
│   │   └── ...
│   ├── patterns/                      # NESTED LEARNING patterns
│   │   ├── decision-patterns.json
│   │   ├── workflow-patterns.json
│   │   └── code-patterns.json
│   ├── checkpoints/                   # Checkpoint snapshots
│   └── database/                      # SQLite databases
│       ├── context.db                 # Main context database
│       └── vector.db                  # ChromaDB vector store
│
├── scripts/
│   └── core/
│       ├── session_export.py          # NEW: Session export automation
│       ├── nested_learning.py         # NEW: NESTED LEARNING processor
│       ├── privacy_control.py         # NEW: Privacy management
│       ├── context_loader.py          # NEW: Context retrieval
│       ├── token_optimizer.py         # NEW: Token optimization
│       └── create_checkpoint.py       # ENHANCED: Integrate with session export
│
├── tests/
│   └── core/
│       ├── test_session_export.py
│       ├── test_nested_learning.py
│       ├── test_privacy_control.py
│       ├── test_context_loader.py
│       └── test_token_optimizer.py
│
├── docs/
│   ├── MEMORY-CONTEXT-architecture.md # NEW: Architecture documentation
│   ├── NESTED-LEARNING-GUIDE.md       # NEW: NESTED LEARNING implementation
│   ├── PRIVACY-CONTROLS-SPEC.md       # NEW: Privacy specification
│   └── TOKEN-OPTIMIZATION-GUIDE.md    # NEW: Token optimization strategies
│
└── SPRINT-1-MEMORY-CONTEXT-tasklist.md # Sprint tasklist with checkboxes

Implementation Plan​

Week 1: Core Infrastructure (Days 1-5)​

Day 1: Session Export Engine​

  • Create session_export.py with conversation extraction
  • Add metadata generation (timestamp, participants, objectives)
  • Implement file change tracking
  • Add decision logging
  • Unit tests for session export

Day 2: Privacy Control Manager​

  • Create privacy_control.py with 4-level model
  • Implement PII detection using spaCy
  • Add automatic redaction
  • Create privacy tagging system
  • Add access control enforcement
  • Unit tests for privacy controls

Day 3: Database Schema & Setup​

  • Design SQLite schema for context storage
  • Create database initialization scripts
  • Setup ChromaDB for vector storage
  • Implement database migrations
  • Add database backup/restore utilities
  • Integration tests for database operations

Day 4: NESTED LEARNING Processor (Part 1)​

  • Create nested_learning.py framework
  • Implement pattern recognition for workflows
  • Add decision pattern extraction
  • Create knowledge graph schema
  • Implement similarity scoring
  • Unit tests for pattern extraction

Day 5: Week 1 Integration & Testing​

  • Integrate session export with checkpoint script
  • End-to-end test: checkpoint → session export → database
  • Add privacy controls to session export
  • Documentation: MEMORY-CONTEXT-architecture.md
  • Code review and refactoring
  • Week 1 checkpoint

Week 2: Intelligence & Optimization (Days 6-10)​

Day 6: NESTED LEARNING Processor (Part 2)​

  • Implement code pattern extraction
  • Add pattern library management
  • Create incremental learning pipeline
  • Implement pattern versioning
  • Add pattern conflict resolution
  • Integration tests for NESTED LEARNING

Day 7: Context Loader​

  • Create context_loader.py with relevance scoring
  • Implement recency weighting
  • Add similarity search via ChromaDB
  • Create token budget manager
  • Implement progressive context loading
  • Unit tests for context loading

Day 8: Token Optimizer​

  • Create token_optimizer.py with semantic compression
  • Implement redundancy elimination
  • Add priority-based selection
  • Create cost tracking system
  • Add A/B testing framework
  • Unit tests for token optimization

Day 9: Integration & Polish​

  • Full system integration test
  • Performance benchmarking (context load time, token reduction)
  • Error handling and edge cases
  • CLI integration (coditect memory load, coditect memory export)
  • Documentation: NESTED-LEARNING-GUIDE.md, PRIVACY-CONTROLS-SPEC.md
  • Security audit (PII detection, privacy enforcement)

Day 10: Final Testing & Documentation​

  • End-to-end user acceptance testing
  • Performance validation (< 5s context load, 40%+ token reduction)
  • Create user guides and API documentation
  • Update all README.md files with MEMORY-CONTEXT features
  • Final code review
  • Sprint +1 completion checkpoint
  • Deploy to all 19 submodules via distributed intelligence

Distributed Intelligence Integration​

How It Works Across All Submodules​

coditect-cloud-backend/.coditect → ../../.coditect
                                  → coditect-core/

Usage from any submodule​

# From coditect-cloud-backend/
cd /path/to/coditect-cloud-backend

# Export session context
python3 .coditect/scripts/core/session_export.py

# Load context for new session
python3 .coditect/scripts/core/context_loader.py --relevance 0.7 --token-budget 8000

# Create checkpoint (auto-exports session)
python3 .coditect/scripts/create_checkpoint.py "Feature X Complete" --auto-commit

Benefits for all 19 projects​

  • ✅ Consistent context management across all repos
  • ✅ Cross-project pattern recognition (e.g., backend patterns inform frontend work)
  • ✅ Team-level knowledge sharing (Level 1 privacy contexts)
  • ✅ Zero configuration per project (works via symlink)

Success Metrics​

Performance Targets​

MetricTargetMeasurement
Session Export Time< 10sTime from checkpoint call to export complete
Context Load Time< 5sTime to load relevant context for new session
Token Reduction40%+Tokens used with optimizer vs. without
Pattern Extraction10+ patterns/weekNESTED LEARNING patterns discovered
Privacy Accuracy99%+PII detection and redaction accuracy
Test Coverage80%+Code coverage across all modules

Quality Targets​

  • ✅ Zero privacy leaks (all PII redacted)
  • ✅ Zero data loss (all sessions recoverable)
  • ✅ Context relevance > 0.8 (user rating)
  • ✅ Cross-session continuity (95%+ tasks resume correctly)

Risk Assessment​

High Risks​

Risk 1: PII Detection Accuracy​

  • Impact: Privacy violations, legal issues
  • Probability: Medium
  • Mitigation:
    • Use proven spaCy models for NLP
    • Add manual review for Level 0 (public) contexts
    • Conservative redaction (false positives OK)
    • Regular audit of exported contexts

Risk 2: Token Optimization Quality​

  • Impact: Loss of critical context, poor session continuity
  • Probability: Medium
  • Mitigation:
    • A/B testing different compression strategies
    • User feedback loop on context quality
    • Configurable compression aggressiveness
    • Fallback to full context if optimization fails

Medium Risks​

Risk 3: ChromaDB Scalability​

  • Impact: Slow context loading as patterns grow
  • Probability: Low
  • Mitigation:
    • Implement pattern archival (archive patterns > 90 days old)
    • Add pattern pruning (remove low-value patterns)
    • Monitor database size and query performance
    • Add Redis caching layer if needed

Risk 4: Cross-Project Compatibility​

  • Impact: Some submodules can't use MEMORY-CONTEXT
  • Probability: Low
  • Mitigation:
    • Test in 3 diverse submodules (backend, frontend, CLI)
    • Document Python version requirements
    • Provide Docker container for consistent environment
    • Add graceful degradation if dependencies missing

Budget Breakdown​

Total Budget: $45,000

Engineering Costs: $40,000​

  • Senior Python Engineer (2 weeks, full-time): $20,000
    • Session export, NESTED LEARNING, context loader
  • ML/NLP Engineer (1 week, full-time): $12,000
    • Token optimization, PII detection, semantic search
  • QA Engineer (1 week, part-time): $8,000
    • Testing, privacy audit, performance validation

Infrastructure Costs: $2,000​

  • ChromaDB Hosting (2 weeks): $200
  • Redis Cloud (optional, 2 weeks): $300
  • Anthropic API Credits (pattern extraction): $1,000
  • Testing & Development Environments: $500

Contingency: $3,000 (6.7%)​

  • Unexpected complexity in PII detection
  • Additional testing cycles
  • Documentation and training materials

Dependencies​

Upstream Dependencies​

  • ✅ Distributed Intelligence Architecture - COMPLETE (19/19 submodules)
  • ✅ Checkpoint Automation System - COMPLETE (create-checkpoint.py)
  • ⏸️ ChromaDB Setup - Required before Day 3
  • ⏸️ spaCy Models - Required before Day 2

Downstream Consumers​

All 19 submodules will benefit:

  • Phase 1 Projects (CODITECT-cloud-backend, frontend, CLI, docs, infrastructure, legal)
  • Phase 2 Projects (marketplace, analytics, automation)
  • Supporting Tools (screenshot automator, workflow analyzer, multi-LLM IDE)
  • Strategic Projects (GTM, agent standards, NESTED LEARNING, blog)

Testing Strategy​

Unit Testing (80%+ coverage)​

  • Session export functions
  • Privacy control logic
  • Pattern extraction algorithms
  • Token optimization strategies
  • Context loading and scoring

Integration Testing​

  • End-to-end: checkpoint → session export → database → context load
  • Privacy enforcement across all components
  • Database operations (SQLite + ChromaDB)
  • CLI integration

Performance Testing​

  • Context load time under various dataset sizes (10, 100, 1000 sessions)
  • Token optimization effectiveness
  • Database query performance
  • Memory usage under load

Security Testing​

  • PII detection accuracy (test with synthetic PII data)
  • Privacy level enforcement
  • Access control verification
  • SQL injection prevention

User Acceptance Testing​

  • Test from 3 different submodules
  • Context quality assessment
  • Session continuity verification
  • Token reduction validation

Documentation Deliverables​

Technical Documentation​

  • MEMORY-CONTEXT-architecture.md - System architecture and design decisions
  • NESTED-LEARNING-GUIDE.md - How to use pattern extraction and learning
  • PRIVACY-CONTROLS-SPEC.md - Privacy model specification and API
  • TOKEN-OPTIMIZATION-GUIDE.md - Optimization strategies and configuration
  • API-REFERENCE.md - Complete API documentation for all modules

User Documentation​

  • MEMORY-CONTEXT-USER-GUIDE.md - How to use MEMORY-CONTEXT system
  • BEST-PRACTICES.md - Best practices for context management
  • TROUBLESHOOTING.md - Common issues and solutions
  • CLI-REFERENCE.md - Command-line interface documentation

Training Materials​

  • MEMORY-CONTEXT-quick-start.md - 15-minute quick start guide
  • DEMO-SCRIPT.md - Live demonstration script
  • Update user-training/ with MEMORY-CONTEXT module

Rollout Plan​

Phase 1: Internal Testing (Day 10)​

  • Deploy to CODITECT-core (primary)
  • Test from 3 submodules:
    • CODITECT-cloud-backend (Python backend)
    • CODITECT-cloud-frontend (JavaScript frontend)
    • CODITECT-cli (Python CLI)

Phase 2: Gradual Rollout (Week 3)​

  • Enable for all Phase 1 Beta projects (P0)
  • Monitor performance and errors
  • Gather user feedback
  • Fix critical issues

Phase 3: Full Deployment (Week 4)​

  • Enable for all 19 submodules
  • Create training materials
  • Conduct team training session
  • Document lessons learned

Success Indicators​

Week 1 Checkpoint​

  • ✅ Session export working and tested
  • ✅ Privacy controls enforced
  • ✅ Database schema operational
  • ✅ Pattern extraction framework ready
  • ✅ Integration with checkpoint script complete

Week 2 Completion​

  • ✅ NESTED LEARNING producing valuable patterns
  • ✅ Context loading < 5s
  • ✅ Token reduction > 40%
  • ✅ All tests passing (80%+ coverage)
  • ✅ Documentation complete
  • ✅ Working in 3+ submodules

Post-Sprint Success (Week 3-4)​

  • ✅ Used in daily development across all Beta projects
  • ✅ Positive user feedback (4/5 rating or higher)
  • ✅ Measurable token cost savings
  • ✅ Zero privacy incidents
  • ✅ Session continuity working reliably

Next Steps After Sprint +1​

Sprint +2: Advanced Features​

  • Multi-user context sharing (team collaboration)
  • Cross-project pattern recognition
  • Context search interface (web UI)
  • Advanced token optimization (LLM-based compression)
  • Integration with agent orchestration system

Long-term Vision​

  • Federated Context - Share patterns across organizations
  • Context Marketplace - Buy/sell valuable patterns
  • AI-Powered Summarization - Automatic context compression
  • Real-time Collaboration - Live context sharing during pair programming

Appendix​

References​

  • NESTED LEARNING: Google Research Paper
  • Privacy Engineering: GDPR, CCPA compliance frameworks
  • Token Optimization: OpenAI/Anthropic best practices

Status: 📋 PLANNED - Ready to Begin Owner: AZ1.AI CODITECT Team Start Date: 2025-11-18 Last Updated: 2025-11-16 Repository: https://github.com/coditect-ai/coditect-core