# STARLOG + CARTON Integration Specification

## Overview

This document outlines future integration between STARLOG MCP and CARTON (knowledge graph) + Context-Alignment MCPs. The integration would provide semantic analysis and graph storage of development session data, enabling advanced queries and insights across project documentation.

## Current State

STARLOG MCP provides structured documentation storage with:
- Registry-based JSON storage 
- Time-based session tracking
- Selective historical retrieval via `retrieve_starlog(date_range, session_id)`

## Proposed Integration Architecture

### Automatic Data Mirroring

Once STARLOG can retrieve data by date ranges, implement automatic background mirroring:

1. **Registry → Neo4j**: Mirror all STARLOG data to Neo4j graph database
2. **Semantic Embeddings**: Generate embeddings for session content, discoveries, and status updates
3. **Automatic Classification**: Use context-alignment MCP to classify and categorize session data

### Integration Points

#### On Session Events
When STARLOG operations occur:
```python
# After start_starlog(), end_starlog(), update_debug_diary()
async def mirror_to_carton(session_data):
    # 1. Store in Neo4j graph
    await carton.add_concept(
        concept_name=f"Session_{session.timestamp}",
        concept=session.content,
        relationships=[
            {"relationship": "part_of", "related": [f"Project_{project_name}"]},
            {"relationship": "follows", "related": [previous_session_id]}
        ]
    )
    
    # 2. Generate embeddings for semantic search
    await context_alignment.analyze_dependencies_and_merge_to_graph(
        repo_name=project_name,
        target_entity=f"session_{session.timestamp}"
    )
```

#### Batch Historical Processing
```python
# Process historical data in batches
sessions = retrieve_starlog(project, date_range="2025-01-01:2025-01-31")
for session in sessions:
    await mirror_session_to_graph(session)
```

### Enhanced Query Capabilities

With graph storage and embeddings, enable advanced queries:

#### Semantic Session Search
```python
# Find sessions similar to current work
similar_sessions = await carton.query_wiki_graph(
    "MATCH (s:Session) WHERE s.content CONTAINS 'authentication' RETURN s"
)
```

#### Cross-Project Pattern Discovery
```python
# Find patterns across multiple projects
patterns = await carton.query_wiki_graph(
    "MATCH (s1:Session)-[:SIMILAR_TO]->(s2:Session) 
     WHERE s1.project <> s2.project 
     RETURN s1.discoveries, s2.discoveries"
)
```

#### Development Timeline Analysis
```python
# Trace feature development across sessions
timeline = await carton.query_wiki_graph(
    "MATCH (s:Session)-[:FOLLOWS*]->(s2:Session)
     WHERE s.project = $project
     RETURN s.timestamp, s.key_discoveries"
)
```

### Data Classification

Use context-alignment MCP to automatically classify session content:

- **Problem Types**: Bug fix, feature implementation, refactor, investigation
- **Domains**: Frontend, backend, database, infrastructure, testing
- **Complexity**: Simple, moderate, complex based on session length and discovery count
- **Impact**: Based on files_updated and testing_results

### Semantic Relationships

Build rich relationship graphs:
- **Temporal**: Session → follows → Session
- **Topical**: Session → relates_to → Concept  
- **Causal**: Problem → leads_to → Solution
- **Hierarchical**: Project → contains → Session

## Implementation Phases

### Phase 1: Basic Mirroring
- Registry data → Neo4j nodes
- Simple temporal relationships
- Date-based retrieval integration

### Phase 2: Semantic Enhancement  
- Embedding generation for session content
- Similarity relationships between sessions
- Semantic search capabilities

### Phase 3: Advanced Analytics
- Cross-project pattern recognition
- Development velocity analysis
- Knowledge evolution tracking
- Automated insight generation

### Phase 4: Intelligent Recommendations
- Context-aware session suggestions
- Similar problem identification
- Best practice recommendations
- Learning pattern detection

## Benefits

### For Individual Developers
- **Pattern Recognition**: "I've solved similar problems before"
- **Context Recovery**: "What was I working on related to authentication?"
- **Learning Acceleration**: Find previous solutions and approaches

### For Teams
- **Knowledge Sharing**: Discover how others solved similar problems
- **Onboarding**: New team members can explore project history semantically
- **Best Practices**: Identify successful patterns across projects

### For Organizations
- **Development Insights**: Understand velocity, complexity, and success patterns
- **Knowledge Management**: Preserve and surface institutional knowledge
- **Process Improvement**: Identify bottlenecks and optimization opportunities

## Technical Considerations

### Performance
- Background processing to avoid blocking STARLOG operations
- Incremental updates vs full rebuilds
- Embedding cache management

### Privacy
- Configurable data mirroring (opt-in/opt-out)
- Sensitive information filtering
- Access control alignment with project permissions

### Scalability  
- Efficient graph traversal for large session histories
- Embedding storage optimization
- Query result caching

## Future Possibilities

### Integration with Other Systems
- **GitHub Integration**: Link sessions to commits, PRs, issues
- **Monitoring Integration**: Correlate sessions with system metrics
- **Documentation**: Automatically update docs based on session discoveries

### AI-Powered Insights
- **Session Summarization**: Generate concise session overviews
- **Problem Prediction**: Identify potential issues based on patterns
- **Solution Recommendation**: Suggest approaches based on similar past sessions
- **Knowledge Gap Detection**: Identify areas needing documentation

## Status

This integration is **future work** - not required for initial STARLOG MCP implementation. The core STARLOG system must be functional first, providing the data foundation for this advanced integration.

Priority: **Later phases after core STARLOG functionality is complete and stable**.