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Architecture Document: Metacogna LangGraph Hooks & Data Models

System Overview​

The Metacogna LangGraph Hooks & Data Models system provides the foundational data structures and orchestration hooks for implementing Symmetrical Intelligence Augmentation (SIA) principles in the Metacogna Subculture Management platform. This system bridges the gap between high-level SIA concepts and practical LangGraph implementation.

Architecture Principles​

SIA Foundation​

  • Symmetrical Intelligence Augmentation: Human and AI intelligence co-evolve through calibrated augmentation
  • Trust Networks: Quantified trust metrics enable reliable cross-cultural collaboration
  • Cultural Translation: Bidirectional communication between organizational subcultures
  • Hierarchical Memory: Four-tier memory architecture for comprehensive context retention

Technical Principles​

  • Type Safety: Pydantic models ensure data integrity and provide IDE support
  • Modular Design: Clear separation between data models, hooks, and utilities
  • Testability: Comprehensive testing framework with high coverage
  • Maintainability: Clean architecture with clear interfaces and documentation

System Context​

graph TB
    A[Metacogna Core System] --> B[LangGraph Hooks & Data Models]
    B --> C[Data Models]
    B --> D[Hooks]
    B --> E[Utilities]
    B --> F[Project Data]

    C --> G[Event Model]
    C --> H[Reward Model]
    C --> I[Indicator Model]
    C --> J[Task Model]
    C --> K[Goal Model]
    C --> L[Profile Model]

    D --> M[Doubt Hook]
    D --> N[Reflection Hook]
    D --> O[Symbiosis Hook]

    E --> P[Task Parser]
    E --> Q[Confidence Scorer]
    E --> R[Jira Adapter]
    E --> S[Alignment Analyzer]

    F --> T[Events JSON]
    F --> U[Rewards JSON]
    F --> V[Indicators JSON]
    F --> W[Tasks JSON]
    F --> X[Alignment Report]

Component Architecture​

Data Models Layer​

Base Model Architecture​

class BaseModel:
    """Base model with shared attributes and validation."""
    id: str
    created_at: datetime
    updated_at: datetime
    version: str
    metadata: Dict[str, Any]

Specialized Models​

  • EventModel: System events with timestamps and context
  • RewardModel: Sparse rewards for reinforcement learning
  • IndicatorModel: System performance indicators
  • TaskModel: Confidence-rated tasks with execution tracking
  • GoalModel: Hierarchical goal structure
  • ProfileModel: Subcultural profile definitions

Hooks Layer​

LangGraph Integration​

class SIAHook(ABC):
    """Abstract base class for SIA hooks."""

    @abstractmethod
    def execute(self, state: State, config: RunnableConfig) -> Dict[str, Any]:
        """Execute hook logic."""
        pass

Hook Implementations​

  • DoubtHook: Uncertainty quantification and confidence assessment
  • ReflectionHook: Meta-cognitive analysis and system improvement
  • SymbiosisHook: Human-AI collaborative decision making

Utilities Layer​

Core Utilities​

  • TaskParser: Parse and validate task definitions
  • ConfidenceScorer: Calculate confidence scores for tasks and decisions
  • JiraAdapter: Integration with Jira for task management
  • AlignmentAnalyzer: Analyze alignment between tasks and goals

Project Data Layer​

Data Storage Architecture​

project/
├── {project_id}/
│   ├── events.json
│   ├── rewards.json
│   ├── indicators.json
│   ├── tasks.json
│   └── alignment_report.md

Data Flow​

  1. Events captured and stored in events.json
  2. Rewards calculated and stored in rewards.json
  3. Indicators computed and stored in indicators.json
  4. Tasks processed with confidence scores
  5. Alignment reports generated periodically

Data Architecture​

Memory Hierarchy Implementation​

Episodic Memory (Redis)​

  • Immediate interaction context
  • Short-term event storage
  • Fast access for recent data

Semantic Memory (Postgres)​

  • Structured knowledge and patterns
  • Long-term data persistence
  • Relational data integrity

Procedural Memory (Weaviate)​

  • Operational procedures and workflows
  • Vector search capabilities
  • Pattern recognition and retrieval

Meta-Memory (Distributed)​

  • Strategic insights and system learning
  • Cross-project knowledge synthesis
  • System evolution tracking

Schema Architecture​

JSON Schema Validation​

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "type": "object",
  "properties": {
    "event": {
      "type": "object",
      "properties": {
        "id": {"type": "string"},
        "timestamp": {"type": "string", "format": "date-time"},
        "type": {"type": "string"},
        "data": {"type": "object"}
      },
      "required": ["id", "timestamp", "type"]
    }
  }
}

Integration Architecture​

LangGraph Integration​

State Management​

@dataclass
class SIAState:
    """LangGraph state for SIA workflows."""
    events: List[EventModel]
    rewards: List[RewardModel]
    indicators: Dict[str, IndicatorModel]
    tasks: List[TaskModel]
    goals: List[GoalModel]
    profiles: Dict[str, ProfileModel]
    trust_scores: Dict[str, float]
    current_phase: str

Hook Execution Flow​

  1. State received from LangGraph
  2. Hook analyzes current state
  3. SIA logic applied (doubt, reflection, symbiosis)
  4. State updated with results
  5. Control returned to LangGraph

External System Integration​

Jira Integration​

  • Task synchronization
  • Status updates
  • Confidence score propagation

Metacogna Core Integration​

  • Event forwarding
  • Trust score updates
  • Cultural translation requests

Security Architecture​

Data Protection​

  • Pydantic validation prevents malformed data
  • JSON schema validation at boundaries
  • Type checking prevents runtime errors

Access Control​

  • Project-level data isolation
  • Hook execution permissions
  • API authentication for external systems

Trust Mechanisms​

  • Confidence scoring for all decisions
  • Uncertainty quantification
  • Transparent reasoning traces

Deployment Architecture​

Package Structure​

metacogna-langgraph-hooks/
├── src/
│   ├── metacogna/
│   │   ├── models/
│   │   ├── hooks/
│   │   ├── utils/
│   │   └── project/
├── tests/
├── docs/
├── pyproject.toml
└── setup.py

Environment Requirements​

  • Python 3.9+
  • Poetry for dependency management
  • Redis for episodic memory
  • PostgreSQL for semantic memory
  • Weaviate for procedural memory

Performance Architecture​

Optimization Strategies​

  • Lazy loading for large datasets
  • Caching for frequently accessed data
  • Asynchronous processing for hooks
  • Connection pooling for databases

Monitoring Points​

  • Hook execution times
  • Data validation performance
  • Memory usage patterns
  • Error rates and recovery times

Testing Architecture​

Test Pyramid​

  • Unit Tests: Individual model and utility functions
  • Integration Tests: Hook execution and data flow
  • System Tests: End-to-end workflows
  • Performance Tests: Load testing and optimization

Test Data Management​

  • Fixtures for consistent test data
  • Mock objects for external dependencies
  • Test utilities for common operations

Evolution Architecture​

Version Management​

  • Semantic versioning for releases
  • Backward compatibility guarantees
  • Migration scripts for data changes
  • Deprecation warnings for old APIs

Extension Points​

  • Plugin architecture for custom hooks
  • Model extension mechanisms
  • Custom utility registration
  • Project-specific data handlers

Quality Attributes​

Reliability​

  • Comprehensive error handling
  • Graceful degradation
  • Data consistency guarantees
  • Recovery mechanisms

Maintainability​

  • Clear code organization
  • Comprehensive documentation
  • Type hints throughout
  • Modular design

Usability​

  • Intuitive APIs
  • Clear error messages
  • Comprehensive examples
  • Developer-friendly tooling

Risks and Mitigations​

Technical Risks​

  • LangGraph Compatibility: Regular integration testing with LangGraph updates
  • Performance Scaling: Load testing and optimization planning
  • Data Model Evolution: Versioned schemas and migration support

Operational Risks​

  • Memory Management: Monitoring and alerting for memory usage
  • Data Persistence: Backup and recovery procedures
  • External Dependencies: Circuit breakers and fallback mechanisms

Conclusion​

This architecture establishes a solid foundation for SIA implementation through well-structured data models, powerful LangGraph hooks, and comprehensive utilities. The modular design ensures maintainability while the performance optimizations support scalable operations. The integration with Metacogna's memory hierarchy and trust networks enables effective subculture management and organizational intelligence augmentation.