# NeoCoder Incarnation Analysis
## Incarnation System Overview
NeoCoder's incarnation system is a key architectural feature that allows the platform to adapt for different specialized use cases while preserving the core Neo4j graph structure. The system can manifest as different "brains" by swapping templates and execution policies.
### Common Schema Motifs
All incarnations share these core elements:
| Element | Purpose | Typical Labels/Relationships |
|---------|---------|------------------------------|
| **Actor** | Represents humans, agents, or tools | `(:Agent)-[:PLAYS_ROLE]->(:Role)` |
| **Intent** | Represents goals such as hypotheses, decisions, lessons | `(:Intent {type})` |
| **Evidence** | Represents documents, metrics, observations | `(:Evidence)-[:SUPPORTS]->(:Intent)` |
| **Outcome** | Represents results such as pass/fail, payoff, grades | `(:Outcome)-[:RESULT_OF]->(:Intent)` |
## Base Incarnation
**File:** `base_incarnation.py`
The original NeoCoder incarnation focused on coding workflows, template management, and incarnation management.
**Purpose:** Provides the core functionality for AI-guided coding workflows.
**Key Features:**
- ActionTemplate management
- Project management
- Workflow execution tracking
- Tool proposal and management
- Cypher snippet management
**Schema Elements:**
- `:AiGuidanceHub`
- `:ActionTemplate`
- `:Project`
- `:WorkflowExecution`
- `:BestPracticesGuide`
**Primary Tools:**
- `get_guidance_hub()` - Entrypoint for AI navigation
- `list_action_templates()` - View available templates
- `get_action_template()` - Get specific template steps
- `log_workflow_execution()` - Record successful workflows
- `propose_tool()` - Suggest new tools
## Research Incarnation
**File:** `research_incarnation.py`
Designed as a scientific research platform for hypothesis tracking and experiments.
**Purpose:** Support scientific research workflows with experiment tracking.
**Key Features:**
- Hypothesis registration and tracking
- Experiment design and execution
- Observation recording
- Statistical analysis
- Publication draft generation
**Schema Elements:**
- `:Hypothesis`
- `:Protocol`
- `:Experiment`
- `:Observation`
**Primary Tools:**
- `register_hypothesis()` - Create a scientific hypothesis
- `create_protocol()` - Design experimental protocols
- `create_experiment()` - Set up an experiment
- `record_observation()` - Record experimental observations
- `compute_statistics()` - Analyze experimental results
- `create_publication_draft()` - Generate publication drafts
## Decision Incarnation
**File:** `decision_incarnation.py`
A decision analysis and evidence tracking system.
**Purpose:** Support structured decision-making processes.
**Key Features:**
- Decision creation and tracking
- Alternative generation and evaluation
- Evidence collection and assessment
- Decision metrics and scoring
**Schema Elements:**
- `:Decision`
- `:Alternative`
- `:Metric`
- `:Evidence`
**Primary Tools:**
- `create_decision()` - Create a new decision
- `add_alternative()` - Add an alternative to a decision
- `add_metric()` - Add an evaluation metric
- `add_evidence()` - Add evidence for/against alternatives
## Knowledge Graph Incarnation
**File:** `knowledge_graph_incarnation.py`
A knowledge graph management system.
**Purpose:** Create and manage semantic knowledge graphs.
**Key Features:**
- Entity management with observations
- Relationship creation and tracking
- Knowledge search and exploration
- Graph visualization support
**Schema Elements:**
- `:Entity`
- `:Observation`
- `[:RELATES_TO {type}]` relationships
**Primary Tools:**
- `create_entities()` - Create entities with observations
- `create_relations()` - Connect entities with typed relationships
- `add_observations()` - Add observations to entities
- `read_graph()` - View the entire knowledge graph
- `search_nodes()` - Find entities by content
- `open_nodes()` - Get detailed information about entities
## Code Analysis Incarnation
**File:** `code_analysis_incarnation.py`
A code analysis system using Abstract Syntax Trees and Abstract Semantic Graphs.
**Purpose:** Deep analysis and understanding of code structures.
**Key Features:**
- Code parsing to AST/ASG representations
- Code structure and complexity analysis
- Code smell detection
- Documentation generation
**Schema Elements:**
- `:CodeFile`
- `:ASTNode`
- `:Analysis`
**Primary Tools:**
- `analyze_codebase()` - Analyze entire directory structures
- `analyze_file()` - Deep analysis of individual files
- `compare_versions()` - Compare different versions of code
- `find_code_smells()` - Identify potential code issues
- `generate_documentation()` - Auto-generate code documentation
## Data Analysis Incarnation
**File:** `data_analysis_incarnation.py`
A data analysis and modeling incarnation.
**Purpose:** Support data analysis workflows.
**Key Features:**
- Data processing and transformation
- Statistical analysis
- Visualization generation
- Model training and evaluation
**Schema Elements:**
- `:Dataset`
- `:Analysis`
- `:Model`
- `:Visualization`
**Primary Tools:**
- (Implementation appears to be in early stages)
- `tool_one()` and `tool_two()` placeholders
## Implementation Assessment
### Technical Implementation
The incarnation system is implemented through:
1. **Base Class Inheritance** - All incarnations extend the `BaseIncarnation` class
2. **Dynamic Loading** - Incarnations are discovered and loaded based on file naming patterns
3. **Tool Registration** - Each incarnation registers its specialized tools with the MCP server
4. **Schema Management** - Each incarnation defines and initializes its own Neo4j schema
5. **Type Enumeration** - IncarnationType enum tracks the available incarnation types
### Integration Points
Each incarnation seamlessly integrates with:
1. **Neo4j Schema** - Through defined schema queries
2. **Tool Registry** - By registering its tools with the MCP server
3. **Guidance Hub** - By creating and managing its own guidance hub
4. **MCP Server** - By implementing a consistent interface
### Maturity Assessment
Based on code review, the incarnations have different levels of implementation maturity:
| Incarnation | Implementation Status | Notes |
|-------------|----------------------|-------|
| Base | Complete | Fully implemented and tested |
| Knowledge Graph | Complete | Fully implemented with robust error handling |
| Research | Partial | Core functionality implemented |
| Decision | Partial | Core functionality implemented |
| Code Analysis | Early Stage | Structure defined but many tools show placeholders |
| Data Analysis | Placeholder | Minimal implementation |
## Extension Opportunities
The incarnation system is designed for extension:
1. **New Incarnations** - Create new specialized versions for different domains
2. **Enhanced Tools** - Add more sophisticated tools to existing incarnations
3. **Cross-Incarnation Integration** - Build tools that leverage multiple incarnations
4. **Visualization Tools** - Add visualization capabilities for different incarnation data
5. **Quantum-Inspired Features** - Implement the planned quantum-inspired schedulers
## Future Development Paths
Based on the implementation roadmap in the README:
1. **LevelEnv ↔ Neo4j Adapter** - For mapping events to graph structures
2. **Amplitude Register (Quantum Layer)** - For superposition states
3. **Scheduler** - For task prioritization based on entropy and impact
4. **TAG Asset Integration** - For vertical information hiding
