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+# AgentFarm DB to Memory System Converter
+
+A comprehensive module for converting data from AgentFarm SQLite databases into a structured memory system. This converter handles the extraction, transformation, and loading of agent data and their associated memories with support for tiering strategies, validation, and configurable error handling.
+
+## Overview
+
+The converter module provides functionality to import agent metadata and memories from an AgentFarm simulation database into a memory system that supports different memory tiers (Short-Term Memory, Intermediate Memory, Long-Term Memory). It's designed to handle large datasets efficiently with batch processing and comprehensive error handling.
+
+## Features
+
+### Core Functionality
+- **Agent Import**: Extracts agent metadata including position, resources, health, genome data, and behavioral parameters
+- **Memory Import**: Processes different types of memories (states, actions, social interactions) with metadata preservation
+- **Memory Tiering**: Intelligent assignment of memories to different tiers based on configurable strategies
+- **Batch Processing**: Efficient handling of large datasets with configurable batch sizes
+- **Validation**: Comprehensive validation of database structure and imported data
+- **Error Handling**: Configurable error handling modes (fail, skip, log)
+
+### Memory Types Supported
+- **State Memories**: Agent state information including position, resources, health, and status
+- **Action Memories**: Agent action records with type, targets, rewards, and resource changes
+- **Interaction Memories**: Social interaction data including type, outcome, and resource transfers
+
+### Tiering Strategies
+- **Simple**: Places all memories in Short-Term Memory (STM)
+- **Step-Based**: Uses time decay to distribute memories across tiers based on simulation timeline
+- **Importance-Aware**: Combines time decay with importance scores for intelligent tier assignment
+
+## Installation
+
+The converter module is part of the AgentMemory project. Ensure you have the required dependencies:
+
+```bash
+pip install sqlalchemy redis
+```
+
+## Quick Start
+
+```python
+from converter import from_agent_farm
+
+# Basic usage with default configuration
+memory_system = from_agent_farm("path/to/agentfarm.db")
+
+# Custom configuration
+memory_system = from_agent_farm(
+    db_path="path/to/agentfarm.db",
+    config={
+        "validate": True,
+        "error_handling": "fail",
+        "batch_size": 200,
+        "tiering_strategy_type": "importance_aware",
+        "memory_config": {
+            "use_mock_redis": False,
+            "stm_config": {
+                "memory_limit": 1000,
+                "ttl": 3600
+            }
+        }
+    }
+)
+```
+
+## Configuration
+
+### Basic Configuration Options
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `validate` | bool | `True` | Enable database and data validation |
+| `error_handling` | str | `"skip"` | Error handling mode: "skip", "fail", or "log" |
+| `batch_size` | int | `100` | Number of records to process in each batch |
+| `show_progress` | bool | `True` | Display progress information during import |
+| `use_mock_redis` | bool | `True` | Use mock Redis for testing |
+
+### Memory Type Mapping
+
+```python
+config = {
+    "memory_type_mapping": {
+        "AgentStateModel": "state",
+        "ActionModel": "action", 
+        "SocialInteractionModel": "interaction"
+    }
+}
+```
+
+### Tiering Strategy Configuration
+
+```python
+config = {
+    "tiering_strategy_type": "importance_aware"  # "simple", "step_based", "importance_aware"
+}
+```
+
+### Import Modes
+
+```python
+config = {
+    "import_mode": "full",  # "full", "incremental"
+    "selective_agents": [1, 2, 3]  # Import specific agents only
+}
+```
+
+## Architecture
+
+### Core Components
+
+#### DatabaseManager (`db.py`)
+- Manages SQLite database connections and sessions
+- Provides schema validation and query utilities
+- Handles connection pooling and error recovery
+
+#### AgentImporter (`agent_import.py`)
+- Extracts agent metadata from the database
+- Supports batch processing and validation
+- Handles agent-specific data transformation
+
+#### MemoryImporter (`memory_import.py`)
+- Processes different memory types (states, actions, interactions)
+- Applies tiering strategies to determine memory placement
+- Manages memory metadata extraction and transformation
+
+#### MemoryTypeMapper (`mapping.py`)
+- Maps database models to memory system types
+- Validates memory data consistency
+- Supports custom type mappings
+
+#### TieringStrategy (`tiering.py`)
+- Implements different strategies for memory tier assignment
+- Supports time-based and importance-based tiering
+- Extensible architecture for custom strategies
+
+#### ConverterConfig (`config.py`)
+- Centralized configuration management
+- Type-safe configuration with validation
+- Default values and extensible options
+
+### Data Flow
+
+1. **Database Connection**: DatabaseManager establishes connection to AgentFarm SQLite database
+2. **Validation**: Optional schema and data validation
+3. **Agent Import**: AgentImporter extracts agent metadata in batches
+4. **Memory Import**: MemoryImporter processes memories for each agent
+5. **Type Mapping**: MemoryTypeMapper converts database models to memory types
+6. **Tiering**: TieringStrategy determines appropriate memory tier
+7. **Memory System**: Imported data is added to the AgentMemorySystem
+
+## Usage Examples
+
+### Basic Import
+
+```python
+from converter import from_agent_farm
+
+# Import with default settings
+memory_system = from_agent_farm("simulation.db")
+print(f"Imported {len(memory_system.agents)} agents")
+```
+
+### Custom Configuration
+
+```python
+from converter import from_agent_farm
+
+config = {
+    "validate": True,
+    "error_handling": "fail",
+    "batch_size": 500,
+    "tiering_strategy_type": "step_based",
+    "memory_config": {
+        "use_mock_redis": False,
+        "stm_config": {
+            "memory_limit": 2000,
+            "ttl": 7200
+        }
+    }
+}
+
+memory_system = from_agent_farm("large_simulation.db", config)
+```
+
+### Selective Import
+
+```python
+# Import only specific agents
+config = {
+    "import_mode": "selective",
+    "selective_agents": [1, 5, 10, 15],
+    "error_handling": "log"
+}
+
+memory_system = from_agent_farm("simulation.db", config)
+```
+
+### Custom Memory Type Mapping
+
+```python
+config = {
+    "memory_type_mapping": {
+        "AgentStateModel": "state",
+        "ActionModel": "action",
+        "SocialInteractionModel": "interaction",
+        "CustomModel": "custom"
+    }
+}
+
+memory_system = from_agent_farm("simulation.db", config)
+```
+
+## Error Handling
+
+The converter supports three error handling modes:
+
+- **`"skip"`** (default): Continue processing, skip problematic records
+- **`"fail"`**: Stop processing and raise exception on first error
+- **`"log"`**: Log errors but continue processing
+
+```python
+# Strict error handling
+config = {"error_handling": "fail"}
+
+# Permissive error handling with logging
+config = {"error_handling": "log"}
+```
+
+## Memory Tiering
+
+### Simple Strategy
+Places all memories in Short-Term Memory (STM).
+
+### Step-Based Strategy
+Distributes memories across tiers based on simulation timeline:
+- Most recent 10% of steps → STM
+- Next 30% of steps → Intermediate Memory (IM)
+- Remaining steps → Long-Term Memory (LTM)
+
+### Importance-Aware Strategy
+Combines time decay with importance scores:
+- High importance (>0.8) → Promoted to STM
+- Medium importance (>0.5) → Promoted to IM
+- Low importance → Uses step-based tiering
+
+## Performance Considerations
+
+### Batch Processing
+- Default batch size: 100 records
+- Increase for better performance with large datasets
+- Decrease if memory usage is a concern
+
+### Memory Usage
+- The converter processes data in batches to manage memory usage
+- Large simulations may require tuning batch sizes and connection pooling
+
+### Database Optimization
+- Ensure proper indexing on agent_id and step_number columns
+- Consider using WAL mode for SQLite databases
+
+## Validation
+
+The converter includes comprehensive validation:
+
+### Database Validation
+- Checks for required tables and columns
+- Validates database schema integrity
+- Ensures data consistency
+
+### Data Validation
+- Validates agent and memory records
+- Checks for required fields and data types
+- Ensures referential integrity
+
+## Logging
+
+The converter uses Python's logging module with configurable levels:
+
+```python
+import logging
+logging.basicConfig(level=logging.INFO)
+
+# Enable debug logging for detailed information
+logging.getLogger('converter').setLevel(logging.DEBUG)
+```
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Database Connection Errors**
+   - Verify database path and permissions
+   - Check SQLite database integrity
+
+2. **Memory Import Failures**
+   - Enable validation to identify data issues
+   - Check error logs for specific problems
+   - Use "log" error handling mode for debugging
+
+3. **Performance Issues**
+   - Increase batch size for large datasets
+   - Monitor memory usage during import
+   - Consider selective import for testing
+
+### Debug Mode
+
+```python
+import logging
+logging.basicConfig(level=logging.DEBUG)
+
+config = {
+    "validate": True,
+    "error_handling": "log",
+    "show_progress": True
+}
+
+memory_system = from_agent_farm("simulation.db", config)
+```
+
+## API Reference
+
+### Main Function
+
+#### `from_agent_farm(db_path: str, config: Optional[Dict] = None) -> AgentMemorySystem`
+
+Imports data from an AgentFarm SQLite database into a memory system.
+
+**Parameters:**
+- `db_path` (str): Path to the AgentFarm SQLite database
+- `config` (dict, optional): Configuration options for the import process
+
+**Returns:**
+- `AgentMemorySystem`: Configured memory system with imported memories
+
+**Raises:**
+- `ValueError`: If database validation fails or import verification fails
+- `SQLAlchemyError`: If there are database connection issues
+
+### Configuration Classes
+
+#### `ConverterConfig`
+Main configuration class with validation and default values.
+
+#### `MemoryTypeMapper`
+Handles mapping between database models and memory types.
+
+#### `TieringStrategy`
+Base class for memory tiering strategies.
+
+## Contributing
+
+When contributing to the converter module:
+
+1. Ensure all new features include comprehensive tests
+2. Update documentation for any API changes
+3. Follow the existing error handling patterns
+4. Add appropriate logging for debugging
+5. Validate configuration parameters in `__post_init__` methods
+
+## License
+
+This module is part of the AgentMemory project. See the main project license for details. 
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