Architecture & Design

Update Summary

Changes Made

Added new section on Enhanced Graceful Shutdown Architecture
Updated Project Structure section to include shutdown components
Added new architectural diagram for shutdown process
Updated Core Components section to include ShutdownCoordinator
Added configuration details for graceful shutdown
Updated Design Patterns section with new component interactions
Updated Safety and Reliability Features section with shutdown validation
Added new section on Shutdown State Persistence

Introduction
Project Structure
Core Components
Architecture Overview
Detailed Component Analysis
Design Patterns
Data Flow and Lifecycle Management
Configuration and State Management
Extensibility and Modularity
Cross-Cutting Concerns
Enhanced Snake Agent Architecture
Safety and Reliability Features
Enhanced Graceful Shutdown Architecture
Shutdown State Persistence

Introduction

The RAVANA system is an experimental open-source Artificial General Intelligence (AGI) framework designed for autonomous, continuous operation. It embodies a modular, state-driven architecture where intelligence emerges from the interaction of specialized components. The system operates through a perpetual cognitive loop that enables self-directed behavior, emotional state tracking, memory formation, reflection, and curiosity-driven exploration.

This document provides a comprehensive architectural overview of the RAVANA system, detailing its component-based design, core orchestration mechanisms, service abstractions, and key design patterns. The analysis is based on direct examination of the codebase, focusing on how components interact, manage state, and evolve over time through learning and reflection. Recent updates include the integration of the Enhanced Snake Agent and the Enhanced Graceful Shutdown system, which adds phased execution, component lifecycle management, and state validation workflows to ensure reliable system termination.

Project Structure

The RAVANA project follows a layered, modular structure that separates concerns into distinct directories:

core/: Contains the central orchestration logic, state management, configuration, and foundational utilities.
modules/: Houses pluggable cognitive modules responsible for specific functions like decision-making, emotional intelligence, and self-reflection.
services/: Provides abstracted access to external resources and data storage systems.
database/: Manages database models and engine configuration.
tests/: Contains unit and integration tests.
actions/: Defines executable actions that the system can perform.

This organization supports the system's philosophy of composability and emergent intelligence, allowing modules to evolve independently while being coordinated by the central AGISystem orchestrator.

Diagram sources

core/system.py
core/state.py
services/memory_service.py
services/knowledge_service.py
core/shutdown_coordinator.py

Core Components

The RAVANA system's architecture revolves around several core components that work together to create an autonomous cognitive agent.

AGISystem Class

The AGISystem class, defined in core/system.py, serves as the singleton orchestrator of the entire system. It is responsible for:

Initializing all modules and services
Managing the main autonomous loop
Holding the shared state of the AGI
Coordinating inter-component communication
Handling graceful shutdown

The AGISystem is instantiated with a database engine and initializes all dependent components during construction. It maintains references to all major modules and services, acting as the central nervous system of the AGI.

SharedState Class

The SharedState class in core/state.py encapsulates the global state of the AGI system. This shared state is accessible to all modules and services, enabling coordinated behavior based on a consistent view of the system's current condition.

Key state elements include:

Mood: A vector representing the AGI's emotional state across multiple dimensions
Current Situation: The most recent situation generated for decision-making
Recent Memories: Short-term episodic memories retrieved for context
Curiosity Topics: Topics generated by the curiosity system for exploration
Search Results: Recent web search results for immediate use
Current Task: The task currently being processed

This centralized state management approach ensures that all components operate with consistent context, enabling coherent behavior across the system.

Section sources

core/system.py
core/state.py

Architecture Overview

The RAVANA system implements a modular component-based architecture with a clear separation between the orchestrator, cognitive modules, and service layers. The architecture follows a layered approach where higher-level cognitive functions are built upon foundational services.

System Context

The system operates as a self-contained agent that interacts with the external world through various channels:

Information Input: RSS feeds, web searches, and user prompts
Action Output: Code generation, file operations, logging, and external API calls
Memory Storage: Persistent storage of episodic and semantic knowledge
Self-Monitoring: Internal state tracking and reflection

Component Interaction

Components interact through well-defined interfaces with the AGISystem acting as the central coordinator. The system follows a dependency injection pattern where the AGISystem injects itself and necessary services into modules during initialization.

Diagram sources

core/system.py
core/state.py
DEVELOPER_GUIDE.md
core/shutdown_coordinator.py

Detailed Component Analysis

AGISystem: The Central Orchestrator

The AGISystem class is the heart of the RAVANA architecture, managing the entire lifecycle of the AGI. It implements a singleton pattern and serves as the primary entry point for all system operations.

Initialization Process

During initialization, the AGISystem:

Creates a database session
Loads configuration settings
Initializes shared models (embedding model, sentiment classifier)
Instantiates all services
Initializes all cognitive modules
Sets up the shared state with initial mood values
Creates and configures the ShutdownCoordinator

The constructor accepts a database engine and configures all dependent components with the necessary resources.

Autonomous Loop

The core of the AGISystem is the run_autonomous_loop method, which executes continuously in a loop. Each iteration follows a structured cognitive process:

Diagram sources

core/system.py
README.md

Section sources

core/system.py

Service Layer Abstraction

The services layer provides abstracted access to external resources and data storage, decoupling business logic from implementation details.

MemoryService

The MemoryService in services/memory_service.py acts as a facade for the episodic memory system. It provides asynchronous methods for:

Retrieving relevant memories based on a query
Saving new memories
Extracting memories from interactions
Consolidating memories for optimization

The service delegates actual memory operations to the episodic memory module while providing a clean, asynchronous interface that integrates with the system's async architecture.

KnowledgeService

The KnowledgeService in services/knowledge_service.py manages the AGI's semantic knowledge base. Key responsibilities include:

Adding new knowledge with deduplication
Semantic search using FAISS vector indexing
Retrieving knowledge by category or recency
Compressing and summarizing knowledge

The service implements a hybrid storage approach with database persistence and in-memory vector search for efficient retrieval.

Section sources

services/memory_service.py
services/knowledge_service.py

Design Patterns

The RAVANA system employs several key design patterns to achieve its architectural goals.

Dependency Injection

The system extensively uses dependency injection to wire components together. The AGISystem injects itself and necessary services into modules during initialization:

self.reflection_module = ReflectionModule(self)
self.experimentation_module = ExperimentationModule(self)
self.action_manager = EnhancedActionManager(self, self.data_service)
self.learning_engine = AdaptiveLearningEngine(self)

This pattern promotes loose coupling, testability, and flexibility in component composition.

Section sources

core/system.py

Registry Pattern

The ActionRegistry in core/actions/registry.py implements the Registry pattern to manage available actions. The registry:

Stores action instances in a dictionary keyed by name
Provides methods to register new actions
Discovers actions automatically through package introspection
Exposes action definitions to the LLM for decision-making

The registry enables dynamic action discovery and extensibility, allowing new actions to be added without modifying core orchestration logic.

Diagram sources

core/actions/registry.py

Section sources

core/actions/registry.py

Strategy Pattern

The AdaptiveLearningEngine implements the Strategy pattern to dynamically adjust the AGI's behavior based on past performance. The engine generates adaptation strategies such as:

Action Prioritization: Prefer high-success actions, avoid low-success ones
Confidence Adjustment: Modify decision confidence based on overall success rate
Exploration vs Exploitation: Balance between trying new actions and using proven ones
Context-Aware Adaptations: Adjust behavior based on situational factors

These strategies are applied during decision-making to improve future performance.

Section sources

modules/adaptive_learning/learning_engine.py

Observer Pattern

While not explicitly implemented as a formal pattern, the system exhibits Observer-like behavior through shared state updates. When one component modifies the shared state (e.g., updating mood), other components can react to these changes in subsequent iterations.

For example, the EmotionalIntelligence module updates the mood vector, which then influences decision-making in the next cycle through the DecisionEngine's access to the shared state.

Data Flow and Lifecycle Management

Cognitive Loop Data Flow

The system's primary data flow follows the autonomous cognitive loop:

Situation Generation: The SituationGenerator creates a context for action based on current state and curiosity topics
Memory Retrieval: Relevant memories are retrieved from the MemoryService to provide context
Decision Making: The DecisionEngine selects an action based on situation, memories, mood, and available actions
Action Execution: The ActionManager executes the selected action, potentially generating new data
State Update: The system updates mood, saves memories, and logs the interaction
Reflection: The ReflectionModule analyzes outcomes and suggests improvements

Component Lifecycle

Components follow a coordinated lifecycle managed by the AGISystem:

Initialization: All components are created and wired together during AGISystem construction
Execution: Components participate in the autonomous loop, processing data and updating state
Background Tasks: Some components run periodic background tasks (e.g., data collection, knowledge compression)
Shutdown: The AGISystem coordinates graceful shutdown, stopping background tasks and closing resources

The system uses asyncio for concurrency, with background tasks managed through the AGISystem's task list.

Section sources

core/system.py
README.md

Configuration and State Management

Configuration System

The Config class in core/config.py provides a centralized configuration system using environment variables with sensible defaults:

class Config:
    DATABASE_URL = os.environ.get("DATABASE_URL", "sqlite:///ravana_agi.db")
    CURIOSITY_CHANCE = float(os.environ.get("CURIOSITY_CHANCE", 0.3))
    REFLECTION_CHANCE = float(os.environ.get("REFLECTION_CHANCE", 0.1))
    LOOP_SLEEP_DURATION = int(os.environ.get("LOOP_SLEEP_DURATION", 10))
    PERSONA_NAME = os.environ.get("PERSONA_NAME", "Ravana")
    PERSONA_CREATIVITY = float(os.environ.get("PERSONA_CREATIVITY", 0.7))

This approach allows for flexible configuration across different environments while providing reasonable defaults for quick setup.

State Management

The system uses a hybrid state management approach:

Shared State: The SharedState object maintains volatile state that persists across iterations
Database Storage: Long-term memories, knowledge, and logs are persisted to the database
File-based Storage: Vector indexes and other large data structures are stored in files

The SharedState is passed by reference to all components, ensuring consistency, while persistent storage provides durability across restarts.

Section sources

core/config.py
core/state.py

Extensibility and Modularity

The RAVANA system is designed for extensibility through its modular architecture.

Pluggable Modules

The system supports pluggable modules that can be added or replaced without modifying core logic. Modules follow a consistent pattern:

Accept the AGISystem in their constructor for access to shared resources
Implement specific cognitive functions
Interact with the system through well-defined interfaces
Update shared state to communicate with other components

Examples include the EmotionalIntelligence, CuriosityTrigger, and ReflectionModule.

Action System

The action system provides a powerful extensibility mechanism. New actions can be added by:

Creating a new Action subclass
Registering it with the ActionRegistry
Making it available to the LLM through the action definitions

The EnhancedActionManager extends this system with multi-modal capabilities, demonstrating how the architecture supports incremental enhancement.

Section sources

core/system.py
core/actions/registry.py
core/enhanced_action_manager.py

Cross-Cutting Concerns

Async Operation

The entire system is built on asyncio for non-blocking operation. Key aspects include:

All service methods are async
Background tasks run concurrently
I/O operations are performed in thread pools when necessary
The main loop uses async/await for coordination

This design enables efficient resource utilization and responsiveness.

Error Handling

The system implements comprehensive error handling:

Try-except blocks around critical operations
Logging of errors with stack traces
Graceful degradation when components fail
Timeout protection for action execution

Errors are logged but do not typically halt the main loop, ensuring system resilience.

Scalability Considerations

The architecture supports scalability through:

Modular design allowing horizontal scaling of components
Asynchronous operation for efficient resource use
Database abstraction for potential migration to more robust systems
Configuration-driven behavior for tuning performance

However, the current implementation is designed for a single-agent use case rather than distributed deployment.

Section sources

core/system.py
core/enhanced_action_manager.py

Enhanced Snake Agent Architecture

The Enhanced Snake Agent is a new component that provides continuous code improvement capabilities through concurrent analysis, experimentation, and enhancement. It operates as a background process within the RAVANA system, using threading and multiprocessing to maximize performance and reliability.

Architecture Overview

The Enhanced Snake Agent uses a multi-layered architecture with distinct threading and multiprocessing components:

Diagram sources

ENHANCED_SNAKE_IMPLEMENTATION.md
core/snake_agent_enhanced.py

Component Integration

The Enhanced Snake Agent integrates with the main AGISystem through a configurable initialization process:

# Initialize Snake Agent if enabled (Enhanced Version)
self.snake_agent = None
if Config.SNAKE_AGENT_ENABLED:
    try:
        # Try enhanced version first, fall back to original if needed
        enhanced_mode = getattr(Config, 'SNAKE_ENHANCED_MODE', True)
        if enhanced_mode:
            from core.snake_agent_enhanced import EnhancedSnakeAgent
            self.snake_agent = EnhancedSnakeAgent(self)
            logger.info("Enhanced Snake Agent initialized and ready")
        else:
            from core.snake_agent import SnakeAgent
            self.snake_agent = SnakeAgent(self)
            logger.info("Standard Snake Agent initialized and ready")
    except Exception as e:
        logger.error(f"Failed to initialize Snake Agent: {e}")
        # Fallback to standard version if enhanced fails
        try:
            from core.snake_agent import SnakeAgent
            self.snake_agent = SnakeAgent(self)
            logger.info("Fallback to standard Snake Agent successful")
        except Exception as fallback_error:
            logger.error(f"Fallback Snake Agent also failed: {fallback_error}")
            self.snake_agent = None

Section sources

core/system.py

Core Components

The Enhanced Snake Agent consists of several key components that work together to provide continuous improvement:

Snake Log Manager: Manages separate log files for different activities with thread-safe logging and JSON structured logging
Threading Manager: Manages file monitoring threads, analysis worker threads, and communication threads
Process Manager: Handles CPU-intensive tasks through worker processes for experiment execution and deep analysis
Continuous File Monitor: Provides real-time file system monitoring using watchdog with hash-based change detection
Parallel Code Analyzer: Performs multi-threaded code analysis with worker pools and intelligent caching
Multiprocess Experimenter: Executes code experiments in isolated sandbox environments with safety validation
Continuous Improvement Engine: Applies safe code improvements with Git integration and automatic rollback capabilities

Section sources

ENHANCED_SNAKE_IMPLEMENTATION.md
core/snake_agent_enhanced.py

Data Flow and Coordination

The Enhanced Snake Agent follows a coordinated workflow for processing file changes and improvements:

Diagram sources

core/snake_agent_enhanced.py
ENHANCED_SNAKE_IMPLEMENTATION.md

Safety and Reliability Features

The Enhanced Snake Agent incorporates multiple safety and reliability features to ensure stable operation and prevent system damage.

Configuration and Environment Variables

The Enhanced Snake Agent is highly configurable through environment variables:

# Enhanced Snake Agent Mode
SNAKE_ENHANCED_MODE=true

# Threading Configuration
SNAKE_MAX_THREADS=8
SNAKE_ANALYSIS_THREADS=3
SNAKE_MONITOR_INTERVAL=2.0

# Multiprocessing Configuration
SNAKE_MAX_PROCESSES=4
SNAKE_TASK_TIMEOUT=300.0
SNAKE_HEARTBEAT_INTERVAL=10.0

# Performance Monitoring
SNAKE_PERF_MONITORING=true
SNAKE_AUTO_RECOVERY=true

# Safety Limits
SNAKE_MAX_QUEUE_SIZE=1000
SNAKE_LOG_RETENTION_DAYS=30

Section sources

core/config.py
ENHANCED_SNAKE_IMPLEMENTATION.md

Safety Mechanisms

The Enhanced Snake Agent implements multiple safety mechanisms:

Sandbox isolation with restricted environment for experiment execution
Resource limits (CPU, memory, time) to prevent system overload
Code validation before execution to detect forbidden operations
Automatic timeout and cleanup for long-running tasks
Backup creation before changes with Git integration
Safety score calculation for changes to assess risk
Critical file protection to prevent modification of essential system files
Automatic rollback on failures to maintain system stability
Graceful shutdown integration with existing shutdown coordinator
Error recovery and auto-restart capabilities
Thread and process health checks to detect and resolve issues
Deadlock prevention through proper resource management

Section sources

ENHANCED_SNAKE_IMPLEMENTATION.md
core/snake_agent_enhanced.py

Performance Metrics

The Enhanced Snake Agent has been validated with the following performance metrics:

Log Processing: 50+ events/second
File Monitoring: Real-time change detection (<2s)
Code Analysis: 3 concurrent worker threads
Experiment Execution: Isolated process safety
Memory Usage: Optimized with caching and cleanup
Thread Safety: Lock-free queues and coordination

These metrics ensure that the Enhanced Snake Agent can operate efficiently without impacting the main RAVANA system performance.

Section sources

ENHANCED_SNAKE_IMPLEMENTATION.md
core/snake_data_models.py

Enhanced Graceful Shutdown Architecture

The Enhanced Graceful Shutdown system provides a robust mechanism for terminating the RAVANA AGI system in a controlled manner, ensuring data integrity and proper resource cleanup. This system introduces a phased execution approach with timeout handling, component lifecycle management, and state persistence workflows.

Architecture Overview

The Enhanced Graceful Shutdown system uses a multi-phase approach to ensure all components are properly terminated:

Diagram sources

core/shutdown_coordinator.py
core/system.py

Component Integration

The ShutdownCoordinator integrates with the main AGISystem through a configurable initialization process:

# Initialize Shutdown Coordinator
self.shutdown_coordinator = ShutdownCoordinator(self)
        
# Register cleanup handlers
self.shutdown_coordinator.register_cleanup_handler(self._cleanup_database_session)
self.shutdown_coordinator.register_cleanup_handler(self._cleanup_models)
self.shutdown_coordinator.register_cleanup_handler(self._save_final_state, is_async=True)
        
# Register MemoryService with shutdown coordinator
self.shutdown_coordinator.register_component(self.memory_service, ShutdownPriority.MEDIUM, is_async=True)
        
# Register Snake Agent cleanup if enabled
if self.snake_agent:
    self.shutdown_coordinator.register_component(self.snake_agent, ShutdownPriority.HIGH, is_async=True)
            
# Register Conversational AI cleanup if enabled
if self.conversational_ai:
    self.shutdown_coordinator.register_cleanup_handler(self._cleanup_conversational_ai, is_async=False)

Section sources

core/system.py

Core Components

The Enhanced Graceful Shutdown system consists of several key components that work together to ensure reliable termination:

ShutdownCoordinator: Central coordinator that manages the shutdown process across multiple phases
ShutdownPhase: Enumeration defining the sequence of shutdown phases from validation to completion
ShutdownPriority: Enumeration defining component shutdown priority levels (HIGH, MEDIUM, LOW)
Shutdownable: Interface that components implement to support graceful shutdown
ComponentRegistration: Class that represents a registered component for shutdown management

Section sources

core/shutdown_coordinator.py

Data Flow and Coordination

The Enhanced Graceful Shutdown system follows a coordinated workflow for terminating the system:

Diagram sources

core/shutdown_coordinator.py

Shutdown State Persistence

The Shutdown State Persistence system ensures that critical system state is preserved across restarts, enabling continuity of operations and recovery from unexpected terminations.

State Collection

The system collects comprehensive state information before shutdown:

async def _collect_system_state(self) -> Dict[str, Any]:
    """Collect system state for persistence."""
    state_data = {
        "shutdown_info": self.shutdown_state,
        "timestamp": datetime.utcnow().isoformat(),
        "version": "1.1"
    }
    
    if not self.agi_system:
        return state_data
    
    try:
        # Collect AGI system state
        agi_state = {}
        
        # Current mood
        if hasattr(self.agi_system, 'emotional_intelligence'):
            agi_state["mood"] = self.agi_system.emotional_intelligence.get_mood_vector()
        
        # Current plans
        if hasattr(self.agi_system, 'current_plan'):
            agi_state["current_plan"] = self.agi_system.current_plan
            agi_state["current_task_prompt"] = getattr(self.agi_system, 'current_task_prompt', None)
        
        # Shared state
        if hasattr(self.agi_system, 'shared_state'):
            shared_state = self.agi_system.shared_state
            agi_state["shared_state"] = {
                "mood": getattr(shared_state, 'mood', {}),
                "current_situation_id": getattr(shared_state, 'current_situation_id', None),
                "current_task": getattr(shared_state, 'current_task', None)
            }
        
        # Research progress
        if hasattr(self.agi_system, 'research_in_progress'):
            agi_state["research_in_progress"] = list(self.agi_system.research_in_progress.keys())
        
        # Invention history
        if hasattr(self.agi_system, 'invention_history'):
            agi_state["invention_history"] = self.agi_system.invention_history[-10:]  # Last 10
        
        # Snake Agent state
        if hasattr(self.agi_system, 'snake_agent') and self.agi_system.snake_agent:
            try:
                snake_state = self.agi_system.snake_agent.state.to_dict()
                agi_state["snake_agent"] = {
                    "state": snake_state,
                    "running": self.agi_system.snake_agent.running,
                    "analysis_count": getattr(self.agi_system.snake_agent, 'analysis_count', 0),
                    "experiment_count": getattr(self.agi_system.snake_agent, 'experiment_count', 0),
                    "communication_count": getattr(self.agi_system.snake_agent, 'communication_count', 0)
                }
            except Exception as e:
                logger.error(f"Error collecting Snake Agent state: {e}")
                agi_state["snake_agent"] = {"error": str(e)}
        
        state_data["agi_system"] = agi_state
        
    except Exception as e:
        logger.error(f"Error collecting AGI system state: {e}")
        state_data["agi_system"] = {"error": str(e)}
    
    return state_data

Section sources

core/shutdown_coordinator.py

Configuration and Environment Variables

The Shutdown State Persistence system is highly configurable through environment variables:

# Graceful Shutdown Configuration
GRACEFUL_SHUTDOWN_ENABLED=true
SHUTDOWN_TIMEOUT=60
FORCE_SHUTDOWN_AFTER=120
SHUTDOWN_HEALTH_CHECK_ENABLED=true
SHUTDOWN_VALIDATION_ENABLED=true
SHUTDOWN_STATE_VALIDATION_ENABLED=true
SHUTDOWN_COMPRESSION_ENABLED=false

# State Persistence Configuration
STATE_PERSISTENCE_ENABLED=true
SHUTDOWN_STATE_FILE="shutdown_state.json"
SHUTDOWN_BACKUP_ENABLED=true
SHUTDOWN_BACKUP_COUNT=5

# Component Shutdown Configuration
COMPONENT_PREPARE_TIMEOUT=10.0
COMPONENT_SHUTDOWN_TIMEOUT=15.0
RESOURCE_CLEANUP_TIMEOUT=30.0

# Specialized Cleanup Configuration
TEMP_FILE_CLEANUP_ENABLED=true
ACTION_CACHE_PERSIST=true
CHROMADB_PERSIST_ON_SHUTDOWN=true

Section sources

core/config.py

Safety and Reliability Features

The Shutdown State Persistence system incorporates multiple safety and reliability features:

State validation before persistence to ensure data integrity
Backup creation with automatic cleanup of old backups
Action cache persistence to maintain performance across restarts
ChromaDB persistence to ensure vector database integrity
JSON integrity validation after writing state files
Error recovery with fallback mechanisms for failed persistence
Version tracking to handle schema changes across versions
Selective state collection to minimize storage requirements
Error logging with detailed shutdown summaries
Force shutdown mechanism when graceful shutdown fails

Section sources

core/shutdown_coordinator.py

Performance Metrics

The Shutdown State Persistence system has been validated with the following performance metrics:

State Collection: <500ms for complete system state
Persistence: <1s for writing state to disk
Backup Creation: <2s for creating timestamped backups
Validation: <100ms for JSON integrity checks
Memory Usage: Optimized with streaming where possible
Error Rate: <0.1% failure rate in persistence operations

These metrics ensure that the Shutdown State Persistence system can operate efficiently without delaying the shutdown process.

Section sources

core/shutdown_coordinator.py
tests/validate_graceful_shutdown.py

Referenced Files in This Document

core/system.py - Updated in recent commit
core/state.py
services/memory_service.py
services/knowledge_service.py
core/config.py
core/actions/registry.py
core/enhanced_action_manager.py
modules/adaptive_learning/learning_engine.py
README.md
DEVELOPER_GUIDE.md
ENHANCED_SNAKE_IMPLEMENTATION.md - Added in recent commit
core/snake_agent_enhanced.py - Added in recent commit
core/snake_data_models.py - Added in recent commit
core/shutdown_coordinator.py - Updated in recent commit

Architecture & Design

Architecture & Design

Update Summary

Table of Contents

Introduction

Project Structure

Core Components

AGISystem Class

SharedState Class

Architecture Overview

System Context

Component Interaction

Detailed Component Analysis

AGISystem: The Central Orchestrator

Initialization Process

Autonomous Loop

Service Layer Abstraction

MemoryService

KnowledgeService

Design Patterns

Dependency Injection

Registry Pattern

Strategy Pattern

Observer Pattern

Data Flow and Lifecycle Management

Cognitive Loop Data Flow

Component Lifecycle

Configuration and State Management

Configuration System

State Management

Extensibility and Modularity

Pluggable Modules

Action System

Cross-Cutting Concerns

Async Operation

Error Handling

Scalability Considerations

Enhanced Snake Agent Architecture

Architecture Overview

Component Integration

Core Components

Data Flow and Coordination

Safety and Reliability Features

Configuration and Environment Variables

Safety Mechanisms

Performance Metrics

Enhanced Graceful Shutdown Architecture

Architecture Overview

Component Integration

Core Components

Data Flow and Coordination

Shutdown State Persistence

State Collection

Configuration and Environment Variables

Safety and Reliability Features

Performance Metrics