Agents

Overview

Agents in RAI are modular components that encapsulate specific functionalities and behaviors. They follow a consistent interface defined by the BaseAgent class and can be combined to create complex robotic systems.

BaseAgent

BaseAgent is the abstract base class for all agent implementations in the RAI framework. It defines the minimal interface that all agents must implement while providing common functionality like logging.

Class Definition

BaseAgent class definition

rai.agents.base.BaseAgent

Bases: ABC

Source code in rai/agents/base.py

class BaseAgent(ABC):
    def __init__(self):
        """Initializes a new agent instance and sets up logging with the class name."""
        self.logger = logging.getLogger(self.__class__.__name__)

    @abstractmethod
    def run(self):
        """Starts the agent's main execution loop.
        In some cases, concrete run implementation may not be needed.
        In that case use pass as a placeholder."""
        pass

    @abstractmethod
    def stop(self):
        """Gracefully terminates the agent's execution and cleans up resources."""
        pass

__init__()

Initializes a new agent instance and sets up logging with the class name.

Source code in rai/agents/base.py

def __init__(self):
    """Initializes a new agent instance and sets up logging with the class name."""
    self.logger = logging.getLogger(self.__class__.__name__)

run() abstractmethod

Starts the agent's main execution loop. In some cases, concrete run implementation may not be needed. In that case use pass as a placeholder.

Source code in rai/agents/base.py

@abstractmethod
def run(self):
    """Starts the agent's main execution loop.
    In some cases, concrete run implementation may not be needed.
    In that case use pass as a placeholder."""
    pass

stop() abstractmethod

Gracefully terminates the agent's execution and cleans up resources.

Source code in rai/agents/base.py

@abstractmethod
def stop(self):
    """Gracefully terminates the agent's execution and cleans up resources."""
    pass

Purpose

The BaseAgent class serves as the cornerstone of RAI's agent architecture, establishing a uniform interface for various agent implementations. This enables:

• Consistent lifecycle management (starting/stopping)
• Standardized logging mechanisms
• Interoperability between different agent types
• Integration with management utilities like AgentRunner

Best Practices

1. Resource Management: Always clean up resources in the stop() method
2. Thread Safety: Use locks for shared resources when implementing multi-threaded agents
3. Error Handling: Implement proper exception handling in long-running agent threads
4. Logging: Use the provided self.logger for consistent logging
5. Graceful Shutdown: Handle interruptions and cleanup properly

Architecture

In the RAI framework, agents typically interact with:

• Connectors: For communication (ROS2, audio devices, etc.)
• Aggregators: For processing and summarizing input data
• Models: For AI capabilities (LLMs, vision models, speech recognition)
• Tools: For implementing specific actions an agent can take

See Also

• Aggregators: For more information on the different types of aggregators in RAI
• Connectors: For more information on the different types of connectors in RAI
• Langchain Integration: For more information on the LangChain integration within RAI
• Multimodal messages: For more information on the multimodal LangChain messages in RAI
• Runners: For more information on the different types of runners in RAI