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Base Agent

The BaseAgent class is the foundation for all PilottAI agents. It provides the core functionality for task execution, tool management, and memory integration.

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Overview

The BaseAgent is responsible for:

  • Executing tasks using LLMs
  • Managing specialized tools
  • Maintaining task context
  • Tracking execution status
  • Storing and retrieving memory

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Class Definition

class BaseAgent:
    def __init__(
            self,
            config: AgentConfig,
            llm_config: Optional[LLMConfig] = None,
            tools: Optional[List[Tool]] = None,
            memory_enabled: bool = True
    ):
        # Core configuration
        self.config = config
        self.id = str(uuid.uuid4())

        # State management
        self.status = AgentStatus.IDLE
        self.current_task: Optional[Task] = None
        self._task_lock = asyncio.Lock()

        # Components
        self.tools = {tool.name: tool for tool in (tools or [])}
        self.memory = Memory() if memory_enabled else None
        self.llm = LLMHandler(llm_config) if llm_config else None

        # Setup logging
        self.logger = self._setup_logger()

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Configuration

The BaseAgent is configured using the AgentConfig class:

from pilottai.core import AgentConfig, AgentRole

config = AgentConfig(
    role="researcher",                  # Agent's role/type
    role_type=AgentRole.WORKER,         # Role classification
    goal="Find accurate information",    # Main objective
    description="Research assistant",    # Brief description
    backstory=None,                      # Optional background story
    tools=["web_search", "text_analyzer"], # Available tools
    required_capabilities=[],            # Required capabilities
    max_iterations=20,                   # Maximum execution iterations
    memory_enabled=True,                 # Enable memory
    verbose=False                        # Verbose logging
)

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Key Methods

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Task Execution

async def execute_task(self, task: Union[Dict, Task]) -> Optional[TaskResult]:
    """Execute a task with proper handling and monitoring."""
    # Implementation details...

The task execution process involves:

  1. Planning execution steps using LLM
  2. Executing each step with proper error handling
  3. Monitoring execution status and timeout
  4. Recording execution in memory
  5. Returning structured results

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Task Suitability Evaluation

async def evaluate_task_suitability(self, task: Dict) -> float:
    """Evaluate how suitable this agent is for a task"""
    # Implementation details...

This method determines how well an agent can handle a specific task by:

  • Checking required capabilities
  • Matching task type with agent specializations
  • Considering current agent load
  • Analyzing task complexity

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Lifecycle Management

async def start(self):
    """Start the agent"""
    # Implementation details...

async def stop(self):
    """Stop the agent"""
    # Implementation details...

These methods handle the agent’s lifecycle, including:

  • Initializing components
  • Setting up connections
  • Updating status
  • Cleaning up resources

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Task Execution Pipeline

The BaseAgent follows a structured approach to task execution:

  1. Task Formatting: Prepare task with context
  2. Execution Planning: Generate a plan using LLM
  3. Step Execution: Execute each step in the plan
  4. Tool Invocation: Use tools as required
  5. Result Summarization: Summarize and format results

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System Prompts

The BaseAgent uses system prompts to guide LLM behavior. The base system prompt follows this template:

You are an AI agent with:
Role: {role}
Goal: {goal}
Backstory: {backstory or 'No specific backstory.'}

Make decisions and take actions based on your role and goal.

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Error Handling

The BaseAgent implements robust error handling:

  • Task timeouts
  • LLM errors
  • Tool execution failures
  • Context validation

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Memory Integration

Agents maintain their own memory instance for:

  • Task history tracking
  • Context preservation
  • Knowledge storage
  • Pattern recognition
# Store task in memory
await self.memory.store_task_start(
    task_id=task.id,
    description=task.description,
    agent_id=self.id
)

# Store result in memory
await self.memory.store_task_result(
    task_id=task.id,
    result=result,
    success=True,
    execution_time=execution_time,
    agent_id=self.id
)

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Extending BaseAgent

To create a specialized agent, extend the BaseAgent class:

from pilottai.core import BaseAgent, AgentConfig

class ResearchAgent(BaseAgent):
    def __init__(self, config: AgentConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.specializations = ["research", "information_gathering"]

    async def evaluate_task_suitability(self, task: Dict) -> float:
        # Custom suitability logic
        base_score = await super().evaluate_task_suitability(task)
        if task.get("type") == "research":
            return min(1.0, base_score + 0.3)
        return base_score

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Examples

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Creating a Basic Agent

from pilottai.core import BaseAgent, AgentConfig, LLMConfig

# Configure LLM
llm_config = LLMConfig(
    model_name="gpt-4",
    provider="openai",
    api_key="your-api-key"
)

# Configure agent
config = AgentConfig(
    role="assistant",
    goal="Help with various tasks",
    description="General assistant agent"
)

# Create agent
agent = BaseAgent(config=config, llm_config=llm_config)

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Executing a Task

# Create task
task = {
    "description": "Summarize the following text",
    "context": {
        "text": "PilottAI is a Python framework for building autonomous multi-agent systems..."
    }
}

# Execute task
result = await agent.execute_task(task)
print(f"Task result: {result.output}")

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API Reference

For a complete reference of all BaseAgent methods and attributes, see the Agent API documentation.