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"""
Agent executor implementation for executing AI agents.
"""
import asyncio
import logging
from typing import Dict, Any, Optional
from uuid import UUID, uuid4
from datetime import datetime
from src.core.interfaces.agent_executor import AgentExecutor
from src.core.entities.agent import Agent, AgentType
from src.core.entities.message import Message
from src.shared.exceptions import DomainException
class AgentExecutorImpl(AgentExecutor):
"""
Implementation of the agent executor interface.
This class handles the execution of different types of agents
and manages their lifecycle during processing.
"""
def __init__(self):
self.logger = logging.getLogger(__name__)
self._active_executions: Dict[UUID, Dict[str, Any]] = {}
async def execute(self, agent: Agent, message: Message) -> Dict[str, Any]:
"""
Execute an agent with a given message.
Args:
agent: The agent to execute
message: The message to process
Returns:
Dictionary containing the execution result
"""
execution_id = uuid4()
start_time = datetime.now()
try:
# Register execution
self._active_executions[execution_id] = {
"agent_id": agent.id,
"message_id": message.id,
"start_time": start_time,
"status": "running"
}
self.logger.info(f"Starting execution {execution_id} for agent {agent.id}")
# Validate agent
validation_result = await self.validate_agent(agent)
if not validation_result.get("valid", False):
raise DomainException(f"Agent validation failed: {validation_result.get('errors', [])}")
# Execute based on agent type
if agent.agent_type == AgentType.FSM_REACT:
result = await self._execute_fsm_react_agent(agent, message)
elif agent.agent_type == AgentType.NEXT_GEN:
result = await self._execute_next_gen_agent(agent, message)
elif agent.agent_type == AgentType.CREW:
result = await self._execute_crew_agent(agent, message)
elif agent.agent_type == AgentType.SPECIALIZED:
result = await self._execute_specialized_agent(agent, message)
else:
raise DomainException(f"Unsupported agent type: {agent.agent_type}")
# Update execution status
execution_time = (datetime.now() - start_time).total_seconds()
self._active_executions[execution_id]["status"] = "completed"
self._active_executions[execution_id]["execution_time"] = execution_time
# Add execution metadata
result["execution_id"] = str(execution_id)
result["execution_time"] = execution_time
self.logger.info(f"Execution {execution_id} completed successfully in {execution_time:.2f}s")
return result
except Exception as e:
execution_time = (datetime.now() - start_time).total_seconds()
self._active_executions[execution_id]["status"] = "failed"
self._active_executions[execution_id]["error"] = str(e)
self._active_executions[execution_id]["execution_time"] = execution_time
self.logger.error(f"Execution {execution_id} failed: {str(e)}")
raise DomainException(f"Agent execution failed: {str(e)}")
async def validate_agent(self, agent: Agent) -> Dict[str, Any]:
"""
Validate an agent before execution.
Args:
agent: The agent to validate
Returns:
Dictionary containing validation result
"""
errors = []
warnings = []
# Check if agent is available
if not agent.is_available:
errors.append("Agent is not available for execution")
# Check agent configuration
if not agent.config:
warnings.append("Agent has no configuration")
# Check model configuration
if not agent.model_config:
warnings.append("Agent has no model configuration")
# Validate agent type
if agent.agent_type not in AgentType:
errors.append(f"Invalid agent type: {agent.agent_type}")
return {
"valid": len(errors) == 0,
"errors": errors,
"warnings": warnings
}
async def get_agent_capabilities(self, agent: Agent) -> Dict[str, Any]:
"""
Get agent capabilities and supported operations.
Args:
agent: The agent to query
Returns:
Dictionary containing agent capabilities
"""
capabilities = {
"agent_type": agent.agent_type.value,
"supported_operations": [],
"tools_available": [],
"model_info": {}
}
# Add capabilities based on agent type
if agent.agent_type == AgentType.FSM_REACT:
capabilities["supported_operations"] = [
"text_processing",
"tool_execution",
"state_management",
"reasoning"
]
elif agent.agent_type == AgentType.NEXT_GEN:
capabilities["supported_operations"] = [
"advanced_reasoning",
"parallel_processing",
"multi_modal_processing",
"learning"
]
elif agent.agent_type == AgentType.CREW:
capabilities["supported_operations"] = [
"multi_agent_coordination",
"task_delegation",
"collaborative_reasoning",
"workflow_management"
]
elif agent.agent_type == AgentType.SPECIALIZED:
capabilities["supported_operations"] = [
"domain_specific_processing",
"expert_knowledge",
"specialized_tools"
]
return capabilities
async def cancel_execution(self, execution_id: UUID) -> bool:
"""
Cancel a running execution.
Args:
execution_id: The execution to cancel
Returns:
True if cancellation was successful, False otherwise
"""
if execution_id not in self._active_executions:
return False
execution = self._active_executions[execution_id]
if execution["status"] == "running":
execution["status"] = "cancelled"
execution["end_time"] = datetime.now()
self.logger.info(f"Execution {execution_id} cancelled")
return True
return False
async def get_execution_status(self, execution_id: UUID) -> Dict[str, Any]:
"""
Get the status of an execution.
Args:
execution_id: The execution to query
Returns:
Dictionary containing execution status
"""
if execution_id not in self._active_executions:
return {"error": "Execution not found"}
execution = self._active_executions[execution_id]
status = {
"execution_id": str(execution_id),
"status": execution["status"],
"agent_id": str(execution["agent_id"]),
"message_id": str(execution["message_id"]),
"start_time": execution["start_time"].isoformat()
}
if "execution_time" in execution:
status["execution_time"] = execution["execution_time"]
if "error" in execution:
status["error"] = execution["error"]
return status
async def _execute_fsm_react_agent(self, agent: Agent, message: Message) -> Dict[str, Any]:
"""Execute an FSM React agent."""
# This would integrate with the existing FSM agent implementation
# For now, return a mock response
await asyncio.sleep(0.1) # Simulate processing time
return {
"response": f"FSM React agent processed: {message.content}",
"confidence": 0.85,
"tools_used": ["text_processor", "reasoning_engine"],
"metadata": {
"agent_type": "fsm_react",
"processing_steps": 3
}
}
async def _execute_next_gen_agent(self, agent: Agent, message: Message) -> Dict[str, Any]:
"""Execute a Next Gen agent."""
await asyncio.sleep(0.2) # Simulate processing time
return {
"response": f"Next Gen agent processed: {message.content}",
"confidence": 0.92,
"tools_used": ["advanced_reasoning", "parallel_processor"],
"metadata": {
"agent_type": "next_gen",
"processing_steps": 5
}
}
async def _execute_crew_agent(self, agent: Agent, message: Message) -> Dict[str, Any]:
"""Execute a Crew agent."""
await asyncio.sleep(0.3) # Simulate processing time
return {
"response": f"Crew agent processed: {message.content}",
"confidence": 0.88,
"tools_used": ["coordinator", "researcher", "executor"],
"metadata": {
"agent_type": "crew",
"crew_size": 3,
"processing_steps": 7
}
}
async def _execute_specialized_agent(self, agent: Agent, message: Message) -> Dict[str, Any]:
"""Execute a Specialized agent."""
await asyncio.sleep(0.15) # Simulate processing time
return {
"response": f"Specialized agent processed: {message.content}",
"confidence": 0.95,
"tools_used": ["domain_expert", "specialized_tool"],
"metadata": {
"agent_type": "specialized",
"domain": "expert",
"processing_steps": 2
}
}
# Alias for backward compatibility
AgentExecutor = AgentExecutorImpl |