agent.py

class BaseAgent():
    """Base class for all AI agents in the system."""
    
    def __init__(self, name, role, provider_config, tools):
        # initialize the agent
        pass
        
    def use_tool(self, tool_name, params):
        """Execute a tool with given parameters"""
        tool = self.tools.get(tool_name)
        result = tool.execute(params)
        return result
        

    def reflect(self, context):
        """
        Perform self-reflection on the current context.
        Returns structured thoughts about the situation.
        """
        # Add available tools to context
        context["available_tools"] = list(self.tools.keys())

        #  there are many ways to implement reflection and context learning
        # it can be heirachy of actions e.g for each process get the observation then
        # pass the observation to the next process and so on
        
        thought_process = {
            "observation": self._observe(context),
            "thought": self._think(context),
            "reasoning": self._reason(context),
            "plan": self._plan(context),
            "criticism": self._self_criticize(context)
        }
        
        self.memory.append(thought_process)
        return thought_process

    def __observe__(self, context):
        """Observe and analyze the current context."""
        pass

    def __think__(self, context):
        """Generate thoughts about the current situation."""
        pass

    def __reason__(self, context):
        """Provide reasoning about the current situation."""
        pass

    def __plan__(self, context):
        """Create a plan based on the current context."""
        pass

    def __self_criticize__(self, context):
        """Provide self-criticism about current thoughts and plans."""
        pass

    def execute_plan(self, plan, context):
        """Execute a plan using available tools""" 
        # the prompt here creates an action road map or list of steps to be executed
        results = []
        for step in plan:
            tool_result = self._execute_plan_step(step, context)
            results.append(tool_result)
            if not tool_result.success:
                break
        return results
    
    def _execute_plan_step(self, step, context):
        """Execute a single step of the plan"""
        pass
    

    def summarize_experience(self):
        """Generate a summary of the agent's experience."""
        prompt = f"Summarize the following experiences as {self.role.value}:\n"
        for memory in self.memory[-5:]:  # Last 5 memories
            prompt += f"\nObservation: {memory.observation}\n"
            prompt += f"Thought: {memory.thought}\n"
            prompt += f"Plan: {', '.join(memory.plan)}\n"
            prompt += "---"
        
        return self.get_completion(prompt)

    def collaborate(self, other_agent: 'BaseAgent', context):
        """
        Collaborate with another agent.
        Returns the results of the collaboration.
        """
        self.logger.info(f"Collaborating with {other_agent.name}")
        my_thoughts = self.reflect(context)
        their_thoughts = other_agent.reflect(context)
        
        return {
            "collaboration_result": {
                f"{self.name}_thoughts": my_thoughts,
                f"{other_agent.name}_thoughts": their_thoughts,
            }
        }

This is a basic template for an AI agent that includes the key components typically found in such systems: memory, observation, reasoning, planning, reflection/self-criticism, and execution with tools. The template provides a starting point.