Notes

Core Building Blocks

1. Foundation Models (Brain)

Implementation Considerations:

Choose base LLMs for general text understanding and generation
Add specialized models for specific tasks (code, images, etc.)
Implement function calling for tool/API interactions
Balance model capabilities vs. resource usage

2. Memory Architecture

Implementation Tips:

Use conversation buffers for short-term context
Implement vector databases (like Pinecone) for efficient retrieval
Design clear memory retention/cleanup policies
Structure data for quick access and updates

3. Function Calling System

Key Components:

# Example Function Schema
functions = [{
    "name": "search_database",
    "description": "Search for records in database",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {"type": "string"},
            "filters": {"type": "object"}
        }
    }
}]
 
# Example Implementation
async def process_user_input(user_input: str):
    # 1. LLM Analysis
    function_call = await llm.analyze_input(user_input, functions)
    
    # 2. Function Execution
    if function_call:
        result = await execute_function(
            function_call.name,
            function_call.parameters
        )
        
    # 3. Response Generation
    response = await llm.generate_response(result)
    return response

4. Tool Integration

Implementation Pattern:

class ToolManager:
    def __init__(self):
        self.tools = {}
    
    def register_tool(self, name: str, tool: callable):
        """Register a new tool with validation"""
        self.tools[name] = tool
    
    async def execute_tool(self, name: str, params: dict):
        """Execute tool with error handling"""
        try:
            tool = self.tools.get(name)
            return await tool(**params)
        except Exception as e:
            logger.error(f"Tool execution failed: {e}")
            return {"error": str(e)}

Best Practices

1. Error Handling

try:
    result = await agent.execute_task(task)
except AgentError as e:
    logger.error(f"Agent error: {e}")
    fallback_result = await fallback_handler.handle(e)

2. Monitoring

class AgentMonitor:
    def log_execution(self, task_id: str, metrics: dict):
        """Log execution metrics"""
        prometheus.push_metrics(task_id, metrics)
    
    def track_performance(self, agent_id: str):
        """Track agent performance"""
        return prometheus.query_metrics(agent_id)

3. Testing

class AgentTest:
    async def test_function_calling(self):
        """Test function calling accuracy"""
        test_inputs = load_test_cases()
        for input in test_inputs:
            result = await agent.process(input)
            assert validate_output(result, input.expected)

Common Patterns

1. ReAct Pattern (Reasoning + Action)

async def react_loop(task: str):
    while not task.completed:
        # Reason about the task
        reasoning = await llm.reason_about(task)
        
        # Decide on action
        action = await llm.decide_action(reasoning)
        
        # Execute action
        result = await execute_action(action)
        
        # Observe results
        task = await update_task(result)

2. Chain of Thought

async def chain_of_thought(problem: str):
    # Break down problem
    steps = await llm.break_down_problem(problem)
    
    # Process each step
    for step in steps:
        result = await process_step(step)
        context.update(result)
    
    return context.final_result

Resources & Tools

💡 Use Cases 🔄 Integration Patterns