Building an AI Agent Is Easy! Here’s Proof..

I’ve created a simple AI agent that shows how anyone with minimal Python knowledge can get started. Let me show you how straightforward it can be!

(Note: Full code example provided below)

4 Core Components🎯

The Brain (AI Model Connection)

At its core, an AI agent just needs to talk to a language model. Here’s how you establish a connection using the Anthropic API:

def __init__(self, anthropic_api_key):
    self.anthropic_client = anthropic.Client(api_key=anthropic_api_key)
    self.models = {
        "haiku": "claude-3-haiku-20240307",
        "sonnet": "claude-3-sonnet-20240229",
    }

The Memory (Action Management)

Register all possible actions that are available for your agent to use. No rocket science here:

# Register single actions
self.actions = {
    "search_wikipedia": self.search_wikipedia,
    "calculate": self.calculate,
    "get_weather": self.get_weather,
    "get_crypto": self.get_crypto_data,
}

# Register automated actions
self.automated_actions = {
    "crypto_tracking": self.start_crypto_scheduler
}

The Instructions (System Prompt)

Here’s where the magic happens. Just tell your AI what it can do:

self.system_prompt = """You are an AI assistant that can perform both automated and one-time actions.
Response Format:
- For automation: 'AUTOMATE: action_name'
- For one-time actions: 'ACTION: action_name: parameter'
- For general queries: Respond naturally
"""

The Executor (Command Processing)

Simply watch for commands and execute them:

# Check for automation command
automate_match = re.search(r'AUTOMATE: (\w+)', assistant_message)
if automate_match:
    action_name = automate_match.group(1)
    result = self.automated_actions[action_name]()

Piecing It All Together 🔄

Let’s see what this could look like:

# Init Anthropic
self.anthropic_client = anthropic.Client(api_key=anthropic_api_key)
self.models = {
    "haiku": "claude-3-haiku-20240307",
    "sonnet": "claude-3-sonnet-20240229",
}

# Create system prompt
self.system_prompt = """You are an AI assistant that ...more"""

# Create automated action
def start_crypto_scheduler(self):
    if not self.scheduler_running:
        self.scheduler_running = True

        # Schedule task for every 10 seconds
        schedule.every(10).seconds.do(self.save_crypto_data)

        # Initial run
        self.save_crypto_data()

        # Start scheduler in separate thread
        scheduler_thread = threading.Thread(target=self.run_scheduler)
        scheduler_thread.daemon = True
        scheduler_thread.start()

# Register action
self.automated_actions = {
    "crypto_tracking": self.start_crypto_scheduler
}

# Excute
def execute(self, user_input):

    # User input : "Can you monitor crypto prices?"
    self.messages.append({"role": "user", "content": user_input})
    conversation_history = [msg for msg in self.messages if msg['role'] != 'system']

    selected_model = self.models.get(model_choice, self.models["haiku"])

    # Get AI response
    response = self.anthropic_client.messages.create(
        model=selected_model,
        messages=conversation_history,
        system=self.system_prompt,
        max_tokens=1000,
        temperature=0.2,
        top_p=1.0,
    )

    assistant_message = response.content[0].text
    self.messages.append({"role": "assistant", "content": assistant_message})

    # Check for automation command
    automate_match = re.search(r'AUTOMATE: (\w+)', assistant_message)
    if automate_match:
        action_name = automate_match.group(1)
        if action_name in self.automated_actions:
            result = self.automated_actions[action_name]()
            return f"Automated task started: {result}"
        return f"Error: Unknown automated action {action_name}"

That’s it! A few simple steps to go from user input to automated task execution.

Real-World Example🚀

Want to try it yourself? Check out the full project code and get started within minutes: https://github.com/unameit10000000/reallysimpleagent

Takeaway 💡

Building an AI agent does not need to be complex. With just basic Python knowledge and a clear structure, you can start building your own agents incrementally and create something powerfull.