Tutorial 1: First Agent in 30 Minutes

import requests
import json

OLLAMA = "http://localhost:11434/api/chat"

# Define the tool the agent can use
tools = [{
    "type": "function",
    "function": {
        "name": "search_web",
        "description": "Search the web for current information. Use this for news, weather, prices, etc.",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "Search query. Be specific."
                }
            },
            "required": ["query"]
        }
    }
}]

def agent_loop(user_input, max_steps=5):
    messages = [
        {"role": "system", "content": "You are a ReAct agent. Search the web when you need current information. Answer directly when you know enough."},
        {"role": "user", "content": user_input}
    ]

    for step in range(max_steps):
        response = requests.post(OLLAMA, json={
            "model": "qwen3.5:4b",
            "messages": messages,
            "tools": tools,
            "stream": False
        })
        msg = response.json()["message"]
        messages.append(msg)

        if msg.get("tool_calls"):
            for tc in msg["tool_calls"]:
                name = tc["function"]["name"]
                args = json.loads(tc["function"]["arguments"])
                print(f"  Step {step+1}: calling {name}({json.dumps(args)})")
                # Execute the tool (in real apps, plug in actual search)
                result = {"result": f"Search results for: {args.get('query', '')}"}
                messages.append({
                    "role": "tool",
                    "name": name,
                    "content": json.dumps(result)
                })
        else:
            return msg["content"]

    return "Step limit reached"

if __name__ == "__main__":
    result = agent_loop("What was the weather in London yesterday?")
    print(f"\nAnswer: {result}")