Tools make agents - “agentic” by enabling them to interact with external systems like searching the web, running SQL, sending an email or calling APIs.

Agno comes with 80+ pre-built toolkits, but in most cases, you will write your own tools. The general syntax is:

import random

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools import tool


@tool(show_result=True, stop_after_tool_call=True)
def get_weather(city: str) -> str:
    """Get the weather for a city."""
    # In a real implementation, this would call a weather API
    weather_conditions = ["sunny", "cloudy", "rainy", "snowy", "windy"]
    random_weather = random.choice(weather_conditions)

    return f"The weather in {city} is {random_weather}."


agent = Agent(
    model=OpenAIChat(model="gpt-4o-mini"),
    tools=[get_weather],
    markdown=True,
)
agent.print_response("What is the weather in San Francisco?", stream=True)

In the example above, the get_weather function is a tool. When it is called, the tool result will be shown in the output because we set show_result=True.

Then, the Agent will stop after the tool call because we set stop_after_tool_call=True.

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Using the Toolkit Class

The Toolkit class provides a way to manage multiple tools with additional control over their execution. You can specify which tools should stop the agent after execution and which should have their results shown.

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.googlesearch import GoogleSearchTools

agent = Agent(
    model=OpenAIChat(id="gpt-4.5-preview"),
    tools=[
        GoogleSearchTools(
            stop_after_tool_call_tools=["google_search"],
            show_result_tools=["google_search"],
        )
    ],
    show_tool_calls=True,
)

agent.print_response("What's the latest about gpt 4.5?", markdown=True)

In this example, the GoogleSearchTools toolkit is configured to stop the agent after executing the google_search function and to show the result of this function.

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