Tool result caching is designed to avoid unnecessary recomputation by storing the results of function calls on disk. This is useful during development and testing to speed up the development process, avoid rate limiting, and reduce costs.
This is supported for all Agno Toolkits

​
On Toolkit

Pass cache_results=True to the Toolkit constructor to enable caching for that Toolkit.
cache_tool_calls.py
import asyncio

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[DuckDuckGoTools(cache_results=True), YFinanceTools(cache_results=True)],
)

asyncio.run(
    agent.aprint_response(
        "What is the current stock price of AAPL and latest news on 'Apple'?",
        markdown=True,
    )
)

​
On @tool

Pass cache_results=True to the @tool decorator to enable caching for that tool.
cache_tool_calls.py
from agno.tools import tool

@tool(cache_results=True)
def get_stock_price(ticker: str) -> str:
    """Get the current stock price of a given ticker"""

    # ... Long running operation

    return f"The current stock price of {ticker} is 100"