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For a conceptual overview of response caching, see Response Caching.
Response caching allows you to cache model responses, which can significantly improve response times and reduce API costs during development and testing.

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Basic Usage

Enable caching by setting cache_response=True when initializing the model. The first call will hit the API and cache the response, while subsequent identical calls will return the cached result.
cache_model_response.py
import time

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

agent = Agent(model=OpenAIChat(id="gpt-4o", cache_response=True))

# Run the same query twice to demonstrate caching
for i in range(1, 3):
    print(f"\n{'=' * 60}")
    print(
        f"Run {i}: {'Cache Miss (First Request)' if i == 1 else 'Cache Hit (Cached Response)'}"
    )
    print(f"{'=' * 60}\n")

    response = agent.run(
        "Write me a short story about a cat that can talk and solve problems."
    )
    print(response.content)
    print(f"\n Elapsed time: {response.metrics.duration:.3f}s")

    # Small delay between iterations for clarity
    if i == 1:
        time.sleep(0.5)

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Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Set your API key

export OPENAI_API_KEY=xxx
3

Install libraries

pip install -U openai agno
4

Run Agent

  python cache_model_response.py