Response Caching

Cache model responses to reduce API calls and costs.

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.

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 OpenAIResponses

agent = Agent(model=OpenAIResponses(id="gpt-5.2", 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)

Usage

1. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Set your API key

   export OPENAI_API_KEY=xxx
   
3. Install dependencies

   uv pip install -U openai agno
   
4. Run Agent

   python cache_model_response.py