> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-agui.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Response Caching

> Cache model responses to reduce API calls and costs.

Response caching allows you to cache model responses, which can significantly improve response times and reduce API costs during development and testing.

<Note>
  For a detailed overview of response caching, see [Response Caching](/models/cache-response).
</Note>

<Note>
  This is different from Anthropic's prompt caching feature. Response caching caches the entire model response, while [prompt caching](/models/providers/native/anthropic/usage/prompt-caching) caches the system prompt to reduce processing time.
</Note>

## 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.

```python cache_model_response.py theme={null}
import time

from agno.agent import Agent
from agno.models.anthropic import Claude

agent = Agent(model=Claude(id="claude-sonnet-4-5", 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

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Set your API key">
    ```bash theme={null}
    export ANTHROPIC_API_KEY=xxx
    ```
  </Step>

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U anthropic agno
    ```
  </Step>

  <Step title="Run Agent">
    ```bash theme={null}
    python cache_model_response.py
    ```
  </Step>
</Steps>