> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-docs-scavio-google-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompt Caching Extended

> Set the extended-cache-ttl beta and extended_cache_time on Claude to hold the cached system prompt for one hour, and compare cache write and read tokens.

Extend caching time for agents using cache with Anthropic models.

<Warning>
  Anthropic retired the source's `claude-sonnet-4-20250514` model on June 15, 2026. Replace it with `claude-sonnet-4-6` before running. See [Anthropic model deprecations](https://platform.claude.com/docs/en/about-claude/model-deprecations).
</Warning>

```python prompt_caching_extended.py theme={null}
"""
This cookbook shows how to extend caching time for agents using cache with Anthropic models.

You can check more about extended prompt caching with Anthropic models here: https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#1-hour-cache-duration-beta
"""

from pathlib import Path

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.utils.media import download_file

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

# Load an example large system message from S3. A large prompt like this would benefit from caching.
txt_path = Path(__file__).parent.joinpath("system_promt.txt")
download_file(
    "https://agno-public.s3.amazonaws.com/prompts/system_promt.txt",
    str(txt_path),
)
system_message = txt_path.read_text()

agent = Agent(
    model=Claude(
        id="claude-sonnet-4-20250514",
        betas=["extended-cache-ttl-2025-04-11"],
        system_prompt=system_message,
        cache_system_prompt=True,  # Activate prompt caching for Anthropic to cache the system prompt
        extended_cache_time=True,  # Extend the cache time from the default to 1 hour
    ),
    system_message=system_message,
    markdown=True,
)

# First run - this will create the cache
response = agent.run(
    "Explain the difference between REST and GraphQL APIs with examples"
)

if response and response.metrics:
    print(f"First run cache write tokens = {response.metrics.cache_write_tokens}")

# Second run - this will use the cached system prompt
response = agent.run(
    "What are the key principles of clean code and how do I apply them in Python?"
)
if response and response.metrics:
    print(f"Second run cache read tokens = {response.metrics.cache_read_tokens}")

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass
```

## Run the Example

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

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

  <Step title="Export your Anthropic API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export ANTHROPIC_API_KEY="your_anthropic_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:ANTHROPIC_API_KEY="your_anthropic_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Update the Claude model">
    Replace `Claude(id="claude-sonnet-4-20250514")` with `Claude(id="claude-sonnet-4-6")` in the saved file.
  </Step>

  <Step title="Run the example">
    Save the code above as `prompt_caching_extended.py`, then run:

    ```bash theme={null}
    python prompt_caching_extended.py
    ```
  </Step>
</Steps>

Full source: [cookbook/90\_models/anthropic/prompt\_caching\_extended.py](https://github.com/agno-agi/agno/blob/main/cookbook/90_models/anthropic/prompt_caching_extended.py)