Cache Team Response

Cache team leader and member responses in two layers.

Demonstrates two-layer caching for team leader and member responses.

caching.py
"""
Cache Team Response
=============================

Demonstrates two-layer caching for team leader and member responses.
"""

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.team import Team

# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
researcher = Agent(
    name="Researcher",
    role="Research and gather information",
    model=OpenAIResponses(id="gpt-5.2", cache_response=True),
)

writer = Agent(
    name="Writer",
    role="Write clear and engaging content",
    model=OpenAIResponses(id="gpt-5.2", cache_response=True),
)

# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
content_team = Team(
    members=[researcher, writer],
    model=OpenAIResponses(id="gpt-5.2", cache_response=True),
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    content_team.print_response(
        "Write a very very very explanation of caching in software"
    )

Cache scope

Each cache_response=True setting caches that model's responses on disk; this is not a cache of the whole Team run. The default directory is ~/.agno/cache/model_responses, with no time-based expiry unless cache_ttl is set. This script makes one team call. Repeat an identical model request to observe a hit; changed messages or generated tool results can prevent reuse.

Run the Example

Set up your virtual environment

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

Install dependencies

uv pip install -U agno openai

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"

Run the example

Save the code above as caching.py, then run:

python caching.py

Full source: cookbook/03_teams/01_quickstart/09_caching.py