caching.py
Run the Example
1
Set up your virtual environment
2
Install dependencies
3
Export your OpenAI API key
4
Run the example
Save the code above as
caching.py, then run:Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Cache team leader and member responses in two layers.
"""
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,
debug_mode=True,
)
# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
content_team.print_response(
"Write a very very very explanation of caching in software"
)
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
Install dependencies
uv pip install -U agno openai
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
Run the example
caching.py, then run:python caching.py
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