learnings_with_agentos.py
"""Server-side example for the learnings REST endpoints.
This sets up an AgentOS instance with a learning-enabled agent so that you can
exercise the /learnings CRUD endpoints from a client (see rest_api_learnings.py).
Run with:
.venvs/demo/bin/python cookbook/05_agent_os/learnings/learnings_with_agentos.py
Then in another terminal, run rest_api_learnings.py to hit the endpoints.
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.learn import LearningMachine
from agno.models.openai import OpenAIResponses
from agno.os import AgentOS
db = SqliteDb(id="learnings-os-demo", db_file="tmp/learnings_os_demo.db")
learning = LearningMachine(
db=db,
model=OpenAIResponses(id="gpt-5.4"),
user_profile=True,
user_memory=True,
namespace="global",
)
assistant = Agent(
name="Assistant",
model=OpenAIResponses(id="gpt-5.4"),
instructions=["You are a helpful assistant. Use what you know about the user."],
db=db,
learning=learning,
)
agent_os = AgentOS(
description="AgentOS exposing the /learnings CRUD endpoints",
agents=[assistant],
)
app = agent_os.get_app()
if __name__ == "__main__":
"""Run the AgentOS.
The learnings endpoints will be available at:
GET /learnings
POST /learnings
GET /learnings/{learning_id}
PATCH /learnings/{learning_id}
DELETE /learnings/{learning_id}
See http://localhost:7777/docs for interactive OpenAPI docs.
"""
agent_os.serve(app="learnings_with_agentos:app", reload=True)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U "agno[os]" openai
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
learnings_with_agentos.py, then run:python learnings_with_agentos.py