always_learn.py
"""
Learning Machines
=================
Set learning=True to turn an agent into a learning machine.
The agent automatically captures:
- User profile: name, role, preferences
- User memory: observations, context, patterns
No explicit tool calls needed. Extraction runs in parallel.
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
db = SqliteDb(db_file="tmp/agents.db")
agent = Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
learning=True,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "alice1@example.com"
# Session 1: Share information naturally
print("\n--- Session 1: Extraction happens automatically ---\n")
agent.print_response(
"Hi! I'm Alice. I work at Anthropic as a research scientist. "
"I prefer concise responses without too much explanation.",
user_id=user_id,
session_id="session_1",
stream=True,
)
lm = agent.learning_machine
lm.user_profile_store.print(user_id=user_id)
lm.user_memory_store.print(user_id=user_id)
# Session 2: New session - agent remembers
print("\n--- Session 2: Agent remembers across sessions ---\n")
agent.print_response(
"What do you know about me?",
user_id=user_id,
session_id="session_2",
stream=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 openai sqlalchemy
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
always_learn.py, then run:python always_learn.py