basic.py
"""
Composition: The Manual Door
============================
learning= is the automatic door: the framework injects context, instructions
and tools for you. This folder is the other door - no learning= at all. You
place the three public surfaces yourself, the way FileSystem composes:
- learning.get_tools(...) the capture tools
- learning.instructions() the guidance block (how to use them)
- learning.build_context(...) the recalled-data block
An agent with no learning= has no automatic capture: the manual door is
agentic by nature - the agent captures by calling the tools you handed it.
Run:
.venvs/demo/bin/python cookbook/08_learning/11_composition/basic.py
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import LearningMachine, LearningMode, UserMemoryConfig
from agno.models.openai import OpenAIResponses
# ---------------------------------------------------------------------------
# Build the machine, place its surfaces by hand
# ---------------------------------------------------------------------------
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
# The manual door injects nothing: without learning= nobody hands the machine
# the agent's model, and capture is a model call.
learning = LearningMachine(
db=db,
model=OpenAIResponses(id="gpt-5.5"),
user_memory=UserMemoryConfig(mode=LearningMode.AGENTIC),
entity_memory=True,
)
USER_ID = "composer@example.com"
agent = Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
tools=[*learning.get_tools(user_id=USER_ID)],
instructions=[
"You are a research assistant.",
learning.instructions(),
],
user_id=USER_ID,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run
# ---------------------------------------------------------------------------
if __name__ == "__main__":
agent.print_response(
"Remember that I prefer sources with primary data, and track the "
"Meridian project - Priya runs it.",
stream=True,
)
print("\n--- what the manual door placed (guidance + data) ---")
print(learning.instructions()[:400])
print("...")
print(learning.build_context(user_id=USER_ID, message="what about meridian?"))
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 "psycopg[binary]" 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 PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql \
-v pgvolume:/var/lib/postgresql \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
docker run -d `
-e POSTGRES_DB=ai `
-e POSTGRES_USER=ai `
-e POSTGRES_PASSWORD=ai `
-e PGDATA=/var/lib/postgresql `
-v pgvolume:/var/lib/postgresql `
-p 5532:5432 `
--name pgvector `
agnohq/pgvector:18
5
Run the example
Save the code above as
basic.py, then run:python basic.py