run.py
"""
Learning Demo: AgentOS Server
=============================
Serves the ops assistant on an AgentOS instance, which exposes the
/learnings CRUD endpoints and powers the Learning pages at os.agno.com.
Requires the pgvector container:
./cookbook/scripts/run_pgvector.sh
Run seed.py first so the Learning pages have data:
.venvs/demo/bin/python cookbook/08_learning/10_demo/seed.py
Then start the server:
.venvs/demo/bin/python cookbook/08_learning/10_demo/run.py
Then open https://os.agno.com, connect to http://localhost:7777, and
browse the Learning section: User Profiles, User Memories, Entity
Memories, Session Context, and Decision Logs.
Interactive API docs are at http://localhost:7777/docs.
"""
from agents import ops_assistant
from agno.os import AgentOS
agent_os = AgentOS(
description="Learning demo: one agent with every learning store enabled",
agents=[ops_assistant],
)
app = agent_os.get_app()
if __name__ == "__main__":
agent_os.serve(app="run:app", reload=True)
agents.py
"""
Learning Demo: Shared Agent
===========================
A single ops assistant with every learning store enabled:
- User Profile: structured fields (name, role, preferences)
- User Memory: unstructured observations about the user
- Session Context: a running summary of each session
- Entity Memory: facts, events, and relationships about external things
- Learned Knowledge: insights that transfer across users (pgvector)
- Decision Log: significant decisions with reasoning
Requires the pgvector container:
./cookbook/scripts/run_pgvector.sh
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge import Knowledge
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.learn import (
LearningMachine,
)
from agno.models.openai import OpenAIResponses
from agno.vectordb.pgvector import PgVector, SearchType
# ---------------------------------------------------------------------------
# Database
# ---------------------------------------------------------------------------
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(id="learning-demo-db", db_url=db_url)
# Learned Knowledge needs a vector store for semantic search.
knowledge = Knowledge(
vector_db=PgVector(
db_url=db_url,
table_name="learning_demo_knowledge",
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
# ---------------------------------------------------------------------------
# Learning Machine: all six stores enabled
# ---------------------------------------------------------------------------
learning = LearningMachine(
db=db,
model=OpenAIResponses(id="gpt-5.5"),
knowledge=knowledge,
user_profile=True,
user_memory=True,
session_context=True,
entity_memory=True,
learned_knowledge=True,
decision_log=True,
)
# ---------------------------------------------------------------------------
# Agent
# ---------------------------------------------------------------------------
ops_assistant = Agent(
id="ops-assistant",
name="Ops Assistant",
model=OpenAIResponses(id="gpt-5.5"),
db=db,
learning=learning,
instructions=[
"You are an engineering operations assistant.",
"Keep answers short and practical.",
"Search your learnings before answering substantive questions.",
"When the user shares a team-wide insight or asks you to remember one, save it with the save_learning tool.",
"When you make a significant recommendation, record it with the log_decision tool, including your reasoning and the alternatives you considered.",
],
markdown=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 pgvector psycopg-binary
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
5
Run the example
Save the code blocks above as
run.py and agents.py in the same directory, then run:python run.py