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Here is a full example of an AgentOS with multiple agents and teams. It also shows how to instantiate agents with a database, knowledge base, and tools.

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Code

cookbook/agent_os/demo.py
"""
AgentOS Demo

Prerequisites:
pip install -U fastapi uvicorn sqlalchemy pgvector psycopg openai ddgs mcp
"""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.os import AgentOS
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.mcp import MCPTools
from agno.vectordb.pgvector import PgVector

# Database connection
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

# Create Postgres-backed memory store
db = PostgresDb(db_url=db_url)

# Create Postgres-backed vector store
vector_db = PgVector(
    db_url=db_url,
    table_name="agno_docs",
)
knowledge = Knowledge(
    name="Agno Docs",
    contents_db=db,
    vector_db=vector_db,
)

# Create your agents
agno_agent = Agent(
    name="Agno Agent",
    model=OpenAIChat(id="gpt-4.1"),
    tools=[MCPTools(transport="streamable-http", url="https://docs.agno.com/mcp")],
    db=db,
    enable_user_memories=True,
    knowledge=knowledge,
    markdown=True,
)

simple_agent = Agent(
    name="Simple Agent",
    role="Simple agent",
    id="simple_agent",
    model=OpenAIChat(id="gpt-5-mini"),
    instructions=["You are a simple agent"],
    db=db,
    enable_user_memories=True,
)

research_agent = Agent(
    name="Research Agent",
    role="Research agent",
    id="research_agent",
    model=OpenAIChat(id="gpt-5-mini"),
    instructions=["You are a research agent"],
    tools=[DuckDuckGoTools()],
    db=db,
    enable_user_memories=True,
)

# Create a team
research_team = Team(
    name="Research Team",
    description="A team of agents that research the web",
    members=[research_agent, simple_agent],
    model=OpenAIChat(id="gpt-5-mini"),
    id="research_team",
    instructions=[
        "You are the lead researcher of a research team! 🔍",
    ],
    db=db,
    enable_user_memories=True,
    add_datetime_to_context=True,
    markdown=True,
)

# Create the AgentOS
agent_os = AgentOS(
    id="agentos-demo",
    agents=[agno_agent],
    teams=[research_team],
)
app = agent_os.get_app()


if __name__ == "__main__":
    agent_os.serve(app="demo:app", port=7777)

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Set Environment Variables

export OPENAI_API_KEY=your_openai_api_key
export OS_SECURITY_KEY=your_security_key  # Optional for authentication
3

Install libraries

pip install -U fastapi uvicorn sqlalchemy pgvector psycopg openai ddgs mcp agno
4

Setup PostgreSQL Database

# Using Docker
docker run -d \
  --name agno-postgres \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -p 5532:5432 \
  pgvector/pgvector:pg17
5

Run Example

python cookbook/agent_os/demo.py