AgentOS Demo

AgentOS with a knowledge-backed agent, a web research team, and Postgres storage.

Here’s a full AgentOS with multiple agents and a team. The Agno Agent combines a Postgres-backed knowledge base with MCP tools for the Agno docs. The Research Team pairs a web search agent with a simple agent. Authentication is optional here; set OS_SECURITY_KEY if you want to require it.

Code

demo.py

"""
AgentOS Demo

Set OS_SECURITY_KEY to enable authentication.

Prerequisites:
uv pip install -U fastapi uvicorn sqlalchemy pgvector psycopg openai ddgs "agno[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.mcp import MCPTools
from agno.tools.websearch import WebSearchTools
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,
    update_memory_on_run=True,
    knowledge=knowledge,
    markdown=True,
)

simple_agent = Agent(
    name="Simple Agent",
    role="Simple agent",
    id="simple_agent",
    model=OpenAIChat(id="gpt-5.2"),
    instructions=["You are a simple agent"],
    db=db,
    update_memory_on_run=True,
)

research_agent = Agent(
    name="Research Agent",
    role="Research agent",
    id="research_agent",
    model=OpenAIChat(id="gpt-5.2"),
    instructions=["You are a research agent"],
    tools=[WebSearchTools()],
    db=db,
    update_memory_on_run=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-4.1"),
    id="research_team",
    instructions=[
        "You are the lead researcher of a research team.",
    ],
    db=db,
    update_memory_on_run=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__":
    # Don't use reload=True here; it can break the MCP connection during the FastAPI lifespan
    agent_os.serve(app="demo:app", port=7777)

Usage

1. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Set Environment Variables

   export OPENAI_API_KEY=your_openai_api_key
   export OS_SECURITY_KEY=your_security_key  # Optional, enables authentication
   
3. Install dependencies

   uv pip install -U fastapi uvicorn sqlalchemy pgvector psycopg openai ddgs "agno[mcp]"
   
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 demo.py