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checkpointing.py
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from agno.os import AgentOS
from agno.tools.websearch import WebSearchTools

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

research_agent = Agent(
    name="Research Agent",
    checkpoint="tool-batch",
    id="research_agent",
    model=OpenAIChat(id="gpt-5.2"),
    instructions=["You are a research agent"],
    tools=[WebSearchTools()],
    db=db,
)

agent_os = AgentOS(
    id="checkpointing-demo",
    name="Checkpointing Demo",
    description="A demo of checkpointing in AgentOS",
    agents=[research_agent],
)

app = agent_os.get_app()

if __name__ == "__main__":
    agent_os.serve(app="checkpointing:app", reload=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]" ddgs fastmcp openai psycopg-binary starlette
3

Export your API keys

export JWT_VERIFICATION_KEY="your_jwt_verification_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"

$Env:JWT_VERIFICATION_KEY="your_jwt_verification_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/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18
5

Run the example

Save the code above as checkpointing.py, then run:
python checkpointing.py
Full source: cookbook/05_agent_os/advanced_demo/checkpointing.py