Schedule management via REST API

Walks the full /schedules REST lifecycle with httpx against a running AgentOS: create, list, patch, disable/enable, trigger, read run history, delete.

Demonstrates creating, listing, updating, enabling/disabling, manually triggering, and deleting schedules.

schedule_management.py

"""Schedule management via REST API.

Demonstrates creating, listing, updating, enabling/disabling,
manually triggering, and deleting schedules.

Prerequisites:
    pip install agno[scheduler] httpx

Usage:
    # First, start the server:
    python cookbook/05_agent_os/scheduler/basic_schedule.py

    # Then run this script:
    python cookbook/05_agent_os/scheduler/schedule_management.py
"""

import httpx

# ---------------------------------------------------------------------------
# Create Example
# ---------------------------------------------------------------------------

BASE_URL = "http://localhost:7777"


def main():
    client = httpx.Client(base_url=BASE_URL, timeout=30)

    # 1. Create a schedule
    print("--- Creating schedule ---")
    resp = client.post(
        "/schedules",
        json={
            "name": "hourly-greeting",
            "cron_expr": "0 * * * *",
            "endpoint": "/agents/greeter/runs",
            "payload": {"message": "Hourly check-in"},
            "timezone": "UTC",
            "max_retries": 2,
            "retry_delay_seconds": 30,
        },
    )
    print(f"  Status: {resp.status_code}")
    schedule = resp.json()
    schedule_id = schedule["id"]
    print(f"  ID: {schedule_id}")
    print(f"  Next run at: {schedule['next_run_at']}")
    print()

    # 2. List all schedules
    print("--- Listing schedules ---")
    resp = client.get("/schedules")
    schedules = resp.json()
    for s in schedules:
        print(f"  {s['name']} (enabled={s['enabled']}, next_run={s['next_run_at']})")
    print()

    # 3. Update the schedule
    print("--- Updating schedule ---")
    resp = client.patch(
        f"/schedules/{schedule_id}",
        json={"description": "Runs every hour on the hour", "max_retries": 3},
    )
    print(f"  Updated description: {resp.json()['description']}")
    print()

    # 4. Disable the schedule
    print("--- Disabling schedule ---")
    resp = client.post(f"/schedules/{schedule_id}/disable")
    print(f"  Enabled: {resp.json()['enabled']}")
    print()

    # 5. Re-enable the schedule
    print("--- Enabling schedule ---")
    resp = client.post(f"/schedules/{schedule_id}/enable")
    print(f"  Enabled: {resp.json()['enabled']}")
    print(f"  Next run at: {resp.json()['next_run_at']}")
    print()

    # 6. Manually trigger
    print("--- Triggering schedule ---")
    resp = client.post(f"/schedules/{schedule_id}/trigger")
    print(f"  Trigger status: {resp.status_code}")
    print(f"  Run: {resp.json()}")
    print()

    # 7. View run history
    print("--- Run history ---")
    resp = client.get(f"/schedules/{schedule_id}/runs")
    runs = resp.json()
    print(f"  Total runs: {len(runs)}")
    for run in runs:
        print(
            f"    attempt={run['attempt']} status={run['status']} triggered_at={run['triggered_at']}"
        )
    print()

    # 8. Delete the schedule
    print("--- Deleting schedule ---")
    resp = client.delete(f"/schedules/{schedule_id}")
    print(f"  Delete status: {resp.status_code}")

    # Verify deletion
    resp = client.get(f"/schedules/{schedule_id}")
    print(f"  Get after delete: {resp.status_code} (expected 404)")


# ---------------------------------------------------------------------------
# Run Example
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    main()

Run the Example

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. Install dependencies

   uv pip install -U "agno[os]" "psycopg[binary]" httpx openai
   
3. Export your API keys

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
4. Run PgVector

   macOS / Linux

   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. Clone Agno

   Clone the repository and run the remaining commands from its root:

   git clone https://github.com/agno-agi/agno.git
   cd agno
   
6. Start the scheduler server

   In another terminal, start Basic Schedule on port 7777:

   python cookbook/05_agent_os/scheduler/basic_schedule.py
   
7. Run the example

   Run the example from the repository root:

   python cookbook/05_agent_os/scheduler/schedule_management.py
   

Full source: cookbook/05_agent_os/scheduler/schedule_management.py