> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-docs-scavio-google-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Scheduling

> Run agents, teams, and workflows on recurring schedules with persisted history and retry controls.

Recurring work such as daily briefs, queue triage, repository syncs, health checks, and reports should use the same runtime as on-demand runs. AgentOS stores schedules and run history in the platform database, invokes existing agent, team, or workflow endpoints, and lets agents manage schedules through `SchedulerTools`.

```python theme={null}
from agno.os import AgentOS

agent_os = AgentOS(
    agents=[agent],
    db=db,
    scheduler=True,
    scheduler_poll_interval=15,    # check for due jobs every N seconds
)
```

The scheduler runs inside the AgentOS process and polls `agno_schedules` every `scheduler_poll_interval` seconds. Keep at least one scheduler-enabled runtime running continuously. Due jobs retry failures up to each schedule's `max_retries`, and every attempt is persisted.

The scheduler fires a due job by calling its endpoint over HTTP, against `http://127.0.0.1:7777` by default. That matches the default `serve()` port. Set `scheduler_base_url` to match when you serve on a different host or port; otherwise schedules fire against the wrong URL.

## Two ways to create schedules

| Pattern                 | How                                                                                                                                                                 |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Agent Managed**       | `SchedulerTools` lets an agent create, list, inspect, delete, enable, disable, and review runs for schedules. Creating a schedule with an existing name updates it. |
| **Manually Registered** | Schedules created in code, registered at startup.                                                                                                                   |

### Agent Managed

Give an agent `SchedulerTools` and it can schedule its own work via chat:

```python theme={null}
from agno.agent import Agent
from agno.tools.scheduler import SchedulerTools

agent = Agent(
    id="my-agent",
    model="openai:gpt-5.4",
    tools=[
        SchedulerTools(
            db=db,
            default_endpoint="/agents/my-agent/runs",
            default_method="POST",
            default_timezone="UTC",
        ),
    ],
)

# In Slack: "@MyAgent post a daily digest of open PRs at 9am ET"
# The agent calls SchedulerTools.create_schedule() with a cron expr.
```

The [Scheduler Tools Agent example](/examples/agent-os/scheduler/scheduler-tools-agent) is a runnable version of this pattern.

### Manually Registered

For schedules that should always exist (the daily digest, the hourly sync, the nightly cleanup), create them in your app's lifespan via `ScheduleManager`:

```python theme={null}
from contextlib import asynccontextmanager
from agno.scheduler import ScheduleManager

@asynccontextmanager
async def lifespan(app):
    manager = ScheduleManager(db=db)
    manager.create(
        name="daily_digest",
        cron="0 9 * * 1-5",                       # weekdays 9am
        endpoint="/agents/my-agent/runs",
        payload={"message": "Create the daily digest."},
        if_exists="update",                       # idempotent on restart
    )
    yield

agent_os = AgentOS(agents=[agent], db=db, scheduler=True, lifespan=lifespan)
```

`if_exists="update"` makes restarts idempotent by updating the existing schedule. Pass `"skip"` to preserve manually edited schedules or `"raise"` (the default) to surface accidental name collisions. This is the pattern Coda uses for [daily digest and repo sync](/deploy/templates/coda/overview).

## Schedule a workflow

Schedules invoke endpoints. Point a schedule at `/workflows/<id>/runs` when recurring work has multiple steps, branches, or review loops. See [Workflow Automation](/use-cases/workflow-automation) and [Workflows](/workflows/overview).

## Schedule runs and observability

When a schedule fires, AgentOS:

1. Looks up the schedule in `agno_schedules` and claims it through a database-backed lease.
2. Calls the configured endpoint (`POST /agents/<id>/runs`, `POST /teams/<id>/runs`, or `POST /workflows/<id>/runs`) over HTTP via `httpx.AsyncClient`. This is the same path an external caller would take, including auth headers.
3. Records the schedule attempt in `agno_schedule_runs` with status, timings, the underlying `run_id` and `session_id` when returned, and any error. The target component persists its run according to its database configuration. Traces require tracing to be enabled.

Schedule runs are queryable from `agno_schedule_runs`. When the target component persists sessions and AgentOS tracing is enabled, the linked run also appears in session and trace views. This Postgres query lists runs fired in the last 24 hours:

```sql theme={null}
SELECT
    s.name,
    sr.status,
    sr.triggered_at,
    (sr.completed_at - sr.triggered_at) AS duration_s
FROM ai.agno_schedule_runs sr
JOIN ai.agno_schedules s ON s.id = sr.schedule_id
WHERE sr.created_at > extract(epoch from NOW() - INTERVAL '24 hours')::bigint
ORDER BY sr.created_at DESC;
```

The `ai.` prefix is the schema `PostgresDb` creates its tables in by default (override with `PostgresDb(db_schema=...)`). Timestamps on schedule runs are stored as epoch seconds (BigInt). For the trace of a specific scheduled run, follow the `run_id` from `agno_schedule_runs` back to `agno_traces`. See [Observability](/features/observability) for the full data model.

## Scheduler in HA

Every replica can run the scheduler loop safely on the backends that implement the scheduler's claim methods: Postgres, SQLite, and MongoDB. Due schedules are claimed through an atomic database-backed lease. The first replica to claim a due job runs it; the others skip.

Deployment configuration can pin scheduler polling to a dedicated replica. See [Scheduler](/agent-os/scheduler/overview) for tuning details.