Scheduling

A useful agent doesn’t only respond. It shows up on its own — morning briefings, daily triage, weekly digests, hourly health checks. AgentOS gives you a cron-style scheduler in-process, so you don’t run a separate worker fleet for any of it. No Celery, no separate cron container, no leader-election service to operate. Schedules live in your db and fire from the same FastAPI process that serves your agents.

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 polls agno_schedules every scheduler_poll_interval seconds, fires due jobs, retries failures, and persists state.

Two ways to create schedules

Pattern	When	Lives in
Agent-driven	Users ask the agent to schedule things in chat	The agent’s own tool calls
Startup-registered	Schedules that should always exist, regardless of who’s chatting	Your app’s lifespan

Agent-driven

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

from agno.tools.scheduler import SchedulerTools

agent = 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 Demo OS ships a Scheduler agent that does exactly this. Talk to it in plain English, it manages cron expressions for you.

Startup-registered

For schedules that should always exist (the daily digest, the hourly sync, the nightly cleanup), register them in your app’s lifespan:

from contextlib import asynccontextmanager
from agno.scheduler import register_schedule

@asynccontextmanager
async def lifespan(app):
    register_schedule(
        name="daily_digest",
        cron="0 9 * * 1-5",       # weekdays 9am
        endpoint="/workflows/daily-digest/runs",
    )
    yield

agent_os = AgentOS(..., scheduler=True, lifespan=lifespan)

Idempotent. Re-registering with the same name updates the existing schedule. Restarts don’t duplicate. This is the pattern Coda uses for daily digest, issue triage, and repo sync.

Workflows for multi-step jobs

Schedules fire single endpoints. When the work is multi-step — research, then outline, then draft, then review — you reach for a workflow. Workflows aren’t strictly a scheduling feature, but they’re the most common thing a schedule fires. A workflow is a typed pipeline. Steps run in order. Parallel runs them concurrently. Loop repeats until a condition holds. Router picks one branch.

from agno.workflow import Workflow, Step, Parallel, Loop, Router, Condition

workflow = Workflow(
    name="content_pipeline",
    steps=[
        Step(name="research", agent=researcher),
        Step(name="outline", agent=outliner),
        Loop(
            name="draft_review",
            step=Step(name="draft_then_review", agent=writer_then_editor),
            until=Condition(expr="quality_score >= 8"),
            max_iterations=3,
        ),
    ],
)

Workflows are first-class AgentOS citizens: /workflows/<id>/runs POST endpoint, schedulable, traced, persisted in the same db.

Pattern	Use when
Sequential	Steps depend on each other
Parallel	Steps are independent and you want fanout
Loop with condition	Quality threshold or max iterations
Router + Condition	Dynamic branching on input
Cross-modal chaining	Output of one agent is input to a different modality (text → speech, code → narration)

For worked examples, see Demo OS.

Schedule runs and observability

When a schedule fires, AgentOS:

Looks up the schedule in agno_schedules.
Hits the configured endpoint (POST /agents/<id>/runs or POST /workflows/<id>/runs) inside the same process.
Records the result in agno_schedule_runs (status, duration, error if any) and the underlying run in agno_sessions and agno_traces like any other run.

That means scheduled work shows up in the same UI, the same SQL queries, and the same trace tree as ad-hoc work. To see what fired in the last 24 hours:

SELECT s.name, sr.status, sr.created_at, sr.duration_ms
FROM agno_schedule_runs sr
JOIN agno_schedules s ON s.id = sr.schedule_id
WHERE sr.created_at > NOW() - INTERVAL '24 hours'
ORDER BY sr.created_at DESC;

For the trace of a specific scheduled run, follow the run_id from agno_schedule_runs back to agno_traces. See Observability for the full data model.

Scheduler in HA

When you scale to multiple replicas, you don’t want every replica firing the same scheduled job. Use leader election (one replica owns scheduling) or pin the scheduler to a single replica via deployment config. See Scheduler for the HA pattern.

Deploy →

Welcome

Tutorials

Runtime

Demo OS

Two ways to create schedules

Agent-driven

Startup-registered

Workflows for multi-step jobs

Schedule runs and observability

Scheduler in HA

Next

​Two ways to create schedules

​Agent-driven

​Startup-registered

​Workflows for multi-step jobs

​Schedule runs and observability

​Scheduler in HA

​Next

Two ways to create schedules

Agent-driven

Startup-registered

Workflows for multi-step jobs

Schedule runs and observability

Scheduler in HA

Next