DBOS | Build Reliable Backends 10x Faster, Scale to Millions with 1 Click

Create reliable workflows

Write your business logic in normal code, with branches, loops, subtasks, and retries. DBOS makes it resilient to any failure.

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@DBOS.workflow()
def checkout_workflow(items):
order = create_order()
reserve_inventory(order, items)
payment_status = process_payment(order, items)
if payment_status == 'paid':
fulfill_order(order)
else:
undo_reserve_inventory(order, items)
cancel_order(order)

Cron jobs made easy

Schedule workflows to run at specific times.
Host them completely serverlessly.

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@DBOS.scheduled("0 * * * *") # Run once an hour
@DBOS.workflow()
def run_hourly(scheduled_time: datetime, actual_time: datetime):
   results = search_hackernews("serverless")
   for comment, url in results:
      post_to_slack(comment, url)

Resilient data pipelines

Build data pipelines that are reliable and observable by default.
DBOS durable queues guarantee all your tasks complete.

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queue = Queue("indexing_queue")

@DBOS.workflow()
def indexing_workflow(urls: List[HttpUrl]):
  handles: List[WorkflowHandle] = []
  for url in urls:
     handle = queue.enqueue(index_document, url)
     handles.append(handle)
indexed_pages = 0
for handle in handles:
     indexed_pages += handle.get_result()
logger.info(f"Indexed {len(urls)} documents totaling {indexed_pages} pages")

Reliable AI agents

Enhance your AI workflows with DBOS, build reliable AI agents with automatic retries and no limit on how long they can run for.

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@DBOS.step(retries_allowed=True, max_attempts=2)
def process_refund(item, reason):
    return f"Processed refund for item {item}, because {reason}"

@DBOS.step(retries_allowed=True, max_attempts=3)
def apply_discount(amount):
    return f"Applied discount of {amount}%"

refunds_agent = Agent(
    name="Refunds Agent",
    instructions="Help the user with a refund. If the reason is that it was too expensive, offer the user a refund code.",
    functions=[process_refund, apply_discount],
)

Kafka event processing

Consume Kafka messages exactly-once, no need to worry about timeouts or offsets.

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@DBOS.kafka_consumer(config,["alerts-topic"])
@DBOS.workflow()
def process_kafka_alerts(msg: KafkaMessage):
  alerts = msg.value.decode()
  for alert in alerts:
    respond_to_alert(alert)

Webhooks & notifications

Effortlessly mix synchronous webhook code with asynchronous event processing. Reliably wait weeks or months for events, then use idempotency and durable execution to process them exactly once.

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@slackapp.message()
def handle_message(request: BoltRequest) -> None:
  event_id = request.body["event_id"]
  with SetWorkflowID(event_id):
    DBOS.start_workflow(message_workflow,     request.body["event"])