Intro to Render Workflows

Render Workflows is an end-to-end orchestration and execution engine for distributed, long-running computation.

Define units of work as standard functions with Render's lightweight SDK, then run them across hundreds of concurrent instances.

Use workflows to orchestrate AI agents, background jobs, data pipelines, and more:

Render bills you for compute usage (prorated by the second) and for temporary retention of your run arguments and return values. Queuing and provisioning are provided at no additional cost.

At a glance

1. You define tasks and flows as standard TypeScript or Python functions and register them with Render.
2. Trigger runs from anywhere: web apps, agents, CI/CD, etc.
   • A task can chain runs of other tasks (or itself!) as illustrated above.
3. Render automatically spins up a separate instance for each run and deprovisions instances as runs complete.

Create your first workflow

Core features

• Tasks and flows as functions

  Define tasks and flows with the Render SDK. Chain runs by invoking one task from another.

• Managed orchestration

  Render handles run queuing, spin-up, and deprovisioning, all at no additional cost.

• Long-running execution

  Runs can execute for up to 24 hours. Customize timeouts per task and per flow.

• Automatic retries

  Render automatically retries a failed task run or flow step according to its settings.

• Flexible compute

  Set CPU and RAM per task and flow. Pay only for the resources you use with the flex plan.

• Rich observability

  Track the status of active and completed runs in the Render Dashboard.

• CLI integration

  Create new workflow services, trigger runs, and view run history from the command line.

• Rapid local development

  Quickly iterate on your task logic by spinning up a local task server.

What's in a workflow?

A workflow service contains a collection of function definitions that you register with Render. You designate each function as one of two types: a task or a flow.

After you register your tasks and flows, you can trigger individual runs of your functions using the Render SDK or API:

Each run executes in its own instance, which Render spins up on demand within seconds. If you trigger more simultaneous runs than your workflow's compute limits allow, Render queues up the excess until resources become available.

A run executes whatever logic you define in its function. For tasks, this can include chaining runs of other tasks in the same workflow:

In this example, we trigger a run of the run_agent task. This task chains runs of other tasks (gather_context, execute_skills, and so on). Each run in the chain also executes in its own instance.

The run_agent task depends on the results of its various chained runs, so it awaits their completion before chaining additional runs and eventually returning its own result.

Defining tasks and flows

Every workflow service pulls its task and flow definitions from a Git provider repo. In this repo, you define tasks and flows as TypeScript or Python functions using the Render SDK.

Here's a single-file workflow definition that defines two basic tasks:

You can define tasks and flows across any number of files and directories, importing frameworks and libraries as needed.

As part of registering your tasks and flows, Render builds your project into a custom image and caches it. Render pushes this image to each run's instance as part of spin-up.

Create your first workflow

Triggering runs

You trigger runs of your tasks and flows from anywhere using the Render SDK or API:

For all the details, see Triggering Workflow Runs.

Workflows versus job queues

Historically, job queue architectures (as managed by Celery, BullMQ, etc.) have required setting up multiple pieces of infrastructure and configuring them to work together:

Render Workflows unifies this architecture into a single, fully managed system:

For new projects that require long-running background execution, Render Workflows helps you move faster with fewer moving parts.

Create your first workflow

FAQ