Nodaro with Make

Make can start any Nodaro workflow from a scenario and receive the finished image, video or audio on a custom webhook. Nodaro does the generation. Make decides when it runs and where the result goes next. Neither direction needs a Nodaro API key, because the workflow's own webhook address starts the run.

How the two connect

1. The HTTP app's Make a request module posts a JSON body, for example a prompt, to the webhook address of a Nodaro workflow.
2. The Nodaro workflow starts with a Webhook Trigger, runs its nodes, and ends with a Webhook Output.
3. The Webhook Output posts the result to a Make Custom webhook, which starts a second scenario with the result.

Step 1: Receive the result in Make

Create the receiving end first, because the Nodaro workflow needs its address.

1. Create a scenario and add the Webhooks › Custom webhook module as its trigger.
2. Click Add next to Webhook, name it, for example "Nodaro result", and click Save.
3. Copy the URL that Make generates. Make now listens for the first request.

Each Make webhook belongs to one scenario. Turn the scenario on when it is ready, so it processes every result as it arrives.

Step 2: Build the Nodaro workflow

1. Add a Webhook Trigger node. Under Output Parameters, add a parameter named prompt of type Text.
2. Connect the prompt output to the prompt input of a Generate Image node, or any other node that makes a result.
3. Add a Webhook Output node. Paste the Make webhook URL into Webhook URL. Under Input Parameters, add image_url of type Image URL, and connect the image to it.
4. Save the workflow. Select the Webhook Trigger and copy its address under Webhook URL.

The address has the form https://app.nodaro.ai/v1/webhooks/<token>. The token works like a password: anyone who has the address can start the workflow.

Step 3: Start the run from Make

1. In the scenario that should start the run, add the HTTP app's Make a request module.
2. Paste the Nodaro address into URL and set Method to POST.
3. Set Body content type to application/JSON and Body input method to Data structure. Add a field named prompt, and map the value from an earlier module.
4. Set Parse response to Yes, so the next modules can map the executionId.

The Data structure method escapes your values. A raw JSON string with mapped values can break when a value contains quotes.

What comes back

Nodaro answers the request at once, before anything is generated. The answer is 202 with the id of the run:

{ "executionId": "3f9e2b1a-7c6d-4e5f-8a9b-0c1d2e3f4a5b", "status": "pending" }

When the run finishes, the Webhook Output posts the result to your custom webhook. The body has one key per Input Parameter, and media values are addresses of files on Nodaro:

{ "image_url": "https://cdn.nodaro.ai/images/2538b658-d2e1-42ee-890a-72ce06089feb.png" }

Run the Nodaro workflow once while the custom webhook listens, and Make learns the structure, so image_url becomes a field you can map. You can also define the structure by hand under the webhook's advanced settings.

Poll instead of a webhook

You can also poll for the result with a personal API token, in the same scenario. Polling works for workflows in your personal projects.

1. After the request module, add the Tools › Sleep module, for example for 30 seconds.
2. Add another Make a request module with Method GET and the URL https://app.nodaro.ai/v1/api/status/<executionId>, with the executionId mapped from the first request.
3. For the token, choose the API key authentication type: put Bearer <your API token> in Key, place it in the header, and name the parameter Authorization.
4. When the status is completed, call https://app.nodaro.ai/v1/api/result/<executionId> the same way. The answer lists the run's outputs with their addresses.

A video can take several minutes, and one Sleep module waits a limited time. For long runs, the webhook method above is simpler. Run a workflow with new input values describes the status and result answers.

Limits to plan for

What it costs

The Webhook Trigger and the Webhook Output cost nothing. You pay the credits of the nodes between them, at the same prices as in the editor. In the example above, each run costs the price of one Nano Banana 2 image, 22 credits. Credits reserved for a node that fails come back automatically.

The same pattern works in n8n and Zapier. Automations covers schedules and the other ways to start a workflow.