AI visibility in Pipedream

Pipedream sits exactly where AI-visibility monitoring wants to live: cron triggers, real code steps when you need them, and connected destinations when you don't. For developers who find n8n's canvas too much and a full repo too heavy, a Pipedream workflow is the right-sized home for "check what the engines say every morning." The integration is honest and thin: one Node (or Python) code step calling POST https://api.agentgeo.org/v1/fetches. No AgentGEO component in Pipedream's registry — and none needed, because the fetch is four lines of code. The one setting that matters: raise the workflow timeout, because a live multi-surface fetch can hold up to 180 seconds.

One call, one run envelope: per-surface records with the verbatim answerText, structured sources[] (title, URL, position), latencyMs and providerRecordId — ready for the next step to diff, store in a Data Store, or post to Slack.

Connect AgentGEO to Pipedream

Create a workflow with a Schedule trigger and a Node code step. Put the key in an environment variable, and set the workflow timeout above 180 seconds in the workflow's settings:

export default defineComponent({
  async run({ steps, $ }) {
    const resp = await fetch("https://api.agentgeo.org/v1/fetches", {
      method: "POST",
      headers: {
        Authorization: `Bearer ${process.env.AGENTGEO_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        query: "best ai visibility tools",
        surfaces: ["chatgpt", "perplexity", "gemini"],
      }),
      signal: AbortSignal.timeout(200_000), // API holds up to 180s
    });
    if (!resp.ok) throw new Error(`agentgeo ${resp.status}`);
    return await resp.json(); // exported for the next step
  },
});

The step's return value is the whole run envelope, so downstream steps get steps.fetch_answers.$return_value.answers with no parsing. Build first on an ag_test_ key — clearly labelled demo records at zero credits — then swap in the ag_live_ key minted in the console (live keys come with a plan).

Workflows worth building

• Morning citation diff. Cron at 7am → fetch your money query across six surfaces → compare sources[] to yesterday's copy in a Data Store → Slack only the changes. Quiet when nothing moved.
• Drop-out pager. Daily Perplexity check; if your domain leaves sources[], page the on-call marketer the same hour.
• Longitudinal archive to S3 or a warehouse. Append each run's JSON to storage you own — the raw-record history that later lets you answer "when did we lose the AI Overview citation?" precisely.
• HTTP-triggered check. Swap the cron for Pipedream's HTTP trigger and give your team a URL that runs an on-demand visibility check from anywhere.

More integrations

The same four lines run in n8n's HTTP node or any runtime — the Node guide and Python guide cover the contract in depth. Timeout-capped platforms like Zapier use a poll pattern instead. See all integrations.