Apify Debug Bundle

Overview

Collect all diagnostic information needed to troubleshoot failed Actor runs and prepare Apify support tickets. Pulls run metadata, logs, dataset samples, and environment info into a single bundle so a support engineer (or you) can diagnose the failure without live access to your account.

Prerequisites

• apify-client installed
• APIFY_TOKEN configured
• A failed or problematic run ID to investigate

Authentication

All API calls authenticate with the APIFY_TOKEN as a Bearer header (Authorization: Bearer $APIFY_TOKEN), and the SDK reads the same token from process.env.APIFY_TOKEN. Get the token from the Apify Console under Settings → Integrations → Personal API tokens. Never commit it — the bundle script redacts any local .env before packaging, and the platform auto-redacts secrets inside run logs.

Instructions

The workflow has four steps. The skeleton below is enough to run it; each step's full implementation lives in implementation.md.

1. Investigate the failed run — pull run summary, dataset stats, and the log tail via the SDK. The core call:

   const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
   const run = await client.run(runId).get();
   const log = await client.run(runId).log().get();
   

2. Create the debug bundle — run apify-debug-bundle.sh <RUN_ID>. It collects environment info, run details, log, a 5-item dataset sample, key-value store keys, a redacted .env, and platform health, then packages everything into a timestamped .tar.gz. Full script in implementation.md.

3. Compare against a good run (optional) — diff a successful and failed run field-by-field to spot the delta ().