Apify debug bundle

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 (compareRuns(successId, failId)).

4. Live-tail a running Actor (optional) — stream logs when the final log is not yet available.

For copy-pasteable code for every step, see implementation.md.

Output

A single timestamped tarball, apify-debug-YYYYMMDD-HHMMSS.tar.gz, containing:

File	Contents
environment.txt	Node/npm versions, installed Apify packages, CLI version
run-details.json	Run status, options, stats, usage, cost
run-log.txt	Full run log (secrets auto-redacted by the platform)
dataset-sample.json	First 5 dataset items
kv-store-keys.json	Key-value store key listing
env-redacted.txt	Local .env with all values redacted
platform-health.json	Apify platform health snapshot

Attach the tarball directly to an Apify support ticket.

Sensitive Data Handling

Always redact before sharing:

• API tokens (apify_api_*)
• Proxy passwords
• PII (emails, names, IPs)
• Custom environment variables

Safe to include:

• Run IDs, Actor IDs, dataset IDs
• Error messages and stack traces
• Run configuration (memory, timeout)
• Platform health status

Escalation Path

1. Check run log for stack trace
2. Compare with a successful run
3. Check Apify Status for outages
4. Create debug bundle
5. Submit to Apify Support with bundle attached

Error Handling

Issue	Cause	Solution
Run not found	Invalid run ID or expired	Unnamed runs expire after 7 days
Log unavailable	Run still in progress	Wait for completion or stream live
Empty dataset	Actor produced no output	Check failedRequestHandler in code
High CU usage	Memory too high or slow execution	Reduce memory, optimize code

Examples

Four worked scenarios — a plain FAILED run, an "it worked yesterday" regression diff, an empty-dataset investigation, and live-tailing a hung run — are in examples.md. The quickest path:

export APIFY_TOKEN="apify_api_..."
./apify-debug-bundle.sh abc123DEF          # → apify-debug-20260717-142530.tar.gz
tar -xzf apify-debug-*.tar.gz && tail -40 apify-debug-*/run-log.txt

See examples.md for the full walkthroughs, including reading the comparison output and interpreting a live tail.

Resources

• Full implementation walkthrough
• Worked examples
• Actor Run API
• Run Log API
• Apify Support Portal

Next Steps

For rate limit issues, see the apify-rate-limits skill.