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To diagnose a GitHub Actions failure, open the workflow run, locate the failed job and step in its graph, then inspect that step’s log. “GitHub Actions log failures” can mean either a workflow run failed or the available logs do not explain why; the steps below cover both, including unexpected skips, reruns and when to consider centralized monitoring.
Find the failed job and step
- In the repository, open Actions, choose the workflow, and select the run you need to diagnose.
- Use the run summary or visualization graph to find the failed job. GitHub says every workflow run generates a graph showing progress; the graph also displays job states and dependencies. Select a job to open its logs. See GitHub Docs: Using the visualization graph.
- Expand the failed step and read its output first. GitHub’s run-log view shows job and step status, and you can search logs, download the archive or use GitHub CLI. You need repository read access to view logs. See GitHub Docs: Using workflow run logs.
Search, download or retrieve logs with the CLI
Search the visible log
Search within the job log for an error message, failing command, or the name of a file or test. The web interface searches only expanded steps, so expand relevant steps before searching.
Download the archive
Download the log archive when you need files beyond the visible step output, want to inspect system-level records, or need to share the evidence with a teammate. A partially rerun workflow’s archive contains only the jobs from that rerun; collect logs from earlier attempts as well if you need the full history.
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These documented commands print all run logs, a particular job’s logs, or only failed-step logs for a job:
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gh run view RUN_ID --log
gh run view --job JOB_ID --log
gh run view --job JOB_ID --log-failedReplace RUN_ID and JOB_ID with the relevant identifiers. You can pipe the output into a text-search utility to find a specific message. See GitHub’s CLI and log instructions.
When normal output is not enough, enable debug logging
Step-level debug output
Set the ACTIONS_STEP_DEBUG secret or variable to true to enable step debug logging. This adds detail to workflow-step output.
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Runner diagnostics
Set ACTIONS_RUNNER_DEBUG to true to enable runner diagnostic logging. GitHub says the downloaded diagnostic archive then includes runner-process and worker-process log files. See GitHub Docs: Enabling debug logging.
Anyone who can run the workflow can enable debug logging for a rerun. Creating or changing repository, organization or environment secrets and variables requires the corresponding write or administrator permissions described by GitHub. Debug output can contain more operational detail; follow your repository’s usual rules for handling and sharing logs and diagnostic archives.
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Rerun with a specific diagnostic question
A rerun can help establish whether a failure is reproducible, and it can be paired with debug logging. GitHub CLI can rerun all jobs, failed jobs or a specific job; add --debug for diagnostic logs.
gh run rerun RUN_ID --debug
gh run rerun RUN_ID --failed --debug
gh run rerun --job JOB_ID --debugChoose the rerun scope that matches what you want to test. A rerun uses the original event’s commit SHA and ref, so it does not automatically test a newer commit. Compare runs against the same code and event when checking whether a failure is reproducible. See GitHub Docs: Re-running workflows and jobs.
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Diagnose jobs that were unexpectedly skipped or run
When a job’s execution does not match expectations, inspect the job-level condition evaluation in the downloaded log archive. Open JOB-NAME/system.txt for the job, then examine:
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These records explain job-level if conditions. For step-level condition details, enable debug logging instead. See GitHub Docs: Viewing job condition expression logs.
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Not every red check is a test failure. If the step output does not point to application code, GitHub’s workflow troubleshooting guide covers other avenues, including billing errors, runner and networking problems, trigger behavior and workflow metrics.
Use APIs or centralized monitoring when the scope demands it
Automate inspection with the REST API
For scripted investigation, GitHub’s workflow-jobs REST API provides job and step status data, along with an endpoint for downloading a job’s logs. The log endpoint redirects to a plain-text log file. Consult GitHub’s workflow-jobs REST API documentation for the endpoints and access requirements.
Decide whether cross-pipeline visibility is worth adding
GitHub’s built-in graph, logs, archive and CLI are often sufficient for diagnosing an individual run. A centralized service becomes relevant when a team needs to investigate trends or see activity across pipelines, repositories or CI providers. Datadog documents CI Visibility for GitHub Actions, including workflow execution views and optional forwarding of job logs to Log Management. Datadog says forwarded logs are billed separately and logs larger than 1 GiB are truncated; see its GitHub Actions setup documentation.
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Before adopting a third-party view, weigh whether it answers a real cross-team monitoring need, and review permissions, log handling, retention and service-specific billing. Centralization adds another place where pipeline data is processed; it is not a prerequisite for reading a failed job’s logs.
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