Incident History

This incident has been resolved. Thank you for your patience and understanding as we addressed this issue. A detailed root cause analysis will be shared as soon as it is available.

This incident has been resolved. Thank you for your patience and understanding as we addressed this issue. A detailed root cause analysis will be shared as soon as it is available.

This incident has been resolved. Thank you for your patience and understanding as we addressed this issue. A detailed root cause analysis will be shared as soon as it is available.

Everything is operating normally.

This incident has been resolved. Thank you for your patience and understanding as we addressed this issue. A detailed root cause analysis will be shared as soon as it is available.

Between June 1, 2026, 23:00 UTC and June 4, 2026 04:11 UTC, customers experienced delays in Dependabot scheduled version updates. Pull request creation for version updates was delayed, with delays increasing over time and reaching up to two days. Approximately 1.5 million repositories with active Dependabot version update configurations were affected. Dependabot security updates were not affected. The primary cause was changes to an internal platform service that routes requests for Dependabot and other services. We mitigated the incident by deploying a fix that enables batch enqueuing of update jobs, which significantly increased processing throughput. Once the backlog was drained, Dependabot returned to normal processing times. To reduce the risk of recurrence, we are working on tuning batch size and concurrency limits for Dependabot update job processing. We are also adding monitoring for job processing lag to enable earlier detection and faster mitigation of similar issues.

On June 2, 2026, between 21:54 UTC and June 3, 2026 06:45 UTC, the Spark service was degraded and users were unable to store or retrieve data for their Spark apps in one of our hosting regions. Users could still make changes to their app configuration during this time. The error rate peaked at 25% of affected requests to the service. Impact was limited to users whose requests were served through a single affected region; 43 users experienced errors during this window.The root cause was a configuration that referenced a service component by a fixed address rather than a dynamic service endpoint. When the component was replaced, requests could no longer reach the fixed address and began to fail. We resolved the incident by updating the configuration to use a our standard service endpoints that are resilient to component replacement. Recovery time was extended because replacing the component required overrides to a temporary deployment safeguard.We are working to add validation that prevents fixed infrastructure addresses from being used in application configuration outside of test environments and to improve our monitoring to reduce our time to detect.

This incident has been resolved. Thank you for your patience and understanding as we addressed this issue. A detailed root cause analysis will be shared as soon as it is available.

On May 28, 2026, between 19:07 UTC and 19:16 UTC, multiple GitHub services experienced elevated error rates. This was due to a change that was partially deployed to an authentication service, causing errors for dependent services including the web experience, REST API, Git operations, and GitHub Actions. At peak impact, 10% of GitHub Actions runs failed to queue or encountered errors while downloading actions. We mitigated the incident by rolling back the change.

We are expanding test coverage and improving our deployment validation process to prevent recurrence of this issue in the future.

On May 28th, 2026, between approximately 18:27 and 20:41 UTC, the GitHub Copilot service was degraded due to an issue with the Responses API of an upstream provider affecting the GPT-5.2, GPT-5.3-Codex, GPT-5.4, and GPT-5.5 models. Requests routed to these models via the Responses API returned elevated error rates, which also affected Copilot coding agent and Copilot code review. No other models were impacted. We mitigated the incident by shifting traffic away from the affected models while the upstream provider deployed a fix. GitHub is working to improve automated failover for the affected models and strengthen monitoring to prevent similar incidents in the future.