[Desktop App] Long-running tasks abort when the selected model reaches capacity
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 45/100
Research direction
Start with the Desktop App reproduction: run a long-running task through multiple tool calls until the selected model reaches capacity, then observe task termination and continuation behavior. Compare the result with the expected queue/retry or model-switch flow, ensuring the task context remains available instead of requiring a new task.
Written by the indexing model from the issue text.
Description
What version of the Codex App are you using?
Version 26.715.21425 (build 5488)
What platform is your computer?
macOS 26.3, Apple Silicon, build 25D125
What issue are you seeing?
While a long-running task is executing, Codex stops with:
"Selected model is at capacity. Please try a different model."
The task is terminated instead of being queued, retried, or safely resumed. I lose the active task context and must abandon it and create a new task to continue working.
Evidence
- First recorded system error: task
019f70f4-26ca-7e22-95ba-15d9e3312737, stopped at2026-07-17 18:46:52 CEST. - Reproduced again in task
019f7103-15f5-7d40-af95-9d594c323e89, stopped at2026-07-17 19:35:57 CEST. - A continuation task had to be created afterward because the original task could not continue.
Steps to reproduce
- Start a long-running task in the Codex Desktop App.
- Let it perform multiple tool calls and background operations.
- Wait until the selected model becomes unavailable.
- Observe that the task terminates with the capacity error.
- Try to continue the same task.
Expected behavior
The app should queue and retry the request, or offer a model switch while preserving the task context. Temporary model capacity should not permanently terminate the task.
Related issue
#33865 reports a similar capacity error for the CLI. This report concerns the Desktop App and loss of task continuity.
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- Rust
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Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
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- Open a pull request that references the issue number.
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