CommandCodeAI / CommandCodeAI/command-code
Background shell tasks return empty logs; provider errors (too_many_images, timeouts) and subagent failures stall long coding sessions
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- 主要語言
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- 星號
- 4k
- 分支
- 350
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描述
Long coding session crippled by tooling failures: empty background-task output, provider errors, lost subagent reports
Environment
- Windows 11, Command Code CLI (npm, v1.39.x line at the time)
- Large Android/Gradle project; long-running builds (1–5 min per gradle invocation)
- One long single-session refactor task (~15 files edited + verification builds + CI screenshot step)
Summary
A large but well-scoped coding task stretched for hours almost entirely because of harness/infra failures, not task complexity. Four classes of failure, each reproducible within the session:
1. Background shell commands return empty output logs (most damaging)
shell_commandwithrun_in_background=truerunninggradlew ... > file.log 2>&1or piped through| powershell ...produced 0-byte output logs for 20–60+ minutes while gradle had actually failed in 40 seconds.- Exit status was never surfaced; the log stayed empty; the only way to learn the real failure was to manually read
~/.gradle/daemon/9.5.0/daemon-*.out.log(the daemon's own log containede: file:///...kt:NN Unresolved reference...compile errors all along). - Piping task output through PowerShell (
| powershell -Command "$input | Select-Object -Last N") also silently lost everything. - Cost: this failure mode alone consumed the majority of the session, with multiple redundant 5–10-minute polling loops staring at empty files.
- Expected: background tasks should surface real exit codes/stderr (or at minimum, the streamed log should contain the process's output as it appears).
2. Repeated mid-session provider errors, each requiring a manual "continue"
Error: 200 Failed to process successful responseError: 500 Cannot connect to API: Connect Timeout Error (172.65.90.20-23:443)Error: 400 Invalid_request_error ... [too_many_images] GLM requests accept at most 8 inline PNG/JPEG/WEBP/GIF ...— a session that reviews screenshots (dev workflow!) becomes unsendable until the user manually compacts. The model can't fix this itself.
3. Subagent runs errored and lost their reports
- One
generalsubagent returned[sub-agent stopped early: the run errored]after 20 minutes, mid-task (had made real edits already). - A separate audit subagent died entirely with its report lost; had to be relaunched from scratch.
- Expected: partial output preserved on subagent error, or automatic retry.
4. Tool-schema friction
search_toolsrepeatedly returnedtodo_writeschema, but calling it kept failing/looping for several turns before it finally worked.
Trace IDs (from the error banners in one session)
- 6b481a97ce2ad552cb4802dc72d0b0ef
- 39a179d53c81ee36fea47ff7f116d066
- 52c2bcc5f22d1a3815bb7b6a4ce81356
- ca6c03357b892c7ca6e9042e6ecb6367
- 2cc56255ce5d8e9aab41e69325ec4e81
Ask
- Surface real exit status + stderr of background and piped shell tasks; don't let empty logs masquerade as "still running."
- Handle the image budget proactively (auto-compact or drop stale images before the provider hard-fails the whole conversation).
- Preserve subagent partial reports when a run errors.
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- 送出 Pull Request,並在描述裡引用這個 Issue 編號。
研究方向
從 shell_command 的 CLI 入口開始,使用 run_in_background=true,重現 Gradle 和 PowerShell 的情況,並將擷取的輸出與 daemon 記錄和結束狀態進行比較。接著追蹤 provider 的 image-budget 錯誤和子代理錯誤處理,在可用時使用列出的 trace IDs。完成的標準是:背景失敗會暴露狀態和 stderr,provider 限制得到處理,發生錯誤的子代理會保留部分報告。
由索引模型根據 Issue 內容生成。
評估
- 技術堆疊
- ai-infra-agents, cli
- 領域
- ai, cli, tooling
- Issue 類型
- 缺陷
- 難度
- 5/5
- 預估耗時
- 一週以上
- 活躍度
- 活躍
- 描述清晰度
- 需要釐清
- 新手友好度
- 25/100