GitHub Copilot SDK Stream Finalization

Discovered: 2026-03-20

Context

Applies to plugins/pipes/github-copilot-sdk/github_copilot_sdk.py when streaming assistant output, handling pending_embeds, and waiting for session.idle.

Finding

Two non-obvious issues can make the pipe feel permanently stuck even when useful work already finished:

If the main queue.get() wait uses the full user-configured TIMEOUT (for example 300s), watchdog logic, "still working" status updates, and synthetic finalization checks only wake up at that same coarse interval.
If pending_embeds are flushed only in the session.idle branch, any timeout/error/missing-idle path can lose already-prepared embeds even though file publishing itself succeeded.

Solution / Pattern

Keep the inactivity limit controlled by TIMEOUT, but poll the local stream queue on a short fixed cadence (for example max 5s) so watchdogs and fallback finalization stay responsive.
Track assistant.turn_end; if session.idle does not arrive shortly afterward, synthesize finalization instead of waiting for the full inactivity timeout.
Flush pending_embeds exactly once via a shared helper that can run from both normal idle finalization and error/timeout finalization paths.
For streamed text/reasoning, use conservative overlap trimming: only strip an overlapping prefix when the incoming chunk still contains new suffix content. Do not drop fully repeated chunks blindly, or legitimate repeated text can be corrupted.

Gotchas

RichUI embed success and streamed-text success are separate paths; a file can be published correctly while chat output still hangs or duplicates.
If assistant.reasoning_delta is streamed, the later complete assistant.reasoning event must be suppressed just like assistant.message, or the thinking block can duplicate.

🛠️ Update 2026-03-21

Fixed Stream Duplication: Fixed text stream overlays (e.g., 🎉 删 🎉 删除成功) when resuming conversation session. Strictly applied _dedupe_stream_chunk(delta, "message_stream_tail") inside assistant.message_delta event handler to prevent concurrent history re-play or multiple stream delivery bug overlays, solving previous gaps in the deployment pipeline.

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