[aw] Smoke Pydantic AI hit engine rate limit (HTTP 429)
I maintainer di solito rispondono entro 1 giorno
Nessuno ha ancora preso questa issue.
Valutazione
- Difficoltà
- 4/5
- Tempo stimato
- 3-5 giorni
- Idoneità per principianti
- 35/100
Direzione di ricerca
Start with .github/workflows/smoke-pydantic.md and the linked workflow run, focusing on the failing Execute Pydantic AI CLI step and its HTTP 429 diagnostics. Then read debug.md and the agentic-workflows skill referenced in the issue; done means identifying the cause and fixing the workflow failure so the run completes.
Scritto dal modello di indicizzazione a partire dal testo della issue.
Descrizione
Workflow Failure
Workflow: Smoke Pydantic AI
Branch: main
Run: https://github.com/github/gh-aw/actions/runs/38325220774
[!WARNING]
Task Could Not Be Completed: The workflow recorded areport_incompletesignal: the task was not completed or completion could not be confirmed.
Error details:
Failure classification: unknown
Driver exit code: 1. The engine driver exited before a terminal safe output was recorded.
Last tool call status: not recorded
Failed tool calls observed: 0
Token budget state: unknown (no exhaustion signal recorded)
Step budget state (LLM invocations): unknown (no exhaustion signal recorded)
Safe-output tools: available at gateway snapshot (registered 7 tools)
Safe-output CLI calls observed: unknown (not recorded)
Safe-output MCP calls observed: 0
Safe-output persistence: not recorded
Agent finished without emitting a terminal safe output; task completion could not be confirmed.
Last engine error type: unknown
Retry attempts observed: 0
A comment or other safe output does not confirm that the requested task was completed. Only inferred runtime status and error codes are reported; agent text is excluded.
Failure Diagnostics
Agent job: agent
Agent job conclusion: failure
Failing step: Execute Pydantic AI CLI
The runtime did not record a specific cause. Check the linked run for failure details.
Only runtime metadata is shown; agent-generated text is excluded.
Action Required
Assign this issue to an agent to debug and fix the issue.
Debug with any coding agent
Use this prompt with any coding agent (GitHub Copilot, Claude, Gemini, etc.):
Debug the agentic workflow failure using https://raw.githubusercontent.com/github/gh-aw/main/debug.md
The failed workflow run is at https://github.com/github/gh-aw/actions/runs/38325220774
Manually invoke the agent
Debug this workflow failure using your favorite Agent CLI and the agentic-workflows prompt.
- Start your agent
- Load the
agentic-workflowsskill from.github/skills/agentic-workflows/SKILL.mdor https://github.com/github/gh-aw/blob/main/.github/skills/agentic-workflows/SKILL.md - Type
debug the agentic workflow smoke-pydantic failure in https://github.com/github/gh-aw/actions/runs/38325220774
[!TIP]
Stop reporting this workflow as a failure
To stop a workflow from creating failure issues, set
report-failure-as-issue: falsein its frontmatter:safe-outputs: report-failure-as-issue: false
Generated from Smoke Pydantic AI · pydantic-ai · 0.225 AIC · ◷
- expires on Oct 13, 2026, 12:06 PM UTC
- Lingua principale
- Go
- Stelle
- 5.4k
- Fork
- 584
- Merge medio
- 3h 38m
- PR unite (30g)
- 826
Preparare l'ambiente
Avvia il container di sviluppo del progetto nel browser, con il tuo account GitHub.
- Include un Dockerfile o un file Docker Compose
- Nessun modello di pull request
- Leggi la guida per i contributori
Come iniziare
- Leggi tutta la issue e poi la guida ai contributi del progetto.
- Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
- Fai un fork del repository e lavora su un branch.
- Apri una pull request che faccia riferimento al numero della issue.
Altre issue di github/gh-aw
-
ai-inspected
Difficoltà 2/5 1-3 ore Idoneità per principianti 76/100
I maintainer di solito rispondono entro 1 giorno
-
automation cookie
Difficoltà 1/5 Meno di un'ora Idoneità per principianti 68/100
github/gh-aw#68990 · 2 commenti ·
I maintainer di solito rispondono entro 1 giorno
-
automated conformance safe-outputs
Difficoltà 2/5 1-3 ore Idoneità per principianti 68/100
I maintainer di solito rispondono entro 1 giorno
-
goroutineresourceleak (81st linter): paren_unwrap_gap across lock/cancel/channel detection, day oneApertacookie sergo
Difficoltà 2/5 1-3 ore Idoneità per principianti 72/100
github/gh-aw#68591 · 1 commento ·
I maintainer di solito rispondono entro 1 giorno
-
agentic-workflows cascade-suspected
Difficoltà 4/5 3-5 giorni Idoneità per principianti 22/100
I maintainer di solito rispondono entro 1 giorno
Tutte le issue di github/gh-aw
Issue simili
-
Difficoltà 2/5 1-3 ore Idoneità per principianti 68/100
I maintainer di solito rispondono entro 1 giorno
-
documentation hacktoberfest priority: low skill: good first issue status: ready for dev
Difficoltà 2/5 Mezza giornata Idoneità per principianti 85/100
hyperledger-labs/fabric-smart-client#2142 ·
I maintainer di solito rispondono entro 1 giorno
-
Difficoltà 2/5 1-3 ore Idoneità per principianti 78/100
numberly/vault-db-injector#107 ·
I maintainer di solito rispondono entro 1 giorno
-
area/import kind/bug
Difficoltà 2/5 1-3 ore Idoneità per principianti 62/100
I maintainer di solito rispondono entro 1 giorno
-
[DOC] Wrong sg_egress_ports range in the Terraform Databricks Guide for AWS Hub & Spoke with Network FirewallForse già presa @akhil9tiet l’ha presa oggi. Apertadocs
Difficoltà 1/5 Meno di un'ora Idoneità per principianti 72/100
databricks/terraform-provider-databricks#6080 · 1 commento ·
I maintainer di solito rispondono entro 3 giorni