klingai-job-monitoring

What this skill does

# Kling AI Job Monitoring

## Overview

Every Kling AI generation returns a `task_id`. This skill covers polling strategies, batch tracking, timeout handling, and callback-based monitoring for the `/v1/videos/text2video`, `/v1/videos/image2video`, and `/v1/videos/video-extend` endpoints.

## Task Lifecycle

| Status | Meaning | Typical Duration |
|--------|---------|-----------------|
| `submitted` | Queued for processing | 0-30s |
| `processing` | Generation in progress | 30-120s (standard), 60-300s (professional) |
| `succeed` | Complete, video URL available | Terminal |
| `failed` | Generation failed | Terminal |

## Polling a Single Task

```python
import jwt, time, os, requests

BASE = "https://api.klingai.com/v1"

def get_headers():
    ak, sk = os.environ["KLING_ACCESS_KEY"], os.environ["KLING_SECRET_KEY"]
    token = jwt.encode(
        {"iss": ak, "exp": int(time.time()) + 1800, "nbf": int(time.time()) - 5},
        sk, algorithm="HS256", headers={"alg": "HS256", "typ": "JWT"}
    )
    return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

def poll_task(endpoint: str, task_id: str, interval: int = 10, timeout: int = 600):
    """Poll with adaptive interval and timeout."""
    start = time.monotonic()
    attempts = 0
    while time.monotonic() - start < timeout:
        time.sleep(interval)
        attempts += 1
        r = requests.get(f"{BASE}{endpoint}/{task_id}", headers=get_headers(), timeout=30)
        data = r.json()["data"]
        status = data["task_status"]
        elapsed = int(time.monotonic() - start)
        print(f"[{elapsed}s] Poll #{attempts}: {status}")

        if status == "succeed":
            return data["task_result"]
        elif status == "failed":
            raise RuntimeError(f"Task failed: {data.get('task_status_msg', 'unknown')}")

        if attempts > 5:
            interval = min(interval * 1.2, 30)
    raise TimeoutError(f"Task {task_id} timed out after {timeout}s")
```

## Batch Job Tracker

```python
from dataclasses import dataclass, field
from datetime import datetime
from typing import Optional

@dataclass
class TrackedTask:
    task_id: str
    endpoint: str
    prompt: str
    status: str = "submitted"
    created_at: float = field(default_factory=time.time)
    result_url: Optional[str] = None
    error_msg: Optional[str] = None

class BatchTracker:
    def __init__(self):
        self.tasks: dict[str, TrackedTask] = {}

    def add(self, task_id, endpoint, prompt):
        self.tasks[task_id] = TrackedTask(task_id=task_id, endpoint=endpoint, prompt=prompt)

    def update_all(self):
        active = [t for t in self.tasks.values() if t.status in ("submitted", "processing")]
        for task in active:
            try:
                r = requests.get(
                    f"{BASE}{task.endpoint}/{task.task_id}",
                    headers=get_headers(), timeout=30
                ).json()
                data = r["data"]
                task.status = data["task_status"]
                if task.status == "succeed":
                    task.result_url = data["task_result"]["videos"][0]["url"]
                elif task.status == "failed":
                    task.error_msg = data.get("task_status_msg")
            except Exception as e:
                print(f"Error polling {task.task_id}: {e}")

    def print_report(self):
        by_status = {}
        for t in self.tasks.values():
            by_status.setdefault(t.status, 0)
            by_status[t.status] += 1
        active = sum(v for k, v in by_status.items() if k in ("submitted", "processing"))
        print(f"\n=== Batch: {len(self.tasks)} tasks, {active} active ===")
        for status, count in sorted(by_status.items()):
            print(f"  {status}: {count}")
```

## Stuck Task Detection

```python
def detect_stuck(tracker: BatchTracker, threshold_sec: int = 600):
    """Flag tasks processing longer than threshold."""
    now = time.time()
    stuck = []
    for t in tracker.tasks.values():
        if t.status in ("submitted", "processing"):
            elapsed = now - t.created_at
            if elapsed > threshold_sec:
                stuck.append((t.task_id, int(elapsed)))
    if stuck:
        print(f"WARNING: {len(stuck)} stuck tasks:")
        for tid, secs in stuck:
            print(f"  {tid}: {secs}s")
    return stuck
```

## Batch Monitor Loop

```python
tracker = BatchTracker()

# Submit batch
for prompt in prompts:
    r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
        "model_name": "kling-v2-master", "prompt": prompt, "duration": "5"
    }).json()
    tracker.add(r["data"]["task_id"], "/videos/text2video", prompt)

# Monitor until all complete
while any(t.status in ("submitted", "processing") for t in tracker.tasks.values()):
    time.sleep(15)
    tracker.update_all()
    tracker.print_report()
    detect_stuck(tracker)
```

## Resources

- [Task Query API](https://app.klingai.com/global/dev/document-api/apiReference/model/textToVideo)
- [Developer Portal](https://app.klingai.com/global/dev)