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Get Started Free →Diagnose and fix common Kling AI API errors. Use when troubleshooting failed video generation or API issues. Trigger with phrases like 'kling ai error', 'klingai not working', 'fix klingai', 'klingai failed'.
.claude/skills/jeremylongshore-klingai-common-errors/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 34% | 0% |
| case-22 | ✓→✓ | = Same ✓ | 45% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 44% | 0% |
| case-21 | ✓→✓ | = Same ✓ | 51% | 0% |
Complete error reference for the Kling AI API. Covers HTTP status codes, task-level failures, JWT issues, and generation-specific problems with tested solutions.
| Code | Error | Cause | Solution | |------|-------|-------|----------| | 400 | Bad Request | Invalid parameters, malformed JSON | Validate all required fields; check model_name is valid | | 401 | Unauthorized | Invalid/expired JWT token | Regenerate JWT; verify AK/SK; check exp claim | | 402 | Payment Required | Insufficient credits | Top up API resource pack or subscription | | 403 | Forbidden | Content policy violation or API disabled | Review prompt against content policy; enable API access | | 404 | Not Found | Invalid task_id or wrong endpoint | Verify task_id; check endpoint path spelling | | 429 | Too Many Requests | Rate limit exceeded | Implement exponential backoff (see pattern below) | | 500 | Internal Server Error | Kling platform issue | Retry after 30s; if persistent, check status page | | 502 | Bad Gateway | Upstream service unavailable | Retry with backoff; typically transient | | 503 | Service Unavailable | System maintenance | Wait and retry; check announcements |
When HTTP returns 200 but task_status is "failed":
| task_status_msg | Cause | Solution | |-------------------|-------|----------| | Content policy violation | Prompt contains restricted content | Remove violent, adult, or copyrighted references | | Image quality too low | Source image is blurry or too small | Use image >= 300x300px, clear and sharp | | Prompt too complex | Too many scene elements | Simplify to 1-2 subjects, clear action | | Generation timeout | Internal processing exceeded limit | Retry; reduce duration from 10s to 5s | | Invalid image format | Unsupported file type | Use JPG, PNG, or WebP | | Mask dimension mismatch | Mask size differs from source | Ensure mask matches source image dimensions exactly |
401 on every requestpython# WRONG — missing headers parameter token = jwt.encode(payload, sk, algorithm="HS256") # CORRECT — include explicit headers token = jwt.encode(payload, sk, algorithm="HS256", headers={"alg": "HS256", "typ": "JWT"})
python# WRONG — token generated once at import time TOKEN = generate_token() # CORRECT — refresh before expiry class TokenManager: def __init__(self, ak, sk): self.ak, self.sk = ak, sk self._token = None self._exp = 0 @property def token(self): if time.time() >= self._exp - 300: # 5 min buffer payload = {"iss": self.ak, "exp": int(time.time()) + 1800, "nbf": int(time.time()) - 5} self._token = jwt.encode(payload, self.sk, algorithm="HS256", headers={"alg": "HS256", "typ": "JWT"}) self._exp = int(time.time()) + 1800 return self._token
pythonimport time import requests def request_with_backoff(method, url, headers, json=None, max_retries=5): """Retry with exponential backoff on 429 and 5xx errors.""" for attempt in range(max_retries): response = method(url, headers=headers, json=json) if response.status_code == 429: retry_after = int(response.headers.get("Retry-After", 2 ** attempt)) print(f"Rate limited. Retrying in {retry_after}s...") time.sleep(retry_after) continue elif response.status_code >= 500: wait = 2 ** attempt print(f"Server error {response.status_code}. Retrying in {wait}s...") time.sleep(wait) continue response.raise_for_status() return response raise RuntimeError(f"Max retries ({max_retries}) exceeded")
When a generation fails, check in order:
model_name matches catalog exactlyduration must be "5" or "10" (string, not int)image_tail, dynamic_masks, and camera_control are mutually exclusivepythonimport logging logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("kling") def debug_request(method, url, headers, json=None): """Log request/response for debugging.""" logger.debug(f"→ {method.__name__.upper()} {url}") logger.debug(f"→ Body: {json}") r = method(url, headers=headers, json=json) logger.debug(f"← Status: {r.status_code}") logger.debug(f"← Body: {r.text[:500]}") return r
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-22 | pass→pass | 20,065 | 12,886 | -36% | 1 | 1 | 0% | 2,748 | 3,980 | +45% | 0 | 0 | — |
case-03 | pass→pass | 21,741 | 19,104 | -12% | 1 | 1 | 0% | 2,809 | 4,036 | +44% | 0 | 0 | — |
case-21 | pass→pass | 16,757 | 20,437 | +22% | 1 | 1 | 0% | 2,817 | 4,267 | +51% | 0 | 0 | — |
case-08 | fail→fail | 17,117 | 19,545 | +14% | 1 | 1 | 0% | 3,203 | 4,190 | +31% | 0 | 0 | — |
case-01 | fail→pass | 43,372 | 33,323 | -23% | 1 | 1 | 0% | 6,941 | 5,812 | -16% | 0 | 0 | — |
case-02 | fail→fail | 31,194 | 16,470 | -47% | 1 | 1 | 0% | 4,153 | 4,867 | +17% | 0 | 0 | — |
case-04 | pass→pass | 26,997 | 15,568 | -42% | 1 | 1 | 0% | 2,587 | 3,450 | +33% | 0 | 0 | — |
case-05 | pass→pass | 15,759 | 10,882 | -31% | 1 | 1 | 0% | 1,814 | 2,365 | +30% | 0 | 0 | — |
case-06 | fail→pass | 20,088 | 19,944 | -1% | 1 | 1 | 0% | 2,723 | 3,640 | +34% | 0 | 0 | — |
case-07 | pass→pass | 9,654 | 6,884 | -29% | 1 | 1 | 0% | 1,743 | 2,808 | +61% | 0 | 0 | — |
case-09 | pass→pass | 17,449 | 18,600 | +7% | 1 | 1 | 0% | 2,009 | 3,418 | +70% | 0 | 0 | — |
case-10 | pass→pass | 17,211 | 16,870 | -2% | 1 | 1 | 0% | 1,972 | 3,301 | +67% | 0 | 0 | — |
case-11 | pass→pass | 8,156 | 11,625 | +43% | 1 | 1 | 0% | 1,328 | 2,682 | +102% | 0 | 0 | — |
case-12 | pass→pass | 14,983 | 9,594 | -36% | 1 | 1 | 0% | 1,915 | 2,133 | +11% | 0 | 0 | — |
case-13 | pass→pass | 16,313 | 15,042 | -8% | 1 | 1 | 0% | 3,184 | 3,432 | +8% | 0 | 0 | — |
case-14 | pass→pass | 16,290 | 12,934 | -21% | 1 | 1 | 0% | 2,880 | 4,045 | +40% | 0 | 0 | — |
case-15 | pass→pass | 11,847 | 8,139 | -31% | 1 | 1 | 0% | 1,993 | 2,028 | +2% | 0 | 0 | — |
case-16 | pass→pass | 15,627 | 16,198 | +4% | 1 | 1 | 0% | 2,468 | 3,383 | +37% | 0 | 0 | — |
case-17 | pass→pass | 15,011 | 10,678 | -29% | 1 | 1 | 0% | 2,435 | 3,282 | +35% | 0 | 0 | — |
case-18 | pass→pass | 16,772 | 15,901 | -5% | 1 | 1 | 0% | 1,923 | 3,390 | +76% | 0 | 0 | — |
case-19 | pass→pass | 18,893 | 15,434 | -18% | 1 | 1 | 0% | 3,575 | 3,428 | -4% | 0 | 0 | — |
case-20 | pass→pass | 19,320 | 27,539 | +43% | 1 | 1 | 0% | 2,886 | 4,581 | +59% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.