Klingai common errors

Kling AI Common Errors

Overview

Complete error reference for the Kling AI API. Covers HTTP status codes, task-level failures, JWT issues, and generation-specific problems with tested solutions.

HTTP Error Codes

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

Task-Level Failures

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

JWT Authentication Errors

Problem: 401 on every request

# 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"})

Problem: Token works then fails after 30 min

# 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

Rate Limit Handling

import 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")

Diagnostic Checklist

When a generation fails, check in order:

1. Auth valid? — Test with a simple GET request first
2. Credits available? — Check balance in developer console
3. Model valid? — Verify model_name matches catalog exactly
4. Parameters valid? — duration must be "5" or "10" (string, not int)
5. Prompt clean? — Remove special characters, keep under 2500 chars
6. Image accessible? — For I2V, verify image URL is publicly accessible
7. Feature exclusivity? — image_tail, dynamic_masks, and camera_control are mutually exclusive

Debug Logging

import 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

Resources

• API Reference
• Content Policy
• Developer Portal