Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/klingai-rate-limits/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill klingai-rate-limits. Copyright stays with the author (MIT).

Kling AI Rate Limits

Overview

Kling AI enforces rate limits per API key. When exceeded, the API returns 429 Too Many Requests. This skill covers detection, backoff strategies, request queuing, and concurrent job management.

Rate Limit Tiers

Exponential Backoff with Jitter

import time, random, requests

def exponential_backoff(attempt: int, base: float = 1.0, max_wait: float = 60.0) -> float:
    """Calculate wait time with jitter to avoid thundering herd."""
    wait = min(base * (2 ** attempt), max_wait)
    jitter = random.uniform(0, wait * 0.5)
    return wait + jitter

def request_with_retry(method, url, headers, json=None, max_retries=5):
    for attempt in range(max_retries + 1):
        response = method(url, headers=headers, json=json, timeout=30)

        if response.status_code == 429:
            if attempt == max_retries:
                raise RuntimeError("Rate limit: max retries exceeded")
            wait = exponential_backoff(attempt)
            print(f"429 rate limited. Waiting {wait:.1f}s (attempt {attempt + 1})")
            time.sleep(wait)
            continue

        if response.status_code >= 500:
            if attempt == max_retries:
                response.raise_for_status()
            time.sleep(exponential_backoff(attempt, base=2.0))
            continue

        response.raise_for_status()
        return response

    raise RuntimeError("Unreachable")

Concurrent Task Limiter (asyncio)

import asyncio

class TaskLimiter:
    """Limit concurrent Kling AI tasks to stay within API tier."""

    def __init__(self, max_concurrent: int = 3):
        self._semaphore = asyncio.Semaphore(max_concurrent)
        self._active = 0

    async def submit(self, coro):
        async with self._semaphore:
            self._active += 1
            try:
                return await coro
            finally:
                self._active -= 1

    @property
    def active_count(self) -> int:
        return self._active

# Usage
limiter = TaskLimiter(max_concurrent=3)
tasks = [limiter.submit(generate_video(p)) for p in prompts]
results = await asyncio.gather(*tasks, return_exceptions=True)

Rate Limit Monitor

class RateLimitMonitor:
    """Track API call frequency and warn before hitting limits."""

    def __init__(self, max_per_minute: int = 30):
        self.max_per_minute = max_per_minute
        self._calls = []

    def record_call(self):
        now = time.time()
        self._calls = [t for t in self._calls if now - t < 60]
        self._calls.append(now)

    @property
    def usage_pct(self) -> float:
        now = time.time()
        recent = sum(1 for t in self._calls if now - t < 60)
        return (recent / self.max_per_minute) * 100

    def wait_if_needed(self):
        if self.usage_pct > 80 and self._calls:
            wait = 60 - (time.time() - self._calls[0])
            if wait > 0:
                print(f"Throttling: waiting {wait:.1f}s ({self.usage_pct:.0f}% of limit)")
                time.sleep(wait)

Request Queue Pattern

from collections import deque
import threading

class RequestQueue:
    """FIFO queue with rate-limit-aware dispatch."""

    def __init__(self, client, max_per_minute: int = 30):
        self.client = client
        self.interval = 60.0 / max_per_minute
        self._queue = deque()

    def enqueue(self, endpoint: str, body: dict, callback=None):
        self._queue.append((endpoint, body, callback))

    def process_all(self):
        while self._queue:
            endpoint, body, callback = self._queue.popleft()
            try:
                result = self.client._post(endpoint, body)
                if callback:
                    callback(result, error=None)
            except Exception as e:
                if callback:
                    callback(None, error=e)
            time.sleep(self.interval)

Error Reference

Prerequisites

• An approved sandbox workload, synthetic or rights-cleared brief, current quota baseline, budget cap, draft-only destination, and a named operator for pause and rollback.

Instructions

1. Exercise limits with bounded draft-only canaries; reject unapproved sources, publishing destinations, or requests that exceed the approved credit budget.
2. Use idempotency keys and backoff, recording aggregate status and credit consumption rather than prompt content or asset URLs.
3. Stop queued work on quota, policy, rights, or retention drift; cancel tasks and restore the prior rate configuration before retrying.
4. Keep a redacted receipt only and delete test artifacts when the approved retention window ends.

Output

Produce a rate-limit receipt with environment, request budget, aggregate response/error counts, credit use, draft-only/policy outcome, pause or rollback action, owner approval, and cleanup proof. Exclude prompts, assets, and credentials.

Error Handling

Examples

env=ci-sandbox; requests=3; budget=30-credits; backoff=enabled; policy=pass; destination=draft-only; cleanup=verified is an acceptable test receipt.

Resources

• API Reference
• Developer Portal