Klingai Sdk Patterns

Kling AI SDK Patterns

Overview

Production-ready client patterns for the Kling AI API. Covers auto-refreshing JWT, typed request/response models, exponential backoff polling, async batch submission, and structured error handling.

Python Client Wrapper

import jwt
import time
import os
import requests
from dataclasses import dataclass, field
from typing import Optional

@dataclass
class KlingConfig:
    access_key: str = field(default_factory=lambda: os.environ["KLING_ACCESS_KEY"])
    secret_key: str = field(default_factory=lambda: os.environ["KLING_SECRET_KEY"])
    base_url: str = "https://api.klingai.com/v1"
    token_buffer_sec: int = 300
    poll_interval_sec: int = 10
    max_poll_attempts: int = 120  # 20 minutes max
    timeout_sec: int = 30

class KlingClient:
    """Production Kling AI client with auto-refreshing JWT."""

    def __init__(self, config: Optional[KlingConfig] = None):
        self.config = config or KlingConfig()
        self._token = None
        self._token_expires = 0

    @property
    def _headers(self) -> dict:
        now = int(time.time())
        if now >= (self._token_expires - self.config.token_buffer_sec):
            payload = {"iss": self.config.access_key, "exp": now + 1800, "nbf": now - 5}
            self._token = jwt.encode(payload, self.config.secret_key,
                                     algorithm="HS256",
                                     headers={"alg": "HS256", "typ": "JWT"})
            self._token_expires = now + 1800
        return {"Authorization": f"Bearer {self._token}",
                "Content-Type": "application/json"}

    def _post(self, path: str, body: dict) -> dict:
        r = requests.post(f"{self.config.base_url}{path}",
                          headers=self._headers, json=body,
                          timeout=self.config.timeout_sec)
        r.raise_for_status()
        return r.json()

    def _get(self, path: str) -> dict:
        r = requests.get(f"{self.config.base_url}{path}",
                         headers=self._headers,
                         timeout=self.config.timeout_sec)
        r.raise_for_status()
        return r.json()

    def _poll_task(self, endpoint: str, task_id: str) -> dict:
        """Poll with exponential backoff until task completes."""
        interval = self.config.poll_interval_sec
        for attempt in range(self.config.max_poll_attempts):
            time.sleep(interval)
            result = self._get(f"{endpoint}/{task_id}")
            status = result["data"]["task_status"]
            if status == "succeed":
                return result["data"]["task_result"]
            elif status == "failed":
                raise KlingGenerationError(result["data"].get("task_status_msg", "Unknown"))
            # Increase interval up to 30s max
            interval = min(interval * 1.2, 30)
        raise KlingTimeoutError(f"Task {task_id} did not complete in time")

    # --- Public API ---

    def text_to_video(self, prompt: str, **kwargs) -> dict:
        body = {"model_name": kwargs.get("model", "kling-v2-master"),
                "prompt": prompt,
                "duration": str(kwargs.get("duration", 5)),
                "aspect_ratio": kwargs.get("aspect_ratio", "16:9"),
                "mode": kwargs.get("mode", "standard")}
        if kwargs.get("negative_prompt"):
            body["negative_prompt"] = kwargs["negative_prompt"]
        if kwargs.get("cfg_scale") is not None:
            body["cfg_scale"] = kwargs["cfg_scale"]
        if kwargs.get("callback_url"):
            body["callback_url"] = kwargs["callback_url"]

        task = self._post("/videos/text2video", body)
        task_id = task["data"]["task_id"]
        if kwargs.get("wait", True):
            return self._poll_task("/videos/text2video", task_id)
        return {"task_id": task_id}

    def image_to_video(self, image_url: str, **kwargs) -> dict:
        body = {"model_name": kwargs.get("model", "kling-v2-1"),
                "image": image_url,
                "duration": str(kwargs.get("duration", 5)),
                "mode": kwargs.get("mode", "standard")}
        if kwargs.get("prompt"):
            body["prompt"] = kwargs["prompt"]

        task = self._post("/videos/image2video", body)
        task_id = task["data"]["task_id"]
        if kwargs.get("wait", True):
            return self._poll_task("/videos/image2video", task_id)
        return {"task_id": task_id}

    def extend_video(self, task_id: str, **kwargs) -> dict:
        body = {"task_id": task_id,
                "prompt": kwargs.get("prompt", ""),
                "duration": str(kwargs.get("duration", 5)),
                "mode": kwargs.get("mode", "standard")}
        result = self._post("/videos/video-extend", body)
        new_task_id = result["data"]["task_id"]
        if kwargs.get("wait", True):
            return self._poll_task("/videos/video-extend", new_task_id)
        return {"task_id": new_task_id}


class KlingError(Exception):
    pass

class KlingGenerationError(KlingError):
    pass

class KlingTimeoutError(KlingError):
    pass

Usage

client = KlingClient()

# Synchronous (waits for result)
result = client.text_to_video(
    "A cat playing piano in a jazz club",
    model="kling-v2-6",
    mode="professional",
    duration=5,
)
print(result["videos"][0]["url"])

# Fire-and-forget (returns task_id)
task = client.text_to_video("Ocean waves at sunset", wait=False)
print(f"Submitted: {task['task_id']}")

Node.js Client

import jwt from "jsonwebtoken";

class KlingClient {
  #token = null;
  #tokenExp = 0;

  constructor(ak = process.env.KLING_ACCESS_KEY, sk = process.env.KLING_SECRET_KEY) {
    this.ak = ak;
    this.sk = sk;
    this.base = "https://api.klingai.com/v1";
  }

  #getHeaders() {
    const now = Math.floor(Date.now() / 1000);
    if (now >= this.#tokenExp - 300) {
      this.#token = jwt.sign(
        { iss: this.ak, exp: now + 1800, nbf: now - 5 },
        this.sk, { algorithm: "HS256", header: { typ: "JWT" } }
      );
      this.#tokenExp = now + 1800;
    }
    return { Authorization: `Bearer ${this.#token}`, "Content-Type": "application/json" };
  }

  async textToVideo(prompt, opts = {}) {
    const res = await fetch(`${this.base}/videos/text2video`, {
      method: "POST",
      headers: this.#getHeaders(),
      body: JSON.stringify({
        model_name: opts.model ?? "kling-v2-master",
        prompt,
        duration: String(opts.duration ?? 5),
        aspect_ratio: opts.aspectRatio ?? "16:9",
        mode: opts.mode ?? "standard",
      }),
    });
    const { data } = await res.json();
    return opts.wait === false ? data : this.#poll("/videos/text2video", data.task_id);
  }

  async #poll(endpoint, taskId, interval = 10000) {
    for (let i = 0; i < 120; i++) {
      await new Promise((r) => setTimeout(r, interval));
      const res = await fetch(`${this.base}${endpoint}/${taskId}`, {
        headers: this.#getHeaders(),
      });
      const { data } = await res.json();
      if (data.task_status === "succeed") return data.task_result;
      if (data.task_status === "failed") throw new Error(data.task_status_msg);
      interval = Math.min(interval * 1.2, 30000);
    }
    throw new Error(`Timeout: task ${taskId}`);
  }
}

Retry Decorator

import functools

def retry_on_transient(max_retries=3, backoff_base=2):
    """Retry on 429 (rate limit) and 5xx (server) errors."""
    def decorator(fn):
        @functools.wraps(fn)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries + 1):
                try:
                    return fn(*args, **kwargs)
                except requests.HTTPError as e:
                    if e.response.status_code in (429, 500, 502, 503) and attempt < max_retries:
                        wait = backoff_base ** attempt
                        time.sleep(wait)
                        continue
                    raise
        return wrapper
    return decorator

# Apply to client methods
KlingClient._post = retry_on_transient()(KlingClient._post)

Resources

• Kling AI Developer Portal
• API Overview

Source: jeremylongshore/claude-code-plugins-plus-skills → skills/.curated/klingai-sdk-patterns/SKILL.md

Also appears in: jeremylongshore/claude-code-plugins-plus-skills/plugins/saas-packs/klingai-pack/skills/klingai-sdk-patterns/SKILL.md