---

name: klingai-text-to-video description: 'Generate videos from text prompts with Kling AI. Use when creating videos from descriptions,

learning prompt techniques, or building T2V pipelines. Trigger with phrases like ''kling ai text to video'',

''klingai prompt'', ''generate video from text'', ''text2video kling''.

' allowed-tools: Read, Write, Edit, Bash(npm:*), Grep version: 1.18.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• kling-ai
• text-to-video
• video-generation compatibility: Designed for Claude Code

---

Kling AI Text-to-Video

Overview

Generate videos from text prompts using the /v1/videos/text2video endpoint. Supports models v1 through v2.6, standard/professional modes, camera control, negative prompts, and native audio (v2.6+).

Endpoint: POST https://api.klingai.com/v1/videos/text2video

Request Parameters

Parameter	Type	Required	Description
model_name	string	Yes	Model version (see model catalog)
prompt	string	Yes	Video description, max 2500 chars
negative_prompt	string	No	What to exclude from generation
duration	string	Yes	"5" or "10" seconds
aspect_ratio	string	No	"16:9" (default), "9:16", "1:1", etc.
mode	string	No	"standard" (default) or "professional"
cfg_scale	float	No	Prompt adherence (0.0-1.0, default 0.5)
camera_control	object	No	Camera movement config
callback_url	string	No	Webhook URL for completion notification

Complete Example — 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"}

# Create text-to-video task
response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "prompt": "Aerial drone shot of a coral reef at golden hour, "
              "tropical fish swimming through crystal clear water, "
              "sun rays penetrating the surface, cinematic 4K",
    "negative_prompt": "blurry, low quality, distorted, watermark",
    "duration": "5",
    "aspect_ratio": "16:9",
    "mode": "professional",
    "cfg_scale": 0.5,
})

task = response.json()
task_id = task["data"]["task_id"]

# Poll for completion
while True:
    time.sleep(15)
    result = requests.get(
        f"{BASE}/videos/text2video/{task_id}", headers=get_headers()
    ).json()

    status = result["data"]["task_status"]
    if status == "succeed":
        video = result["data"]["task_result"]["videos"][0]
        print(f"Video URL: {video['url']}")
        print(f"Duration: {video['duration']}s")
        break
    elif status == "failed":
        raise RuntimeError(result["data"]["task_status_msg"])
    # else: submitted/processing — keep polling

With Camera Control

# Camera movement types: pan, tilt, zoom, roll
response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "prompt": "A medieval castle on a cliff at sunrise, fog in the valley",
    "duration": "5",
    "mode": "standard",
    "camera_control": {
        "type": "simple",
        "config": {
            "horizontal": 5,    # pan right (negative = left), range -10 to 10
            "vertical": 0,      # tilt (negative = down, positive = up)
            "zoom": 3,          # zoom in (positive) or out (negative)
            "roll": 0,          # rotation
            "pan": 0,           # dolly left/right
            "tilt": -2,         # dolly up/down
        }
    },
})

Rule: Only one non-zero field in config for type: "simple".

With Native Audio (v2.6 only)

response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "prompt": "A jazz band performing in a dimly lit club, saxophone solo, "
              "audience clapping, warm amber lighting",
    "duration": "10",
    "mode": "professional",
    "motion_has_audio": True,  # generates synchronized audio
})

Prompt Engineering Tips

Technique	Example
Scene + action + style	"A samurai walking through cherry blossoms, cinematic slow motion"
Lighting cues	"golden hour", "neon-lit", "overcast diffused light"
Camera language	"close-up", "wide establishing shot", "tracking shot"
Negative prompt	"blurry, watermark, text overlay, distorted faces"
Material/texture	"brushed steel", "hand-painted watercolor", "photorealistic"

Cost Reference

Duration	Standard	Professional
5 seconds	10 credits	35 credits
10 seconds	20 credits	70 credits

Error Handling

Error	Cause	Fix
400 invalid prompt	Empty or >2500 chars	Check prompt length
400 invalid model	Unsupported model_name	Use valid model ID from catalog
402 insufficient credits	Not enough credits	Top up account
task_status: failed	Content policy violation or complexity	Simplify prompt, remove restricted content

Prerequisites

• An approved brief, rights-cleared or synthetic reference material, an authorized workspace and budget, a content-policy review, and a named owner for publication and removal.

Instructions

1. Create a sandbox draft from an approved brief; do not include private individuals, protected material, or unverified claims in prompts or uploads.
2. Verify the requested duration, style, destination, credit budget, content-policy status, and draft-only setting before submission.
3. Review one watermarked canary render for policy, rights, and quality; halt on a policy or attribution concern and delete the draft rather than publishing it.
4. Promote only after owner approval and retain a redacted production receipt; remove temporary assets at the approved retention boundary.

Output

Produce a render receipt with brief ID, approved source classification, model/mode, duration, credit estimate, policy and rights-review outcome, draft destination, approver, retention/removal reference, and task ID. Exclude prompt text, identities, and credentials.

Examples

brief=synthetic-product-demo; source=rights-cleared; mode=standard; duration=5s; policy=pass; destination=draft-only; approval=pending; cleanup=24h is a safe canary request.

Resources

• Text-to-Video API
• Camera Control Guide
• Content Guidelines