/videos

Feature	Supported
Cost Tracking	✅
Logging	✅ (Full request/response logging)
Fallbacks	✅ (Between supported models)
Load Balancing	✅
Guardrails Support	✅ Content moderation and safety checks
Proxy Server Support	✅ Full proxy integration with virtual keys
Spend Management	✅ Budget tracking and rate limiting
Supported Providers	openai, azure, gemini, vertex_ai, runwayml

提示

LiteLLM follows the OpenAI Video Generation API specification

LiteLLM Python SDK Usage

Quick Start

from litellm import video_generation, video_status, video_content
import os
import time

os.environ["OPENAI_API_KEY"] = "sk-.."

# Generate video
response = video_generation(
    model="openai/sora-2",
    prompt="A cat playing with a ball of yarn in a sunny garden",
    seconds="8",
    size="720x1280"
)

print(f"Video ID: {response.id}")
print(f"Initial Status: {response.status}")

# Check status until video is ready
while True:
    status_response = video_status(
        video_id=response.id
    )
    
    print(f"Current Status: {status_response.status}")
    
    if status_response.status == "completed":
        break
    elif status_response.status == "failed":
        print("Video generation failed")
        break
    
    time.sleep(10)  # Wait 10 seconds before checking again

# Download video content when ready
video_bytes = video_content(
    video_id=response.id
)

# Save to file
with open("generated_video.mp4", "wb") as f:
    f.write(video_bytes)

Async Usage

from litellm import avideo_generation, avideo_status, avideo_content
import os, asyncio

os.environ["OPENAI_API_KEY"] = "sk-.."

async def test_async_video(): 
    response = await avideo_generation(
        model="openai/sora-2",
        prompt="A cat playing with a ball of yarn in a sunny garden",
        seconds="8",
        size="720x1280"
    )
    
    print(f"Video ID: {response.id}")
    print(f"Initial Status: {response.status}")
    
    # Check status until video is ready
    while True:
        status_response = await avideo_status(
            video_id=response.id
        )
        
        print(f"Current Status: {status_response.status}")
        
        if status_response.status == "completed":
            break
        elif status_response.status == "failed":
            print("Video generation failed")
            break
        
        await asyncio.sleep(10)  # Wait 10 seconds before checking again
    
    # Download video content when ready
    video_bytes = await avideo_content(
        video_id=response.id
    )
    
    # Save to file
    with open("generated_video.mp4", "wb") as f:
        f.write(video_bytes)

asyncio.run(test_async_video())

Video Status Checking

from litellm import video_status

status_response = video_status(
    video_id="video_1234567890"
)

print(f"Video Status: {status_response.status}")
print(f"Created At: {status_response.created_at}")
print(f"Model: {status_response.model}")

List Videos

For listing videos, you need to specify the provider since there's no video_id to decode from:

from litellm import video_list

# List videos from OpenAI
videos = video_list(custom_llm_provider="openai")

for video in videos:
    print(f"Video ID: {video['id']}")

Video Generation with Reference Image

from litellm import video_generation

# Video generation with reference image
response = video_generation(
    model="openai/sora-2",
    prompt="A cat playing with a ball of yarn in a sunny garden",
    input_reference=open("path/to/image.jpg", "rb"),  # Reference image as file object
    seconds="8",
    size="720x1280"
)

print(f"Video ID: {response.id}")

Video Remix (Video Editing)

from litellm import video_remix

# Video remix with reference image
response = video_remix(
    model="openai/sora-2",
    prompt="Make the cat jump higher",
    input_reference=open("path/to/image.jpg", "rb"),  # Reference image as file object
    seconds="8"
)

print(f"Video ID: {response.id}")

Optional Parameters

response = video_generation(
    model="openai/sora-2",
    prompt="A cat playing with a ball of yarn in a sunny garden",
    seconds="8",                    # Video duration in seconds
    size="720x1280",               # Video dimensions
    input_reference=open("path/to/image.jpg", "rb"),  # Reference image as file object
    user="user_123"                # User identifier for tracking
)

Azure Video Generation

from litellm import video_generation
import os

os.environ["AZURE_OPENAI_API_KEY"] = "your-azure-api-key"
os.environ["AZURE_OPENAI_API_BASE"] = "https://your-resource.openai.azure.com/"
os.environ["AZURE_OPENAI_API_VERSION"] = "2024-02-15-preview"

response = video_generation(
    model="azure/sora-2",
    prompt="A cat playing with a ball of yarn in a sunny garden",
    seconds="8",
    size="720x1280"
)

print(f"Video ID: {response.id}")

LiteLLM Proxy Usage

LiteLLM provides OpenAI API compatible video endpoints for complete video generation workflow:

• /videos - Generate new videos
• /videos/remix - Edit existing videos with reference images
• /videos/status - Check video generation status
• /videos/retrieval - Download completed videos

Setup

Add this to your litellm proxy config.yaml

model_list:
  - model_name: sora-2
    litellm_params:
      model: openai/sora-2
      api_key: os.environ/OPENAI_API_KEY
  - model_name: azure-sora-2
    litellm_params:
      model: azure/sora-2
      api_key: os.environ/AZURE_OPENAI_API_KEY
      api_base: os.environ/AZURE_OPENAI_API_BASE

Start litellm

litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

Test video generation request

curl --location 'http://localhost:4000/v1/videos' \
--header 'Content-Type: application/json' \
--header 'x-litellm-api-key: sk-1234' \
--data '{
    "model": "sora-2",
    "prompt": "A beautiful sunset over the ocean"
}'

Test video status request

curl --location 'http://localhost:4000/v1/videos/{video_id}' \
--header 'x-litellm-api-key: sk-1234'

Test video retrieval request

curl --location 'http://localhost:4000/v1/videos/{video_id}/content' \
--header 'x-litellm-api-key: sk-1234' \
--output video.mp4

Test video remix request

curl --location --request POST 'http://localhost:4000/v1/videos/{video_id}/remix' \
--header 'Content-Type: application/json' \
--header 'x-litellm-api-key: sk-1234' \
--data '{
    "prompt": "New remix instructions"
}'

Test video list request (requires custom_llm_provider)

# Note: video_list requires custom_llm_provider since there's no video_id to decode from
curl --location 'http://localhost:4000/v1/videos?custom_llm_provider=openai' \
--header 'x-litellm-api-key: sk-1234'

# Or using header
curl --location 'http://localhost:4000/v1/videos' \
--header 'x-litellm-api-key: sk-1234' \
--header 'custom-llm-provider: azure'

Character, Edit, and Extension Endpoints

LiteLLM proxy also supports these OpenAI-compatible video routes:

• POST /v1/videos/characters
• GET /v1/videos/characters/{character_id}
• POST /v1/videos/edits
• POST /v1/videos/extensions

Routing Behavior (target_model_names, encoded IDs, and provider overrides)

• POST /v1/videos/characters supports target_model_names like POST /v1/videos.
• When target_model_names is provided on character creation, LiteLLM encodes the returned character_id with routing metadata.
• GET /v1/videos/characters/{character_id} accepts encoded character IDs directly. LiteLLM decodes the ID internally and routes with the correct model/provider metadata.
• POST /v1/videos/edits and POST /v1/videos/extensions support both:
  • plain video.id
  • encoded video.id values returned by LiteLLM
• custom_llm_provider can be supplied using the same patterns as other proxy endpoints:
  • header: custom-llm-provider
  • query: ?custom_llm_provider=...
  • body: custom_llm_provider (or extra_body.custom_llm_provider where applicable)

Character create with target_model_names

curl --location 'http://localhost:4000/v1/videos/characters' \
--header 'Authorization: Bearer sk-1234' \
-F 'name=hero' \
-F 'target_model_names=gpt-4' \
-F 'video=@/path/to/character.mp4'

Example response (encoded id):

{
  "id": "character_...",
  "object": "character",
  "created_at": 1712697600,
  "name": "hero"
}

Get character using encoded character_id

curl --location 'http://localhost:4000/v1/videos/characters/character_...' \
--header 'Authorization: Bearer sk-1234'

Video edit with encoded video.id

curl --location 'http://localhost:4000/v1/videos/edits' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
  "prompt": "Make this brighter",
  "video": { "id": "video_..." }
}'

Video extension with provider override from extra_body

curl --location 'http://localhost:4000/v1/videos/extensions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
  "prompt": "Continue this scene",
  "seconds": "4",
  "video": { "id": "video_..." },
  "extra_body": { "custom_llm_provider": "openai" }
}'

Test Azure video generation request

curl http://localhost:4000/v1/videos \
  -H "Authorization: Bearer sk-1234" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "azure-sora-2",
    "prompt": "A cat playing with a ball of yarn in a sunny garden",
    "seconds": "8",
    "size": "720x1280"
  }'

Using OpenAI Client with LiteLLM Proxy

You can use the standard OpenAI Python client to interact with LiteLLM's video endpoints. This provides a familiar interface while leveraging LiteLLM's provider abstraction and proxy features.

Setup

First, configure your OpenAI client to point to your LiteLLM proxy:

from openai import OpenAI

# Point the OpenAI client to your LiteLLM proxy
client = OpenAI(
    api_key="sk-1234",  # Your LiteLLM proxy API key
    base_url="http://localhost:4000/v1"  # Your LiteLLM proxy URL
)

Video Generation

Generate a new video using the OpenAI client interface:

# Basic video generation
response = client.videos.create(
    model="sora-2",
    prompt="A cat playing with a ball of yarn in a sunny garden",
    seconds=8,
    size="720x1280"
)

print(f"Video ID: {response.id}")
print(f"Status: {response.status}")

Video Generation with Reference Image

Create a video using a reference image:

# Video generation with reference image
response = client.videos.create(
    model="sora-2",
    prompt="Add clouds to the video",
    seconds=4,
    input_reference=open("/path/to/your/image.jpg", "rb")
)

print(f"Video ID: {response.id}")
print(f"Status: {response.status}")

Video Status Checking

Check the status of a video generation:

# Check video status
status_response = client.videos.retrieve(
    video_id="video_6900378779308191a7359266e59b53fc01cd6bbd27a70763"
)

print(f"Status: {status_response.status}")
print(f"Progress: {status_response.progress}%")

# Poll until completion
import time

while status_response.status not in ["completed", "failed"]:
    time.sleep(10)  # Wait 10 seconds
    status_response = client.videos.retrieve(
        video_id="video_6900378779308191a7359266e59b53fc01cd6bbd27a70763"
    )
    print(f"Current status: {status_response.status}")

List Videos

Get a list of your videos:

# List all videos
videos = client.videos.list()

for video in videos.data:
    print(f"Video ID: {video.id}, Status: {video.status}")

Download Video Content

Download the completed video:

# Download video content
response = client.videos.download_content(
    video_id="video_68fa2938848c8190bb718f977503aba6092ab18d68938fed"
)

# Save the video to file
with open("generated_video.mp4", "wb") as f:
    f.write(response.content)

print("Video downloaded successfully!")

Video Remix (Editing)

Edit an existing video with new instructions:

# Remix/edit an existing video
response = client.videos.remix(
    video_id="video_68fa2574bdd88190873a8af06a370ff407094ddbc4bbb91b",
    prompt="Slow the cloud movement",
    seconds=8
)

print(f"Remix Video ID: {response.id}")
print(f"Status: {response.status}")

Complete Workflow Example

Here's a complete example showing the full video generation workflow:

from openai import OpenAI
import time

# Initialize client
client = OpenAI(
    api_key="sk-1234",
    base_url="http://localhost:4000/v1"
)

# 1. Generate video
print("Generating video...")
response = client.videos.create(
    model="sora-2",
    prompt="A serene lake with mountains in the background",
    seconds=8,
    size="1280x720"
)

video_id = response.id
print(f"Video generation started. ID: {video_id}")

# 2. Poll for completion
print("Waiting for video to complete...")
while True:
    status = client.videos.retrieve(video_id=video_id)
    print(f"Status: {status.status}")
    
    if status.status == "completed":
        print("Video generation completed!")
        break
    elif status.status == "failed":
        print("Video generation failed!")
        break
    
    time.sleep(10)

# 3. Download video
if status.status == "completed":
    print("Downloading video...")
    video_content = client.videos.download_content(video_id=video_id)
    
    with open(f"video_{video_id}.mp4", "wb") as f:
        f.write(video_content.content)
    
    print("Video saved successfully!")

# 4. Optional: Remix the video
print("Creating a remix...")
remix_response = client.videos.remix(
    video_id=video_id,
    prompt="Add gentle ripples to the lake surface"
)

print(f"Remix started. ID: {remix_response.id}")

Request/Response Format

資訊

LiteLLM follows the OpenAI Video Generation API specification.

See the official OpenAI Video Generation documentation for complete details.

Example Request

{
    "model": "openai/sora-2",
    "prompt": "A cat playing with a ball of yarn in a sunny garden",
    "seconds": "8",
    "size": "720x1280",
    "user": "user_123"
}

Request Parameters

Parameter	Type	Required	Description
model	string	Yes	The video generation model to use (e.g., "openai/sora-2")
prompt	string	Yes	Text description of the desired video
seconds	string	No	Video duration in seconds (e.g., "8", "16")
size	string	No	Video dimensions (e.g., "720x1280", "1280x720")
input_reference	file object	No	Reference image for video generation or editing (both generation and remix)
user	string	No	User identifier for tracking
video_id	string	Yes (status/retrieval)	Video ID for status checking or retrieval

Video Generation Request Example

For video generation:

{
  "model": "sora-2",
  "prompt": "A cat playing with a ball of yarn in a sunny garden",
  "seconds": "8",
  "size": "720x1280"
}

For video generation with reference image:

{
  "model": "sora-2",
  "prompt": "A cat playing with a ball of yarn in a sunny garden",
  "input_reference": open("path/to/image.jpg", "rb"),  # File object
  "seconds": "8",
  "size": "720x1280"
}

For video status check:

{
  "video_id": "video_1234567890",
  "model": "sora-2"
}

For video retrieval:

{
  "video_id": "video_1234567890",
  "model": "sora-2"
}

Response Format

The response follows OpenAI's video generation format with the following structure:

{
    "id": "video_6900378779308191a7359266e59b53fc01cd6bbd27a70763",
    "object": "video",
    "status": "queued",
    "created_at": 1761621895,
    "completed_at": null,
    "expires_at": null,
    "error": null,
    "progress": 0,
    "remixed_from_video_id": null,
    "seconds": "4",
    "size": "720x1280",
    "model": "sora-2",
    "usage": {
        "duration_seconds": 4.0
    }
}

Response Fields

Field	Type	Description
id	string	Unique identifier for the video
object	string	Always "video" for video responses
status	string	Video processing status ("queued", "processing", "completed")
created_at	integer	Unix timestamp when the video was created
model	string	The model used for video generation
size	string	Video dimensions
seconds	string	Video duration in seconds
usage	object	Token usage and duration information

Supported Providers

Provider	Link to Usage
OpenAI	Usage
Azure	Usage
Gemini	Usage
Vertex AI	Usage
RunwayML	Usage