Seedance-2-API: A Python Wrapper for ByteDance's Seedance Video Generation APIs

What Seedance-2-API Does and Who It Is For

ByteDance's Seedance model family generates video from text prompts and static images. Seedance-2-API is a Python wrapper that makes these APIs callable from Python code without writing raw HTTP requests. All API calls are routed through muapi.ai, a third-party API relay service that provides access to ByteDance's Seedance models.

The library covers three model variants. Seedance 2.0 is positioned for maximum output quality. Seedance 2 Mini is a lighter variant described in the README as faster and cheaper, with pricing starting at approximately $0.073 per second as documented in the README. Seedance 2.5 adds higher resolution output (the README describes it as native 4K) and longer clip durations. Each variant has separate text-to-video and image-to-video endpoint methods in the SDK.

The primary audience is Python developers building applications that need programmatic video generation, content creators who want to automate video production pipelines, and developers integrating Seedance into AI assistant workflows via MCP. An MCP server is bundled with the package so that tools like Claude, Cursor, and other MCP-compatible assistants can call Seedance generation directly.

Seedance Model Variants and What They Generate

The README documents four capability areas across the model family: text-to-video generation from written descriptions, image-to-video animation starting from a static image, realistic human face generation, and consistent character generation across frames.

Seedance 2.0 is the baseline model for standard quality output. Seedance 2 Mini is the cost-optimized option; the README describes it as outperforming Seedance 2.0 Fast at a fraction of the cost. Seedance 2.5 is the most recent release covered by the SDK, adding native 4K output, clips up to 30 seconds, native audio generation, and support for up to 30 images, 10 videos, and 10 audio references as inputs per call.

The README includes links to separate playground interfaces for each model and mode combination on muapi.ai. These playgrounds allow testing without writing code and are referenced as the fastest way to evaluate whether a specific model's output style matches your use case.

The `CHARACTER_CONSISTENCY.md` file at the top level of the repository documents the character consistency feature but its contents were not available in the repository file listing for detailed review.

Getting an API Key and Installing the Library

Access requires a muapi.ai account and an API key. The `.env.example` file in the repository shows the single required environment variable:

MUAPI_API_KEY=your_muapi_api_key_here

Set this variable in a `.env` file in your project root before running any generation. The library uses `python-dotenv` to load it automatically.

The package has three runtime dependencies:

requests
python-dotenv
mcp[cli]

Install from the repository or via the package index using the package name `seedance-2-api` as declared in `pyproject.toml`. The package requires Python 3.7 or later, as specified in the `pyproject.toml` `requires-python` field.

The `mcp[cli]` dependency is required even if you do not use the MCP server, since it is listed as a runtime dependency rather than an optional extra. This adds the Model Context Protocol SDK to the installation. Teams that want only the HTTP wrapper without MCP can install from `requirements.txt` but will get the same three packages.

MCP Server Integration for AI Assistants

The `mcp_server.py` file at the top level implements an MCP server that exposes Seedance's generation capabilities to MCP-compatible AI assistants. When running as an MCP server, the Seedance API becomes callable by tools like Claude, Cursor, or any other assistant that supports MCP tool calls. The assistant can invoke text-to-video or image-to-video generation as part of its response to a prompt, without the user needing to write Python code.

The README lists a companion repository, `seedance-2-mcp`, described as a focused MCP server for Seedance 2 from Claude, Cursor, and other AI assistants. This suggests the MCP functionality in the main library is a lighter implementation and the companion repository is the more feature-complete option if MCP integration is the primary use case.

A separate `seedance-2.5-mcp` repository is also listed in the README for Seedance 2.5 with 720p/480p route selection. The main library and the dedicated MCP repositories appear to have overlapping functionality, and the README does not document when to prefer one over the other.

The muapi.ai Relay: What This Means for Developers

All API calls from this library go through muapi.ai rather than to ByteDance directly. This has several practical consequences.

Billing, rate limits, and terms of service are those of muapi.ai, not ByteDance. Any downtime, pricing changes, or policy changes at muapi.ai affect all applications built on this library. The README presents muapi.ai as the delivery mechanism for the API; it is not optional or swappable without modifying the library.

The README mentions that the Seedance model family has a more permissive content policy compared to other AI video models, and the library and companion repositories include Spicy variant endpoints (referenced in `Seedance-2-Spicy-API` and `Seedance-2.5-Spicy-API` in the related projects section) for relaxed-moderation use cases. These are separate repositories and separate muapi.ai endpoint tiers; the standard library endpoints use the default moderation settings.

For teams that need a direct contract with ByteDance, SLA guarantees, or the ability to audit data handling between their application and the model provider, the relay architecture of this library is a disqualifying constraint. The library provides no way to point API calls at a different endpoint.

Limitations and Version Status

The package is at version 0.1.0 as declared in `pyproject.toml` and `setup.py`. There are no GitHub releases with published changelogs. Breaking changes between commits are not tracked in any visible versioning scheme.

The README lists several companion repositories for related use cases: `seedance2-comfyui` for running Seedance inside ComfyUI, `n8n-nodes-seedance2` for n8n workflow automation, and `seedance-2-generator` as a Next.js SaaS template. The main Python library does not cover these use cases; each requires its own separate tool.

The `CHARACTER_CONSISTENCY.md` file is present but was not fully available for review. Character consistency across multiple generated clips, which the README cites as a key capability, appears to be documented there. Teams building applications that depend on character consistency across sessions should review that file before committing to the library.

The README does not document error handling, rate limit behavior, or what happens when a generation request fails or times out. These are operational details that the library consumer would need to discover through testing.

Comparison with Direct HTTP Calls to the muapi.ai API

The practical alternative to this library is calling the muapi.ai REST API directly using the `requests` library, without the wrapper. muapi.ai exposes documented endpoint URLs for each Seedance model variant; a developer who reads the API documentation can make generation calls with a few lines of Python without installing this package.

The wrapper's value over direct calls is the unified method interface across model variants, the bundled MCP server that does not need to be built from scratch, and any request/response normalization the library applies. The `seedance_api.py` source file at the top level contains the wrapper implementation, and the README describes it as the Python module the package exposes.

For a single integration project, the wrapper saves setup time. For teams who need precise control over request structure, retry logic, or who want to integrate Seedance into an existing HTTP client with custom middleware, direct calls may be simpler than working with or around the wrapper.

Editorial conclusion

Python developers who want a tested SDK for Seedance's text-to-video and image-to-video models, with an MCP server for integration into Claude, Cursor, or similar tools, will find this library covers the basics with minimal dependencies. The hard dependency on muapi.ai means all traffic and billing goes through that third-party relay, not directly to ByteDance; teams that need direct provider relationships or SLA guarantees cannot use this library as-is. The package is at version 0.1.0 and has no documented versioning policy; API breaking changes between minor versions are possible. Before using it in production, verify current muapi.ai pricing for each model variant, since the README's pricing figures were accurate at the time of writing and are subject to change.

Frequently asked questions

Official sources

1. Anil-matcha/Seedance-2-API on GitHub
2. Issues
3. License: MIT
4. Project website
5. README