AGPL-3.0 open-source multimodal GenAI workflow studio (canvas + Python plugin engine) that connects to DeepSeek Harness via the dsh-tongflow studio plugin; run it as a desktop app, Docker container or from source.
DSH integration
Compatible
Inferred
Safety audit
Unaudited
Last verified
2026-08-21
License
AGPL-3.0
01What can it help you accomplish?
Design multimodal generative workflows that turn text into images, talking-head videos and music videos
Finished media assets from one canvas — fused images, a lip-synced talking-head avatar video, or a complete music video (lyrics + song + characters + scenes + storyboard)
Creators and content teams who want generative-AI workflow automation across text, image, video and audio without writing code
Automate end-to-end pipeline execution instead of re-running steps manually
Workflows built once from AI-model nodes run node by node, or in one click via Execute Mode's run button
Operators and teams who need repeatable, one-click workflow automation over multimodal generation tasks
Self-host a fully local, account-free workflow automation studio
A running studio at http://localhost:3000 with plugins and credentials managed in-app; all data kept in a local /data volume (SQLite db, uploads, settings)
Individuals and teams who want their own workflow-automation studio without cloud accounts or external data storage
02How to install into DeepSeek Harness
Prerequisites
- Desktop path: macOS (Universal — Apple Silicon & Intel) or Windows x64 machine, plus a Google or WeChat account to sign in
- Self-host from source: Node with pnpm, and a Python 3.10+ interpreter on PATH
- Self-host with Docker: a Docker runtime — no Node / Python / pnpm setup required
Installation steps
- 01
Download the installer for your platform (TongFlow-mac-universal.dmg or TongFlow-win-x64.msi from GitHub Releases), install it, and open it
- 02
Sign in with Google or WeChat and start creating — the cloud studio manages plugins and execution
- 03
Self-host from source: run `pnpm install`, `pnpm plugins:install`, then `pnpm start:prod` and open http://localhost:3000
- 04
Self-host with Docker: `docker run -d -p 3000:3000 -v tongflow-data:/data -v tongflow-plugins:/plugins ghcr.io/tong-io/tongflow:latest`, then open http://localhost:3000
- 05
Self-host: install the capability plugins you need from the in-app plugin manager, and add credentials in Settings
Verify the integration
- Open http://localhost:3000 and the canvas is live (source or Docker self-host)
03DSH integration and capability boundaries
dsh-tongflow — the DeepSeek Harness studio plugin listed in the repo description and topics; TongFlow's plugin-based architecture lets every platform package its own independent plugin around the studio
Multimodal canvas workflows (add / transform / combine)
text, images, audio, video, documents, URLs and 3D models added as canvas nodes→chained generative results — images, video, music, speech, 3D — arranged with just three operations, no manual node connecting
generation nodes call external model providers through the installed pluginsBroad modality coverage (text, image, video, audio, 3D)
any supported modality or file format→generation, editing, understanding, upscaling, lip sync, motion capture, music creation, OCR, speech synthesis/recognition and more as out-of-the-box nodes
Official plugin catalog — API, router and GPU/CPU plugins
plugins for OpenAI, Gemini, DeepSeek, ByteDance, xAI, Runway, OpenRouter, Replicate, fal and Modal-based GPU/CPU nodes→per-node model pickers and capability implementations installed from the in-app plugin manager, usable immediately without restart
first plugin install needs network access to GitHubofficial GPU/CPU plugins run on Modal and need MODAL_TOKEN_ID / MODAL_TOKEN_SECRETCustom plugins via ABI contract + Python SDK
a small Python package annotated against the ABI-generated types via the tongflow Python SDK→new capability nodes that pick one or more ABI slots and supply the implementation
on first run a plugin provisions a shared Python venv and installs its requirements.txt from PyPI (needs network)
04Who is it for? When not to use it?
Good for
- Creators and content teams who want generative-AI workflow automation across text, image, video and audio without writing code
- Operators and teams who need repeatable, one-click workflow automation over multimodal generation tasks
- Individuals and teams who want their own workflow-automation studio without cloud accounts or external data storage
Not for
- macOS builds are not yet notarized with Apple, so Gatekeeper blocks the first launch; you must clear the quarantine flag once with `xattr -cr /Applications/TongFlow.app`.
- The self-host image ships no plugins — the first plugin install needs network access to GitHub, and the first run installs Python dependencies from PyPI; Modal-backed plugins additionally need a Modal token.
05Compatibility, maintenance and safety notes
- macOS builds are not yet notarized with Apple, so Gatekeeper blocks the first launch; you must clear the quarantine flag once with `xattr -cr /Applications/TongFlow.app`.
- The self-host image ships no plugins — the first plugin install needs network access to GitHub, and the first run installs Python dependencies from PyPI; Modal-backed plugins additionally need a Modal token.
- Capability nodes execute through external model providers (OpenAI / Gemini / OpenRouter-style API plugins, or Modal GPU), so real workloads need provider API keys or a Modal account — Modal's free tier is capped at $30/month of GPU compute.
AGPL-3.0 · actively maintained (latest release v0.3.4, 2026-08-19)
06Frequently asked questions
How does TongFlow connect to DeepSeek Harness?
The repository ships dsh-tongflow, the DeepSeek Harness studio plugin (listed in the repo description and topics). TongFlow's plugin-based design lets every platform package its own independent plugin around the studio; this page is based on the TongFlow studio README, which documents the desktop app, self-hosting and the plugin system.
How do I install TongFlow?
Download the macOS universal dmg or Windows msi from GitHub Releases, install and open it, then sign in with Google or WeChat. To self-host, run `docker run -d -p 3000:3000 -v tongflow-data:/data -v tongflow-plugins:/plugins ghcr.io/tong-io/tongflow:latest`, or run from source with `pnpm install`, `pnpm plugins:install`, `pnpm start:prod`.
Do I need API keys?
Keys are optional and plugin-specific: add them in the in-app Settings dialog or pass them at launch — supported keys include OPENROUTER_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY and MODAL_TOKEN_ID / MODAL_TOKEN_SECRET. Official GPU/CPU plugins run on Modal with up to $30/month of free GPU compute.
Why does macOS block the first launch?
The builds are not yet notarized with Apple, so Gatekeeper reports "TongFlow is damaged". Move the app to Applications, run `xattr -cr /Applications/TongFlow.app` once, and it opens normally. Download installers from the Releases page directly.
Where does my data live when self-hosting?
Everything writable lives in the /data volume — SQLite database, uploads and settings. Plugin credentials are stored locally by the in-app Settings editor and take effect without a restart; self-hosting gives you a fully local, account-free TongFlow.
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08Data and sources
TongFlow’s plugin-based design lets every platform package its own independent plugins, and we provide at least one offi…
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-08-21. Found an error? Submit a correction.
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