What is VideoClaw?
VideoClaw is the AI storyboard and video generation workspace inside Synclip. It gives you a node-based canvas where every element of a production — project outline, character assets, scene assets, episode scripts, shot storyboards, and rendered video clips — lives as an interconnected node you can inspect and regenerate at any time.
The name is a deliberate nod to OpenClaw, the open-source storyboard pipeline that popularised node-based film pre-production. VideoClaw extends that idea with live AI generation: instead of manually drawing boards or hunting for stock images, every node can be filled in seconds by the underlying model stack.
Under the hood, a multi-agent system handles the creative pipeline: a Router agent classifies your chat input, a Planner agent structures episodes and shots, a Writer agent drafts scripts and prompts, and a Director agent reviews output for consistency before anything reaches the canvas.
- Node-based production canvas — every asset is a draggable, editable node
- AI script analysis — extract characters, scenes, and episode structure automatically
- Storyboard image generation via Nano Banana (fast, cinematic, cost-efficient)
- Video generation: Sora 2 / Veo 3.1 Fast / Grok Video — pick per shot
- Multi-agent pipeline: Router → Planner → Writer → Director
- Per-user AI token quota — usage tracked transparently on the energy bar
How VideoClaw Works — From Script to Screen
The full pipeline runs in a single persistent canvas. Each stage builds on the last.
Stage 1 · Project Setup & Script Ingestion
Create a new project and paste or write your script into the Script Source node. VideoClaw's analysis engine reads the raw text and extracts a structured project outline: title, logline, core conflict, characters, scenes, and episode list. All extracted assets are seeded as nodes on the canvas automatically.
- Paste any plain-text script, treatment, or synopsis
- AI extracts characters (name, role, archetype, appearance tags)
- AI extracts locations and scene descriptions
- Episode list is generated from act structure or explicit scene headers
Stage 2 · Character & Scene Asset Generation
Each character node gets an auto-generated visual profile. Nano Banana renders a reference portrait from the appearance tags extracted in Stage 1. Scene nodes receive a concept art image. These visuals act as the ground truth for all subsequent storyboard frames — keeping faces and locations consistent across the entire production.
Stage 3 · Episode Script & Shot Planning
Select an episode node and ask the AI to generate the full scene-by-scene script. The Writer agent drafts dialogue, action lines, and camera direction. A shot list is then generated from the script — each shot becomes a Shot Script node containing location, time of day, action summary, dialogue excerpt, and a storyboard image slot.
Stage 4 · Storyboard Image Generation
The Planner agent writes a Nano Banana-optimised prompt for every shot — styled to match the project's global visual style or the episode-level override. One click triggers batch image generation across all shots. You can also regenerate individual frames without affecting the rest of the board.
Stage 5 · Video Rendering
Each storyboard frame is the starting point for video generation. Connect the image to a VideoGen node, choose your model (Sora 2 for cinematic quality, Veo 3.1 Fast for speed and first/last frame control, Grok Video for 3-ratio cinematic output), write or import the shot prompt, and generate. All video clips are stored in the canvas and can be previewed inline.
Key Features at a Glance
Everything you need to run a full AI pre-production and production pipeline:
| Feature | Detail |
|---|---|
| Script analysis | Auto-extract characters, scenes, logline, and episode structure from raw text |
| Character portraits | Nano Banana generates reference portraits from appearance tags |
| Scene concept art | AI generates location reference images for each scene node |
| Shot scripting | AI writes scene-by-scene scripts with dialogue and camera direction |
| Shot prompt generation | Auto-generate Nano Banana image prompts and video prompts per shot |
| Batch storyboard generation | Generate all shot images in one operation |
| Multi-model video generation | Sora 2 / Veo 3.1 Fast / Grok Video — selectable per node |
| Visual style inheritance | Set a global style on the outline node; override per episode or per shot |
| AI chat interface | Natural language control — describe what you want and the agent routes the action |
| AI token energy bar | Real-time usage display with monthly quota by subscription tier |
Storyboard Images — Nano Banana Under the Hood
All storyboard image generation in VideoClaw runs on Nano Banana, Synclip's fast image model. The Writer agent automatically translates each shot's script description into a Nano Banana-optimised prompt — applying the project's visual style, the character's appearance tags, and the scene's location reference. You get production-ready boards in seconds, not hours.
- Cinematic composition prompts generated automatically per shot
- Project visual style applied globally (e.g. "cyberpunk neon", "ink wash", "film noir")
- Character appearance tags injected into every frame they appear in
- One-click batch generation across all shots in an episode
- Individual frame regeneration without disrupting adjacent shots
Tip: Set the Visual Style field on the Project Outline node before generating any images. This string is prepended to every Nano Banana prompt in the project, ensuring a consistent look across all 100+ frames of a full episode.
Video Generation — Three Models, One Canvas
VideoClaw exposes all three of Synclip's video models directly in the canvas. Each VideoGen node lets you choose the model that fits the shot:
| Model | Resolution | Max Duration | Best For |
|---|---|---|---|
| Sora 2 | 720p | 15 s | Cinematic quality, character-focused shots, artistic sequences |
| Veo 3.1 Fast | 720p | 25 s | Fast iteration, first/last frame control, multi-reference image shots |
| Grok Video | 720p | 15 s | 3-ratio output (3:2 / 2:3 / 1:1), auto-thumbnail, linear pricing |
Each model has different strengths — Veo 3.1 Fast is the most cost-efficient for long clips; Sora 2 delivers the highest cinematic quality; Grok Video adds auto-thumbnail generation and flexible aspect ratios. You can mix models freely across shots in the same episode.
Recommended Workflow for a Short Film
A typical 5-minute short film (3 acts, 9 scenes, ~30 shots) can be fully storyboarded and video-generated in under 2 hours using this sequence:
Step 1 · Write or paste your treatment (15 min)
Prepare a 500–2000 word treatment or full script. Include character names, scene headers (INT./EXT.), and action lines. The more structure you give the parser, the cleaner the extraction.
Step 2 · Run script analysis and review extracted assets (10 min)
Click "Parse Content" on the Script Source node. Review the extracted characters and scenes. Add missing details (appearance tags, personality traits) by editing the asset nodes directly or asking the AI to enrich them.
Step 3 · Generate character portraits and scene concept art (20 min)
Select all character nodes and trigger portrait generation. Do the same for scene nodes. This gives the storyboard a consistent visual reference for every subsequent frame.
Step 4 · Generate episode scripts and shot lists (15 min)
For each episode node, click "Generate Script". The Writer agent drafts the full scene-by-scene script and auto-creates shot nodes. Review the shot list and adjust camera direction or action beats as needed.
Step 5 · Batch generate all storyboard images (20 min)
Ask the AI to generate all storyboard images for the episode. The system writes Nano Banana prompts per shot and runs batch generation. Regenerate any frames that need adjustment.
Step 6 · Render video for key shots (40 min)
Select the shots that need video (hero moments, transitions, action beats). Connect each to a VideoGen node, choose your model, and generate. Use Veo 3.1 Fast for speed, Sora 2 for the most important cinematic moments.
VideoClaw vs Manual Pre-Production vs Other AI Tools
How VideoClaw compares for a 30-shot episode:
| Task | VideoClaw | Manual (Figma/Notion) | Generic AI (ChatGPT + Midjourney) |
|---|---|---|---|
| Script to shot list | Automatic (< 2 min) | 2–4 hours | 30–60 min (manual prompting) |
| Character reference portraits | Batch, 1 click | Manual illustration or stock | Manual per character |
| Storyboard frame generation | Batch, style-consistent | Manual sketch or stock | Manual, no style inheritance |
| Video rendering | Inline, 3 model choices | Requires separate video tool | Requires separate tool |
| Style consistency | Global style + per-shot override | Manual | None (prompt-by-prompt) |
| Asset organisation | Node canvas, auto-linked | Manual folder structure | None |
FAQ
What is the relationship between VideoClaw and OpenClaw?
OpenClaw is an open-source storyboard pipeline project that popularised node-based film pre-production. VideoClaw is Synclip's implementation of a similar concept — extended with live AI generation (script analysis, image generation, video rendering) and a built-in multi-agent chat interface. VideoClaw is a hosted product; OpenClaw is self-hosted.
Do I need to write a full screenplay to use VideoClaw?
No. You can start with a short treatment (a few paragraphs describing your story), a synopsis, or even a bullet-point outline. The AI parser is designed to extract useful structure from whatever you give it, and you can fill in missing details interactively through the chat interface.
How is AI token usage tracked?
Every LLM call inside VideoClaw (script analysis, script generation, prompt writing, chat routing) is tracked against your monthly AI token quota. The energy bar in the header shows your current usage in real time. Quotas reset monthly and scale with your subscription tier.
Can I use my own images or existing storyboard frames?
Yes. Every image slot in the canvas (storyboard frame, character portrait, scene concept) accepts an uploaded image. You can mix AI-generated and manually uploaded assets freely. Uploaded images can also be used as reference inputs for video generation.
Which video model should I use for storyboard-to-video?
For most shots, Veo 3.1 Fast is the best starting point — fast, cost-efficient, and supports using the storyboard frame as a reference image. Use Sora 2 for hero cinematic moments where quality matters most. Use Grok Video when you need a specific aspect ratio (e.g. 1:1 for social) or want auto-generated thumbnails.