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Modern web applications are moving past simple WYSIWYG interfaces toward intelligent, generative design workspaces. If you are building a document or PDF generation platform using GrapesJS, adding an AI design partner is the logical next step.
Here is a look behind the curtain at how we integrated the Vercel AI SDK, structural tool calling (Structured Outputs), and advanced UI components to create an AI sidebar capable of manipulating a drag-and-drop document canvas in real time.
Implementation Note: This documentation provides a conceptual blueprint for setting up an AI design partner layout. Real-world setups will require deeper integration, such as handling unique asset uploads, implementing custom Canvas Spot behaviours, and resolving user workspace parameters inside your API routes.
1. The Architecture: Streamed Actions & Page Scopes
To give users a contextual AI assistant, the chat interface needs to understand what the user is currently working on. We look at the GrapesJS editor state to see if a specific component is selected or if the user is targeting the entire document root.
This context, alongside the current canvas schema, is sent to a Next.js App Router API route. The backend leverages streamText from the AI SDK paired with google/gemini-3.5-flash to process complex layout requirements with a dedicated reasoning budget.
The Next.js API Route (/api/chat/route.ts)
2. Defining the Tool Canvas (GrapesJS Bridge)
The AI changes the document layout by executing tightly validated Zod schemas that map directly to GrapesJS API methods. Instead of letting the LLM guess raw HTML structures, we give it clear layout primitives like columns, rows, tables, and typography tweaks.
Accompanying Schemas & Tool Definitions
For the createTools wrapper to cleanly process intent, the AI SDK needs explicit Zod blueprints telling it exactly what properties each canvas operation expects. Below are the foundational schema shapes and their corresponding tool hooks.
1. Validation Schemas (schemas.ts)
2. Registering the Tools (tools.ts)
These configurations match the Zod definitions to human-readable instructions that the model reads to determine when to call specific actions.
Connecting AI to GrapesJS
On the client, we wrap the active GrapesJS editor instance inside a custom tool hook (createTools). When the AI SDK fires a tool call event, the corresponding local editor operation runs automatically.
3. The Client UI: Seamless Sidebar Interaction
On the frontend, we utilize highly specialized, composable AI elements to create a sleek conversational workspace next to our main canvas. Using useChat from @ai-sdk/react, we can intercept incoming stream components, manage multi-part message attachments, and automatically fire off reciprocal tool execution states. For the UI I found the chatbot example to be a great starting point, paired with the documentation of the message component itself.
4. Establishing Strict System Guardrails
The secret sauce to making an AI template builder reliable is deterministic layout rules in your system instructions. Without strong constraints, LLMs will default to loose formatting tricks that break clean print PDF generation. You'll want to take your time here as this is very crucial and you'll find this is mostly all there is to most custom AI assistants.
By limiting the AI's capability to an exact tool schema and combining it with the granular DOM control of GrapesJS, developers can provide a bulletproof, high-fidelity generative document workspace that just works.