Bubble Lab is an open-source workflow automation platform designed for developers who demand full control, type safety, and transparency. Unlike low-code tools that lock users into proprietary formats, Bubble Lab generates clean, production-ready TypeScript code from visual workflows. It enables developers to describe complex automation tasks in natural language via its AI assistant, Pearl, and then immediately see the resulting code—complete with type definitions, error handling, and performance metrics. This approach bridges the gap between visual drag-and-drop interfaces and full-code flexibility, making it ideal for engineers building AI-powered workflows that need to be integrated into existing codebases or CI/CD pipelines.
What You Get
- Prompt-to-Workflow Generation - Describe your automation goal in natural language, and Pearl (Bubble Lab’s AI assistant) generates a complete TypeScript workflow using composable ‘Bubbles’—reusable integration nodes. This eliminates manual coding for common tasks like API calls or data transformations.
- Full Observability - Every workflow execution provides detailed logs, token usage statistics for AI models (e.g., Google Gemini), cost tracking, memory usage, and execution duration. This enables debugging, optimization, and billing transparency for AI-powered flows.
- Export as Production TypeScript - All workflows are compiled into clean, human-readable TypeScript code that you own and can deploy anywhere. No vendor lock-in—exported code runs independently in your environment.
- n8n and Other Workflow Import - Seamlessly migrate existing workflows from platforms like n8n into Bubble Lab’s TypeScript format, preserving logic while gaining type safety and observability.
- Type-Safe Bubbles - Each integration (e.g., RedditScrapeTool, AIAgentBubble) is a TypeScript class with strict interfaces. This ensures compile-time safety and IDE autocompletion while chaining operations via .action().
Common Use Cases
- Building AI-powered data aggregators - Create a workflow that scrapes Reddit, summarizes top posts with Gemini, and pushes results to a database—fully exportable as 50 lines of TypeScript.
- Automating multi-step API workflows - Chain HTTP calls, data transformations, and AI analysis (e.g., extract insights from emails → summarize → send Slack alert) with full execution tracing and cost tracking.
- Problem: Manual workflow maintenance → Solution: Exportable code - Teams tired of maintaining brittle JSON-based automations in n8n or Zapier can migrate to Bubble Lab, gaining type safety and the ability to version control and test their workflows like regular code.
- DevOps teams managing AI agents - Developers building LLM-powered automation pipelines need to monitor token usage and performance. Bubble Lab’s built-in metrics make it ideal for production AI workflows that must be audited and optimized.
Under The Hood
BubbleLabAI is a visual flow-based development platform that enables users to build and deploy complex workflows through an intuitive interface. It combines AI-powered automation with a modular bubble architecture, allowing for composable and extensible workflow components.
Architecture
The system adopts a modular monolithic structure with well-defined workspaces and applications that interact through shared packages and interfaces.
- Clear separation of frontend, API, and core logic components with distinct responsibilities
- Modular organization supports reusability and maintainability across different parts of the system
- Component-based design promotes scalability and easier debugging through encapsulated logic
Tech Stack
The project leverages a modern TypeScript ecosystem with React for frontend and Hono for backend, emphasizing developer experience and type safety.
- Built primarily with TypeScript and React, using Vite for fast development and Hono as a lightweight backend framework
- Extensive use of TanStack Router, Clerk, and Monaco Editor for routing, authentication, and code editing
- Employs Bun for runtime execution and testing, alongside Tailwind CSS for styling and Vite for builds
Code Quality
The codebase reflects a moderate level of quality with strong testing practices and component organization, though some inconsistencies and debt remain.
- Comprehensive test suite includes unit, integration, and end-to-end scenarios with good coverage of logic paths
- Error handling is present throughout the codebase, though some propagation patterns are inconsistent or underused
- Consistent naming and component structure support readability, although deviations exist in style and implementation
- Technical debt is evident in duplicated logic and over-reliance on environment variables, signaling areas for refactoring
What Makes It Unique
BubbleLabAI distinguishes itself through its innovative use of visual flow editors and AI-native integration within a modular system.
- Modular bubble components support embedded AI logic and service triggers, enabling flexible workflow composition
- Real-time execution logs and debugging are integrated into a unified visualization layer for enhanced developer insight
- Native AI agent interaction and contextual prompting capabilities are embedded directly into the flow builder interface
- Strong type safety is enforced through TypeScript and Zod schemas while maintaining visual flexibility in design