GolemUI

What it does

GolemUI is an open source library for generating forms dynamically from JSON definitions. A single JSON document describes both the data structure and the UI layout. The library renders forms in React, Angular, Vue, and Lit. It includes 28 headless components and supports conditional logic, validation rules, and named form states within the JSON definition itself.

Who it is for

The library targets developers building applications that need dynamic, data-driven forms. It is particularly useful for teams working with AI agents and large language models, since the JSON format is machine-readable and GolemUI provides an MCP server for AI-assisted form generation. Developers prioritizing content security policy compliance may find the library's strict CSP support relevant.

Pricing

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How it stands out

GolemUI uses a single JSON document for both data and UI, whereas some competitors require two separate files (a schema and a UI schema) to express the same form. The library enforces strict Content Security Policy by default with no configuration needed, while several competing libraries (JSON Forms, RJSF, ngx-formly) rely on eval and become inert or fail to validate under CSP constraints like script-src 'self'. GolemUI includes built-in machine-readable documentation designed for LLM integration, with references at standardized URLs. The library provides an MCP server to validate form definitions programmatically.

What a founder should check

A developer considering a competing product should verify how many documents are required to define a form with conditional logic and custom validation. Some libraries claim to need only one but actually require a second schema file for any conditional rule, which adds authoring complexity and sync overhead.

Second, test the chosen library under strict Content Security Policy headers (script-src 'self'). Many form engines rely on JavaScript eval for schema compilation or expression evaluation, which silently fails under CSP. This is a hard blocking issue in security-sensitive environments.

Third, check whether the library supports AI-assisted form generation natively. If LLM integration is a planned feature, verify whether the form definition format is documented in a way that existing AI agents can consume without custom training.