Anmol Baranwal writes a comprehensive guide to AG-UI, an open protocol that standardizes how agents communicate with user-facing applications by replacing custom streaming layers with a unified event stream. The protocol connects agentic backends to frontends across any framework and surface, defining 31 event types grouped into 8 categories covering runs, text, tool calls, reasoning, state, activity, subagents, and custom events. It operates alongside MCP (agent-to-tool) and A2A (agent-to-agent) as the user-facing layer, and its 1.0 specification is stable, backwards compatible, and backed by major tech companies.
- Google, Microsoft, Amazon, and Oracle have adopted the protocol
- SDKs for TypeScript, Python, and .NET are generated from a shared JSON Schema, with community SDKs for Go, Ruby, Rust, Dart, Kotlin, Java, and C++
- Transport-agnostic design supports HTTP SSE, WebSockets, and Protobuf binary format
- AG-UI 1.0 adds subagent support, multimodal tool results, human-in-the-loop interrupts, and token usage tracking
- CopilotKit Intelligence uses captured interaction data with Automatic Learning to extract insights and generate Skills that improve agent behavior over time
- Google, Microsoft, Amazon, and Oracle have adopted the protocol
- SDKs for TypeScript, Python, and .NET are generated from a shared JSON Schema, with community SDKs for Go, Ruby, Rust, Dart, Kotlin, Java, and C++
- Transport-agnostic design supports HTTP SSE, WebSockets, and Protobuf binary format
- AG-UI 1.0 adds subagent support, multimodal tool results, human-in-the-loop interrupts, and token usage tracking
- CopilotKit Intelligence uses captured interaction data with Automatic Learning to extract insights and generate Skills that improve agent behavior over time
json-render is a generative UI framework that allows developers to create dynamic, personalized user interfaces from prompts using AI. It focuses on reliability and predictability by utilizing predefined components and actions. The framework streamlines UI development through a three-step process: defining a catalog of components, letting AI generate JSON based on prompts, and then instantly rendering the UI as the JSON streams in.
Key features include guardrails to constrain AI output, streaming for progressive rendering, support for both React and React Native, data binding capabilities, and the ability to export generated UIs as standalone React code. This enables rapid prototyping and the creation of unique interfaces with minimal runtime dependencies.
Key features include guardrails to constrain AI output, streaming for progressive rendering, support for both React and React Native, data binding capabilities, and the ability to export generated UIs as standalone React code. This enables rapid prototyping and the creation of unique interfaces with minimal runtime dependencies.
Vercel has open‑sourced json‑render, a framework it calls "Generative UI" that lets AI models produce structured user interfaces from natural language prompts. The library uses Zod schemas to define a catalog of allowed components and actions, and an LLM generates a JSON specification that the renderer maps to real implementations. json‑render supports React, Vue, Svelte, Solid, React Native and more, and ships with 36 pre‑built shadcn/ui components. The project has already garnered 13,000 stars and 200 releases, and has sparked discussion on the future of constraint‑based UI generation and the role of AI in the rendering layer.
This article discusses the concept of Generative UI, explaining how it builds upon Generative AI to create more interactive and user-friendly experiences. It outlines the technical considerations for implementing Generative UI, including linking applications with LLMs, data sources, and tools like React Server Components and vector databases.
