Business Software for OpenUI

The Agent Development Kit (ADK) is a flexible, open-source framework for building and deploying AI agents. It is tightly integrated with Google’s ecosystem, including Gemini models, and supports popular large language models (LLMs). ADK simplifies the development of both simple and complex AI agents, providing a structured environment for building dynamic workflows and multi-agent systems. With built-in tools for orchestration, deployment, and evaluation, ADK helps developers create scalable, modular AI solutions that can be easily deployed on platforms like Gemini Enterprise Agent Platform or Cloud Run.

LangChain is a powerful, composable framework designed for building, running, and managing applications powered by large language models (LLMs). It offers an array of tools for creating context-aware, reasoning applications, allowing businesses to leverage their own data and APIs to enhance functionality. LangChain’s suite includes LangGraph for orchestrating agent-driven workflows, and LangSmith for agent observability and performance management. Whether you're building prototypes or scaling full applications, LangChain offers the flexibility and tools needed to optimize the LLM lifecycle, with seamless integrations and fault-tolerant scalability.

Gain precision and control with LangGraph to build agents that reliably handle complex tasks. Build and scale agentic applications with LangGraph Platform. LangGraph's flexible framework supports diverse control flows – single agent, multi-agent, hierarchical, sequential – and robustly handles realistic, complex scenarios. Ensure reliability with easy-to-add moderation and quality loops that prevent agents from veering off course. Use LangGraph Platform to templatize your cognitive architecture so that tools, prompts, and models are easily configurable with LangGraph Platform Assistants. With built-in statefulness, LangGraph agents seamlessly collaborate with humans by writing drafts for review and awaiting approval before acting. Easily inspect the agent’s actions and "time-travel" to roll back and take a different action to correct course.

Mastra is a powerful TypeScript framework for building intelligent AI agents that can execute tasks, access knowledge bases, and maintain memory persistently within workflows. This framework simplifies the process of creating and deploying AI-powered agents by leveraging TypeScript’s capabilities to streamline development. With features like customizable agent instructions, memory, and task orchestration, Mastra provides developers with the tools to build and scale AI agents for various applications, from personal assistants to specialized domain experts.

The Vercel AI SDK is a free, open source TypeScript toolkit from the creators of Next.js that gives developers unified, high-level primitives to build AI-powered features quickly across any model provider by changing a single line of code. It abstracts common complexities like streaming responses, multi-turn tool execution, error handling and recovery, and model switching while remaining framework-agnostic so builders can go from idea to working application in minutes. With a unified provider API, developers can generate typed objects, compose generative UIs, and deliver instant, streamed AI responses without reinventing plumbing, and the SDK includes documentation, cookbooks, a playground, and community-driven extensibility to accelerate development. It handles the hard parts under the hood while exposing enough control to get under the hood when needed, making integration with multiple LLMs seamless.

A2UI is a declarative UI protocol designed to enable AI agents to generate rich, interactive user interfaces that render natively across web, mobile, and desktop environments without executing arbitrary code. Instead of relying on text-only interactions or unsafe approaches like sending HTML or JavaScript, A2UI allows agents to communicate UI intent through structured JSON messages that describe components, layouts, and data bindings, which client applications then render using their own trusted, pre-approved components. This approach separates UI generation from UI execution, ensuring that interfaces are secure, consistent with the host application’s design system, and adaptable across platforms. It is designed to be LLM-friendly, using a flat, streaming JSON structure that allows agents to incrementally build and update interfaces in real time, enabling progressive rendering and responsive user experiences.

AG-UI is an open, lightweight, event-based protocol that standardizes how AI agents connect to user-facing applications. Built for simplicity and flexibility, it enables seamless integration between AI agents, real-time user context, and user interfaces. AG-UI is designed for agent-human interaction: during agent executions, backends emit events compatible with standard AG-UI event types, and agent backends can accept simple AG-UI-compatible inputs as arguments. It works with any event transport, including SSE, WebSockets, webhooks, and other streaming systems, while providing a flexible middleware layer that ensures compatibility across diverse environments. AG-UI brings agents into user-facing applications and complements the wider agentic protocol stack: MCP gives agents tools, A2A allows agents to communicate with other agents, and AG-UI connects agents directly to the user interface.

CopilotKit is an enterprise-grade agentic frontend stack designed to help developers build AI-powered applications with generative user interfaces. The platform enables seamless integration between user-facing applications and agentic backends through its AG-UI protocol, which supports bi-directional communication. It provides tools and SDKs for modern frameworks like React, Angular, and Next.js, allowing developers to quickly implement AI features. CopilotKit supports generative UI, enabling AI agents to dynamically render and update interface components in real time. The platform also includes features like chat components, conversation threading, and persistent state management for maintaining context across sessions. Developers can connect their preferred AI models, frameworks, and agents without being locked into a specific ecosystem.