What is Aihu? Features, Pricing & Tutorial (2026 Guide)

The Problem Aihu Solves

Modern agentic workflows often rely on "generative UI" where an LLM emits HTML or JSON fragments that vanish as soon as the next interaction occurs. This approach forces developers to treat user interfaces as throwaway assets, leading to brittle code, lack of state persistence, and a disjointed user experience. Every time an agent updates the UI, the state is often lost or requires complex, manual re-hydration.

Engineers building agent-ready apps struggle with this "ephemeral UI" trap, as they must balance the need for dynamic agent control with the requirement for stable, testable, and reusable frontend components. The status quo usually involves building complex bespoke wrappers or forcing agents to manipulate standard DOM elements without a structured contract.

Aihu changes this by providing a persistent, standards-based bridge. By using a Rust-based compiler to emit actual Web Components with built-in llms.txt and MCP (Model Context Protocol) manifest support, Aihu ensures the UI is not only visible to the user but directly controllable by the agent. In this tutorial, you'll learn exactly how to use Aihu — step by step.

How to Get Started with Aihu in 5 Minutes

1. Ensure you have Bun installed on your system, as it is the recommended runtime for Aihu projects.
2. Open your terminal and run npx create-aihu my-app --template agent to scaffold a new project with the required agentic boilerplate.
3. Navigate into your new directory using cd my-app and install the necessary dependencies with bun install.
4. Start your development environment by running bun run dev, which initializes both the local component server and the agent capability bridge.
5. Open your browser to the local development URL provided in your terminal to view your live, agent-ready component.

How to Use Aihu: Complete Tutorial

Step 1: Architecting with .aihu Single-File Components

At the core of the framework is the .aihu file, which encapsulates your markup, reactive state, styles, and the AI agent interface within a single, unified file. When you define a component, you are essentially writing a standard Web Component that carries its own machine-readable metadata. To create a new component, simply create a .aihu file and use the framework's reactive signals for state management. This ensures that when an agent updates the component via the bridge, the UI reacts immediately without full-page reloads or virtual DOM diffing.

Step 2: Defining the Agent Surface

The unique power of Aihu lies in its @agent interface, which you declare directly within your component file. This tells the Rust compiler to generate the necessary MCP manifest that an AI agent uses to "understand" your component's capabilities. You must define the actions your component supports, such as changing a label, updating a task list, or toggling a state; the agent then uses these tools to interact with the DOM elements directly.

Step 3: Leveraging the Reactive Runtime

Aihu uses @aihu/signals for reactivity and @aihu/arbor for targeted DOM updates. Unlike frameworks that rely on a virtual DOM, Aihu’s approach targets specific DOM nodes for updates, which accounts for its significant performance edge. When building your UI, leverage these signals to manage your state; the runtime handles the synchronization between the browser's DOM and the agent's instructions, ensuring the UI remains stable even through page refreshes or complex agent interactions.

Aihu: Pros & Cons

Pros	Cons
Persistent, non-disposable UI components.	Requires learning a specific .aihu syntax.
Standards-based (vanilla Web Components).	Emerging ecosystem compared to React or Vue.
Zero runtime dependencies and sub-2kB size.	Version 1.0.x is still in early active development.
Built-in auth and policy management.	Limited third-party component library support.

Aihu Pricing: Free vs Paid

Aihu is an open-source project, which means the entire framework—including the Rust-based compiler, the reactive runtime, and the CLI tools—is completely free to use under its current license. There is no "pro" version or hidden tiering; the project is intended to be a foundational infrastructure for agentic applications.

Because the framework is distributed via open-source repositories and npm, you have full control over your implementation without needing to pay for platform-specific services. You are free to deploy your Aihu applications to any host that supports standard Web Components, including Vercel and Cloudflare, using the provided first-party adapters.

👉 Check the latest pricing and repository updates on the official Aihu GitHub repository.

Who is Aihu Best For?

For AI Engineers: Aihu provides a standardized way to build "agent-aware" interfaces that go beyond basic prompts. It allows you to define clear capability bridges, ensuring your agents interact with reliable, predictable UI tools rather than hallucinated or ephemeral HTML.

For Frontend Developers: The framework offers a high-performance alternative to traditional heavy frameworks. By compiling to vanilla Web Components, it removes the need for hydration steps and bloat, making it ideal for performance-critical applications that need to integrate with autonomous agents.

For SaaS Founders: If you are building the next generation of agentic applications where the UI needs to be trusted, styled, and long-lived, Aihu is the right choice. Its built-in support for auth and policy management means you can spend less time building the infrastructure and more time refining the agent's utility.

Alternatives to Aihu

Developers currently working in this space often look at React with custom hooks, Lit, or various "Generative UI" patterns found in libraries like Vercel AI SDK. While these offer familiar workflows, they often treat the UI as a side effect rather than a first-class, persistent citizen. Aihu distinguishes itself by treating the AI agent as a primary user of the interface at the architectural level, whereas most alternatives treat agent interaction as an afterthought. If your primary goal is to build long-term, durable interfaces that agents can safely manipulate, Aihu’s focus on standard-compliant, agent-native components makes it the most specialized tool for the task.

Final Verdict: Is Aihu Worth It?

Aihu is an excellent choice for teams looking to move beyond the "disposable" UI paradigm that currently dominates the agentic space. While the learning curve for .aihu syntax and the relative youth of the project are considerations, the performance gains and the elegance of having agent capabilities baked into the component format are significant advantages for serious agent-based development.

📋 Disclosure: This is an independent tutorial based on Aihu's publicly available documentation and website content as of June 17, 2026. GitNeural is not affiliated with, sponsored by, or endorsed by Aihu or github.com. Pricing and features may have changed — always verify on the official Aihu website.