agent-native

Product Introduction

1. Definition: Agent-Native is an open-source TypeScript framework for building full-stack, agentic applications. It falls into the technical categories of AI agent frameworks, autonomous application platforms, and collaborative human-AI development environments.
2. Core Value Proposition: It exists to unify the fragmented tooling required for AI agents to operate effectively within real-world applications. Its primary value is providing a pre-integrated environment where autonomous AI agents have direct, contextual access to the user interface (UI), shared application state, a PostgreSQL database, user permissions, and deployment infrastructure, enabling seamless collaboration between users and agents.

Main Features

1. Unified Action System (defineAction()): This is the core architectural innovation. Developers define a capability (e.g., "create document," "update calendar event") once using the defineAction() function. This single action definition is then automatically callable by the AI agent, the React-based user interface, HTTP API clients, and external integrations. It eliminates redundant code and ensures consistency across all interaction surfaces.
2. Integrated Agent-UI Context: The framework provides a shared application state layer. This means the AI agent has real-time awareness of what the user is viewing, selecting, or editing within the React UI. This context allows the agent to act intelligently and specifically, such as editing a currently open document or commenting on a selected data point, enabling true embedded collaboration.
3. Full-Stack Foundation with BYO Stack: Agent-Native comes pre-wired with essential production infrastructure: authentication, user organizations, role-based permissions, sharing models, and PostgreSQL data persistence. Crucially, it is open-source and allows developers to "Bring Your Own Stack"—choosing their preferred Large Language Model (LLM), hosting provider (like Vercel or AWS), and maintaining full control over their application code repository.

Problems Solved

1. Pain Point: It solves the problem of agentic tool fragmentation. Traditionally, developers building AI agents must manually cobble together separate systems for the UI, agent logic, data access, memory, and permissions, leading to complex, brittle integrations. Agent-Native provides a cohesive, batteries-included framework.
2. Target Audience: The primary personas are full-stack TypeScript/React developers and product teams building interactive, AI-powered SaaS applications. Secondary users include product managers and entrepreneurs who need to prototype and deploy agentic features rapidly without building foundational infrastructure from scratch.
3. Use Cases: Essential scenarios include: building an AI-assisted design tool where an agent modifies layouts in real-time; creating an analytics dashboard where an agent generates charts from natural language queries; developing a collaborative content platform where an agent drafts and edits documents alongside human users.

Unique Advantages

1. Differentiation: Unlike low-level AI SDKs (e.g., LangChain, LlamaIndex) that only provide agent orchestration, Agent-Native includes the full application context (UI, data, auth). Unlike traditional web frameworks (e.g., Next.js), it has agentic collaboration built into its core, not added as an afterthought.
2. Key Innovation: The "One Action" paradigm is its key innovation. By defining capabilities once that are natively accessible to both the UI and the AI agent, it creates a symmetric development model. This drastically reduces complexity and enables the creation of applications where human and AI interactions are fundamentally intertwined, not siloed.

Frequently Asked Questions (FAQ)

1. What is the Agent-Native framework used for? The Agent-Native framework is used for developing autonomous, interactive web applications where AI agents and human users collaborate directly within a shared interface, such as AI-powered design tools, analytics dashboards, content creation platforms, and intelligent productivity suites.
2. How does Agent-Native handle AI agent memory and data? Agent-Native provides shared PostgreSQL data storage as a built-in primitive, ensuring both users and agents read from and write to the same source of truth. It also includes constructs for skills and persistent context, allowing agents to maintain and reuse expertise across sessions within the application's defined permissions.
3. Can I deploy an Agent-Native application to my own servers? Yes, Agent-Native is an open-source TypeScript framework designed for self-hosting. You can choose your own deployment infrastructure (e.g., Vercel, AWS, Google Cloud) and connect your own PostgreSQL database and LLM API keys, maintaining full ownership and control of your deployment.
4. What programming language is required to use Agent-Native? Agent-Native requires TypeScript (or JavaScript) and React knowledge, as it is a full-stack framework built on these technologies. Familiarity with Node.js and modern web development practices is essential for development.
5. How does Agent-Native compare to using OpenAI's Assistants API? While OpenAI's Assistants API provides cloud-hosted agent threads and file search, Agent-Native is a full-stack application framework you host yourself. It integrates the agent directly into your application's UI and business logic, with built-in user authentication, database, and permissions, offering deeper customization and data control for complex product builds.