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Basic Information

Tambo AI is a React package and developer toolkit for building AI-powered applications that use generative UI, enabling users to interact with applications via natural language. It functions as a client-side registry of React components that an LLM can instantiate and manipulate. The repo provides patterns and runtime pieces for registering UI components with typed props schemas, exposing programmatic tools to the model, wrapping apps in a provider to connect to Tambo services, and optionally routing requests through MCP servers. It includes templates and example components to bootstrap chat apps with generative UX and points to a hosted backend that has been open-sourced. The package targets developers who want to let models render interactive React components and call typed tools within conversational interfaces.

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App Details

Features
Component registry for LLM-driven UI where components are registered with name, description, implementation and zod propsSchema. TamboProvider and TamboMcpProvider to configure API keys, components, tools and MCP servers. React hooks and primitives such as useTambo, useTamboThreadInput, useMessageContext and useTamboComponentState for managing threads, inputs, component state and streaming responses. Ability to register tools with typed function schemas so the model can call application code. Example components and templates including a WeatherCard demo and an AI chat template to scaffold projects. Integration with a UI component library and ready-made templates. Development prerequisites and an MIT license. Quick start commands and docs for getting started are included.
Use Cases
Tambo helps developers rapidly create conversational and generative user interfaces by giving LLMs access to real React components and typed tools rather than plain text outputs. It simplifies wiring an app to an AI backend by providing a provider layer, thread hooks, and component state hooks so messages can render interactive UI elements directly. Typed schemas via zod ensure component props and tool calls are validated, reducing runtime errors. Templates and a component library accelerate prototyping, while MCP server support enables flexible transport and deployment architectures. Streaming responses and example usage patterns make it easier to build responsive chat experiences. The open-sourced backend and community resources support contribution and production adoption.

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