"Every AI feature you ship has a hidden tax. Often, you write the same logic twice. Need a weather tool? You write the OpenWeatherMap fetch on your server, then write the client-side schema and validation again. Database query tool? Implement it server-side, duplicate the interface client-side. User geolocation? Same story. This isn't just an annoying problem; it's an expensive one. Codebases bloat, types drift, and months later it's unclear which implementation is the source of truth."
"TanStack AI addresses this problem with isomorphic tools. You define a tool once using toolDefinition(), then explicitly choose where it executes with .server() or .client(). The definition and types remain the same across environments, eliminating duplication. In the rest of this article, we'll walk through how isomorphic tools work by building a sample AI assistant and contrast this model with the approach used by Vercel AI SDK. We'll also examine how TanStack AI reduces vendor lock-in and enforces stronger type safety."
AI features frequently require duplicated logic across server and client, such as fetching data, replicating schema validation, or mirroring database query interfaces. Duplication causes codebase bloat, type drift, and uncertainty about the source of truth as implementations diverge over time. TanStack AI provides isomorphic tools via toolDefinition(), allowing a tool to be defined once and bound to execution environments with .server() or .client() while preserving shared definitions and types. This approach eliminates redundant implementations, improves type safety through unified typings, reduces vendor lock-in, and contrasts with SDKs that require separate server and client tool implementations.
Read at LogRocket Blog
Unable to calculate read time
Collection
[
|
...
]