ai-core

name	ai-core
description	Entry point for TanStack AI skills. Routes to chat-experience, tool-calling, media-generation, structured-outputs, adapter-configuration, ag-ui-protocol, middleware, custom-backend-integration, and debug-logging. Use chat() not streamText(), openaiText() not createOpenAI(), toServerSentEventsResponse() not manual SSE, middleware hooks not onEnd callbacks.
type	core
library	tanstack-ai
library_version	0.10.0

TanStack AI — Core Concepts

TanStack AI is a type-safe, provider-agnostic AI SDK. Server-side functions live in @tanstack/ai and provider adapter packages. Client-side hooks live in framework packages (@tanstack/ai-react, @tanstack/ai-solid, etc.). Always import from the framework package on the client — never from @tanstack/ai-client directly (unless vanilla JS).

Sub-Skills

Need to...	Read
Build a chat UI with streaming	ai-core/chat-experience/SKILL.md
Add tool calling (server, client, or both)	ai-core/tool-calling/SKILL.md
Generate images, video, speech, or transcriptions	ai-core/media-generation/SKILL.md
Get typed JSON responses from the LLM	ai-core/structured-outputs/SKILL.md
Choose and configure a provider adapter	ai-core/adapter-configuration/SKILL.md
Implement AG-UI streaming protocol server-side	ai-core/ag-ui-protocol/SKILL.md
Add analytics, logging, or lifecycle hooks	ai-core/middleware/SKILL.md
Connect to a non-TanStack-AI backend	ai-core/custom-backend-integration/SKILL.md
Turn on/off debug logging, pipe into pino/winston	ai-core/debug-logging/SKILL.md
Set up Code Mode (LLM code execution)	See @tanstack/ai-code-mode package skills

Quick Decision Tree

• Setting up a chatbot? → ai-core/chat-experience
• Adding function calling? → ai-core/tool-calling
• Generating media (images, audio, video)? → ai-core/media-generation
• Need structured JSON output? → ai-core/structured-outputs
• Choosing/configuring a provider? → ai-core/adapter-configuration
• Building a server-only AG-UI backend? → ai-core/ag-ui-protocol
• Adding analytics or post-stream events? → ai-core/middleware
• Connecting to a custom backend? → ai-core/custom-backend-integration
• Turning on debug logging to trace chunks/tools/middleware? → ai-core/debug-logging
• Debugging mistakes? → Check Common Mistakes in the relevant sub-skill

Critical Rules

1. This is NOT the Vercel AI SDK. Use chat() not streamText(). Use openaiText() not createOpenAI(). Import from @tanstack/ai, not ai.
2. Import from framework package on client. Use @tanstack/ai-react (or solid/vue/svelte/preact), not @tanstack/ai-client.
3. Use toServerSentEventsResponse() to convert streams to HTTP responses. Never implement SSE manually.
4. Use middleware for lifecycle events. No onEnd/onFinish callbacks on chat() — use middleware: [{ onFinish: ... }].
5. Ask the user which adapter and model they want. Suggest the latest model. Also ask if they want Code Mode.
6. Tools must be passed to both server and client. Server gets the tool in chat({ tools }), client gets the definition in useChat({ clientTools }).

Version

Targets TanStack AI v0.10.0.