ai-sdk-core

Orchestrate multi-provider LLM workflows with AI SDK v5/v6.

961|99|Updated Oct 20, 2025

Search Tags:#agents #vercel-ai #structured-output #text-generation #backend-ai #tool-calling #ai-sdk

Ready to run

npx skills add https://github.com/jezweb/claude-skills --skill ai-sdk-core

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-sdk-core
Source: https://github.com/jezweb/claude-skills/tree/main/skills/ai-sdk-core
Command: npx skills add https://github.com/jezweb/claude-skills --skill ai-sdk-core

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires npm, node, and includes scripts (resource) and references (resource) and templates (resource) components.

What problem does it solve?

This Skill eliminates the complexity of implementing AI SDK v5/v6, saving developers hours of configuration and troubleshooting time.

Core Features & Use Cases

Multi-Provider AI: Seamlessly switch between OpenAI, Anthropic, Google, and Cloudflare Workers AI with a consistent API.
Production-Ready: Get your AI backend running with comprehensive error handling and optimization patterns.
Use Case: Imagine you need to migrate from AI SDK v4 to v5. Use this Skill to automatically update all breaking changes and implement best practices.

Quick Start

Install the required packages: npm install ai @ai-sdk/openai @ai-sdk/anthropic @ai-sdk/google zod

Use this Skill to generate text with the latest models: const result = await generateText({ model: openai('gpt-5.1'), prompt: 'Hello' });

Frequently Asked Questions about ai-sdk-core

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build multi-provider LLM workflows with a single API across OpenAI, Anthropic, and Google?▼

Multi-provider LLM workflows use AI SDK v5/v6 to abstract provider differences behind a consistent generateText and streamText API. Define your model once with openai(), anthropic(), or google() and switch providers without rewriting orchestration logic, enabling failover and cost optimization across OpenAI, Anthropic, Google, and Cloudflare Workers AI.

How do I implement tool calling and agent-based actions with human-in-the-loop approvals?▼

Tool calling with AI SDK involves defining tool schemas, passing them to generateText or streamText, and handling tool use events. Human-in-the-loop approvals intercept tool execution before action, route decisions to a reviewer, and resume the agent workflow based on approval status, enabling safe autonomous agent patterns in production.

What's the best way to migrate from AI SDK v4 to v5 without breaking existing code?▼

AI SDK v5 introduces breaking changes in function signatures and streaming patterns. Migration involves updating generateText and streamObject calls, replacing deprecated provider imports, and refactoring streaming handlers. This Skill provides automated migration guidance and best practices to apply updates systematically across your codebase.

Can I use structured output with Zod schemas across multiple LLM providers?▼

Structured output with Zod uses generateObject and streamObject to enforce schema validation at the API level. AI SDK v5/v6 adapts Zod schemas to provider-specific formats—OpenAI uses JSON schema, Anthropic uses structured output blocks—so you write one schema and it works consistently across all providers.

How do I set up streaming text and object generation for real-time responses?▼

Streaming with AI SDK uses streamText and streamObject to emit tokens and partial objects as they arrive. Set up event listeners for text chunks or object updates, pipe output to the client, and handle backpressure. This enables real-time AI responses in Next.js, Cloudflare Workers, and Vercel without waiting for full completion.

Does AI SDK work with document reranking for retrieval-augmented generation (RAG)?▼

Document reranking for RAG filters and ranks retrieved chunks by relevance before passing them to the LLM. AI SDK v5/v6 integrates with RAG workflows by accepting reranked context in prompts and tool definitions, reducing token consumption and improving output quality when augmenting generation with external knowledge.