Next.js AI Template

Integrates Vercel's AI SDK with Next.js Server Components to stream LLM responses directly to the client using React's streaming primitives. The template demonstrates server-side API route handlers that invoke language models (OpenAI, Anthropic, etc.) and pipe streamed tokens through Next.js's built-in streaming infrastructure, avoiding client-side latency and enabling progressive UI updates without explicit WebSocket management.

Demonstrates using the AI SDK's structured output mode to constrain LLM responses to a predefined JSON schema, with automatic parsing and validation. The template shows how to define TypeScript interfaces, convert them to JSON schemas, and invoke models with schema constraints so responses are guaranteed to parse as valid structured data without post-hoc validation.

The template includes working examples of common AI application patterns: simple text generation, streaming chat, structured output extraction, and tool-calling agents. Each example is a complete, runnable implementation that developers can study, modify, or copy into their own projects. Examples are organized by pattern and include both API routes and client-side code.

The template is optimized for deployment on Vercel, with automatic environment variable management, serverless function optimization, and edge runtime support. Vercel's deployment platform automatically detects Next.js projects and applies optimizations like automatic code splitting and edge caching. The template includes configuration for Vercel-specific features like edge middleware and analytics.

Provides a provider-agnostic abstraction for tool calling (function calling) across OpenAI, Anthropic, and other LLM providers. The template demonstrates defining tools as TypeScript functions, registering them with the AI SDK, and automatically routing model-selected tool calls back to the appropriate handler. The SDK handles provider-specific tool definition formats (OpenAI's function schema vs. Anthropic's tool_use blocks) transparently.

Demonstrates building multi-turn agent loops where the model iteratively calls tools, receives results, and decides next steps. The template shows how to structure agent state (conversation history, tool results, reasoning steps) and implement a loop that continues until the model reaches a terminal state (e.g., 'stop' or 'final_answer'). State is managed in-memory or via Next.js request context, with no external persistence layer required for basic workflows.

The template uses Vercel's AI SDK to abstract over multiple LLM providers (OpenAI, Anthropic, Google, Cohere, Ollama) through a unified client interface. Developers specify the provider via environment variables and use the same API to invoke models, eliminating provider-specific code paths. The SDK handles authentication, request formatting, and response parsing for each provider internally.

Demonstrates building Next.js API routes (in the App Router's route.ts pattern) that act as thin wrappers around LLM provider calls. These routes handle authentication, parameter validation, error handling, and response formatting. The template shows how to structure routes to support both streaming and non-streaming responses, with proper HTTP headers and error codes.

Provides React hooks (useChat, useCompletion) that manage client-side state for LLM interactions, including message history, loading states, and error handling. These hooks abstract away the HTTP request/response cycle and streaming mechanics, allowing developers to focus on UI logic. The template demonstrates how to use these hooks to build chat interfaces with minimal boilerplate.

The template demonstrates using environment variables to configure which LLM provider and model to use at runtime. This allows developers to switch between providers (OpenAI, Anthropic, etc.) or models (GPT-4, Claude 3, etc.) without code changes, enabling A/B testing, cost optimization, and gradual rollouts. Configuration is typically done via .env.local for development and environment variables in deployment platforms (Vercel, etc.).

The template uses TypeScript to define request and response types for API routes, enabling compile-time validation and IDE autocomplete. Request bodies are typed as interfaces, and responses are typed as discriminated unions or specific types. This catches type mismatches at development time and provides documentation through type definitions.

The template provides a pre-configured Next.js project structure optimized for AI applications, with example API routes, client components, and utility functions already in place. This eliminates the need to set up routing, authentication, and error handling from scratch. Developers can immediately start building AI features by modifying example files rather than creating new ones.