Next.js AI Template

Integrates Vercel AI SDK with Next.js App Router Server Components to stream LLM responses directly to the client using ReadableStream and Server-Sent Events. Leverages Next.js server-side rendering pipeline to execute AI calls server-side, then streams chunked responses through the HTTP response body without requiring separate API routes, enabling real-time token-by-token updates in React components via useEffect hooks.

Enables LLMs to generate strictly-typed JSON responses by passing JSON Schema definitions to the AI SDK, which enforces schema compliance at the model level (via provider-specific structured output APIs like OpenAI's JSON mode or Anthropic's tool use). The template demonstrates schema definition patterns and response parsing that guarantee type-safe outputs without post-hoc validation, integrating with TypeScript for compile-time type checking.

Demonstrates patterns for updating React component state as LLM response chunks arrive via streaming, enabling real-time token-by-token display in the UI. The template shows how to use useEffect hooks to consume streamed responses, update state incrementally, and handle stream completion. Integrates with Next.js Server Components to stream responses directly from the server without requiring separate WebSocket connections.

Provides patterns for maintaining conversation history across multiple turns, managing context windows, and implementing memory strategies (e.g., summarization, sliding window). The template demonstrates how to store and retrieve conversation messages, format them for the LLM, and handle context length limits. Includes examples of system prompts that reference conversation history and techniques for summarizing old messages to stay within token limits.

Provides a complete development environment setup including Next.js configuration, environment variable management for LLM API keys, and local development server setup. The template includes example .env.local files, next.config.js configuration for AI SDK compatibility, and development scripts for running the application. Integrates with Next.js's development server (as documented in vercel/next.js Development Server and Hot Module Replacement) to enable hot reloading during AI feature development.

Implements a schema-based function registry that abstracts tool definitions across multiple LLM providers (OpenAI, Anthropic, Ollama) using a unified interface. The template demonstrates how to define tools as TypeScript functions with JSON Schema parameters, pass them to the AI SDK, and handle tool execution callbacks. The AI SDK automatically translates tool definitions to provider-specific formats (OpenAI function_calling, Anthropic tool_use) and manages the request-response loop for tool invocation.

Demonstrates patterns for building multi-turn agent loops where the LLM iteratively decides actions, executes tools, and refines responses based on tool results. The template shows how to maintain conversation state across multiple LLM calls, handle tool execution results, and implement termination conditions (e.g., max iterations, explicit stop signals). State is managed in React component state or passed through Server Component props, enabling stateless server-side execution compatible with Next.js's serverless architecture.

Provides patterns for Client Components to invoke AI capabilities through Next.js API routes, enabling interactive AI features in browser-based UIs. The template demonstrates how to create API routes that call the Vercel AI SDK, handle streaming responses via fetch with ReadableStream, and update React state as chunks arrive. This pattern separates client-side UI logic from server-side LLM execution, allowing Client Components to trigger AI operations without direct SDK access.

Provides helper functions and patterns for constructing well-formatted prompts and message arrays compatible with the Vercel AI SDK's message format (role-based: user, assistant, system). The template demonstrates system prompt definition, conversation history management, and prompt templating patterns that ensure consistent message formatting across different LLM providers. Includes examples of few-shot prompting and instruction-following patterns.

Demonstrates error handling strategies for LLM API failures, rate limits, and malformed responses. The template shows how to catch SDK exceptions, implement retry logic with exponential backoff, and provide user-friendly error messages. Includes patterns for handling partial responses (e.g., stream interruptions) and graceful degradation when LLM providers are unavailable.

Leverages TypeScript's type system to provide compile-time safety for AI SDK operations, including type inference for structured outputs, tool parameters, and message formats. The template demonstrates how TypeScript generics and utility types ensure that LLM responses match expected schemas and that tool parameters are correctly typed. This enables IDE autocomplete and catches type mismatches before runtime.

Demonstrates how to integrate the Vercel AI SDK with Next.js's App Router architecture, including Server Components, Server Actions, and API routes. The template shows how to execute LLM calls within Server Components (avoiding client-side SDK exposure), use Server Actions for form submissions with AI processing, and structure API routes for streaming responses. Leverages Next.js's request handling pipeline (as documented in vercel/next.js architecture) to manage LLM execution within the server-side rendering context.

Enables switching between different LLM providers (OpenAI, Anthropic, Ollama, etc.) by changing a single model parameter, without modifying application code. The template demonstrates how the Vercel AI SDK abstracts provider-specific APIs and configuration, allowing the same code to work with different models. Includes patterns for environment-based provider selection and model configuration (temperature, max tokens, etc.).