{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"next-js-ai-template","slug":"next-js-ai-template","name":"Next.js AI Template","type":"template","url":"https://github.com/vercel/next.js/tree/canary/examples/ai","page_url":"https://unfragile.ai/next-js-ai-template","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"next-js-ai-template__cap_0","uri":"capability://text.generation.language.server.side.streaming.text.generation.with.react.server.components","name":"server-side streaming text generation with react server components","description":"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.","intents":["Stream LLM completions to users without building custom API endpoints","Display real-time token generation in chat interfaces with minimal latency","Avoid exposing API keys to the client by keeping all LLM calls server-side"],"best_for":["Full-stack developers building chat applications with Next.js","Teams wanting streaming AI responses without custom backend infrastructure"],"limitations":["Streaming only works with Server Components; Client Components require separate API routes","No built-in request deduplication — concurrent identical requests will each spawn separate LLM calls","Stream cancellation on client disconnect may not immediately terminate server-side LLM processing"],"requires":["Next.js 13.4+ with App Router enabled","Vercel AI SDK 2.0+","API key for OpenAI, Anthropic, or compatible LLM provider","Node.js 18+"],"input_types":["text prompts","conversation history (array of messages)","system instructions"],"output_types":["streamed text chunks","complete text on stream end"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_1","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"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.","intents":["Generate structured data (e.g., extracted entities, categorized content) with guaranteed schema compliance","Avoid parsing errors from malformed LLM JSON responses","Maintain type safety between LLM outputs and application code"],"best_for":["Developers building data extraction pipelines with LLMs","Teams needing reliable structured outputs for downstream processing"],"limitations":["Not all LLM providers support structured output — fallback to post-hoc validation required for unsupported models","Schema complexity affects token usage and latency; deeply nested schemas may increase costs by 15-25%","Requires explicit schema definition per output type; no automatic schema inference from TypeScript types"],"requires":["Vercel AI SDK 2.0+","LLM provider with structured output support (OpenAI, Anthropic, Mistral)","JSON Schema definition for desired output structure","TypeScript 4.7+ (optional, for type inference)"],"input_types":["text prompts","JSON Schema object"],"output_types":["JSON object matching schema","typed TypeScript object"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_10","uri":"capability://text.generation.language.real.time.ui.updates.with.streaming.response.chunks","name":"real-time ui updates with streaming response chunks","description":"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.","intents":["Display LLM responses token-by-token as they arrive, improving perceived performance","Update chat UI in real-time without waiting for complete response","Provide visual feedback that the AI is actively processing"],"best_for":["Chat applications and conversational UIs","User-facing AI features where perceived latency matters"],"limitations":["Streaming adds complexity to state management; partial responses may be displayed before completion","Network latency affects perceived responsiveness; slow connections may show chunked updates","No built-in handling for stream interruptions; incomplete responses remain in UI state","Browser support for ReadableStream varies; older browsers may not support streaming"],"requires":["React 18+ (for useEffect and state management)","Browser support for ReadableStream API","Next.js Server Components or API routes for streaming responses"],"input_types":["streamed response chunks (text)"],"output_types":["updated React component state","rendered UI with partial/complete response"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_11","uri":"capability://memory.knowledge.conversation.memory.and.context.management","name":"conversation memory and context management","description":"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.","intents":["Maintain conversation context across multiple user-AI exchanges","Implement memory strategies to stay within LLM context windows","Reference previous conversation turns in new responses"],"best_for":["Chat applications and conversational agents","Multi-turn AI interactions requiring context awareness"],"limitations":["No built-in persistence — conversation history is lost on page refresh unless saved to database","Context window limits require manual management; no automatic summarization","Storing full conversation history increases token usage and latency for each new request","No built-in privacy controls; sensitive information in conversation history is not automatically redacted"],"requires":["Vercel AI SDK 2.0+","React state or database for storing conversation history","Understanding of LLM context windows and token limits"],"input_types":["conversation messages (array of user/assistant messages)","new user input"],"output_types":["updated conversation history","LLM response in context of history"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_12","uri":"capability://automation.workflow.development.environment.setup.and.configuration","name":"development environment setup and configuration","description":"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.","intents":["Quickly set up a development environment for AI-powered Next.js applications","Configure API keys and environment variables securely","Enable hot reloading for rapid iteration on AI features"],"best_for":["Developers new to Next.js and AI SDK integration","Teams standardizing development environment setup"],"limitations":["Environment variable management is basic; no built-in secret rotation or encryption","Hot reloading may not work correctly with streaming responses in development","No built-in development-only mocking of LLM responses; requires manual setup"],"requires":["Node.js 18+","npm or pnpm package manager","API keys for LLM providers (OpenAI, Anthropic, etc.)"],"input_types":["environment variables","next.config.js configuration"],"output_types":["running development server","hot-reloaded application"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_2","uri":"capability://tool.use.integration.tool.calling.with.multi.provider.function.registry","name":"tool calling with multi-provider function registry","description":"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.","intents":["Enable LLMs to call application functions (e.g., database queries, API calls) as part of reasoning","Build multi-step agent workflows where LLMs decide which tools to invoke","Support provider-agnostic tool definitions that work across OpenAI, Anthropic, and other models"],"best_for":["Developers building AI agents with external tool access","Teams wanting to switch LLM providers without redefining tools"],"limitations":["Tool execution is synchronous in the template; async tool chains require custom orchestration","No built-in error recovery — tool failures don't automatically trigger retries or fallback tools","Tool definitions must be manually kept in sync with actual function signatures; no automatic schema generation from TypeScript function types"],"requires":["Vercel AI SDK 2.0+","LLM provider with tool calling support (OpenAI, Anthropic, Ollama)","TypeScript function definitions with JSON Schema parameter descriptions","API key for chosen LLM provider"],"input_types":["tool definitions (name, description, parameters schema)","user prompts","tool execution callbacks"],"output_types":["tool invocation requests (name + parameters)","tool execution results","final LLM response"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_3","uri":"capability://planning.reasoning.multi.step.agent.workflows.with.state.persistence","name":"multi-step agent workflows with state persistence","description":"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.","intents":["Build agents that reason over multiple steps and refine answers based on tool results","Implement conversation memory across multiple LLM calls within a single session","Create workflows where LLMs decide when to stop iterating vs. continue reasoning"],"best_for":["Developers building complex AI agents with multi-step reasoning","Teams implementing research assistants or autonomous task executors"],"limitations":["No built-in persistence — agent state is lost on page refresh unless explicitly saved to database","Each iteration adds latency (LLM call + tool execution); typical 3-5 step workflows add 5-15 seconds","No automatic loop termination detection; requires explicit max-iteration limits to prevent infinite loops","Tool execution errors don't automatically trigger recovery; failed tools require manual error handling in the loop"],"requires":["Vercel AI SDK 2.0+","Next.js 13.4+ with App Router","LLM provider with tool calling support","Tool definitions matching agent's action space"],"input_types":["initial user prompt","tool definitions","conversation history","iteration limit"],"output_types":["final agent response","execution trace (tools called, results)","conversation history with all turns"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_4","uri":"capability://tool.use.integration.client.side.ai.integration.with.api.route.abstraction","name":"client-side ai integration with api route abstraction","description":"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.","intents":["Add AI features to interactive Client Components (e.g., chat inputs, form assistants)","Keep LLM API keys server-side while exposing AI functionality to the frontend","Stream LLM responses to the client and update UI in real-time"],"best_for":["Frontend developers building interactive AI-powered UIs","Teams with strict separation between client and server concerns"],"limitations":["API route overhead adds ~50-100ms latency per request compared to direct Server Component calls","Streaming requires manual ReadableStream handling in client code; no built-in React hooks for streaming","CORS and authentication must be manually configured for API routes","No automatic request deduplication — concurrent identical requests spawn separate LLM calls"],"requires":["Next.js 13.4+ with App Router","Vercel AI SDK 2.0+","API route handler (e.g., app/api/chat/route.ts)","Client-side fetch or axios for HTTP requests"],"input_types":["JSON request body (prompt, messages, parameters)","HTTP headers (authentication, content-type)"],"output_types":["streamed text chunks via ReadableStream","complete response on stream end"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_5","uri":"capability://text.generation.language.prompt.engineering.and.message.formatting.utilities","name":"prompt engineering and message formatting utilities","description":"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.","intents":["Construct consistent, well-formatted prompts that work across multiple LLM providers","Manage conversation history with proper role attribution (user, assistant, system)","Implement few-shot prompting and instruction-following patterns"],"best_for":["Developers new to LLM prompt engineering","Teams standardizing prompt formats across multiple AI features"],"limitations":["No automatic prompt optimization — developers must manually tune prompts for quality","Message formatting is provider-agnostic but doesn't account for provider-specific quirks (e.g., Claude's preferred instruction format)","No built-in prompt versioning or A/B testing framework"],"requires":["Vercel AI SDK 2.0+","Understanding of LLM message formats (role, content)"],"input_types":["system instructions (string)","user messages (string)","conversation history (array of messages)"],"output_types":["formatted message array","complete prompt string"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_6","uri":"capability://safety.moderation.error.handling.and.fallback.patterns.for.llm.failures","name":"error handling and fallback patterns for llm failures","description":"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.","intents":["Handle LLM API errors gracefully without crashing the application","Implement retry logic for transient failures (rate limits, timeouts)","Provide meaningful error messages to users when AI features fail"],"best_for":["Production applications requiring robust error handling","Teams building user-facing AI features with SLA requirements"],"limitations":["Retry logic is basic — no exponential backoff with jitter by default","No built-in circuit breaker pattern; repeated failures don't automatically disable AI features","Error messages are generic; provider-specific error codes require custom mapping","Stream interruptions may leave partial responses in UI state"],"requires":["Vercel AI SDK 2.0+","Error handling in try-catch blocks or promise chains"],"input_types":["LLM API responses","error objects from SDK"],"output_types":["error messages","fallback responses","retry signals"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_7","uri":"capability://code.generation.editing.typescript.type.inference.for.ai.sdk.operations","name":"typescript type inference for ai sdk operations","description":"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.","intents":["Catch type errors in AI SDK usage at compile time","Enable IDE autocomplete for AI SDK methods and parameters","Ensure structured outputs match expected TypeScript types"],"best_for":["TypeScript projects requiring type safety","Teams using IDEs with strong TypeScript support (VS Code, WebStorm)"],"limitations":["Type inference requires explicit type annotations for complex schemas","Runtime behavior may not match TypeScript types if schemas are incorrectly defined","Type checking adds build time (~5-10 seconds for typical projects)"],"requires":["TypeScript 4.7+","Vercel AI SDK with TypeScript definitions","tsconfig.json with strict mode enabled"],"input_types":["TypeScript type definitions","JSON Schema objects"],"output_types":["typed LLM responses","compile-time type errors"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_8","uri":"capability://automation.workflow.integration.with.next.js.app.router.and.server.components","name":"integration with next.js app router and server components","description":"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.","intents":["Execute LLM calls server-side within Next.js's rendering pipeline","Use Server Actions to process form submissions with AI","Stream LLM responses through Next.js's HTTP response handling"],"best_for":["Next.js developers building full-stack AI applications","Teams leveraging Server Components for server-side logic"],"limitations":["Requires Next.js 13.4+ with App Router; incompatible with Pages Router","Server Components cannot directly use client-side hooks; state management requires Client Component wrappers","Streaming only works with specific response types; some LLM provider responses may not stream properly"],"requires":["Next.js 13.4+ with App Router enabled","Vercel AI SDK 2.0+","Understanding of Server Components and Server Actions"],"input_types":["user prompts from forms or API requests","Server Component props"],"output_types":["streamed responses","server-side rendered HTML with AI content"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__cap_9","uri":"capability://tool.use.integration.provider.agnostic.llm.model.selection.and.configuration","name":"provider-agnostic llm model selection and configuration","description":"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.).","intents":["Switch between LLM providers without rewriting application code","Test different models (GPT-4, Claude, Llama) with minimal changes","Configure model-specific parameters (temperature, max tokens) consistently across providers"],"best_for":["Teams evaluating multiple LLM providers","Projects requiring flexibility to switch providers based on cost or performance"],"limitations":["Provider-specific features (e.g., vision capabilities, extended context windows) require conditional code","Model behavior varies across providers; same prompt may produce different results","Cost and latency differ significantly between providers; no automatic cost optimization"],"requires":["Vercel AI SDK 2.0+","API keys for desired LLM providers","Environment variables for provider configuration"],"input_types":["model name (string)","provider configuration (API key, base URL)"],"output_types":["LLM responses from selected provider"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"next-js-ai-template__headline","uri":"capability://app.builders.ai.powered.next.js.application.template","name":"ai-powered next.js application template","description":"A starter template for building AI-integrated applications using Next.js, featuring minimal boilerplate and support for streaming text generation, structured output, and multi-step workflows.","intents":["best Next.js AI template","Next.js template for AI applications","AI integration in Next.js projects","how to build AI apps with Next.js","Next.js starter for AI development"],"best_for":["developers looking to integrate AI into web apps"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["app-builders"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Next.js 13.4+ with App Router enabled","Vercel AI SDK 2.0+","API key for OpenAI, Anthropic, or compatible LLM provider","Node.js 18+","LLM provider with structured output support (OpenAI, Anthropic, Mistral)","JSON Schema definition for desired output structure","TypeScript 4.7+ (optional, for type inference)","React 18+ (for useEffect and state management)","Browser support for ReadableStream API","Next.js Server Components or API routes for streaming responses"],"failure_modes":["Streaming only works with Server Components; Client Components require separate API routes","No built-in request deduplication — concurrent identical requests will each spawn separate LLM calls","Stream cancellation on client disconnect may not immediately terminate server-side LLM processing","Not all LLM providers support structured output — fallback to post-hoc validation required for unsupported models","Schema complexity affects token usage and latency; deeply nested schemas may increase costs by 15-25%","Requires explicit schema definition per output type; no automatic schema inference from TypeScript types","Streaming adds complexity to state management; partial responses may be displayed before completion","Network latency affects perceived responsiveness; slow connections may show chunked updates","No built-in handling for stream interruptions; incomplete responses remain in UI state","Browser support for ReadableStream varies; older browsers may not support streaming","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.693Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=next-js-ai-template","compare_url":"https://unfragile.ai/compare?artifact=next-js-ai-template"}},"signature":"Pql4Jxk42zk1YagIPVD8cDOJCWz04My5noXz2ZatqzzxqvNFXzRVGl/VFW3/EllNX56QkmmO107vPfeeJZudDA==","signedAt":"2026-06-22T19:04:14.391Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/next-js-ai-template","artifact":"https://unfragile.ai/next-js-ai-template","verify":"https://unfragile.ai/api/v1/verify?slug=next-js-ai-template","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}