{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-vercel--ai","slug":"vercel--ai","name":"ai","type":"framework","url":"https://ai-sdk.dev","page_url":"https://unfragile.ai/vercel--ai","categories":["app-builders"],"tags":["anthropic","artificial-intelligence","gemini","generative-ai","generative-ui","javascript","language-model","llm","nextjs","openai","react","svelte","typescript","vercel","vue"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-vercel--ai__cap_0","uri":"capability://text.generation.language.multi.provider.unified.text.generation.with.streaming","name":"multi-provider unified text generation with streaming","description":"Abstracts text generation across 15+ LLM providers (OpenAI, Anthropic, Google, Azure, Mistral, Cohere, etc.) through a single generateText() and streamText() API. Uses a provider-agnostic message format that normalizes differences in API schemas, token counting, and finish reasons across providers. Internally converts to provider-specific formats via adapter layers (e.g., convert-to-openai-messages.ts, convert-to-anthropic-messages.ts) and handles streaming via unified ReadableStream abstraction.","intents":["Build an AI app that can switch between OpenAI and Anthropic without changing application code","Stream LLM responses to the client while maintaining consistent behavior across providers","Get accurate token counts and usage metrics regardless of which provider is used","Handle provider-specific quirks (like Anthropic's system prompt handling) transparently"],"best_for":["Teams building multi-provider LLM applications to avoid vendor lock-in","Developers prototyping with different models and needing quick provider swaps","Production applications requiring fallback providers or cost optimization"],"limitations":["Provider-specific features (e.g., OpenAI's vision_detail parameter) require manual provider selection, not abstracted","Streaming adds ~50-100ms latency due to adapter layer normalization","Token counting accuracy varies by provider; some providers don't expose token counts in streaming mode","Custom provider parameters must be passed through provider-specific options objects, breaking abstraction"],"requires":["TypeScript 4.5+","Node.js 18+ or edge runtime (Cloudflare Workers, Vercel Edge Functions)","API keys for at least one provider (OpenAI, Anthropic, Google, etc.)","@ai-sdk/core package"],"input_types":["text prompts","message arrays with role/content structure","system prompts","structured tool definitions"],"output_types":["text strings (generateText)","streaming text via ReadableStream (streamText)","usage metadata (tokens, cost estimates)","finish reason (stop, length, tool-calls, etc.)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_1","uri":"capability://data.processing.analysis.schema.based.structured.output.generation.with.type.safety","name":"schema-based structured output generation with type safety","description":"Generates JSON or structured data matching a Zod schema or TypeScript type definition using the Output API. Works by embedding the schema into the prompt or using provider-native structured output modes (OpenAI's JSON mode, Anthropic's tool_choice=required with a single tool). Validates responses against the schema and automatically retries on validation failure. Provides full TypeScript type inference so the returned object is properly typed.","intents":["Extract structured data (e.g., entities, sentiment, categories) from unstructured text with guaranteed schema compliance","Generate JSON APIs responses from LLM outputs without manual parsing or validation","Build type-safe LLM pipelines where downstream code knows the exact shape of LLM outputs","Reduce hallucination and invalid outputs by constraining the model to a specific schema"],"best_for":["Data extraction pipelines requiring reliable structured outputs","API builders wrapping LLM calls with guaranteed response schemas","TypeScript teams leveraging type inference for end-to-end type safety"],"limitations":["Schema complexity is limited; deeply nested or recursive schemas may cause token bloat or model confusion","Validation failures trigger retries, adding latency (typically 1-3 additional API calls per request)","Some providers (Mistral, Cohere) don't support native structured output, falling back to prompt-based schema injection","Large schemas (>10KB serialized) can exceed context windows or cause performance degradation"],"requires":["Zod library for schema definition (or compatible schema library)","TypeScript 4.7+ for type inference","Provider supporting structured output (OpenAI, Anthropic, Google) or fallback to prompt-based approach"],"input_types":["text prompts","Zod schemas or TypeScript type definitions","message arrays"],"output_types":["typed JavaScript objects matching the schema","validation errors with detailed failure reasons"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_10","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation.across.providers","name":"token counting and cost estimation across providers","description":"Provides accurate token counting for inputs and outputs across different providers, enabling cost estimation before or after API calls. Uses provider-specific tokenizers (OpenAI's cl100k_base, Anthropic's Claude tokenizer, Google's tokenizer) to count tokens accurately. Integrates with pricing data to estimate costs. Works with both streaming and non-streaming responses.","intents":["Estimate LLM API costs before making requests","Track token usage for billing or budget management","Optimize prompts by understanding token counts","Compare costs across different providers and models"],"best_for":["Cost-conscious applications with budget constraints","Organizations tracking LLM spending per user or project","Applications optimizing prompts for token efficiency"],"limitations":["Token counting accuracy varies by provider; some providers don't expose exact tokenizers","Streaming responses don't provide token counts until completion","Cost estimation is approximate; actual costs may vary due to pricing changes or provider-specific rules","Custom models or fine-tuned models may have different token counts than base models"],"requires":["TypeScript 4.5+","@ai-sdk/core package","Provider API keys for accurate tokenization"],"input_types":["text prompts","message arrays","model names"],"output_types":["token counts (input, output, total)","cost estimates (USD or other currency)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_11","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.exponential.backoff","name":"error handling and retry logic with exponential backoff","description":"Implements automatic retry logic with exponential backoff for transient errors (rate limits, timeouts, temporary provider outages). Distinguishes between retryable errors (429, 503) and non-retryable errors (401, 404). Configurable retry count and backoff strategy. Integrates with middleware for custom error handling and recovery logic.","intents":["Handle rate limiting and temporary provider outages gracefully","Automatically retry failed requests without application-level logic","Configure retry behavior per request or globally","Implement custom error recovery strategies via middleware"],"best_for":["Production applications requiring resilience to provider outages","High-volume applications prone to rate limiting","Applications with strict SLA requirements"],"limitations":["Retries increase latency; worst-case latency can be 30+ seconds with default backoff","Retries increase API costs if the underlying error is not transient","Custom retry logic requires middleware; not configurable via simple options","Exponential backoff may not be optimal for all error scenarios"],"requires":["TypeScript 4.5+","@ai-sdk/core package"],"input_types":["LLM requests","retry configuration (max retries, backoff strategy)"],"output_types":["successful LLM responses after retries","error objects with retry metadata"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_12","uri":"capability://code.generation.editing.type.safe.function.definitions.with.zod.schema.integration","name":"type-safe function definitions with zod schema integration","description":"Enables defining tool functions with full type safety using Zod schemas for parameter validation. Converts Zod schemas to JSON Schema for provider function calling APIs. Provides TypeScript type inference on function parameters and return types. Validates function arguments at runtime and provides detailed error messages on validation failure.","intents":["Define tool functions with compile-time type safety","Validate tool function arguments at runtime","Generate JSON Schema from TypeScript types automatically","Build type-safe agentic workflows with guaranteed parameter types"],"best_for":["TypeScript teams prioritizing type safety","Applications with complex tool definitions","Teams using Zod for runtime validation"],"limitations":["Zod schema complexity is limited; deeply nested schemas may cause issues","Type inference is limited to Zod-defined types; custom types require manual schema definition","JSON Schema generation may not capture all Zod constraints (e.g., custom validators)","Runtime validation adds ~5-10ms overhead per tool call"],"requires":["TypeScript 4.7+","Zod library","@ai-sdk/core package"],"input_types":["Zod schemas","TypeScript function definitions"],"output_types":["typed function parameters","JSON Schema for provider APIs","validation errors"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_13","uri":"capability://automation.workflow.edge.runtime.compatibility.and.serverless.deployment","name":"edge runtime compatibility and serverless deployment","description":"Designed to run on edge runtimes (Cloudflare Workers, Vercel Edge Functions, Deno Deploy) and serverless platforms (AWS Lambda, Google Cloud Functions) with minimal dependencies. Uses only standard Web APIs (fetch, ReadableStream, TextEncoder) to ensure compatibility. Avoids Node.js-specific APIs that aren't available in edge runtimes. Supports streaming responses in edge environments.","intents":["Deploy AI applications to edge runtimes for lower latency","Build serverless AI functions without Node.js dependencies","Stream LLM responses from edge functions","Reduce cold start time by using lightweight edge runtimes"],"best_for":["Applications requiring low-latency LLM responses","Serverless deployments on Vercel, Cloudflare, or AWS Lambda","Global applications using edge networks"],"limitations":["Edge runtimes have limited memory and execution time; large models or long-running agents may timeout","Some providers may have higher latency from edge locations","Debugging edge functions is difficult; limited logging and error reporting","Cold start latency is still significant for first request (typically 100-500ms)"],"requires":["Edge runtime (Cloudflare Workers, Vercel Edge Functions, etc.) or serverless platform","TypeScript 4.5+","@ai-sdk/core package"],"input_types":["HTTP requests","LLM provider configuration"],"output_types":["HTTP responses with streaming LLM output"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_14","uri":"capability://code.generation.editing.generative.ui.component.streaming.with.react","name":"generative ui component streaming with react","description":"Enables streaming AI-generated React components to the client in real-time using React Server Components and createStreamableUI(). The LLM generates component code or descriptions, which are converted to React components and streamed to the client as they're generated. Supports progressive rendering where UI updates arrive incrementally, improving perceived performance.","intents":["Build applications where the LLM generates UI components on-the-fly","Stream AI-generated dashboards or reports to the client","Create progressive rendering experiences with incremental UI updates","Generate complex UIs from natural language descriptions"],"best_for":["Next.js 13+ applications with Server Components","Generative UI applications","Real-time dashboard or report generation"],"limitations":["Requires Next.js 13+ with App Router; not portable to other frameworks","Streaming JSX requires careful serialization; complex components may fail","Interactivity in streamed components requires 'use client' boundaries, complicating architecture","Debugging streaming components is difficult; errors in streamed code are hard to trace","Performance depends on component complexity; large components may cause latency"],"requires":["Next.js 13+ with App Router","React 18.3+ (experimental RSC features)","@ai-sdk/rsc package","TypeScript 4.5+"],"input_types":["text prompts describing desired UI","LLM provider configuration","component templates or examples"],"output_types":["streamed React components","progressive UI updates"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_2","uri":"capability://tool.use.integration.agentic.tool.calling.with.multi.step.reasoning.and.state.management","name":"agentic tool calling with multi-step reasoning and state management","description":"Enables LLMs to call external tools (functions, APIs) through a schema-based function registry. The SDK manages the agentic loop: LLM decides which tool to call, SDK executes the tool, returns results to LLM, LLM reasons about results and calls next tool, etc. Uses provider-native function calling APIs (OpenAI's function_calling, Anthropic's tool_use) with automatic message formatting. Supports parallel tool calls, tool result streaming, and custom tool execution logic via middleware.","intents":["Build AI agents that can interact with APIs, databases, or external systems autonomously","Create multi-step workflows where the LLM decides which tools to use based on reasoning","Implement retrieval-augmented generation (RAG) where the LLM decides when to search a knowledge base","Enable LLM-powered automation that can take actions in external systems"],"best_for":["Teams building AI agents or autonomous workflows","Applications requiring LLM-driven API orchestration","RAG systems where the LLM decides which documents to retrieve"],"limitations":["Tool calling adds 1-3 extra API calls per agent step, increasing latency and cost","No built-in state persistence; requires external database for multi-turn agent memory","Tool execution errors don't automatically trigger recovery; requires custom error handling middleware","Parallel tool calls not supported by all providers (e.g., Mistral has limited support)","Context window grows with each tool call; long agent runs may exceed token limits"],"requires":["TypeScript 4.5+","Provider supporting function calling (OpenAI, Anthropic, Google, Mistral)","Tool definitions as TypeScript functions or Zod schemas","@ai-sdk/core package"],"input_types":["text prompts","tool definitions (functions with Zod schemas)","message history"],"output_types":["tool calls with arguments","tool results","final LLM response after tool execution","agent step history"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_3","uri":"capability://automation.workflow.framework.agnostic.reactive.chat.ui.integration","name":"framework-agnostic reactive chat ui integration","description":"Provides hooks and composables for React, Vue, Svelte, and Angular that manage chat state (messages, loading, errors) and handle streaming responses from the server. The @ai-sdk/react package exports useChat() hook that manages message history, sends requests to a server endpoint, streams responses, and updates UI reactively. Similar composables exist for Vue (useChat), Svelte (createChat), and Angular. Handles optimistic message updates, automatic scroll-to-bottom, and attachment management.","intents":["Build a chat UI that streams LLM responses in real-time without manual fetch/stream handling","Manage chat message history and state without Redux or other state management libraries","Add file attachments to chat messages and send them to the server","Handle loading states, errors, and retries automatically"],"best_for":["Frontend developers building chat interfaces with React, Vue, Svelte, or Angular","Teams using Next.js, SvelteKit, Nuxt, or other full-stack frameworks","Rapid prototyping of chatbot UIs without custom state management"],"limitations":["Requires a server endpoint that implements the AI SDK's expected request/response format (not compatible with arbitrary LLM APIs)","State is client-side only; refreshing the page loses chat history (requires manual persistence to localStorage or database)","Streaming requires server-sent events (SSE) or ReadableStream support; not compatible with simple REST endpoints","Attachment handling is basic; large files or multiple attachments may cause performance issues","No built-in support for voice input/output or rich media beyond text and images"],"requires":["React 16.8+ (for hooks) or Vue 3+, Svelte 3+, Angular 12+","Server endpoint returning streaming responses (compatible with AI SDK's streamText or generateText)","@ai-sdk/react, @ai-sdk/vue, @ai-sdk/svelte, or @ai-sdk/angular package"],"input_types":["user text input","file attachments (images, documents)","message history"],"output_types":["streamed chat responses","message arrays with metadata","loading and error states"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_4","uri":"capability://automation.workflow.react.server.components.rsc.integration.for.server.side.streaming","name":"react server components (rsc) integration for server-side streaming","description":"The @ai-sdk/rsc package enables streaming AI responses directly from React Server Components to the client, bypassing traditional API endpoints. Uses React's experimental createStreamableUI() and createStreamableValue() APIs to stream JSX and data updates. Allows rendering AI-generated UI components on the server and streaming them to the client as they're generated, enabling real-time progressive rendering.","intents":["Stream AI-generated UI components from the server to the client in real-time","Build generative UI applications where the LLM generates React components","Avoid API endpoint boilerplate by streaming directly from Server Components","Create progressive rendering experiences where UI updates arrive incrementally"],"best_for":["Next.js 13+ applications using App Router and Server Components","Teams building generative UI or AI-powered content generation","Applications requiring real-time UI updates from LLM outputs"],"limitations":["Requires Next.js 13+ with App Router; not compatible with Pages Router or other frameworks","React Server Components are still experimental; API may change in future React versions","Streaming JSX requires careful serialization; complex component trees may fail to serialize","Debugging streaming RSC is difficult; errors in streamed components are hard to trace","Client-side interactivity in streamed components requires additional setup with 'use client' boundaries"],"requires":["Next.js 13+ with App Router","React 18.3+ (experimental RSC features)","@ai-sdk/rsc package","TypeScript 4.5+"],"input_types":["text prompts","LLM provider configuration","React components to stream"],"output_types":["streamed JSX elements","streamed data values","progressive UI updates"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_5","uri":"capability://image.visual.provider.native.image.video.and.audio.processing","name":"provider-native image, video, and audio processing","description":"Supports multimodal inputs (images, videos, audio) through provider-specific APIs. For images: accepts base64, URLs, or file buffers and passes them to OpenAI Vision, Google Gemini, or Anthropic Claude with vision capabilities. For audio: transcribes via OpenAI Whisper or Google Speech-to-Text. For video: some providers support video frames or direct video input. Handles format conversion and provider-specific constraints (e.g., image size limits, supported formats).","intents":["Analyze images or screenshots using vision-capable LLMs","Transcribe audio files to text using speech-to-text models","Build multimodal AI applications that process images, video, and text together","Extract text from images (OCR) using vision models"],"best_for":["Applications requiring image analysis or OCR","Voice-based AI assistants using speech-to-text","Multimodal AI systems combining text, image, and audio inputs"],"limitations":["Image size limits vary by provider (OpenAI: 20MB, Anthropic: varies by model); large images must be resized","Video support is limited; only some providers support direct video input, others require frame extraction","Audio transcription is provider-specific; no unified transcription API across providers","Image format support varies (JPEG, PNG, GIF, WebP); some formats require conversion","Vision model quality and cost vary significantly by provider; no abstraction for provider switching"],"requires":["Provider supporting vision/audio (OpenAI, Anthropic, Google)","Image/audio files in supported formats","API keys for the chosen provider"],"input_types":["images (base64, URLs, file buffers)","audio files (WAV, MP3, etc.)","video files (for providers supporting video)","text prompts describing what to analyze"],"output_types":["text descriptions of images","transcribed text from audio","structured data extracted from images","analysis results"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_6","uri":"capability://automation.workflow.middleware.based.observability.and.telemetry.integration","name":"middleware-based observability and telemetry integration","description":"Implements a middleware system that intercepts LLM calls and tool executions, enabling logging, monitoring, and tracing without modifying application code. Middleware receives request/response metadata (tokens, latency, cost, errors) and can send it to observability platforms (Langfuse, OpenTelemetry, custom backends). Built on a chain-of-responsibility pattern where each middleware can log, modify, or reject requests. Integrates with Vercel AI Gateway for centralized monitoring.","intents":["Monitor LLM API usage and costs across the application","Trace multi-step agent executions for debugging and optimization","Log all LLM interactions for compliance or audit purposes","Integrate with observability platforms (Datadog, New Relic, Langfuse) without code changes"],"best_for":["Production applications requiring cost tracking and usage monitoring","Teams using observability platforms (Langfuse, OpenTelemetry, etc.)","Applications with compliance requirements for LLM interaction logging"],"limitations":["Middleware adds ~10-50ms latency per LLM call due to serialization and network I/O","Middleware can't modify LLM responses without breaking type safety; limited to logging and metrics","Custom middleware requires understanding the middleware chain pattern; not intuitive for beginners","Observability platform integrations are provider-specific; no unified abstraction"],"requires":["TypeScript 4.5+","@ai-sdk/core package","Optional: observability platform SDK (Langfuse, OpenTelemetry, etc.)"],"input_types":["LLM request metadata (model, tokens, latency)","tool execution metadata","error information"],"output_types":["logs sent to observability platforms","metrics and traces","cost estimates"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_7","uri":"capability://tool.use.integration.vercel.ai.gateway.with.provider.routing.and.cost.optimization","name":"vercel ai gateway with provider routing and cost optimization","description":"A managed gateway service that routes LLM requests across multiple providers, enabling automatic failover, load balancing, and cost optimization. Sits between the application and LLM providers, intercepting requests and deciding which provider to use based on cost, latency, or availability. Provides centralized monitoring, rate limiting, and caching. Requires minimal code changes; applications point to the gateway instead of individual providers.","intents":["Automatically switch between providers based on cost or availability","Implement provider failover without application-level logic","Cache LLM responses to reduce API costs","Monitor and optimize LLM spending across the organization"],"best_for":["Organizations with high LLM API costs looking to optimize","Applications requiring high availability across multiple providers","Teams wanting centralized LLM monitoring and cost tracking"],"limitations":["Adds network latency (typically 50-200ms) due to gateway hop","Gateway is a single point of failure; requires redundancy for production","Caching is limited to deterministic requests; doesn't work with streaming or dynamic prompts","Provider routing logic is opaque; difficult to debug why a specific provider was chosen","Requires Vercel account and additional infrastructure costs"],"requires":["Vercel account and AI Gateway access","AI SDK configured to use gateway endpoint","API keys for underlying providers"],"input_types":["LLM requests (text, images, etc.)","routing policies (cost, latency, provider preference)"],"output_types":["LLM responses from selected provider","routing metadata (which provider was used, cost)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_8","uri":"capability://tool.use.integration.langchain.and.llamaindex.adapter.integration","name":"langchain and llamaindex adapter integration","description":"Provides adapters that allow AI SDK models and tools to be used within LangChain and LlamaIndex ecosystems. Exposes AI SDK providers as LangChain LanguageModel objects and LlamaIndex LLM objects, enabling seamless integration with existing LangChain chains or LlamaIndex pipelines. Allows using AI SDK's unified provider abstraction within LangChain's agent framework or LlamaIndex's RAG pipeline.","intents":["Use AI SDK providers within existing LangChain applications","Integrate AI SDK with LlamaIndex RAG pipelines","Migrate from LangChain to AI SDK without rewriting existing code","Combine AI SDK's provider abstraction with LangChain's agent framework"],"best_for":["Teams with existing LangChain or LlamaIndex codebases","Developers wanting to leverage AI SDK's provider abstraction in LangChain","Gradual migration paths from LangChain to AI SDK"],"limitations":["Adapter is a thin wrapper; doesn't expose all AI SDK features to LangChain/LlamaIndex","Type safety is reduced at adapter boundaries; some LangChain/LlamaIndex APIs are not fully typed","Performance overhead from adapter layer (typically <10ms per call)","Maintenance burden; adapters must be updated when LangChain or LlamaIndex APIs change"],"requires":["LangChain 0.1+ or LlamaIndex 0.9+","AI SDK @ai-sdk/core package","TypeScript 4.5+"],"input_types":["LangChain chains or LlamaIndex pipelines","AI SDK provider configuration"],"output_types":["LangChain LanguageModel objects","LlamaIndex LLM objects"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vercel--ai__cap_9","uri":"capability://data.processing.analysis.message.content.normalization.and.multimodal.handling","name":"message content normalization and multimodal handling","description":"Normalizes message content across different formats and providers, handling text, images, tool calls, and tool results in a unified structure. Converts between provider-specific message formats (OpenAI's content arrays, Anthropic's content blocks, Google's parts) transparently. Supports multimodal messages with mixed text and image content, and manages tool call/result message types for agentic workflows.","intents":["Build applications that handle mixed text and image content in messages","Normalize messages across different provider APIs without manual conversion","Manage tool call and tool result messages in agentic workflows","Support rich message types (text, images, tool calls) in chat applications"],"best_for":["Multimodal AI applications combining text and images","Agentic systems with tool calling","Applications supporting multiple LLM providers"],"limitations":["Complex message structures (deeply nested multimodal content) may not serialize correctly","Some providers have limitations on message structure (e.g., Anthropic doesn't support images in all positions)","Tool result formatting is provider-specific; some providers have strict requirements","Message history can grow large with images; no built-in compression or summarization"],"requires":["TypeScript 4.5+","@ai-sdk/core package"],"input_types":["text content","image content (base64, URLs)","tool call objects","tool result objects"],"output_types":["normalized message arrays","provider-specific message formats"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["TypeScript 4.5+","Node.js 18+ or edge runtime (Cloudflare Workers, Vercel Edge Functions)","API keys for at least one provider (OpenAI, Anthropic, Google, etc.)","@ai-sdk/core package","Zod library for schema definition (or compatible schema library)","TypeScript 4.7+ for type inference","Provider supporting structured output (OpenAI, Anthropic, Google) or fallback to prompt-based approach","Provider API keys for accurate tokenization","TypeScript 4.7+","Zod library"],"failure_modes":["Provider-specific features (e.g., OpenAI's vision_detail parameter) require manual provider selection, not abstracted","Streaming adds ~50-100ms latency due to adapter layer normalization","Token counting accuracy varies by provider; some providers don't expose token counts in streaming mode","Custom provider parameters must be passed through provider-specific options objects, breaking abstraction","Schema complexity is limited; deeply nested or recursive schemas may cause token bloat or model confusion","Validation failures trigger retries, adding latency (typically 1-3 additional API calls per request)","Some providers (Mistral, Cohere) don't support native structured output, falling back to prompt-based schema injection","Large schemas (>10KB serialized) can exceed context windows or cause performance degradation","Token counting accuracy varies by provider; some providers don't expose exact tokenizers","Streaming responses don't provide token counts until completion","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7702438977312991,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:21.997Z","last_commit":"2026-05-02T18:40:35Z"},"community":{"stars":23979,"forks":4311,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=vercel--ai","compare_url":"https://unfragile.ai/compare?artifact=vercel--ai"}},"signature":"tHY9YarKr5PXmZEstgIo8w0IXyaoGTv/Ke/eX7X5+x2InmCXHjFNz+i0vs3m8/yCn9ksAEWqifsG1HLkZrXNDg==","signedAt":"2026-06-22T07:54:45.646Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vercel--ai","artifact":"https://unfragile.ai/vercel--ai","verify":"https://unfragile.ai/api/v1/verify?slug=vercel--ai","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"}}