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The SDK sends the schema to the provider (if supported natively like OpenAI's JSON mode or Anthropic's structured output), or implements client-side validation and retry logic to ensure the LLM produces valid JSON matching the schema.","intents":["I want the LLM to return structured data (e.g., extracted entities, categorized content) that I can immediately use in my application","I need guaranteed JSON output that matches my application's data model without manual parsing","I want to extract information from unstructured text and get it back in a predictable format"],"best_for":["data extraction pipelines","applications requiring structured LLM outputs for downstream processing","teams building form-filling or entity extraction features"],"limitations":["Schema validation adds latency; complex schemas may require multiple retries","Not all providers support native JSON mode; fallback to client-side validation is slower","Very large schemas (>10KB) may exceed context limits or cause parsing errors","Nested or recursive schemas have limited support"],"requires":["JSON schema definition for the desired output format","Provider with JSON mode support (OpenAI, Anthropic) or fallback validation logic","Node.js 16+ for schema validation"],"input_types":["text prompts","JSON schema definitions","validation rules"],"output_types":["validated JSON objects","parsed and typed data structures","validation error messages if schema mismatch"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-tanstack-ai__cap_4","uri":"capability://data.processing.analysis.embedding.generation.and.vector.storage.integration","name":"embedding generation and vector storage integration","description":"Provides a unified interface for generating embeddings from text using multiple providers (OpenAI, Cohere, Hugging Face, local models), with built-in integration points for vector databases (Pinecone, Weaviate, Supabase, etc.). 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Supports both simple string interpolation and more complex template engines, allowing developers to define reusable prompt patterns with placeholders for dynamic content like user input, context, or retrieved documents.","intents":["I want to define prompt templates once and reuse them across different parts of my application","I need to safely inject user input into prompts without manual string concatenation","I want to conditionally include sections of prompts based on runtime variables"],"best_for":["applications with multiple prompt patterns","teams standardizing prompt engineering across projects","developers building prompt management systems"],"limitations":["No built-in prompt versioning or A/B testing","Template syntax is basic; complex logic requires custom functions","No automatic prompt optimization or cost estimation based on template content","Variable injection is not escaped by default; developers must sanitize user input"],"requires":["Template definitions with placeholder syntax","Variable values to interpolate","Node.js 16+"],"input_types":["template strings with placeholders","variable objects","formatting options"],"output_types":["interpolated prompt strings","formatted messages ready for LLM"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-tanstack-ai__cap_7","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation","name":"token counting and cost estimation","description":"Calculates token counts for prompts and completions using provider-specific tokenizers (tiktoken for OpenAI, Anthropic's tokenizer, etc.), and estimates API costs based on model pricing. 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Distinguishes between retryable errors (429, 503) and permanent failures (401, 404), and provides hooks for custom error handling and recovery strategies.","intents":["I want my application to automatically retry failed LLM requests without manual error handling","I need to handle rate limits gracefully without losing requests","I want to implement custom error recovery logic for specific failure scenarios"],"best_for":["production applications requiring reliability","high-volume LLM applications prone to rate limiting","teams implementing resilient AI pipelines"],"limitations":["Retry logic adds latency; exponential backoff can delay responses by seconds","No built-in circuit breaker pattern; cascading failures may still occur","Retries consume tokens and incur costs even if ultimately unsuccessful","Custom error handlers may introduce application-specific logic that breaks portability"],"requires":["Configuration for retry count and backoff strategy","Error handling callbacks (optional)","Node.js 16+"],"input_types":["LLM requests","retry configuration (max attempts, backoff multiplier)","error handler functions"],"output_types":["successful responses after retries","final error if all retries exhausted","retry metadata (attempt count, backoff delay)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-tanstack-ai__cap_9","uri":"capability://code.generation.editing.typescript.type.generation.from.llm.schemas","name":"typescript type generation from llm schemas","description":"Automatically generates TypeScript types from JSON schemas used in structured output or tool definitions, providing compile-time type safety for LLM responses and tool parameters. Integrates with the SDK's structured output and tool calling to ensure type consistency between schema definitions and application code.","intents":["I want TypeScript types for LLM responses so I get IDE autocomplete and compile-time checking","I need to ensure my tool definitions and their implementations have matching types","I want to avoid manually writing TypeScript interfaces that duplicate my JSON schemas"],"best_for":["TypeScript projects using TanStack AI","teams with strict type safety requirements","developers building type-safe AI agents"],"limitations":["Type generation is one-way; changes to TypeScript types don't update schemas","Complex recursive or circular schemas may not generate valid TypeScript","Generated types can be verbose for deeply nested schemas","Requires TypeScript 4.5+ for full feature support"],"requires":["TypeScript 4.5+","JSON schema definitions","Build step to generate types (or runtime generation)"],"input_types":["JSON schema definitions","schema metadata (description, examples)"],"output_types":["TypeScript type definitions","interface declarations","type guards (optional)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ or modern browser runtime","API keys for at least one supported provider (OpenAI, Anthropic, Google, Azure, etc.)","@tanstack/ai package installed via npm","Node.js 16+ with async/await support","Provider that supports streaming (OpenAI, Anthropic, Google, etc.)","Client-side code capable of handling async iterators or event streams","React 16.8+ (for hooks)","Next.js 13+ (for server actions)","@tanstack/ai package","Node.js 16+"],"failure_modes":["Normalization layer adds ~50-100ms overhead per request due to schema mapping","Not all provider-specific features (e.g., vision capabilities, function calling variants) are uniformly exposed","Streaming responses require provider-specific handling for error recovery","Streaming state is ephemeral — no built-in persistence of partial responses","Error recovery mid-stream may result in incomplete outputs","Some providers (e.g., Azure OpenAI) have different stream termination semantics requiring custom handling","Hooks are React-specific; no Vue, Svelte, or vanilla JS support","Server actions are Next.js-specific; not compatible with other frameworks","State management is local to the component; no global state integration (Redux, Zustand, etc.)","Streaming requires WebSocket or Server-Sent Events; not compatible with all hosting environments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.33037660997791557,"quality":0.34,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.474Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":100632,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=tanstack-ai","compare_url":"https://unfragile.ai/compare?artifact=tanstack-ai"}},"signature":"2n4FzzHAM6WtaZp7JoET9/IWGulVCI6mL3sXzZDLkWy4bD3rnPnJ1JLr/SR0lqhGh57yJJNnYVbd5P4L827xCQ==","signedAt":"2026-06-21T11:31:49.646Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tanstack-ai","artifact":"https://unfragile.ai/tanstack-ai","verify":"https://unfragile.ai/api/v1/verify?slug=tanstack-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"}}