{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-openrouter","slug":"openrouter","name":"OpenRouter","type":"webapp","url":"https://openrouter.ai/","page_url":"https://unfragile.ai/openrouter","categories":["llm-apis"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-openrouter__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.request.routing.with.unified.api","name":"multi-provider llm request routing with unified api","description":"Routes API requests to multiple LLM providers (OpenAI, Anthropic, Google, Meta, Mistral, etc.) through a single standardized endpoint, abstracting provider-specific API schemas and authentication. Implements a request normalization layer that translates unified OpenRouter API calls into provider-native formats, handling differences in parameter naming, token counting, and response structures across 100+ models.","intents":["I want to switch between different LLM providers without rewriting my application code","I need to compare model outputs across providers using the same API interface","I want to load-balance requests across multiple providers to optimize cost and latency","I need a single authentication mechanism instead of managing multiple API keys per provider"],"best_for":["AI application developers building provider-agnostic LLM applications","teams evaluating multiple models without vendor lock-in","startups prototyping with cost-sensitive model selection"],"limitations":["Adds network hop latency (~50-200ms) compared to direct provider APIs","Provider-specific features (vision, function calling nuances) may not be fully exposed","Rate limiting is aggregated across providers, not per-provider","Some advanced provider parameters may be lost in normalization layer"],"requires":["OpenRouter API key","HTTP client library (curl, axios, requests, etc.)","Valid API credentials for underlying providers (implicit via OpenRouter)"],"input_types":["JSON request body with messages array","model identifier string","optional system prompt"],"output_types":["JSON response with completion text","token usage metadata","provider attribution"],"categories":["tool-use-integration","api-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openrouter__cap_1","uri":"capability://tool.use.integration.model.agnostic.function.calling.with.schema.translation","name":"model-agnostic function calling with schema translation","description":"Enables function calling across providers with different native function-calling implementations (OpenAI's tool_choice, Anthropic's tool_use, etc.) by accepting a unified JSON schema and translating it to each provider's format. Handles response parsing to extract function calls regardless of provider-specific response structure, normalizing tool_calls into a consistent format.","intents":["I want to use function calling with any model without rewriting tool definitions","I need to switch models mid-project without refactoring function calling logic","I want to compare function calling performance across providers using identical schemas"],"best_for":["developers building agent systems with provider flexibility","teams using function calling as a core feature across multiple models"],"limitations":["Some providers have limited or no function calling support, falling back to text extraction","Complex nested schemas may not translate perfectly across all providers","Tool choice behavior differs between providers (some don't support forced tool selection)"],"requires":["OpenRouter API key","JSON schema definition for functions","model that supports function calling (not all do)"],"input_types":["JSON schema array defining available functions","messages array with function call requests"],"output_types":["normalized tool_calls array with function name and arguments","fallback text extraction if native function calling unavailable"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openrouter__cap_2","uri":"capability://data.processing.analysis.cost.optimized.model.selection.with.pricing.metadata","name":"cost-optimized model selection with pricing metadata","description":"Exposes real-time pricing data (input/output token costs) for all available models, enabling developers to programmatically select models based on cost-performance tradeoffs. Provides model metadata including context window size, training data cutoff, and capabilities, allowing cost-aware routing logic without manual price lookups.","intents":["I want to automatically select the cheapest model that meets my quality requirements","I need to estimate API costs before making requests to budget my application","I want to compare cost-per-capability across different models to optimize spend"],"best_for":["cost-sensitive startups and indie developers","teams building multi-model applications with budget constraints","applications with variable workloads needing dynamic model selection"],"limitations":["Pricing data updates may lag actual provider pricing by hours","Does not account for volume discounts or custom pricing agreements","Cost optimization requires application-level logic; no built-in cost router"],"requires":["OpenRouter API key","ability to parse JSON metadata responses"],"input_types":["model list request"],"output_types":["JSON array with model metadata including pricing, context window, capabilities"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openrouter__cap_3","uri":"capability://text.generation.language.streaming.response.handling.with.provider.normalization","name":"streaming response handling with provider normalization","description":"Supports Server-Sent Events (SSE) streaming for real-time token generation across all providers, normalizing streaming response formats (OpenAI's delta objects, Anthropic's content_block_delta, etc.) into a unified stream format. Handles stream interruption, error propagation, and graceful fallback to non-streaming responses.","intents":["I want to stream model responses to users in real-time regardless of which provider I'm using","I need to handle streaming errors and reconnection without breaking my application","I want to build interactive chat interfaces with live token-by-token updates"],"best_for":["web and mobile applications requiring real-time user feedback","chat interfaces and conversational AI applications","developers building streaming-first LLM applications"],"limitations":["Streaming adds complexity to error handling and retry logic","Some providers have different streaming latency characteristics","Stream interruption handling varies by provider and network conditions","Requires client-side SSE support (not all environments have it)"],"requires":["OpenRouter API key","HTTP client with SSE support (fetch API, axios, etc.)","stream parameter set to true in request"],"input_types":["standard LLM request with stream=true flag"],"output_types":["Server-Sent Events stream with normalized delta objects","stop_reason and usage metadata at stream end"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openrouter__cap_4","uri":"capability://data.processing.analysis.request.logging.and.analytics.with.provider.attribution","name":"request logging and analytics with provider attribution","description":"Automatically logs all API requests and responses with metadata including provider, model, tokens used, latency, and cost. Provides dashboard and API access to request history, enabling usage analytics, cost tracking, and performance monitoring across all providers without application-level instrumentation.","intents":["I want to track API usage and costs across all models and providers in one place","I need to monitor model performance and latency to optimize my application","I want to audit all LLM requests for compliance and debugging purposes"],"best_for":["teams managing multiple LLM applications","applications with compliance or audit requirements","developers optimizing LLM costs and performance"],"limitations":["Logging adds minimal latency but requires additional storage","Log retention policies may limit historical data access","Real-time analytics may have slight delays (minutes to hours)","Sensitive prompt/response data is logged (privacy consideration)"],"requires":["OpenRouter API key","access to OpenRouter dashboard or analytics API"],"input_types":["automatic capture of all API requests"],"output_types":["JSON logs with request/response metadata","aggregated analytics dashboards","cost and usage reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openrouter__cap_5","uri":"capability://data.processing.analysis.context.window.and.token.counting.with.model.specific.accuracy","name":"context window and token counting with model-specific accuracy","description":"Provides accurate token counting for each model using model-specific tokenizers (not generic approximations), accounting for differences in how providers count tokens (e.g., OpenAI vs. Anthropic token boundaries). Exposes context window limits and handles context overflow warnings before requests are sent.","intents":["I want to know exactly how many tokens my request will use before sending it","I need to ensure my prompts fit within a model's context window without trial-and-error","I want to compare token efficiency across different models for the same task"],"best_for":["developers building token-aware applications","applications with strict context window constraints","cost-sensitive applications optimizing token usage"],"limitations":["Token counting may be approximate for some models (not all providers expose exact tokenizers)","Special tokens and formatting may affect actual token count vs. estimate","Context window limits are static; dynamic limits (e.g., based on load) not exposed"],"requires":["OpenRouter API key","model identifier"],"input_types":["text string or messages array"],"output_types":["token count integer","context window limit","overflow warnings"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openrouter__cap_6","uri":"capability://automation.workflow.fallback.and.retry.logic.with.provider.failover","name":"fallback and retry logic with provider failover","description":"Implements automatic failover to alternative providers/models when a request fails, with configurable retry policies (exponential backoff, max retries, timeout handling). Transparently switches providers based on availability, error type, and user-defined fallback chains without requiring application-level retry logic.","intents":["I want my application to automatically retry failed requests with a different provider","I need to ensure high availability by falling back to alternative models if one provider is down","I want to handle rate limiting and transient errors without manual retry logic"],"best_for":["production applications requiring high availability","applications sensitive to provider outages","teams wanting to reduce operational complexity of error handling"],"limitations":["Failover adds latency (retry time + provider switch overhead)","Not all errors are retryable (e.g., authentication failures)","Fallback chains must be configured per-application","Cost implications of retries across multiple providers"],"requires":["OpenRouter API key","optional: custom retry policy configuration"],"input_types":["standard LLM request with optional fallback_models parameter"],"output_types":["successful response from primary or fallback provider","error with provider attribution if all fallbacks exhausted"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openrouter__cap_7","uri":"capability://search.retrieval.model.capability.filtering.and.discovery","name":"model capability filtering and discovery","description":"Exposes structured metadata about model capabilities (vision support, function calling, long context, etc.) enabling programmatic filtering and discovery. Allows querying models by capability (e.g., 'find all models with vision support under $0.01 per 1K tokens') without manual research or hardcoded model lists.","intents":["I want to find all models that support vision without manually checking each provider's docs","I need to select a model that supports function calling and fits my budget","I want to discover new models that match my application's requirements automatically"],"best_for":["developers building capability-aware model selection logic","applications with dynamic model requirements","teams evaluating new models as they become available"],"limitations":["Capability metadata may lag actual model capabilities by days/weeks","Some capabilities are subjective (e.g., 'good at reasoning') and not standardized","Filtering logic must be implemented by application; no built-in smart selection"],"requires":["OpenRouter API key","ability to parse and filter JSON metadata"],"input_types":["capability filter query (e.g., vision=true, function_calling=true)"],"output_types":["JSON array of models matching filter criteria with full metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openrouter__cap_8","uri":"capability://automation.workflow.request.rate.limiting.and.quota.management","name":"request rate limiting and quota management","description":"Manages rate limits and quotas across multiple providers, tracking usage per model, provider, and time window. Implements client-side rate limiting to prevent hitting provider limits, with configurable quota policies and transparent quota enforcement without application-level tracking.","intents":["I want to stay within my API quota without manually tracking usage","I need to distribute requests fairly across multiple models and providers","I want to implement usage-based pricing tiers without custom quota logic"],"best_for":["multi-tenant applications with per-user quotas","applications with strict budget constraints","teams managing shared API keys across multiple services"],"limitations":["Client-side rate limiting doesn't prevent provider-side rate limit errors","Quota enforcement adds latency (checking before each request)","Distributed systems may have quota synchronization issues","No built-in quota reset scheduling; requires manual configuration"],"requires":["OpenRouter API key","optional: custom quota policy configuration"],"input_types":["quota configuration (requests per minute, tokens per day, etc.)"],"output_types":["request allowed/denied decision","quota usage metrics","quota reset time"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openrouter__cap_9","uri":"capability://memory.knowledge.prompt.caching.and.response.deduplication","name":"prompt caching and response deduplication","description":"Caches identical prompts and their responses to avoid redundant API calls, with configurable cache TTL and invalidation policies. Detects duplicate requests and returns cached responses transparently, reducing latency and costs for repeated queries without application-level caching logic.","intents":["I want to avoid paying for duplicate API calls when users ask the same question","I need to speed up responses for frequently asked questions using cached results","I want to implement prompt caching without building custom cache infrastructure"],"best_for":["applications with repetitive user queries (FAQs, documentation)","cost-sensitive applications with high query overlap","chat applications with common follow-up questions"],"limitations":["Cache hits only work for identical prompts (no semantic similarity matching)","Cache invalidation must be manually configured or time-based","Cached responses may become stale if model behavior changes","Privacy consideration: cached responses stored on OpenRouter servers"],"requires":["OpenRouter API key","optional: cache configuration (TTL, invalidation policy)"],"input_types":["standard LLM request"],"output_types":["cached response (if hit) or fresh response (if miss)","cache hit/miss indicator in response metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key","HTTP client library (curl, axios, requests, etc.)","Valid API credentials for underlying providers (implicit via OpenRouter)","JSON schema definition for functions","model that supports function calling (not all do)","ability to parse JSON metadata responses","HTTP client with SSE support (fetch API, axios, etc.)","stream parameter set to true in request","access to OpenRouter dashboard or analytics API","model identifier"],"failure_modes":["Adds network hop latency (~50-200ms) compared to direct provider APIs","Provider-specific features (vision, function calling nuances) may not be fully exposed","Rate limiting is aggregated across providers, not per-provider","Some advanced provider parameters may be lost in normalization layer","Some providers have limited or no function calling support, falling back to text extraction","Complex nested schemas may not translate perfectly across all providers","Tool choice behavior differs between providers (some don't support forced tool selection)","Pricing data updates may lag actual provider pricing by hours","Does not account for volume discounts or custom pricing agreements","Cost optimization requires application-level logic; no built-in cost router","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=openrouter","compare_url":"https://unfragile.ai/compare?artifact=openrouter"}},"signature":"zfbGwTLK++bF/GTL+OMxueK5kWlAf5RMfH3wfIgUO5ot9RkbwCKRmjXpkQ9J/Tybj4RbDe2HyBrrjeQ3J3H9Aw==","signedAt":"2026-06-20T12:11:39.841Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openrouter","artifact":"https://unfragile.ai/openrouter","verify":"https://unfragile.ai/api/v1/verify?slug=openrouter","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"}}