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Implements a decision tree that evaluates incoming prompts against tier criteria and selects the lowest-cost model capable of handling the request, reducing API spend while maintaining quality thresholds.","intents":["I want to use cheaper models for simple queries and reserve expensive models for complex reasoning tasks","I need to optimize LLM API costs by routing requests intelligently based on complexity","I want to define custom rules for which model tier handles which types of requests"],"best_for":["teams managing multi-model LLM deployments with budget constraints","developers building cost-conscious chatbots or agents","organizations with heterogeneous model availability (local + cloud models)"],"limitations":["tier definitions are static at configuration time — no dynamic tier adjustment based on real-time model performance","no built-in cost tracking or analytics per tier — requires external logging to measure savings","routing decisions are synchronous — adds latency if tier evaluation logic is complex"],"requires":["Node.js 14+ or compatible JavaScript runtime","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","configuration file defining model tiers and selection criteria"],"input_types":["text prompts","structured routing metadata (e.g., complexity score, user tier)"],"output_types":["selected model identifier","routing decision metadata"],"categories":["planning-reasoning","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kb-labs-llm-router__cap_1","uri":"capability://automation.workflow.automatic.fallback.chaining.across.model.providers","name":"automatic fallback chaining across model providers","description":"Automatically cascades requests to alternative models when the primary model fails, times out, or returns an error. Maintains a fallback chain (e.g., GPT-4 → Claude → Llama) and transparently retries with the next model in sequence without requiring application-level retry logic, with configurable backoff and circuit-breaker patterns.","intents":["I want requests to automatically retry with a different model if the primary one fails","I need to handle provider outages gracefully by falling back to alternative models","I want to define a priority order of models so the best available one is always used"],"best_for":["production systems requiring high availability across multiple LLM providers","teams without dedicated DevOps infrastructure for complex retry logic","applications serving latency-sensitive users who can't tolerate provider downtime"],"limitations":["fallback chains are linear — no intelligent selection of next model based on error type","no built-in cost tracking across fallback attempts — may incur unexpected charges if fallbacks are frequent","timeout and retry behavior must be configured per chain — no adaptive tuning based on historical performance","fallback latency compounds if multiple models fail in sequence"],"requires":["Node.js 14+","credentials for at least 2 LLM providers","fallback chain configuration (ordered list of models)"],"input_types":["text prompts","error types and status codes"],"output_types":["LLM response from first successful model in chain","fallback attempt metadata (which model was used, how many retries)"],"categories":["automation-workflow","reliability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kb-labs-llm-router__cap_2","uri":"capability://planning.reasoning.request.aware.routing.with.metadata.driven.model.selection","name":"request-aware routing with metadata-driven model selection","description":"Routes requests to models based on attached metadata (e.g., user tier, request priority, domain) rather than just request content. Evaluates metadata against routing rules at request time to select the optimal model, enabling use cases like 'premium users get GPT-4, free users get GPT-3.5' or 'code generation requests use specialized models'. Metadata can be attached by middleware or application logic before routing.","intents":["I want to serve different model tiers to different user segments (e.g., premium vs. free)","I need to route requests to specialized models based on the task type (code, translation, summarization)","I want to prioritize certain requests (e.g., paying customers) to use faster/better models"],"best_for":["SaaS platforms with tiered user models","multi-tenant applications requiring per-tenant model policies","systems with diverse request types (code, text, analysis) requiring specialized models"],"limitations":["metadata schema is not enforced — requires application discipline to attach consistent metadata","routing rules are evaluated synchronously — complex rule sets may add measurable latency","no built-in audit trail of routing decisions — requires external logging for compliance","metadata-based routing can lead to unpredictable model selection if rules conflict"],"requires":["Node.js 14+","application code to attach metadata to requests","routing rules configuration (metadata patterns → model mappings)"],"input_types":["text prompts","metadata object (user_id, tier, task_type, priority, etc.)"],"output_types":["selected model identifier","routing decision with metadata context"],"categories":["planning-reasoning","multi-tenancy"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kb-labs-llm-router__cap_3","uri":"capability://tool.use.integration.model.provider.abstraction.with.unified.interface","name":"model provider abstraction with unified interface","description":"Provides a single API surface for interacting with multiple LLM providers (OpenAI, Anthropic, Ollama, etc.) by normalizing their different request/response formats into a common schema. Handles provider-specific quirks (token limits, parameter names, response structures) transparently, allowing applications to switch providers without code changes. Implements adapter pattern with provider-specific implementations for each API.","intents":["I want to write code once and switch between different LLM providers without rewriting","I need to abstract away provider-specific API differences in my application","I want to test my application with different models without changing business logic"],"best_for":["teams evaluating multiple LLM providers","applications requiring provider portability","developers building LLM-agnostic frameworks or libraries"],"limitations":["abstraction may not expose provider-specific advanced features (e.g., vision capabilities, function calling nuances)","lowest-common-denominator API may limit access to cutting-edge model capabilities","provider-specific error handling is normalized — may lose granular error context","performance characteristics vary significantly across providers — abstraction doesn't hide latency differences"],"requires":["Node.js 14+","API keys for desired providers","provider-specific SDK or HTTP client (handled by router)"],"input_types":["normalized prompt object (role, content, system message, etc.)"],"output_types":["normalized response object (text, tokens, finish_reason, etc.)"],"categories":["tool-use-integration","abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kb-labs-llm-router__cap_4","uri":"capability://automation.workflow.dynamic.model.availability.detection.and.circuit.breaking","name":"dynamic model availability detection and circuit breaking","description":"Monitors model availability in real-time by tracking request success/failure rates and response times, automatically removing models from rotation when they exceed error thresholds or timeout consistently. Implements circuit breaker pattern that temporarily disables failing models and periodically tests them for recovery, preventing cascading failures and wasted API calls to unavailable endpoints.","intents":["I want the router to automatically stop using a model if it's consistently failing","I need to detect provider outages and route around them without manual intervention","I want to prevent wasted API calls to models that are temporarily unavailable"],"best_for":["production systems requiring automatic failover","teams without 24/7 on-call monitoring","applications with strict latency SLAs that can't tolerate slow models"],"limitations":["circuit breaker state is in-memory — not shared across multiple instances without external state store","availability detection is reactive (based on failures) not proactive (health checks)","no built-in metrics export — requires custom logging to understand circuit breaker behavior","recovery testing (half-open state) may send requests to still-failing models"],"requires":["Node.js 14+","configuration for error thresholds and circuit breaker timeouts","optional: external state store (Redis) for distributed circuit breaker state"],"input_types":["request results (success/failure, latency)"],"output_types":["circuit breaker state (open/closed/half-open)","availability metrics"],"categories":["automation-workflow","reliability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kb-labs-llm-router__cap_5","uri":"capability://automation.workflow.request.batching.and.cost.aggregation.across.models","name":"request batching and cost aggregation across models","description":"Groups multiple requests destined for the same model and sends them in batch operations where supported (e.g., OpenAI Batch API), reducing per-request overhead and API costs. Tracks costs per model and aggregates them for billing/analytics, providing visibility into which models are consuming budget. Implements batching with configurable window sizes and timeout thresholds to balance latency vs. cost savings.","intents":["I want to reduce API costs by batching requests to models that support batch operations","I need to track and report on LLM spending by model and user","I want to optimize throughput for non-latency-sensitive workloads by batching requests"],"best_for":["applications with high request volume and flexible latency requirements","teams needing detailed cost attribution per model","batch processing pipelines (data labeling, content generation, analysis)"],"limitations":["batching adds latency — not suitable for real-time interactive applications","not all providers support batch APIs — fallback to individual requests for unsupported models","batch cost savings vary by provider — may not be significant for all use cases","batching state is in-memory — requires external persistence for distributed systems"],"requires":["Node.js 14+","providers that support batch operations (OpenAI Batch API, etc.)","configuration for batch window size and timeout"],"input_types":["multiple text prompts"],"output_types":["batch results with cost per request","aggregated cost metrics"],"categories":["automation-workflow","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kb-labs-llm-router__cap_6","uri":"capability://data.processing.analysis.context.aware.prompt.optimization.and.token.management","name":"context-aware prompt optimization and token management","description":"Automatically optimizes prompts before sending to models by truncating context, removing redundant information, or reformatting based on model token limits and capabilities. Tracks token usage per request and model, enforcing hard limits to prevent exceeding context windows. Implements strategies like sliding window context, summarization, or hierarchical chunking to fit large contexts into model limits while preserving semantic meaning.","intents":["I want to automatically fit large documents into model context windows without manual truncation","I need to track token usage and prevent exceeding model limits","I want to optimize prompts for different models with different context window sizes"],"best_for":["applications processing long documents or large conversation histories","teams managing multiple models with different context limits","systems requiring predictable token costs"],"limitations":["automatic optimization may lose important context — requires tuning per use case","token counting is approximate for some models — actual usage may differ","optimization strategies (summarization, chunking) add latency before API calls","no built-in semantic understanding — optimization is heuristic-based"],"requires":["Node.js 14+","tokenizer for target models (built-in or external)","configuration for optimization strategies and token limits"],"input_types":["text prompts","context documents","conversation histories"],"output_types":["optimized prompts","token usage metrics"],"categories":["data-processing-analysis","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kb-labs-llm-router__cap_7","uri":"capability://data.processing.analysis.performance.profiling.and.model.benchmarking","name":"performance profiling and model benchmarking","description":"Collects latency, throughput, and quality metrics for each model in the routing configuration, enabling data-driven decisions about tier assignments and fallback ordering. Provides built-in benchmarking tools to compare models on representative workloads, with support for custom evaluation metrics. Stores historical performance data to identify trends and detect performance regressions.","intents":["I want to benchmark different models on my specific workloads to choose the best tier assignments","I need to track model performance over time to detect regressions or improvements","I want data-driven insights into which models are fastest/cheapest for my use cases"],"best_for":["teams optimizing model selection for specific domains","organizations with strict SLA requirements","developers evaluating new models before production deployment"],"limitations":["benchmarking requires running representative workloads — adds upfront cost and time","performance metrics are workload-specific — results may not generalize","historical data storage requires external persistence","no built-in statistical significance testing — requires manual interpretation"],"requires":["Node.js 14+","representative test dataset","optional: external metrics storage (database, time-series DB)"],"input_types":["test prompts","expected outputs (for quality evaluation)"],"output_types":["latency metrics (p50, p95, p99)","throughput metrics","cost per request","quality scores"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or compatible JavaScript runtime","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","configuration file defining model tiers and selection criteria","Node.js 14+","credentials for at least 2 LLM providers","fallback chain configuration (ordered list of models)","application code to attach metadata to requests","routing rules configuration (metadata patterns → model mappings)","API keys for desired providers","provider-specific SDK or HTTP client (handled by router)"],"failure_modes":["tier definitions are static at configuration time — no dynamic tier adjustment based on real-time model performance","no built-in cost tracking or analytics per tier — requires external logging to measure savings","routing decisions are synchronous — adds latency if tier evaluation logic is complex","fallback chains are linear — no intelligent selection of next model based on error type","no built-in cost tracking across fallback attempts — may incur unexpected charges if fallbacks are frequent","timeout and retry behavior must be configured per chain — no adaptive tuning based on historical performance","fallback latency compounds if multiple models fail in sequence","metadata schema is not enforced — requires application discipline to attach consistent metadata","routing rules are evaluated synchronously — complex rule sets may add measurable latency","no built-in audit trail of routing decisions — requires external logging for compliance","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.15788683286660288,"quality":0.26,"ecosystem":0.55,"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":1896,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kb-labs-llm-router","compare_url":"https://unfragile.ai/compare?artifact=kb-labs-llm-router"}},"signature":"MhC2ScD2TTbjPFWWBrK7UqeX2YgKst9sq5c6SEoa4GzbvcaZ0SF1MpBM0JsaLDdLVysT7YjeS06GCxXJj3+aDw==","signedAt":"2026-06-20T00:42:54.383Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kb-labs-llm-router","artifact":"https://unfragile.ai/kb-labs-llm-router","verify":"https://unfragile.ai/api/v1/verify?slug=kb-labs-llm-router","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"}}