{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-portkey-ai--gateway","slug":"portkey-ai--gateway","name":"gateway","type":"api","url":"https://portkey.ai/features/ai-gateway","page_url":"https://unfragile.ai/portkey-ai--gateway","categories":["llm-apis"],"tags":["ai-gateway","gateway","generative-ai","hacktoberfest","langchain","llm","llm-gateway","llmops","llms","mcp","mcp-client","mcp-gateway","mcp-servers","model-router","openai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-portkey-ai--gateway__cap_0","uri":"capability://tool.use.integration.multi.provider.request.routing.with.fallback.and.load.balancing","name":"multi-provider request routing with fallback and load balancing","description":"Routes incoming requests across 70+ AI providers (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Azure OpenAI, Cohere, etc.) using configurable strategies including fallback chains, load balancing, and conditional routing. Implements recursive target orchestration via tryTargetsRecursively() that attempts providers sequentially with exponential backoff retry logic (up to 5 attempts), automatically falling back to next provider on failure. Supports single-target, fallback, and load-balanced modes with provider-specific request/response transformation.","intents":["Route requests to multiple LLM providers without rewriting application code","Implement automatic failover when primary provider is unavailable or rate-limited","Distribute load across multiple providers to reduce latency and cost","Switch providers conditionally based on request parameters or provider health","Recursively chain providers so fallback targets can themselves have fallbacks"],"best_for":["Teams building multi-provider LLM applications to avoid vendor lock-in","Production systems requiring high availability across provider outages","Cost-optimization scenarios needing dynamic provider selection","LLMOps platforms managing customer requests across heterogeneous provider ecosystems"],"limitations":["Retry logic adds latency on provider failures (exponential backoff up to 5 attempts)","Recursive fallback chains require careful configuration to avoid cascading timeouts","Provider-specific API incompatibilities still require request/response transformation per provider","No built-in cost optimization — requires external logic to select cheapest provider"],"requires":["Node.js 18+, Cloudflare Workers, Bun, or Deno runtime","Valid API keys for target providers in environment or request headers","Configuration schema defining targets array with provider, apiKey, and routing strategy"],"input_types":["JSON request body (chat completions, text generation, embeddings format)","HTTP headers with provider-specific credentials and routing config","Configuration objects specifying target providers and fallback chains"],"output_types":["JSON response matching OpenAI API format (transformed from provider-native format)","Streaming responses via Server-Sent Events (SSE) for compatible providers","Error responses with provider-specific error codes normalized to OpenAI schema"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_1","uri":"capability://data.processing.analysis.provider.agnostic.request.response.transformation","name":"provider-agnostic request/response transformation","description":"Abstracts provider-specific API differences by transforming incoming requests to provider-native formats and normalizing responses back to OpenAI-compatible schema. Each provider (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Azure OpenAI, Cohere) has dedicated transformation logic that maps request parameters (model, messages, temperature, etc.) to provider-specific payloads and transforms provider responses into unified format. Handles streaming responses, token counting, and function-calling schemas across heterogeneous provider APIs.","intents":["Write application code once against OpenAI API and run against any provider without changes","Normalize responses from different providers into consistent schema for downstream processing","Handle provider-specific quirks (e.g., Azure endpoint structure, Bedrock model IDs) transparently","Support streaming responses from providers with different streaming protocols","Map function-calling schemas across providers with different function definition formats"],"best_for":["Application developers wanting provider-agnostic code","Teams migrating between providers without refactoring","Multi-provider SaaS platforms needing unified API surface","LLM frameworks (LangChain, etc.) integrating with gateway"],"limitations":["Not all provider features map 1:1 — some provider-specific capabilities may be unavailable","Transformation adds ~50-100ms latency per request for complex mappings","Custom provider parameters require passthrough headers or extended config","Streaming response transformation requires buffering some data, reducing streaming latency benefits"],"requires":["Provider API keys configured in environment or request headers","Request body conforming to OpenAI chat completions or embeddings schema","Knowledge of which providers support which features (e.g., function calling, vision)"],"input_types":["JSON request body in OpenAI chat completions format (messages, model, temperature, etc.)","HTTP headers with provider-specific credentials and configuration","Optional function definitions in OpenAI function-calling schema"],"output_types":["JSON response in OpenAI chat completions format (choices, usage, finish_reason)","Streaming responses as Server-Sent Events with delta objects","Normalized error responses with provider error codes mapped to standard codes"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_10","uri":"capability://automation.workflow.multi.runtime.deployment.support","name":"multi-runtime deployment support","description":"Built on Hono lightweight web framework supporting deployment across multiple runtime environments: Node.js, Cloudflare Workers, Bun, and Deno. Single codebase compiles to each runtime with minimal changes, enabling deployment flexibility. Runtime-specific features (e.g., real-time SSE log streaming) are conditionally available. Supports both HTTP server mode (Node.js, Bun) and serverless/edge function mode (Cloudflare Workers, Deno). Configuration and provider integrations are runtime-agnostic.","intents":["Deploy gateway to preferred runtime without rewriting code","Run gateway as edge function on Cloudflare Workers for global latency reduction","Deploy to Node.js for traditional server deployments with full feature support","Use Bun or Deno for alternative JavaScript runtimes","Migrate between runtimes without application changes"],"best_for":["Teams wanting runtime flexibility without code changes","Global deployments using Cloudflare Workers for edge execution","Organizations with existing Node.js infrastructure","Developers preferring alternative runtimes (Bun, Deno)"],"limitations":["Some features unavailable on certain runtimes (e.g., SSE log streaming not on Cloudflare Workers)","Runtime-specific performance characteristics vary (Cloudflare Workers have CPU time limits)","Debugging and monitoring differ per runtime","Dependency compatibility may vary across runtimes"],"requires":["Target runtime installed (Node.js 18+, Cloudflare Workers account, Bun, or Deno)","Build tooling for target runtime","Environment variables and configuration for target runtime"],"input_types":["HTTP requests in any runtime environment","Configuration via environment variables or headers"],"output_types":["HTTP responses in any runtime environment","Logs and observability data (format varies by runtime)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_11","uri":"capability://tool.use.integration.model.agnostic.api.endpoint.routing","name":"model-agnostic api endpoint routing","description":"Routes requests to appropriate provider endpoints based on model identifier, abstracting provider-specific endpoint structures. Supports model aliasing so applications can reference models by friendly names (e.g., 'gpt-4') and gateway maps to provider-specific model IDs (e.g., 'gpt-4-turbo-preview'). Handles provider-specific endpoint variations (Azure endpoint structure, Bedrock model ARNs, etc.) transparently. Enables model switching without application code changes by updating configuration.","intents":["Route requests to correct provider endpoint based on model name","Implement model aliasing so applications reference friendly names","Switch models without application code changes by updating configuration","Handle provider-specific model ID formats (Bedrock ARNs, Azure model names) transparently","Support multiple versions of same model across providers"],"best_for":["Multi-provider applications wanting model abstraction","Teams experimenting with different models without code changes","Cost optimization scenarios switching between model versions","Organizations standardizing on friendly model names across providers"],"limitations":["Model aliasing requires configuration maintenance as provider models change","Not all models available on all providers — aliasing must map to available models","Provider-specific model capabilities (e.g., vision, function calling) vary — aliasing may hide incompatibilities","No automatic model capability detection — applications must know which models support which features"],"requires":["Model alias configuration mapping friendly names to provider-specific model IDs","Request specifying model name (friendly or provider-specific)","Provider API keys for target model"],"input_types":["JSON request body with model field (friendly name or provider-specific ID)","Configuration objects mapping model aliases to provider models"],"output_types":["Request routed to correct provider endpoint with provider-specific model ID","Response from provider with model metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_12","uri":"capability://tool.use.integration.function.calling.schema.normalization.across.providers","name":"function-calling schema normalization across providers","description":"Normalizes function-calling schemas across providers with different function definition formats (OpenAI, Anthropic, Google, etc.). Transforms function definitions from OpenAI format to provider-native format before transmission, and transforms provider-native function calls back to OpenAI format in responses. Supports function calling for providers that implement it, with graceful degradation for providers without native function-calling support. Handles tool_choice parameter mapping and function execution context.","intents":["Write function-calling code once against OpenAI schema and run against any provider","Normalize function definitions across providers with different schemas","Transform function calls from providers back to OpenAI format for application processing","Support function calling across heterogeneous provider ecosystem","Handle provider-specific function-calling quirks (tool_choice, parallel calls, etc.)"],"best_for":["Applications using function calling across multiple providers","Teams wanting provider-agnostic function-calling code","Multi-provider agent systems requiring tool use","LLM frameworks (LangChain, etc.) integrating with gateway"],"limitations":["Not all providers support function calling — graceful degradation required","Function-calling schemas differ significantly — some provider features may be unavailable","Transformation adds latency for complex function definitions","Provider-specific function-calling behaviors (parallel calls, tool_choice) may not map cleanly"],"requires":["Function definitions in OpenAI format (name, description, parameters)","Provider support for function calling","Provider API keys and configuration"],"input_types":["JSON request body with functions array in OpenAI format","tool_choice parameter specifying function selection strategy"],"output_types":["Response with function calls in OpenAI format (tool_calls array)","Function call context (id, name, arguments) for application processing"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_13","uri":"capability://planning.reasoning.conditional.routing.based.on.request.parameters","name":"conditional routing based on request parameters","description":"Routes requests to different providers based on conditional logic evaluating request parameters (model, message length, user metadata, etc.). Supports rule-based routing where conditions trigger provider selection, enabling sophisticated routing strategies beyond simple fallback or load balancing. Conditions can reference request fields, user context, and provider metadata. Enables A/B testing by routing subset of requests to experimental providers, cost optimization by routing expensive requests to cheaper providers, and capability-based routing by selecting providers supporting required features.","intents":["Route requests to different providers based on model or request parameters","Implement A/B testing by routing subset of requests to experimental providers","Optimize costs by routing expensive requests to cheaper providers","Route to providers supporting specific capabilities (vision, function calling, etc.)","Implement user-specific routing rules (premium users to faster providers, etc.)"],"best_for":["Cost optimization scenarios with heterogeneous provider pricing","A/B testing new providers or models","Capability-based routing where different providers support different features","User-tier-based routing (premium users to faster/better providers)","Complex routing logic beyond simple fallback or load balancing"],"limitations":["Conditional routing logic adds latency for rule evaluation","Complex routing rules are difficult to debug and maintain","No built-in A/B testing framework — requires custom condition logic","Routing decisions are made per-request without learning from outcomes"],"requires":["Routing rules configuration specifying conditions and target providers","Request parameters to evaluate conditions against","Provider configuration for all potential routing targets"],"input_types":["JSON request body with model, messages, and user metadata","Routing rules configuration with conditions and provider targets"],"output_types":["Request routed to selected provider based on condition evaluation","Routing decision metadata for observability"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_2","uri":"capability://memory.knowledge.intelligent.request.caching.with.semantic.and.simple.modes","name":"intelligent request caching with semantic and simple modes","description":"Implements dual-mode caching system supporting both simple (exact-match) and semantic (embedding-based similarity) caching with configurable TTL. Simple caching stores responses keyed by request hash, returning cached results for identical requests within TTL window. Semantic caching uses embeddings to match semantically similar requests and return cached responses, reducing redundant API calls for paraphrased queries. Caching decisions are configurable per request via headers or configuration, with cache invalidation and TTL management built-in.","intents":["Reduce API costs by caching responses to identical or similar requests","Improve latency for frequently asked questions or common queries","Implement semantic deduplication so 'What is AI?' and 'Tell me about artificial intelligence' hit same cache","Configure cache TTL and invalidation policies per request or globally","Monitor cache hit rates and effectiveness via observability hooks"],"best_for":["Cost-sensitive applications with repetitive user queries","Customer support chatbots handling similar questions repeatedly","Batch processing systems with overlapping requests","Applications with budget constraints wanting to maximize API efficiency"],"limitations":["Semantic caching requires embedding model (adds latency and cost for cache misses)","Cache storage is in-memory by default — no persistence across server restarts","Semantic similarity threshold tuning required to avoid false positives","Cache invalidation strategy must be explicitly configured; no automatic invalidation on provider model updates"],"requires":["Cache mode configuration (simple or semantic) in request headers or config","TTL value in seconds for cache expiration","For semantic caching: embedding model endpoint and configuration"],"input_types":["JSON request body (chat completions, embeddings, text generation)","HTTP headers specifying cache mode, TTL, and semantic similarity threshold","Configuration objects with caching strategy and provider settings"],"output_types":["Cached JSON response matching original provider response format","Cache metadata (hit/miss, age, similarity score for semantic matches)","Cache statistics via observability hooks for monitoring"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_3","uri":"capability://safety.moderation.hooks.based.guardrails.and.request.response.mutation.system","name":"hooks-based guardrails and request/response mutation system","description":"Extensible plugin architecture with 22+ built-in guardrails and mutators that intercept requests and responses at defined lifecycle points. Hooks execute before request transmission (pre-request), after response receipt (post-response), and on errors, enabling validation, transformation, and security enforcement. Guardrails (validation hooks) reject requests/responses based on policies (PII detection, prompt injection, content filtering, etc.). Mutators transform requests/responses (e.g., prompt rewriting, response formatting). Custom hooks can be registered via plugin system with access to request context, provider info, and configuration.","intents":["Enforce security policies (detect and block PII, prompt injection, jailbreak attempts) before sending to provider","Filter or transform responses (remove sensitive data, reformat output, apply custom logic)","Implement compliance checks (content moderation, bias detection) on requests and responses","Add custom business logic (prompt rewriting, response enrichment) without modifying application code","Monitor and log all requests/responses through standardized hook interface"],"best_for":["Regulated industries (healthcare, finance) requiring compliance guardrails","Multi-tenant SaaS platforms needing per-tenant security policies","Teams implementing custom validation or transformation logic","Organizations requiring audit trails and request/response inspection"],"limitations":["Hook execution adds latency (typically 10-50ms per hook depending on complexity)","Guardrails are heuristic-based and may have false positives/negatives","Custom hooks require TypeScript/JavaScript knowledge and plugin registration","No built-in distributed caching for hook results — each request re-evaluates"],"requires":["Hook configuration specifying which hooks to enable and their parameters","For custom hooks: TypeScript plugin implementing Hook interface","Provider API keys and configuration for hooks that need provider context"],"input_types":["Request objects (messages, model, parameters) for pre-request hooks","Response objects (choices, usage, finish_reason) for post-response hooks","Error objects for error hooks","Configuration objects specifying hook parameters (e.g., PII patterns, injection signatures)"],"output_types":["Modified request objects (transformed prompts, filtered parameters)","Modified response objects (filtered content, reformatted output)","Rejection decisions with error messages for guardrail violations","Observability data (hook execution logs, policy violations) via logging hooks"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_4","uri":"capability://automation.workflow.automatic.retry.with.exponential.backoff.and.circuit.breaker","name":"automatic retry with exponential backoff and circuit breaker","description":"Implements resilience patterns including automatic retries (up to 5 attempts) with exponential backoff for transient failures, and circuit breaker pattern to prevent cascading failures when providers are unhealthy. Retry logic distinguishes between retryable errors (rate limits, timeouts, 5xx) and permanent errors (4xx auth failures). Circuit breaker tracks provider health and temporarily stops sending requests to unhealthy providers, with configurable thresholds and recovery strategies. Integrates with timeout configuration to enforce maximum request duration.","intents":["Automatically recover from transient provider failures without application intervention","Reduce failed requests due to rate limiting by retrying with backoff","Prevent cascading failures by stopping requests to unhealthy providers","Configure retry behavior per provider or globally","Monitor provider health and circuit breaker state via observability"],"best_for":["Production systems requiring high availability and fault tolerance","Applications with variable provider reliability","Cost-sensitive systems wanting to maximize successful requests","Multi-provider setups where some providers are less reliable than others"],"limitations":["Retries increase latency on failures (exponential backoff can add seconds for 5 attempts)","Circuit breaker may reject valid requests if health threshold is too aggressive","Retry logic cannot distinguish between transient and permanent failures in all cases","No built-in adaptive retry strategy based on provider-specific error patterns"],"requires":["Configuration specifying max retries (default 5), backoff multiplier, and timeout","Circuit breaker thresholds (failure count, recovery timeout)","Provider API keys and network connectivity"],"input_types":["HTTP request with retry and timeout configuration in headers or config","Provider response or error indicating retry eligibility"],"output_types":["Successful response after retry (if transient failure recovered)","Error response with retry count and final failure reason","Circuit breaker state (open/closed) via observability hooks"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_5","uri":"capability://safety.moderation.request.validation.and.ssrf.protection","name":"request validation and ssrf protection","description":"Validates incoming requests against configuration schema (Options and Targets) before transmission to providers, enforcing required fields, parameter types, and value constraints. Implements Server-Side Request Forgery (SSRF) protection by validating provider URLs against allowlist and preventing requests to internal IP ranges (127.0.0.1, 10.0.0.0/8, etc.). Configuration inheritance and merging allows request-level overrides of global settings while maintaining security constraints. Schema validation uses strict type checking and format validation for model names, API keys, and endpoints.","intents":["Reject malformed requests before they reach providers, reducing wasted API calls","Prevent SSRF attacks by validating provider URLs and blocking internal IP access","Enforce consistent request format across all applications using the gateway","Provide clear error messages for invalid requests to aid debugging","Support configuration inheritance so request-level settings override defaults safely"],"best_for":["Multi-tenant gateways requiring strict request validation","Security-conscious deployments in untrusted network environments","Teams wanting to enforce consistent API contracts","Compliance-heavy organizations requiring request validation audit trails"],"limitations":["Schema validation adds ~5-10ms latency per request","SSRF protection relies on IP allowlist — may block legitimate regional providers","Configuration schema is opinionated and may not support all provider-specific parameters","Custom validation logic requires extending schema or using hooks"],"requires":["Configuration schema defining valid Options and Targets","SSRF allowlist configuration specifying permitted provider URLs","Request body conforming to schema (model, messages, parameters, etc.)"],"input_types":["JSON request body with model, messages, parameters, and provider configuration","HTTP headers with API keys and request-level overrides","Configuration objects defining schema constraints and SSRF rules"],"output_types":["Validation success (request forwarded to provider)","Validation error response with specific field and constraint violation details","SSRF rejection with blocked URL and reason"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_6","uri":"capability://automation.workflow.streaming.response.handling.with.server.sent.events","name":"streaming response handling with server-sent events","description":"Handles streaming responses from providers via Server-Sent Events (SSE) protocol, buffering and transforming provider-native streaming formats into OpenAI-compatible delta objects. Supports streaming for chat completions, text generation, and embeddings where applicable. Streaming responses are transmitted to client in real-time with proper SSE formatting, allowing applications to display responses incrementally. Integrates with hooks system to allow custom streaming transformations and monitoring.","intents":["Stream responses from providers to clients in real-time for better UX","Transform provider-specific streaming formats (e.g., Anthropic's streaming) to OpenAI format","Monitor streaming responses via hooks for logging and compliance","Handle streaming errors gracefully with proper SSE error formatting","Support streaming across multiple providers with fallback (if primary provider fails mid-stream)"],"best_for":["Chat applications and conversational interfaces requiring real-time responses","Long-form text generation where incremental output improves UX","Applications with bandwidth constraints wanting to stream rather than buffer","Compliance-heavy systems needing to inspect streaming responses"],"limitations":["Streaming response transformation requires buffering some data, reducing latency benefits vs direct streaming","Fallback to secondary provider mid-stream is complex and may result in incomplete responses","Streaming error handling is limited — errors mid-stream may not be recoverable","Client must support SSE protocol; not compatible with simple HTTP polling"],"requires":["Provider support for streaming (not all providers support all endpoints)","Client supporting Server-Sent Events (EventSource API or equivalent)","Request with stream=true parameter"],"input_types":["JSON request body with stream=true and chat completions or text generation parameters","HTTP headers with provider credentials and streaming configuration"],"output_types":["Server-Sent Events stream with delta objects (role, content, finish_reason)","Streaming error events with error code and message","Final completion event with usage statistics"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_7","uri":"capability://automation.workflow.configuration.management.with.environment.variables.and.header.overrides","name":"configuration management with environment variables and header overrides","description":"Supports multi-source configuration with hierarchy: environment variables (lowest priority), configuration files/objects, and HTTP request headers (highest priority). Configuration schema defines Options (global settings like timeout, retries) and Targets (provider-specific settings like model, apiKey, endpoint). Configuration inheritance allows request-level settings to override defaults while maintaining constraints. Environment variables are loaded via src/utils/env.ts with support for .env files and runtime overrides. Headers can override any configuration parameter for per-request customization.","intents":["Configure gateway behavior via environment variables for containerized deployments","Override configuration per request via HTTP headers without restarting gateway","Support multi-tenant scenarios where each request specifies its own provider and credentials","Manage secrets (API keys) via environment variables rather than configuration files","Implement configuration inheritance so request-level settings override defaults"],"best_for":["Containerized deployments (Docker, Kubernetes) using environment variables","Multi-tenant SaaS platforms where each request specifies its own provider","Development teams wanting to test different configurations without redeployment","Security-conscious deployments managing secrets via environment variables"],"limitations":["Configuration hierarchy (env vars < config < headers) may be confusing for complex setups","Header-based overrides expose configuration in HTTP logs — sensitive data should use env vars","Configuration validation happens at request time — invalid configs cause request failures","No built-in configuration hot-reload — changes require server restart or header override"],"requires":["Environment variables for sensitive data (API keys, endpoints)",".env file or runtime environment setup","Configuration schema defining valid Options and Targets","HTTP headers for per-request overrides (optional)"],"input_types":["Environment variables (PORTKEY_*, provider-specific keys)","Configuration objects in request body or files","HTTP headers with x-portkey-* prefix for overrides"],"output_types":["Merged configuration object used for request processing","Configuration validation errors if schema constraints violated","Observability data showing which configuration source was used"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_8","uri":"capability://automation.workflow.observability.and.logging.with.real.time.sse.streaming","name":"observability and logging with real-time sse streaming","description":"Provides comprehensive observability via request/response logging, usage analytics, and real-time log streaming. Logs capture request parameters, provider selection, response metadata (tokens, latency), and errors. Usage analytics track API costs, token consumption, and provider performance. Real-time SSE log streaming (Node.js only) allows clients to subscribe to gateway logs and monitor requests as they execute. Integrates with hooks system to allow custom logging and monitoring logic. Supports structured logging for easy parsing and analysis.","intents":["Monitor gateway behavior and provider performance in real-time","Track API costs and token consumption per provider and request","Debug issues by inspecting request/response logs with full context","Implement custom monitoring and alerting based on gateway events","Audit all requests and responses for compliance and security"],"best_for":["Production deployments requiring operational visibility","Cost-tracking systems monitoring API spend across providers","Debugging and troubleshooting multi-provider request flows","Compliance-heavy organizations requiring audit trails","Teams implementing custom monitoring and alerting"],"limitations":["Real-time SSE log streaming only available on Node.js (not Cloudflare Workers, Deno)","Logging adds ~5-10ms latency per request","Log storage is in-memory by default — no persistence across server restarts","Usage analytics require token counting which may not be available for all providers"],"requires":["Node.js runtime for real-time SSE log streaming","Logging configuration specifying log level and format","Client supporting SSE for real-time log subscription"],"input_types":["Request objects (model, messages, provider, parameters)","Response objects (choices, usage, finish_reason, latency)","Error objects with error codes and messages","Custom logging events from hooks"],"output_types":["Structured logs with request ID, timestamp, provider, latency, tokens, cost","Real-time SSE stream of log events for live monitoring","Usage analytics aggregated by provider, model, time period","Error logs with full context for debugging"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-portkey-ai--gateway__cap_9","uri":"capability://automation.workflow.timeout.and.request.duration.enforcement","name":"timeout and request duration enforcement","description":"Enforces maximum request duration via configurable timeout settings, preventing requests from hanging indefinitely on slow or unresponsive providers. Timeout applies to entire request lifecycle including retries, so total duration is bounded. Supports per-provider timeout overrides and global defaults. Timeout errors are distinguished from other failures and trigger appropriate retry logic (timeouts are retryable). Integrates with circuit breaker to mark providers as unhealthy if they consistently timeout.","intents":["Prevent requests from hanging indefinitely on slow providers","Enforce SLA compliance by bounding request duration","Distinguish timeout failures from other errors for better retry decisions","Configure different timeouts for different providers based on expected latency","Monitor timeout rates per provider to identify performance issues"],"best_for":["Production systems with strict SLA requirements","Multi-provider setups with variable provider latency","Cost-sensitive systems wanting to avoid wasted API calls on slow providers","Real-time applications (chat, search) requiring bounded latency"],"limitations":["Timeout applies to entire request lifecycle including retries — may be too aggressive for slow providers","Timeout errors interrupt streaming responses mid-stream","No adaptive timeout based on provider historical latency","Timeout configuration is global or per-provider, not per-request"],"requires":["Timeout configuration in seconds (default typically 30-60s)","Per-provider timeout overrides (optional)","Provider API keys and network connectivity"],"input_types":["HTTP request with timeout configuration in headers or config","Provider response or timeout event"],"output_types":["Timeout error response with timeout duration and elapsed time","Retry attempt if timeout is retryable","Circuit breaker state update if provider consistently times out"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 18+, Cloudflare Workers, Bun, or Deno runtime","Valid API keys for target providers in environment or request headers","Configuration schema defining targets array with provider, apiKey, and routing strategy","Provider API keys configured in environment or request headers","Request body conforming to OpenAI chat completions or embeddings schema","Knowledge of which providers support which features (e.g., function calling, vision)","Target runtime installed (Node.js 18+, Cloudflare Workers account, Bun, or Deno)","Build tooling for target runtime","Environment variables and configuration for target runtime","Model alias configuration mapping friendly names to provider-specific model IDs"],"failure_modes":["Retry logic adds latency on provider failures (exponential backoff up to 5 attempts)","Recursive fallback chains require careful configuration to avoid cascading timeouts","Provider-specific API incompatibilities still require request/response transformation per provider","No built-in cost optimization — requires external logic to select cheapest provider","Not all provider features map 1:1 — some provider-specific capabilities may be unavailable","Transformation adds ~50-100ms latency per request for complex mappings","Custom provider parameters require passthrough headers or extended config","Streaming response transformation requires buffering some data, reducing streaming latency benefits","Some features unavailable on certain runtimes (e.g., SSE log streaming not on Cloudflare Workers)","Runtime-specific performance characteristics vary (Cloudflare Workers have CPU time limits)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3560900628554843,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:58:24.502Z","last_commit":"2026-03-25T09:33:55Z"},"community":{"stars":11564,"forks":1031,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=portkey-ai--gateway","compare_url":"https://unfragile.ai/compare?artifact=portkey-ai--gateway"}},"signature":"bDUBfUDhouVR8tvtFjY16HKGpQ3M6cvVJOooljaraI0H9WzPb927H7e5SJysWQFocYd+lu/dfV1TOc8yPK9KAA==","signedAt":"2026-06-20T07:10:00.158Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/portkey-ai--gateway","artifact":"https://unfragile.ai/portkey-ai--gateway","verify":"https://unfragile.ai/api/v1/verify?slug=portkey-ai--gateway","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"}}