{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-kong--kong","slug":"kong--kong","name":"kong","type":"platform","url":"https://konghq.com/install/","page_url":"https://unfragile.ai/kong--kong","categories":["deployment-infra"],"tags":["ai","ai-gateway","api-gateway","api-management","apis","artificial-intelligence","cloud-native","devops","kubernetes","kubernetes-ingress","kubernetes-ingress-controller","llm-gateway","llm-ops","mcp","mcp-gateway","microservice","microservices","openai-proxy","reverse-proxy","serverless"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-kong--kong__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.api.routing.with.unified.interface","name":"multi-provider llm api routing with unified interface","description":"Kong routes LLM requests to multiple AI providers (OpenAI, Anthropic, Azure, Ollama, etc.) through a single standardized API endpoint, translating request/response formats between providers' native schemas. The gateway maintains a provider registry with format adapters that normalize chat completion, embedding, and streaming requests into provider-specific protocols, enabling seamless provider switching and fallback without client-side changes.","intents":["Route LLM traffic across multiple providers without rewriting client code","Implement provider failover and load balancing for AI workloads","Standardize LLM API contracts across heterogeneous backend providers","Avoid vendor lock-in by abstracting provider-specific API differences"],"best_for":["Teams building multi-cloud AI applications","Organizations standardizing on a single LLM API surface","Enterprises requiring provider redundancy for critical AI services"],"limitations":["Format translation adds ~50-150ms latency per request depending on provider complexity","Streaming responses require buffering strategy to normalize chunking behavior across providers","Custom provider-specific parameters may require passthrough configuration","Rate limiting and quota management must be coordinated across multiple provider accounts"],"requires":["Kong 3.4+","Valid API keys for target LLM providers","Network connectivity to provider endpoints","Configuration of provider routes via Admin API or declarative config"],"input_types":["JSON (chat completion requests, embedding requests)","Streaming request bodies","HTTP headers with authentication tokens"],"output_types":["JSON (normalized chat completion responses, embeddings)","Server-Sent Events (SSE) for streaming responses","HTTP headers with usage metadata"],"categories":["tool-use-integration","ai-gateway"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_1","uri":"capability://safety.moderation.llm.request.response.transformation.and.enrichment","name":"llm request/response transformation and enrichment","description":"Kong intercepts LLM API requests and responses to apply transformations including prompt injection detection, token counting, cost calculation, response filtering, and header injection. The transformation pipeline uses Lua plugins that execute before requests reach the LLM provider and after responses return, enabling cost tracking, security scanning, and response normalization without modifying client or backend code.","intents":["Detect and block prompt injection attacks before they reach LLM providers","Calculate and track token usage and costs per request for billing/chargeback","Filter or redact sensitive data from LLM responses","Inject authentication headers or request IDs for audit trails","Normalize response formats across different LLM providers"],"best_for":["Organizations requiring LLM cost visibility and chargeback","Security-conscious teams implementing defense-in-depth against prompt injection","Multi-tenant platforms needing per-user/per-org cost tracking","Enterprises with compliance requirements for LLM audit trails"],"limitations":["Token counting requires model-specific tokenizer libraries (adds ~20-50ms per request)","Prompt injection detection uses heuristics/regex patterns, not guaranteed to catch all attacks","Response filtering on large outputs (>100K tokens) may impact latency","Cost calculation requires real-time provider pricing data that may lag actual billing"],"requires":["Kong 3.4+","Lua plugin development knowledge or pre-built transformation plugins","Token counter libraries (tiktoken, transformers) if implementing token-based cost tracking","Access to provider pricing APIs or static pricing configuration"],"input_types":["JSON request bodies (chat completion, embedding requests)","HTTP headers (authorization, custom metadata)","Request context (user ID, organization, model name)"],"output_types":["Modified JSON request bodies","Modified JSON response bodies","Audit logs with transformation metadata","Cost/usage metrics"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_10","uri":"capability://automation.workflow.control.plane.and.data.plane.separation.for.hybrid.deployments","name":"control plane and data plane separation for hybrid deployments","description":"Kong supports a hybrid architecture where a control plane (Admin API, configuration management) is separated from data planes (request processing) that connect to the control plane via RPC. The control plane manages configuration and pushes updates to data planes, which apply changes without restarting. Data planes can be deployed in different environments (on-prem, cloud, edge) and sync configuration from the control plane, enabling centralized management with distributed request processing.","intents":["Manage multiple Kong deployments from a single control plane","Deploy Kong data planes in different environments (on-prem, cloud, edge) with centralized configuration","Update configuration on data planes without downtime","Implement multi-region or multi-cloud API gateway deployments","Scale data planes independently from control plane"],"best_for":["Large organizations with multiple deployment environments","Multi-region or multi-cloud deployments requiring centralized management","Teams needing to update configuration without data plane downtime","Enterprises with strict separation between management and data planes"],"limitations":["Control plane becomes a single point of failure for configuration updates; requires high availability setup","Data planes must maintain persistent connection to control plane; network partitions cause stale configuration","Configuration push latency adds delay between control plane update and data plane application (~1-5 seconds)","Debugging configuration issues across multiple data planes is more complex than single-node deployments","RPC communication between control plane and data planes adds operational complexity"],"requires":["Kong 2.1+ (for hybrid mode)","Control plane Kong instance with database (PostgreSQL/Cassandra)","Data plane Kong instances configured to connect to control plane","Network connectivity between control plane and all data planes","TLS certificates for control plane-data plane communication"],"input_types":["Configuration updates (routes, services, plugins, consumers)","RPC messages from control plane to data planes"],"output_types":["Configuration pushed to data planes","Data plane acknowledgment of configuration updates","Metrics and logs from data planes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_11","uri":"capability://tool.use.integration.automatic.mcp.server.generation.from.rest.apis","name":"automatic mcp server generation from rest apis","description":"Kong can automatically generate MCP servers from existing REST APIs by introspecting API schemas (OpenAPI/Swagger) and converting REST endpoints into MCP tools. The generated MCP server exposes REST endpoints as callable tools with parameter schemas derived from API specifications, enabling LLM agents to interact with REST APIs via MCP without manual MCP server implementation.","intents":["Expose existing REST APIs as MCP tools for LLM agents without manual server implementation","Enable LLM agents to discover and call REST API endpoints via MCP","Convert OpenAPI/Swagger specifications into MCP tool schemas automatically","Reduce manual work of implementing MCP servers for existing REST APIs"],"best_for":["Organizations with existing REST APIs wanting to expose them to LLM agents","Teams building agentic AI systems with access to multiple REST APIs","Platforms reducing manual MCP server implementation effort","Enterprises wanting to leverage existing API investments for AI"],"limitations":["Automatic generation works best with well-documented OpenAPI/Swagger specs; incomplete specs require manual adjustment","Complex REST patterns (e.g., pagination, streaming) may not map cleanly to MCP tools","Generated MCP tools inherit REST API limitations (latency, rate limits, error handling)","Authentication/authorization must be configured separately; automatic generation doesn't infer security requirements","Generated tools may expose unintended API endpoints; requires explicit allowlisting"],"requires":["Kong 3.5+","OpenAPI/Swagger specification for REST API","REST API endpoints accessible from Kong","Configuration of MCP server generation rules"],"input_types":["OpenAPI/Swagger specification","REST API endpoint configuration"],"output_types":["MCP server with auto-generated tools","MCP tool schemas derived from API specifications","MCP tool implementations that call REST endpoints"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_12","uri":"capability://automation.workflow.openresty.nginx.based.reverse.proxy.with.lua.extensibility","name":"openresty/nginx-based reverse proxy with lua extensibility","description":"Kong is built on OpenResty (Nginx + Lua JIT), providing a high-performance reverse proxy foundation with Lua scripting for custom logic. The Nginx core handles connection management, TLS termination, and HTTP protocol processing, while Lua runs in the request processing pipeline for plugins, routing, and transformations. This architecture enables Kong to handle high request volumes (>10K req/sec per node) while remaining extensible via Lua without requiring C module compilation.","intents":["Route and proxy API requests with high throughput and low latency","Implement custom gateway logic in Lua without recompiling Nginx","Terminate TLS connections and forward to backend services","Handle connection pooling and keep-alive for backend services","Scale to handle thousands of concurrent connections"],"best_for":["High-traffic API platforms requiring low-latency proxying","Teams wanting Nginx-like performance with Lua extensibility","Organizations needing to avoid C module compilation for customization","Deployments requiring efficient resource utilization (CPU, memory)"],"limitations":["Lua JIT compilation adds startup overhead (~100-500ms) but provides fast execution","Blocking operations in Lua (e.g., synchronous HTTP calls) block the entire request pipeline","Lua memory usage is per-worker; high-memory plugins can increase Kong memory footprint","Debugging Lua errors requires understanding Nginx error logs and Lua stack traces","OpenResty version updates may require testing for compatibility with custom plugins"],"requires":["OpenResty 1.25+","Lua 5.1+ knowledge for custom plugins","Understanding of Nginx request processing phases","Sufficient system resources (CPU, memory) for expected request volume"],"input_types":["HTTP requests (method, path, headers, body)","Nginx/Lua context (connection state, request metadata)"],"output_types":["Proxied HTTP requests to backends","HTTP responses from backends","Lua-generated responses (error pages, redirects)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_13","uri":"capability://automation.workflow.kong.manager.ui.for.visual.configuration.and.monitoring","name":"kong manager ui for visual configuration and monitoring","description":"Kong Manager is a web-based UI that provides visual configuration of routes, services, plugins, and consumers without requiring Admin API calls or YAML editing. The UI displays real-time metrics (request count, latency, error rates), plugin status, and upstream health, enabling operators to manage Kong via a dashboard. The UI integrates with Kong's Admin API and supports role-based access control for multi-user environments.","intents":["Configure Kong routes, services, and plugins via visual UI instead of API calls","Monitor API traffic and performance metrics in real-time","View upstream health status and plugin execution logs","Manage consumers and credentials via UI","Implement role-based access control for Kong management"],"best_for":["Operators preferring visual configuration over API/YAML","Teams with non-technical stakeholders needing visibility into API gateway","Organizations requiring audit trails of configuration changes","Multi-user environments needing role-based access control"],"limitations":["UI may lag behind Admin API in supporting new Kong features","Complex configurations (e.g., advanced plugin parameters) may be easier via API/YAML","UI performance may degrade with very large configurations (>10K routes)","Role-based access control requires Kong Enterprise; open-source version has limited RBAC","UI requires separate deployment and maintenance; adds operational overhead"],"requires":["Kong 2.0+","Kong Manager deployment (separate from Kong Gateway)","Admin API access from Kong Manager to Kong Gateway","Web browser for accessing UI"],"input_types":["UI form inputs (route paths, service URLs, plugin parameters)","Admin API responses (configuration, metrics)"],"output_types":["Configuration updates via Admin API","Metrics dashboards","Audit logs of configuration changes"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_2","uri":"capability://tool.use.integration.model.context.protocol.mcp.traffic.governance.and.routing","name":"model context protocol (mcp) traffic governance and routing","description":"Kong provides native MCP server support, routing MCP client requests to backend MCP servers with authentication, authorization, and observability. The gateway implements MCP protocol handling via Lua plugins that parse MCP JSON-RPC messages, enforce access control policies, and forward requests to configured MCP server upstreams, enabling centralized governance of agentic LLM-to-tool interactions.","intents":["Route MCP client requests to multiple backend MCP servers with load balancing","Enforce authentication and authorization policies on MCP tool access","Monitor and log all MCP interactions for audit and debugging","Implement rate limiting and quota management per MCP client or tool","Expose RESTful APIs as MCP servers without manual server implementation"],"best_for":["Teams building agentic AI systems with tool access control requirements","Organizations needing centralized governance of LLM-to-tool interactions","Enterprises requiring audit trails for tool usage by AI agents","Platforms exposing multiple backend services as MCP tools"],"limitations":["MCP streaming responses require careful buffering to avoid blocking the gateway","Complex MCP schemas with nested tool definitions may require schema validation overhead","Authorization policies must be defined per tool or tool group; fine-grained per-parameter control requires custom plugins","MCP server discovery and health checking adds operational complexity vs static routing"],"requires":["Kong 3.5+","MCP-compatible clients (Claude, custom agents)","Backend MCP servers or REST APIs to expose as MCP","Configuration of MCP routes and authorization policies"],"input_types":["JSON-RPC 2.0 MCP protocol messages","MCP tool call requests with parameters","MCP resource access requests"],"output_types":["JSON-RPC 2.0 MCP responses","Tool execution results","MCP resource data","Audit logs with tool access metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_3","uri":"capability://automation.workflow.dynamic.request.routing.with.regex.and.semantic.path.matching","name":"dynamic request routing with regex and semantic path matching","description":"Kong's router uses a tree-based matching algorithm that supports exact path matching, regex patterns, and semantic matching (e.g., matching by HTTP method, hostname, headers) to route requests to backend services. The router compiles routes into an optimized tree structure at startup, enabling O(1) lookup for exact matches and efficient regex evaluation for pattern-based routes, with support for route priorities and weighted load balancing across multiple upstreams.","intents":["Route API requests to different backend services based on path patterns and HTTP metadata","Implement API versioning by routing /v1/* and /v2/* to different backend services","Route requests based on hostname, headers, or query parameters for multi-tenant scenarios","Distribute traffic across multiple backend instances with configurable load balancing algorithms","Implement canary deployments by routing a percentage of traffic to new backend versions"],"best_for":["Microservices architectures with dynamic service discovery","Multi-tenant platforms requiring request routing based on tenant metadata","Teams implementing API versioning or gradual rollouts","Organizations with complex routing rules (path-based, header-based, host-based)"],"limitations":["Regex route matching is slower than exact matches; complex regex patterns can add 1-5ms latency","Route tree is compiled at startup; dynamic route changes require reload (brief downtime or graceful reload)","Semantic matching on custom headers requires explicit route configuration; no automatic header-based routing","Weighted load balancing requires manual weight configuration; no automatic traffic-based rebalancing"],"requires":["Kong 2.0+","Route configuration via Admin API or declarative config","Backend service endpoints (upstreams) registered in Kong","Understanding of Kong's route matching priority rules"],"input_types":["HTTP request path, method, hostname, headers, query parameters","Route configuration (path patterns, regex, priorities)"],"output_types":["Routed request to selected upstream","HTTP response from upstream","Routing decision metadata (matched route, upstream)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_4","uri":"capability://automation.workflow.health.checking.and.automatic.upstream.failover","name":"health checking and automatic upstream failover","description":"Kong continuously monitors backend service health using active (periodic HTTP requests) and passive (request failure detection) health checks, automatically removing unhealthy upstreams from the load balancing pool and restoring them when health recovers. The health checker runs in a separate Lua coroutine, tracks health state per upstream, and integrates with the load balancer to skip unhealthy targets, enabling transparent failover without client-side retry logic.","intents":["Automatically detect and remove unhealthy backend services from traffic","Implement transparent failover to healthy upstreams without client awareness","Monitor backend service health with configurable check intervals and thresholds","Restore traffic to recovered services automatically","Reduce cascading failures by failing fast on unhealthy backends"],"best_for":["High-availability deployments requiring automatic failover","Microservices architectures with dynamic service instances","Teams without sophisticated service mesh infrastructure","Applications requiring transparent resilience without client-side changes"],"limitations":["Active health checks add overhead (~1-5 requests/second per upstream depending on interval)","Passive health checks rely on request failures; may not detect slow/degraded services","Health check configuration is per-upstream; no automatic per-service-type defaults","Health state is local to each Kong node; distributed health state requires external coordination"],"requires":["Kong 2.0+","Backend services with health check endpoints (HTTP GET/HEAD)","Configuration of health check intervals, thresholds, and timeout values","Network connectivity from Kong to all backend service health endpoints"],"input_types":["Upstream configuration (health check path, interval, timeout)","HTTP responses from health check endpoints","Request failure events from load balancer"],"output_types":["Health state per upstream (healthy/unhealthy)","Load balancer pool updates (add/remove upstreams)","Health check metrics (check count, failure count)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_5","uri":"capability://tool.use.integration.plugin.based.request.response.middleware.pipeline","name":"plugin-based request/response middleware pipeline","description":"Kong implements a plugin system where Lua-based plugins hook into the request/response lifecycle at multiple phases (init, access, header_filter, body_filter, log) and execute in a defined order. Plugins can read/modify requests and responses, access Kong context (route, service, consumer), and interact with external systems via HTTP or database calls. The Plugin Development Kit (PDK) provides a standardized API for common operations (authentication, rate limiting, logging), and plugins are loaded from the filesystem or database at startup.","intents":["Implement custom authentication schemes (OAuth2, JWT, API keys, mutual TLS)","Apply rate limiting, quota management, and request throttling","Add request/response logging and metrics collection","Transform requests/responses (header injection, body modification)","Implement custom business logic (request validation, authorization, enrichment)"],"best_for":["Teams building custom API gateway functionality","Organizations with non-standard authentication or authorization requirements","Platforms requiring extensibility without forking Kong","Developers comfortable with Lua or willing to learn it"],"limitations":["Lua plugin development requires Lua expertise; no Python/JavaScript plugin support","Plugin execution is synchronous; long-running operations block the request pipeline","Plugin ordering is implicit (based on phase and plugin priority); complex dependencies require careful configuration","Plugin state is not shared across Kong nodes; distributed state requires external coordination","Plugin errors can crash the Kong worker process if not properly error-handled"],"requires":["Kong 2.0+","Lua 5.1+ knowledge for custom plugin development","Plugin Development Kit (PDK) documentation and examples","Plugin schema definition for configuration validation"],"input_types":["HTTP request (method, path, headers, body)","Kong context (route, service, consumer, upstream)","Plugin configuration (custom parameters)"],"output_types":["Modified HTTP request","Modified HTTP response","Logs and metrics","External API calls (authentication services, logging backends)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_6","uri":"capability://automation.workflow.declarative.configuration.with.schema.validation.and.migrations","name":"declarative configuration with schema validation and migrations","description":"Kong supports declarative configuration via YAML/JSON files that define routes, services, plugins, and consumers, with a schema system that validates configuration against defined types and constraints. The configuration can be loaded in DB-less mode (in-memory) or synced to a database (PostgreSQL, Cassandra) with automatic migrations that handle schema changes across Kong versions. The schema system uses Lua-based validators that check types, required fields, and custom constraints before configuration is applied.","intents":["Define API gateway configuration as code (routes, services, plugins, consumers)","Validate configuration before deployment to catch errors early","Version control gateway configuration in Git","Implement infrastructure-as-code for API gateway management","Migrate configuration across Kong versions automatically"],"best_for":["Teams practicing infrastructure-as-code and GitOps","Organizations with frequent configuration changes","Microservices platforms with dynamic service discovery","Teams wanting to avoid manual Admin API calls for configuration"],"limitations":["DB-less mode requires reloading Kong to apply configuration changes (brief downtime)","Database mode requires external database (PostgreSQL/Cassandra) for state persistence","Schema validation is strict; invalid configuration is rejected entirely (no partial application)","Migrations are one-way; downgrading Kong versions may require manual schema adjustments","Large configurations (>10K routes) may have slow load times due to schema validation overhead"],"requires":["Kong 2.1+ (for declarative config)","YAML or JSON configuration file","PostgreSQL 9.5+ or Cassandra 3.11+ (for database mode)","Understanding of Kong's configuration schema"],"input_types":["YAML/JSON configuration files","Kong schema definitions","Migration scripts"],"output_types":["Validated configuration","Database schema (if using database mode)","Configuration errors and validation messages"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_7","uri":"capability://safety.moderation.consumer.based.authentication.and.authorization","name":"consumer-based authentication and authorization","description":"Kong implements a consumer model where API clients (users, applications, services) are registered as consumers with associated credentials (API keys, OAuth2 tokens, JWT, mutual TLS certificates). Authentication plugins verify credentials against the consumer database, and authorization plugins check consumer attributes (groups, roles, custom metadata) to enforce access control. The consumer model integrates with Kong's plugin system, enabling plugins to apply different policies to different consumers.","intents":["Authenticate API clients using multiple credential types (API keys, OAuth2, JWT, mTLS)","Authorize API access based on consumer attributes (groups, roles, custom metadata)","Implement per-consumer rate limiting and quota management","Track API usage per consumer for billing and analytics","Revoke access by disabling or deleting consumers without changing backend code"],"best_for":["Multi-tenant API platforms with per-consumer access control","Organizations requiring fine-grained authentication and authorization","Teams implementing API monetization with per-consumer billing","Enterprises needing to revoke access quickly without backend changes"],"limitations":["Consumer database must be managed via Kong Admin API or declarative config; no automatic sync from external identity providers","Consumer attributes are static; dynamic attributes require external authorization service integration","Consumer-based rate limiting is per-Kong-node; distributed rate limiting requires external coordination","Consumer credentials are stored in Kong database; no built-in integration with external secret stores (requires plugins)"],"requires":["Kong 2.0+","Consumer registration via Admin API or declarative config","Authentication plugin (api-key, oauth2, jwt, mtls)","Authorization plugin (acl, rbac) if implementing role-based access"],"input_types":["Consumer configuration (username, custom attributes)","Credentials (API keys, OAuth2 tokens, JWT, certificates)","HTTP requests with credentials in headers/query parameters"],"output_types":["Authenticated consumer identity","Authorization decision (allow/deny)","Consumer metadata in request context"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_8","uri":"capability://safety.moderation.rate.limiting.and.quota.management.with.distributed.state","name":"rate limiting and quota management with distributed state","description":"Kong provides rate limiting plugins that enforce request quotas per consumer, API, or global level using sliding window or fixed window algorithms. The rate limiter tracks request counts in Redis or Kong's local memory, with distributed state coordination via Redis to ensure accurate limits across multiple Kong nodes. The rate limiting policy is configurable per route/service/consumer, and can enforce different limits for different consumers or APIs.","intents":["Enforce per-consumer rate limits to prevent API abuse","Implement tiered rate limiting (different limits for different consumer tiers)","Enforce global API rate limits to protect backend services","Track quota usage for billing and analytics","Return rate limit headers (X-RateLimit-Remaining, X-RateLimit-Reset) to clients"],"best_for":["Public APIs requiring protection against abuse","Multi-tenant platforms with per-consumer rate limiting","Organizations implementing API monetization with usage-based billing","Teams needing to protect backend services from overload"],"limitations":["Local memory rate limiting is not accurate across multiple Kong nodes; requires Redis for distributed accuracy","Redis adds latency (~5-10ms per request) and introduces a dependency on Redis availability","Rate limit window resets are not perfectly synchronized across nodes; brief overages possible","Quota tracking requires persistent state; local memory rate limiting is lost on Kong restart","Complex rate limiting rules (e.g., per-user-per-endpoint) require custom plugin implementation"],"requires":["Kong 2.0+","Rate limiting plugin (rate-limiting, rate-limiting-advanced)","Redis 3.0+ (for distributed rate limiting across multiple Kong nodes)","Configuration of rate limit policies per route/service/consumer"],"input_types":["HTTP request with consumer identity","Rate limit policy configuration (requests per window, window size)","Redis state (request counts)"],"output_types":["Rate limit decision (allow/reject)","HTTP 429 response if rate limit exceeded","Rate limit headers (X-RateLimit-Remaining, X-RateLimit-Reset)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kong--kong__cap_9","uri":"capability://data.processing.analysis.request.response.logging.and.metrics.collection","name":"request/response logging and metrics collection","description":"Kong provides logging plugins that capture request/response metadata (method, path, status, latency, consumer, upstream) and send logs to external systems (syslog, HTTP endpoints, files, Datadog, Splunk, etc.). The logging pipeline runs in the log phase after the response is sent, collecting metrics like request latency, upstream response time, and request/response sizes. Metrics can be exported to monitoring systems (Prometheus, StatsD) for real-time dashboards and alerting.","intents":["Log all API requests for audit and debugging","Collect request latency and performance metrics","Track API usage per consumer for billing and analytics","Export metrics to monitoring systems (Prometheus, Datadog, Splunk)","Implement distributed tracing by injecting trace IDs into requests"],"best_for":["Organizations requiring comprehensive API audit trails","Teams implementing observability and monitoring","Platforms tracking API usage for billing","Enterprises with compliance requirements for request logging"],"limitations":["Logging adds latency (~5-20ms per request depending on destination)","High-volume logging (>10K requests/sec) may overwhelm external logging systems","Log buffering is per-Kong-node; distributed log aggregation requires external system","Sensitive data in logs requires careful filtering to avoid logging credentials or PII","Metrics cardinality (unique label combinations) can explode with high-cardinality dimensions (e.g., per-user metrics)"],"requires":["Kong 2.0+","Logging plugin (http-log, syslog, file-log, datadog, etc.)","External logging system (syslog server, HTTP endpoint, Datadog, Splunk, etc.)","Metrics exporter (Prometheus plugin, StatsD plugin) if collecting metrics"],"input_types":["HTTP request/response metadata","Kong context (route, service, consumer, upstream)","Request/response timing information"],"output_types":["Structured logs (JSON, syslog format)","Metrics (request count, latency, status codes)","Trace IDs for distributed tracing"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Kong 3.4+","Valid API keys for target LLM providers","Network connectivity to provider endpoints","Configuration of provider routes via Admin API or declarative config","Lua plugin development knowledge or pre-built transformation plugins","Token counter libraries (tiktoken, transformers) if implementing token-based cost tracking","Access to provider pricing APIs or static pricing configuration","Kong 2.1+ (for hybrid mode)","Control plane Kong instance with database (PostgreSQL/Cassandra)","Data plane Kong instances configured to connect to control plane"],"failure_modes":["Format translation adds ~50-150ms latency per request depending on provider complexity","Streaming responses require buffering strategy to normalize chunking behavior across providers","Custom provider-specific parameters may require passthrough configuration","Rate limiting and quota management must be coordinated across multiple provider accounts","Token counting requires model-specific tokenizer libraries (adds ~20-50ms per request)","Prompt injection detection uses heuristics/regex patterns, not guaranteed to catch all attacks","Response filtering on large outputs (>100K tokens) may impact latency","Cost calculation requires real-time provider pricing data that may lag actual billing","Control plane becomes a single point of failure for configuration updates; requires high availability setup","Data planes must maintain persistent connection to control plane; network partitions cause stale configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4227508315158364,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:01.479Z","last_commit":"2026-03-27T17:42:51Z"},"community":{"stars":43304,"forks":5127,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kong--kong","compare_url":"https://unfragile.ai/compare?artifact=kong--kong"}},"signature":"JjwYQy4JE8xM9JSuSkttBBeRXQCzW09x1HrKg+qbjfY5IOr3ISsyO4VWQ/DkYnYma3ArR3dA/qPGrF//Aog8Bw==","signedAt":"2026-06-21T00:05:16.967Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kong--kong","artifact":"https://unfragile.ai/kong--kong","verify":"https://unfragile.ai/api/v1/verify?slug=kong--kong","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"}}