{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_apipark","slug":"apipark","name":"APIPark","type":"api","url":"https://apipark.com","page_url":"https://unfragile.ai/apipark","categories":["llm-apis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_apipark__cap_0","uri":"capability://tool.use.integration.unified.multi.provider.api.gateway","name":"unified-multi-provider-api-gateway","description":"Abstracts provider-specific API differences (OpenAI, Anthropic, etc.) behind a single standardized REST endpoint, translating incoming requests to each provider's native format and normalizing responses back to a unified schema. Uses request/response middleware layers to handle protocol translation without requiring client-side code changes when switching models.","intents":["Switch between ChatGPT, Claude, and other LLMs without rewriting API calls","Test multiple models against the same codebase to compare outputs","Reduce cognitive load of managing separate API documentation and authentication for each provider","Prototype multi-model strategies without vendor lock-in"],"best_for":["Individual developers and small teams building prototypes","Startups evaluating which LLM provider offers best cost/quality tradeoff","Teams migrating between AI providers without application refactoring"],"limitations":["Normalization layer may lose provider-specific response fields (e.g., OpenAI's logprobs, Anthropic's stop_reason details)","Latency overhead from request/response translation adds ~50-150ms per call depending on payload size","No intelligent routing based on model capabilities, cost, or latency — all requests go to specified provider","Limited support for streaming responses across all providers with consistent behavior"],"requires":["API keys for at least one supported LLM provider (OpenAI, Anthropic, etc.)","HTTP client library (curl, axios, requests, etc.)","Network connectivity to APIPark gateway and upstream providers"],"input_types":["JSON request bodies with messages array","Provider-specific parameters (temperature, max_tokens, etc.)","System prompts and conversation history"],"output_types":["Normalized JSON response with message content","Usage statistics (tokens consumed, cost estimates)","Provider metadata (model version, response latency)"],"categories":["tool-use-integration","api-gateway"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_apipark__cap_1","uri":"capability://tool.use.integration.provider.agnostic.api.key.management","name":"provider-agnostic-api-key-management","description":"Centralizes storage and rotation of API credentials for multiple LLM providers in a single secure vault, allowing developers to submit requests with a single APIPark API key rather than managing separate keys per provider. Uses credential mapping to route requests to the correct provider endpoint with injected authentication headers.","intents":["Store OpenAI, Anthropic, and other provider keys in one place instead of scattered across environment variables","Rotate provider credentials without updating application code","Share multi-provider access with team members without exposing individual provider keys","Audit which team member used which provider and when"],"best_for":["Small teams sharing development environments","Developers managing multiple side projects with different provider preferences","Organizations evaluating multiple LLM providers simultaneously"],"limitations":["Centralizing credentials creates a single point of failure — compromise of APIPark account exposes all provider keys","No granular permission controls (e.g., restrict user A to Claude-only, user B to GPT-4 only)","Audit logs limited to request-level metadata; no detailed credential access tracking","No automatic credential rotation or expiration policies"],"requires":["APIPark account with free or paid tier","Valid API keys from at least one LLM provider","HTTPS connection to APIPark dashboard for credential submission"],"input_types":["API key strings from OpenAI, Anthropic, etc.","Provider identifier (e.g., 'openai', 'anthropic')","Optional credential labels/aliases"],"output_types":["APIPark API key for client authentication","Credential status (active, expired, revoked)","Usage metrics per provider credential"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_apipark__cap_2","uri":"capability://tool.use.integration.model.switching.without.code.changes","name":"model-switching-without-code-changes","description":"Enables runtime model selection via request parameters or configuration without modifying application code, using a provider/model parameter in the API request to route to different LLM endpoints. The gateway maintains a registry of supported models and their provider mappings, allowing clients to specify 'gpt-4' or 'claude-3-opus' and have the request routed transparently.","intents":["A/B test different models (GPT-4 vs Claude) with the same prompt and compare outputs","Switch from GPT-4 to GPT-3.5-turbo for cost reduction without code deployment","Gradually migrate user traffic from one model to another by adjusting request parameters","Support multiple model versions simultaneously for different user cohorts"],"best_for":["Product teams running model comparison experiments","Cost-conscious developers optimizing LLM spend","Applications requiring model flexibility for different use cases"],"limitations":["No intelligent fallback if requested model is unavailable — returns error instead of degrading to alternative","Model registry must be manually maintained; no automatic detection of new provider models","Parameter differences between models (e.g., GPT-4's vision capabilities vs Claude's) still require conditional client logic","No cost estimation or quota enforcement per model"],"requires":["APIPark API key","Knowledge of supported model identifiers (gpt-4, claude-3-opus, etc.)","HTTP client capable of sending JSON request bodies with model parameter"],"input_types":["Model identifier string (e.g., 'gpt-4', 'claude-3-sonnet')","Provider name (optional, inferred from model ID if unambiguous)","Standard LLM request parameters (messages, temperature, etc.)"],"output_types":["LLM response from selected model","Metadata indicating which model was used","Cost/token information for the selected model"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_apipark__cap_3","uri":"capability://tool.use.integration.vendor.lock.in.reduction.through.api.abstraction","name":"vendor-lock-in-reduction-through-api-abstraction","description":"Reduces switching costs between LLM providers by abstracting away provider-specific API contracts, response formats, and parameter names. When a developer wants to migrate from OpenAI to Anthropic, they only need to change the model parameter rather than refactoring request/response handling code, since APIPark normalizes both to a common schema.","intents":["Migrate from OpenAI to Anthropic (or vice versa) with minimal code changes","Avoid rewriting application logic if a provider raises prices or degrades service quality","Negotiate better rates with providers knowing you can easily switch","Future-proof application against provider API changes"],"best_for":["Startups concerned about provider dependency","Applications with high LLM API costs where provider negotiation is critical","Teams building multi-year products that may outlive current provider dominance"],"limitations":["Abstraction is incomplete — provider-specific features (vision, function calling nuances, streaming behavior) still require conditional logic","Normalization may degrade performance for provider-specific optimizations (e.g., OpenAI's logprobs for token probability analysis)","Switching providers still requires testing to ensure output quality is acceptable","Cost differences between providers are not abstracted — budget planning still requires provider-specific knowledge"],"requires":["APIPark account","API keys for target providers","Willingness to accept normalized API contract rather than provider-specific optimizations"],"input_types":["Standard LLM request format (messages, system prompt, parameters)","Model identifier to specify target provider"],"output_types":["Normalized response format independent of provider","Metadata about which provider was used"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_apipark__cap_4","uri":"capability://tool.use.integration.free.tier.multi.provider.experimentation","name":"free-tier-multi-provider-experimentation","description":"Provides a no-credit-card-required free tier that allows developers to test multiple LLM providers and compare outputs without financial commitment. The free tier includes rate limiting and usage caps but removes the friction of entering payment information, enabling rapid prototyping and model evaluation.","intents":["Evaluate multiple LLM providers (OpenAI, Anthropic, etc.) before committing to paid accounts","Prototype multi-model applications without upfront cost","Test APIPark's gateway functionality before purchasing paid tier","Teach students or teams about LLM APIs without requiring corporate credit cards"],"best_for":["Individual developers and students learning LLM integration","Startups in pre-revenue stage evaluating provider options","Teams prototyping internal tools before budget approval"],"limitations":["Free tier includes rate limiting (likely <100 requests/day or similar) preventing production use","Usage caps may be insufficient for meaningful model comparison (e.g., only 10 requests per model)","Free tier may have higher latency or lower priority than paid tiers","Limited to basic features; advanced routing, analytics, or monitoring likely requires paid tier","No SLA or uptime guarantee on free tier"],"requires":["Email address for account creation","API keys from at least one LLM provider (OpenAI, Anthropic, etc.)","No credit card required"],"input_types":["Standard LLM requests (messages, model, parameters)"],"output_types":["LLM responses from selected provider","Usage statistics (requests remaining, quota status)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_apipark__cap_5","uri":"capability://tool.use.integration.single.endpoint.request.routing","name":"single-endpoint-request-routing","description":"Routes all LLM requests through a single APIPark endpoint URL regardless of target provider, using request parameters to determine which provider/model to invoke. Implements a request router that parses the model identifier, looks up the corresponding provider endpoint, and forwards the request with translated parameters and injected credentials.","intents":["Simplify client code by using one endpoint URL instead of managing separate URLs for OpenAI, Anthropic, etc.","Centralize request logging and monitoring across all providers","Implement provider-agnostic request middleware (rate limiting, caching, etc.)","Enable dynamic provider selection based on runtime conditions"],"best_for":["Applications with multiple LLM integrations","Teams wanting centralized observability across providers","Microservices architectures where a single gateway simplifies service discovery"],"limitations":["Single endpoint becomes a bottleneck if not properly scaled — all provider traffic flows through APIPark infrastructure","Adds network latency (request must traverse APIPark gateway before reaching provider)","Debugging is more complex — errors could originate from APIPark routing layer or upstream provider","No built-in request batching or multiplexing across providers"],"requires":["APIPark API key","Single endpoint URL provided by APIPark (e.g., https://api.apipark.com/v1/chat/completions)","HTTP client library"],"input_types":["JSON request body with model identifier and LLM parameters","Authorization header with APIPark API key"],"output_types":["JSON response from target provider","HTTP status codes indicating success or failure"],"categories":["tool-use-integration","api-gateway"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_apipark__cap_6","uri":"capability://data.processing.analysis.normalized.response.schema.across.providers","name":"normalized-response-schema-across-providers","description":"Translates provider-specific response formats (OpenAI's chat completion format, Anthropic's message format, etc.) into a unified response schema that clients can parse consistently. The normalization layer extracts relevant fields (content, tokens used, finish reason) and maps them to a common structure, hiding provider differences from application logic.","intents":["Parse LLM responses with the same code regardless of which provider was used","Extract token usage and cost information in a consistent format","Handle finish reasons (stop, max_tokens, etc.) uniformly across providers","Build provider-agnostic response handling middleware"],"best_for":["Applications supporting multiple LLM providers","Teams building abstractions over LLM APIs","Cost tracking and monitoring systems that need provider-agnostic metrics"],"limitations":["Normalization loses provider-specific fields (e.g., OpenAI's logprobs, Anthropic's stop_reason details)","Some providers include additional metadata that doesn't map to normalized schema","Streaming responses may not normalize cleanly across providers with different chunk formats","Error responses from different providers may not map cleanly to unified error schema"],"requires":["APIPark API key","Understanding of normalized response schema structure","HTTP client to parse JSON responses"],"input_types":["Provider-specific LLM responses (OpenAI chat completion, Anthropic message, etc.)"],"output_types":["Normalized JSON response with fields: content, tokens_used, finish_reason, model, provider","Optional: provider-specific metadata in separate field"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_apipark__cap_7","uri":"capability://data.processing.analysis.request.parameter.translation.across.providers","name":"request-parameter-translation-across-providers","description":"Translates client request parameters (temperature, max_tokens, top_p, etc.) from a normalized format into provider-specific parameter names and formats. For example, converts a generic 'max_tokens' parameter to OpenAI's 'max_tokens' field and Anthropic's 'max_tokens' field, handling differences in parameter naming, valid ranges, and default values.","intents":["Use the same parameter names regardless of target provider","Avoid learning provider-specific parameter conventions (OpenAI vs Anthropic vs others)","Automatically validate parameters against provider constraints (e.g., temperature range)","Build provider-agnostic prompt engineering tools"],"best_for":["Developers building multi-provider LLM applications","Tools that need to work with multiple providers without conditional logic","Teams standardizing on a parameter naming convention"],"limitations":["Some provider-specific parameters don't have equivalents in other providers (e.g., OpenAI's logprobs)","Parameter ranges differ between providers (e.g., temperature 0-2 for OpenAI vs 0-1 for others)","Translation may lose nuance — a parameter that works well on one provider may behave differently on another","No validation that translated parameters will produce equivalent results across providers"],"requires":["APIPark API key","Knowledge of normalized parameter names","HTTP client to send JSON requests"],"input_types":["Normalized request parameters: temperature, max_tokens, top_p, top_k, frequency_penalty, presence_penalty, etc.","Model identifier to determine target provider"],"output_types":["Provider-specific request format with translated parameters","Validation errors if parameters are out of range for target provider"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["API keys for at least one supported LLM provider (OpenAI, Anthropic, etc.)","HTTP client library (curl, axios, requests, etc.)","Network connectivity to APIPark gateway and upstream providers","APIPark account with free or paid tier","Valid API keys from at least one LLM provider","HTTPS connection to APIPark dashboard for credential submission","APIPark API key","Knowledge of supported model identifiers (gpt-4, claude-3-opus, etc.)","HTTP client capable of sending JSON request bodies with model parameter","APIPark account"],"failure_modes":["Normalization layer may lose provider-specific response fields (e.g., OpenAI's logprobs, Anthropic's stop_reason details)","Latency overhead from request/response translation adds ~50-150ms per call depending on payload size","No intelligent routing based on model capabilities, cost, or latency — all requests go to specified provider","Limited support for streaming responses across all providers with consistent behavior","Centralizing credentials creates a single point of failure — compromise of APIPark account exposes all provider keys","No granular permission controls (e.g., restrict user A to Claude-only, user B to GPT-4 only)","Audit logs limited to request-level metadata; no detailed credential access tracking","No automatic credential rotation or expiration policies","No intelligent fallback if requested model is unavailable — returns error instead of degrading to alternative","Model registry must be manually maintained; no automatic detection of new provider models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:29.133Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=apipark","compare_url":"https://unfragile.ai/compare?artifact=apipark"}},"signature":"+SLwXi2EqYd0O7ZVYln4LdBOZYtPWv2ZP7skn0Ff2vg8tHm2kaWJZw4BRDFVbut7etuA3R7+8ljeFyDcGbNGAQ==","signedAt":"2026-06-20T23:44:08.622Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/apipark","artifact":"https://unfragile.ai/apipark","verify":"https://unfragile.ai/api/v1/verify?slug=apipark","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"}}