{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_anon","slug":"anon","name":"Anon","type":"product","url":"https://www.anon.com","page_url":"https://unfragile.ai/anon","categories":["app-builders"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_anon__cap_0","uri":"capability://tool.use.integration.multi.provider.api.abstraction.layer","name":"multi-provider api abstraction layer","description":"Routes AI requests through a unified HTTP/REST interface that translates calls to multiple downstream providers (OpenAI, Anthropic, etc.) without requiring application code changes. Implements a provider-agnostic request/response normalization layer that maps different model APIs (chat completions, embeddings, function calling) to a canonical schema, handling protocol differences and authentication transparently.","intents":["Switch between Claude and GPT models without rewriting integration code","Test multiple AI providers in production without deploying new versions","Reduce vendor lock-in by abstracting away provider-specific API contracts","Consolidate multiple provider integrations into a single SDK/HTTP endpoint"],"best_for":["Mid-market SaaS teams using 2+ AI providers simultaneously","Engineering teams wanting to avoid vendor lock-in without custom wrapper code","Organizations needing rapid provider switching for cost optimization or model evaluation"],"limitations":["Adds 50-200ms latency per request due to server-side routing and normalization overhead","Cannot expose provider-specific advanced features (e.g., OpenAI's vision_detail parameter, Anthropic's extended thinking) without custom configuration","Abstraction layer may not support bleeding-edge model releases immediately upon provider availability","Request/response transformation may lose fidelity for complex streaming scenarios or structured outputs"],"requires":["API key for at least one supported provider (OpenAI, Anthropic, etc.)","Anon account and authentication credentials","HTTP client library or SDK (language-specific)"],"input_types":["text prompts","JSON request objects","structured function calling schemas"],"output_types":["text completions","JSON structured responses","streaming token sequences"],"categories":["tool-use-integration","api-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anon__cap_1","uri":"capability://automation.workflow.centralized.credential.and.api.key.management","name":"centralized credential and api key management","description":"Provides a single dashboard and secure vault for storing and rotating API keys across multiple AI providers, eliminating the need to scatter credentials across environment variables, config files, or CI/CD secrets. Uses encryption at rest and role-based access control to manage which applications and team members can access which provider credentials, with audit logging for compliance.","intents":["Manage API keys for 5+ providers from one dashboard instead of scattered .env files","Rotate provider credentials without redeploying applications","Grant different team members access to different provider accounts","Audit which applications used which provider credentials and when"],"best_for":["Teams with multiple developers needing secure credential sharing","Organizations with compliance requirements (SOC 2, HIPAA) needing audit trails","Multi-tenant SaaS platforms where different customers use different providers"],"limitations":["Centralized credential storage creates a single point of failure if Anon's vault is compromised","Requires trust in Anon's encryption and security practices — no option for self-hosted credential management","Credential rotation may require brief downtime if not implemented with graceful fallback logic","No built-in support for temporary/short-lived credentials (e.g., AWS STS tokens)"],"requires":["Anon account with admin or credential management permissions","Valid API keys from target providers (OpenAI, Anthropic, etc.)","HTTPS connectivity to Anon's credential service"],"input_types":["API key strings","provider account identifiers"],"output_types":["credential references (opaque tokens)","audit logs (JSON)"],"categories":["automation-workflow","security-credential-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anon__cap_2","uri":"capability://automation.workflow.cross.platform.request.routing.with.provider.failover","name":"cross-platform request routing with provider failover","description":"Routes incoming requests to specified AI providers with automatic failover to secondary providers if the primary is unavailable or rate-limited. Implements health checks, circuit breaker patterns, and request queuing to gracefully degrade service rather than returning errors. Supports weighted load balancing across providers for cost optimization or performance tuning.","intents":["Automatically fall back to Claude if OpenAI hits rate limits","Distribute requests across providers to optimize cost per token","Ensure application availability even if one provider experiences an outage","A/B test different providers without manual request routing logic"],"best_for":["Applications requiring high availability and resilience to provider outages","Cost-conscious teams wanting to dynamically route to cheapest available provider","Teams evaluating multiple providers and needing transparent traffic splitting"],"limitations":["Failover adds latency (health checks, circuit breaker state transitions) — typically 100-500ms for first failover attempt","Weighted routing requires manual tuning; no built-in ML-based optimization for cost/latency tradeoffs","Failover logic cannot guarantee consistency across retries if providers return different outputs for same input","Request queuing may cause unbounded latency growth under sustained high load if all providers are saturated"],"requires":["At least 2 configured provider credentials","Network connectivity to all failover providers","Configuration of failover policy (sequential, weighted, round-robin)"],"input_types":["AI request objects (prompts, function calls, etc.)"],"output_types":["AI responses from selected provider","routing metadata (which provider handled request, failover count)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anon__cap_3","uri":"capability://tool.use.integration.unified.streaming.response.handling","name":"unified streaming response handling","description":"Normalizes streaming responses from different providers (OpenAI's Server-Sent Events, Anthropic's event stream format) into a canonical streaming protocol that applications consume via a single interface. Handles backpressure, chunk buffering, and error recovery within streams without requiring provider-specific parsing logic.","intents":["Stream responses from any provider using identical client-side code","Switch providers mid-stream without breaking client connections","Handle streaming errors (provider timeout, network interruption) with automatic recovery"],"best_for":["Real-time chat applications needing provider-agnostic streaming","Web/mobile apps where streaming latency is user-visible","Teams wanting to test streaming behavior across providers without code duplication"],"limitations":["Streaming normalization adds 20-50ms per chunk due to buffering and transformation overhead","Cannot preserve provider-specific streaming metadata (e.g., token probabilities, logprobs) without custom extensions","Backpressure handling may cause buffer bloat if client consumes slower than provider produces","Switching providers mid-stream may lose context if providers have different token counting or state management"],"requires":["HTTP/2 or WebSocket support for streaming","Client-side streaming parser compatible with Anon's canonical format"],"input_types":["streaming request objects"],"output_types":["normalized streaming chunks (JSON or binary)","stream metadata (completion reason, token counts)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anon__cap_4","uri":"capability://data.processing.analysis.request.response.logging.and.analytics","name":"request/response logging and analytics","description":"Captures all requests and responses flowing through Anon's abstraction layer, storing structured logs with provider, model, latency, token counts, and cost metadata. Provides queryable analytics dashboard and export APIs for cost analysis, performance monitoring, and usage auditing across all integrated providers.","intents":["Track total AI spending across multiple providers in one dashboard","Identify which models and providers are slowest for optimization","Audit which applications and users made which AI requests for compliance","Export usage data for billing or cost allocation across teams"],"best_for":["Finance/ops teams needing consolidated AI cost visibility","Engineering teams optimizing for latency and cost tradeoffs","Organizations with regulatory requirements for request auditing"],"limitations":["Logging adds 10-30ms latency per request due to async write operations","Log retention policies may limit historical data availability (e.g., 90-day retention window)","Cost calculations depend on accurate token counting — may diverge from actual provider billing if token counting differs","Analytics queries on large datasets (millions of requests) may be slow without proper indexing"],"requires":["Anon account with analytics/logging permissions","Sufficient log storage quota (typically included in paid plans)"],"input_types":["request/response objects (captured automatically)"],"output_types":["structured logs (JSON)","analytics dashboards (web UI)","CSV/JSON exports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anon__cap_5","uri":"capability://tool.use.integration.function.calling.schema.translation","name":"function calling schema translation","description":"Translates function calling schemas between different provider formats (OpenAI's tools format, Anthropic's tool_use format, etc.) so applications define functions once and Anon handles provider-specific serialization. Validates function arguments against schemas and routes function execution requests back to the application with normalized payloads.","intents":["Define tool schemas once and use with any provider without reformatting","Switch providers without rewriting function calling logic","Validate function arguments before sending to provider to catch schema mismatches early"],"best_for":["Agentic applications using function calling across multiple providers","Teams building provider-agnostic tool integrations","Applications needing strict schema validation before provider calls"],"limitations":["Schema translation adds 20-50ms latency per function calling request","Cannot preserve provider-specific function calling features (e.g., OpenAI's parallel function calling, Anthropic's tool_choice parameter) without custom configuration","Schema validation is limited to JSON Schema — no support for custom validation logic","Function execution routing requires application to implement callback endpoint for Anon to invoke"],"requires":["Function schemas defined in JSON Schema format","Application endpoint for receiving function execution requests","Support for at least one provider's function calling API"],"input_types":["JSON Schema function definitions","function execution requests"],"output_types":["provider-specific tool definitions","normalized function execution results"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anon__cap_6","uri":"capability://memory.knowledge.model.version.and.capability.mapping","name":"model version and capability mapping","description":"Maintains a registry of supported models across all providers with capability metadata (context window, vision support, function calling, cost per token). Allows applications to query available models and automatically select compatible models based on required capabilities, abstracting away model naming differences and deprecation.","intents":["Find which models support vision across all providers without checking each provider's docs","Automatically upgrade to newer model versions without code changes","Select cheapest model that meets capability requirements (e.g., 'needs vision and 100k context')"],"best_for":["Applications supporting multiple models and needing capability-based selection","Teams wanting to automatically adopt new models as providers release them","Cost-optimization workflows needing to find cheapest model with required features"],"limitations":["Model registry may lag provider releases by days or weeks","Capability metadata is manually curated — may be incomplete or inaccurate for new models","Automatic model selection cannot account for behavioral differences between models (e.g., reasoning quality, instruction-following)","Model deprecation handling requires manual updates to routing rules"],"requires":["Anon account with access to model registry API"],"input_types":["capability requirements (JSON object)"],"output_types":["list of compatible models with metadata","recommended model selection"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anon__cap_7","uri":"capability://automation.workflow.rate.limiting.and.quota.management","name":"rate limiting and quota management","description":"Enforces per-application, per-user, and per-provider rate limits and quotas at the Anon layer, preventing individual applications from exhausting provider rate limits and impacting other users. Implements token bucket algorithms with configurable burst allowances and provides quota status APIs for applications to check remaining limits before making requests.","intents":["Prevent one misbehaving application from consuming all provider quota","Implement per-user rate limits for multi-tenant SaaS without provider-side configuration","Check remaining quota before making expensive requests to avoid failures"],"best_for":["Multi-tenant SaaS platforms needing per-customer rate limiting","Teams with shared provider accounts wanting to isolate usage across applications","Applications needing predictable quota behavior across providers"],"limitations":["Rate limiting adds 5-20ms latency per request for quota checks","Quota enforcement is soft — applications can still exceed limits if they ignore quota status responses","Distributed rate limiting across multiple Anon servers requires shared state (Redis, etc.) which adds complexity","Provider-side rate limits may still be hit if multiple Anon instances share same provider account"],"requires":["Configuration of rate limit policies (requests/minute, tokens/day, etc.)","Anon account with quota management permissions"],"input_types":["rate limit policy definitions"],"output_types":["quota status (remaining requests, reset time)","rate limit exceeded errors"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anon__cap_8","uri":"capability://automation.workflow.request.caching.and.response.deduplication","name":"request caching and response deduplication","description":"Caches identical requests and returns cached responses without hitting providers, reducing latency and costs for repeated queries. Uses content-addressable caching keyed by normalized request hash (model, prompt, parameters) with configurable TTL and cache invalidation policies. Deduplicates concurrent identical requests to prevent thundering herd.","intents":["Reduce costs by caching responses to frequently asked questions","Speed up repeated queries by serving from cache instead of provider","Prevent multiple concurrent requests for same query from hitting provider multiple times"],"best_for":["Applications with high query repetition (FAQ bots, documentation search)","Cost-sensitive applications where caching ROI justifies added complexity","Teams wanting to reduce provider load without changing application code"],"limitations":["Caching adds 10-30ms latency for cache lookups and invalidation checks","Cache hit rate depends on query patterns — low for highly variable inputs (e.g., personalized recommendations)","Cached responses may become stale if provider behavior changes (model updates, system prompt changes)","Cache invalidation is manual or TTL-based — no automatic invalidation when provider models are updated"],"requires":["Distributed cache backend (Redis, Memcached) for multi-instance deployments","Configuration of cache TTL and invalidation policies"],"input_types":["AI request objects"],"output_types":["cached or fresh responses","cache metadata (hit/miss, age)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anon__cap_9","uri":"capability://text.generation.language.request.transformation.and.prompt.templating","name":"request transformation and prompt templating","description":"Allows applications to define reusable prompt templates with variable substitution and request transformations (e.g., prepending system prompts, appending context) that apply automatically to all requests. Supports Jinja2-style templating with access to request metadata, environment variables, and user context for dynamic prompt construction.","intents":["Apply consistent system prompts across all requests without modifying application code","Inject user context or conversation history into prompts automatically","Test different prompt variations by changing templates without redeploying"],"best_for":["Teams wanting to centralize prompt management without code changes","Applications needing dynamic prompt construction based on user context","Organizations A/B testing prompt variations across users"],"limitations":["Template processing adds 5-20ms latency per request","Complex templates with many variables may be hard to debug when results are unexpected","Template syntax errors are caught at request time, not deployment time","No built-in support for conditional logic beyond basic variable substitution"],"requires":["Template definitions in Jinja2 or similar format","Access to request context and user metadata for variable substitution"],"input_types":["prompt templates (text with variables)","request context (JSON)"],"output_types":["transformed requests with substituted prompts"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["API key for at least one supported provider (OpenAI, Anthropic, etc.)","Anon account and authentication credentials","HTTP client library or SDK (language-specific)","Anon account with admin or credential management permissions","Valid API keys from target providers (OpenAI, Anthropic, etc.)","HTTPS connectivity to Anon's credential service","At least 2 configured provider credentials","Network connectivity to all failover providers","Configuration of failover policy (sequential, weighted, round-robin)","HTTP/2 or WebSocket support for streaming"],"failure_modes":["Adds 50-200ms latency per request due to server-side routing and normalization overhead","Cannot expose provider-specific advanced features (e.g., OpenAI's vision_detail parameter, Anthropic's extended thinking) without custom configuration","Abstraction layer may not support bleeding-edge model releases immediately upon provider availability","Request/response transformation may lose fidelity for complex streaming scenarios or structured outputs","Centralized credential storage creates a single point of failure if Anon's vault is compromised","Requires trust in Anon's encryption and security practices — no option for self-hosted credential management","Credential rotation may require brief downtime if not implemented with graceful fallback logic","No built-in support for temporary/short-lived credentials (e.g., AWS STS tokens)","Failover adds latency (health checks, circuit breaker state transitions) — typically 100-500ms for first failover attempt","Weighted routing requires manual tuning; no built-in ML-based optimization for cost/latency tradeoffs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.133Z","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=anon","compare_url":"https://unfragile.ai/compare?artifact=anon"}},"signature":"TIX+UToWPg+x2mLei0fdlh1kQi0Yv0/91dkp5ycU6JzijMAMRcP0inxzHilemyCw3NTH7wujTMgnoQ0LEZlfDw==","signedAt":"2026-06-21T07:34:28.243Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anon","artifact":"https://unfragile.ai/anon","verify":"https://unfragile.ai/api/v1/verify?slug=anon","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"}}