{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"helicone","slug":"helicone","name":"Helicone","type":"platform","url":"https://helicone.ai","page_url":"https://unfragile.ai/helicone","categories":["observability"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"helicone__cap_0","uri":"capability://tool.use.integration.proxy.based.llm.request.interception.and.routing","name":"proxy-based llm request interception and routing","description":"Helicone acts as a transparent HTTP/HTTPS proxy that intercepts all outbound LLM API calls from applications to external providers (OpenAI, Anthropic, etc.) without requiring code changes. Requests are routed through Helicone's gateway infrastructure, logged, and forwarded to the target provider with response data captured for observability. The proxy pattern enables one-line integration by replacing provider API endpoints with Helicone's proxy URL, maintaining full API compatibility while capturing request/response metadata.","intents":["I want to monitor all LLM API calls my application makes without modifying application code","I need to intercept and log requests to multiple LLM providers through a single gateway","I want to add caching and rate limiting to LLM calls without application-level changes"],"best_for":["teams building LLM applications who need observability without refactoring","multi-provider LLM applications requiring centralized request routing","developers wanting to add gateway features (caching, rate limiting) post-deployment"],"limitations":["Proxy adds network latency (~50-200ms estimated) for each request round-trip through Helicone infrastructure","Requires network connectivity to Helicone's gateway; no offline mode available","Streaming responses may have higher latency overhead due to proxy buffering requirements","No built-in request transformation or payload modification at proxy layer"],"requires":["HTTP/HTTPS client library in application (standard in all languages)","Network access to Helicone's proxy endpoints","Valid Helicone API key for authentication","Ability to modify API endpoint URLs in application configuration"],"input_types":["HTTP requests (JSON payloads)","LLM provider API schemas (OpenAI, Anthropic, Cohere, etc.)"],"output_types":["HTTP responses (JSON)","Request/response metadata for logging"],"categories":["tool-use-integration","gateway-middleware"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_1","uri":"capability://data.processing.analysis.comprehensive.request.logging.with.metadata.extraction","name":"comprehensive request logging with metadata extraction","description":"Helicone automatically captures and stores all LLM API request/response pairs with extracted metadata including model name, token counts, latency, cost, user identifiers, and custom properties. Logs are persisted in a queryable database with configurable retention periods (7 days free tier to forever on enterprise). The logging system operates asynchronously to minimize impact on application latency and supports batch ingestion at rates from 10 logs/min (hobby) to 30,000 logs/min (enterprise).","intents":["I want to see a complete audit trail of all LLM API calls my application made","I need to extract structured data from logs (model, tokens, latency) for analysis","I want to query historical logs to debug production issues or analyze usage patterns"],"best_for":["production LLM applications requiring audit trails and compliance logging","teams analyzing LLM usage patterns and performance metrics","developers debugging LLM application behavior in production"],"limitations":["Data retention limited by tier: 7 days (hobby), 1 month (pro), 3 months (team), forever (enterprise only)","Storage quota of 1 GB free with usage-based overage charges (~$0.97/GB estimated)","Ingestion rate limits may cause log drops during traffic spikes if tier limits exceeded","No real-time streaming of logs; dashboard updates on polling interval (frequency unknown)"],"requires":["Active Helicone account with appropriate tier for retention needs","Network connectivity to Helicone logging infrastructure","LLM provider API compatibility (works with all major providers)"],"input_types":["LLM API requests (JSON)","LLM API responses (JSON)","Custom metadata/properties (key-value pairs on Pro+ tiers)"],"output_types":["Structured log records with extracted metadata","Queryable log database","Log exports (format unknown)"],"categories":["data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_10","uri":"capability://text.generation.language.interactive.llm.playground.with.prompt.testing","name":"interactive llm playground with prompt testing","description":"Helicone's Playground is an interactive web interface for testing LLM prompts and models in real-time. Users can write prompts, select models, adjust parameters (temperature, max tokens, etc.), and execute requests against live LLM providers. The Playground supports testing against datasets and comparing outputs across models or prompt versions. Results are displayed with metadata (latency, cost, tokens) and can be saved for later reference.","intents":["I want to quickly test and iterate on prompts without writing code","I need to compare outputs across different models or prompt versions","I want to evaluate prompts against a test dataset before deploying to production"],"best_for":["non-technical users (product managers, content creators) testing LLM prompts","prompt engineers iterating on prompts with immediate feedback","teams evaluating models before production deployment"],"limitations":["Playground parameter support unknown; unclear which LLM parameters are exposed","Batch testing against datasets may be slow for large datasets","No mention of prompt templates or parameterization for dynamic testing","Results are not automatically saved; manual save required","No version control integration; unclear how Playground results relate to Prompts feature"],"requires":["Helicone account (tier requirements unknown)","Web browser access to Helicone dashboard","LLM provider API keys configured in Helicone"],"input_types":["Prompt text (string)","Model selection","LLM parameters (temperature, max tokens, etc.)","Test datasets (optional)"],"output_types":["LLM responses (text)","Metadata (latency, cost, tokens)","Comparison results across models/prompts"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_11","uri":"capability://tool.use.integration.multi.provider.llm.support.with.unified.api.abstraction","name":"multi-provider llm support with unified api abstraction","description":"Helicone's proxy gateway abstracts away provider-specific API differences, enabling applications to switch between LLM providers (OpenAI, Anthropic, Cohere, etc.) with minimal code changes. The gateway translates requests to provider-specific formats and normalizes responses, exposing a unified interface. Provider selection can be configured per request or globally, with fallback logic for provider failures. This abstraction enables cost optimization and redundancy without application-level provider handling.","intents":["I want to switch between LLM providers without rewriting application code","I need to use multiple LLM providers for different use cases (cost vs. quality tradeoff)","I want to implement provider failover for high availability"],"best_for":["applications requiring flexibility to switch LLM providers","cost-conscious teams optimizing provider selection by use case","organizations needing high availability with multi-provider redundancy"],"limitations":["Supported providers list unknown; unclear which providers are supported beyond 'all major providers'","Provider-specific features (vision, function calling, streaming) may not be fully abstracted","Request/response translation overhead unknown; may add latency for some providers","Provider API key management and rotation details unknown","No mention of provider-specific error handling or retry logic"],"requires":["Helicone gateway integration","API keys for selected LLM providers","Provider configuration in Helicone dashboard"],"input_types":["LLM requests (normalized format)"],"output_types":["LLM responses (normalized format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_12","uri":"capability://tool.use.integration.rest.api.with.tiered.rate.limiting.and.access.control","name":"rest api with tiered rate limiting and access control","description":"Helicone exposes a REST API for programmatic access to logs, analytics, and configuration. The API supports querying request logs, retrieving cost data, managing prompts, and configuring alerts. Rate limits are tiered by subscription level (10 calls/min hobby, 1,000 calls/min team). API authentication uses API keys with optional IP whitelisting. The API enables building custom dashboards, reports, and integrations without dashboard access.","intents":["I want to programmatically query LLM logs and metrics for custom reporting","I need to integrate Helicone data with external analytics or BI tools","I want to automate prompt management and configuration via API"],"best_for":["developers building custom dashboards or reports from Helicone data","teams integrating Helicone with data warehouses or BI tools","organizations automating Helicone configuration and management"],"limitations":["API endpoints and schema unknown; unclear what operations are supported","Rate limits may be restrictive for high-volume data exports (10 calls/min hobby tier)","API documentation quality unknown; unclear if SDKs are available","No mention of pagination or result limits for large queries","API access not available on hobby tier (implied by comparison table)"],"requires":["Helicone Pro+ tier for API access","API key generated in Helicone dashboard","HTTP client library for making API requests"],"input_types":["REST API requests (JSON payloads)"],"output_types":["JSON responses with log data, metrics, or configuration"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_13","uri":"capability://automation.workflow.on.premises.deployment.and.data.residency","name":"on-premises deployment and data residency","description":"Helicone offers on-premises deployment option (enterprise tier only) enabling organizations to run the entire observability platform within their own infrastructure. On-prem deployments provide data residency compliance, network isolation, and full control over retention and access. The deployment includes the proxy gateway, logging backend, dashboard, and API. Organizations maintain their own infrastructure and are responsible for scaling, backups, and updates.","intents":["I need to comply with data residency requirements (GDPR, HIPAA, etc.)","I want to keep LLM request data within my organization's network","I need full control over data retention, access, and compliance"],"best_for":["enterprises with strict data residency or compliance requirements","organizations handling sensitive data (healthcare, finance) requiring data isolation","teams needing full control over infrastructure and scaling"],"limitations":["On-prem deployment requires enterprise tier; no pricing information available","Deployment architecture and infrastructure requirements unknown","Scaling, backup, and disaster recovery are customer responsibility","Update and maintenance procedures unknown; unclear if automatic updates available","Support model for on-prem deployments unknown"],"requires":["Enterprise tier subscription","Infrastructure to host Helicone (compute, storage, networking)","Kubernetes or Docker expertise (assumed based on typical on-prem deployments)","Network connectivity between application and on-prem Helicone instance"],"input_types":["LLM API requests (same as cloud deployment)"],"output_types":["Observability data stored in on-prem infrastructure"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_2","uri":"capability://data.processing.analysis.cost.tracking.and.attribution.by.user.session","name":"cost tracking and attribution by user/session","description":"Helicone automatically calculates LLM API costs per request based on provider pricing (tokens × rate) and aggregates costs by user, session, or custom properties. Cost data is displayed in the dashboard with breakdowns by model, provider, and time period. The system supports custom user identifiers and session tracking to enable cost attribution and chargeback analysis. Cost calculations are performed server-side using current provider pricing rates.","intents":["I want to understand how much each user or feature is costing in LLM API spend","I need to track cost trends over time and identify expensive models or usage patterns","I want to implement cost-based rate limiting or chargeback to users"],"best_for":["SaaS companies building LLM features and needing cost attribution per customer","teams with multi-tenant LLM applications requiring usage-based billing","organizations optimizing LLM spend and identifying cost reduction opportunities"],"limitations":["Cost calculations depend on Helicone's pricing database; may lag behind provider price changes","No real-time cost alerts or anomaly detection mentioned; requires manual dashboard review","Cost attribution requires explicit user/session identifiers in requests; no automatic user detection","No integration with billing systems; cost data export format and frequency unknown"],"requires":["Helicone account (all tiers)","Custom user/session identifiers passed in request headers or metadata","Access to Helicone dashboard for cost visualization"],"input_types":["LLM API requests with user/session identifiers","Token count data from provider responses"],"output_types":["Cost per request (USD)","Aggregated cost reports by user/session/model","Cost trend data over time"],"categories":["data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_3","uri":"capability://memory.knowledge.intelligent.request.caching.with.provider.agnostic.deduplication","name":"intelligent request caching with provider-agnostic deduplication","description":"Helicone's caching layer intercepts LLM requests at the proxy level and stores responses in a distributed cache, returning cached results for identical or semantically similar requests without calling the LLM provider. The cache supports configurable TTL and eviction policies, with cache hits/misses tracked in logs. Caching works transparently across all LLM providers by matching request payloads (model, prompt, parameters) and returning stored responses, reducing API costs and latency for repeated queries.","intents":["I want to reduce LLM API costs by caching responses to frequently asked questions","I need faster response times for repeated user queries without modifying application code","I want to see cache hit rates and understand which queries are being cached"],"best_for":["LLM applications with high query repetition (FAQ bots, documentation assistants)","cost-sensitive applications where reducing API calls is critical","teams wanting to add caching without application refactoring"],"limitations":["Cache matching is exact payload matching; no semantic similarity matching mentioned","Cache invalidation strategy and TTL configuration options unknown","No cache warming or preloading capabilities mentioned","Streaming responses may not be cacheable due to proxy buffering requirements","Cache storage limits and per-request cache size constraints unknown"],"requires":["Helicone gateway integration (proxy setup)","Requests routed through Helicone proxy","Cache configuration in Helicone dashboard (details unknown)"],"input_types":["LLM API requests (JSON)"],"output_types":["Cached LLM responses (JSON)","Cache hit/miss metadata in logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_4","uri":"capability://automation.workflow.rate.limiting.and.request.throttling.with.automatic.fallbacks","name":"rate limiting and request throttling with automatic fallbacks","description":"Helicone enforces rate limits at the gateway level, throttling requests based on configurable per-user, per-model, or global limits. When rate limits are exceeded, the system can automatically fall back to alternative models or providers (e.g., GPT-4 → GPT-3.5-turbo) to maintain service availability. Rate limit policies are configured in the dashboard and applied uniformly across all application instances without code changes. Fallback logic is defined as rules mapping primary models to alternatives.","intents":["I want to prevent any single user from consuming excessive LLM API quota","I need to gracefully degrade service by falling back to cheaper models when rate limits are hit","I want to enforce per-model rate limits to balance usage across different LLM providers"],"best_for":["multi-tenant SaaS applications needing per-user rate limiting","cost-conscious teams wanting to enforce spending caps per user/feature","applications requiring high availability with automatic fallback to alternative models"],"limitations":["Fallback logic configuration details unknown; unclear how complex rule definitions can be","No mention of rate limit metrics or quota reset schedules (hourly, daily, monthly unknown)","Fallback to alternative models may produce different quality outputs; no quality guarantees","Rate limit enforcement adds latency for request evaluation (~10-50ms estimated)","No client-side rate limit headers or quota remaining information mentioned"],"requires":["Helicone gateway integration","Rate limit policy configuration in dashboard","Alternative model definitions for fallback rules"],"input_types":["LLM API requests with user identifiers"],"output_types":["Rate-limited responses or fallback model responses","Rate limit status in response headers (unknown format)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_5","uri":"capability://data.processing.analysis.user.session.and.interaction.analytics","name":"user session and interaction analytics","description":"Helicone tracks user sessions and interactions across multiple LLM requests, aggregating metrics like session duration, request count, cost per session, and user engagement patterns. Custom properties can be attached to requests to enable segmentation by feature, cohort, or experiment. Analytics are visualized in the dashboard with filters and breakdowns by user, time period, and custom dimensions. Session tracking requires explicit user identifiers in request headers or metadata.","intents":["I want to understand how users are interacting with my LLM features (session length, request frequency)","I need to segment usage analytics by feature, user cohort, or experiment variant","I want to identify high-value users or features based on usage and cost metrics"],"best_for":["product teams analyzing LLM feature adoption and engagement","teams running A/B tests on LLM models or prompts with usage metrics","SaaS companies understanding user behavior and LLM feature ROI"],"limitations":["Session tracking requires explicit user identifiers; no automatic session detection","Custom properties are Pro+ tier only; hobby tier limited to basic analytics","Analytics are dashboard-only; no programmatic API for exporting analytics data mentioned","Real-time analytics unavailable; dashboard updates on polling interval","No cohort analysis or retention metrics mentioned; basic aggregation only"],"requires":["Helicone Pro+ tier for custom properties and advanced analytics","User identifiers passed in request metadata","Custom property definitions configured in dashboard"],"input_types":["LLM API requests with user identifiers and custom properties"],"output_types":["Session aggregation metrics (duration, request count, cost)","User engagement analytics","Custom dimension breakdowns"],"categories":["data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_6","uri":"capability://data.processing.analysis.helicone.query.language.hql.for.advanced.log.querying","name":"helicone query language (hql) for advanced log querying","description":"HQL is a custom query language (Pro+ tier) enabling developers to write complex queries against the request log database to extract, filter, and aggregate data. HQL supports filtering by request properties (model, user, cost, latency), aggregation functions (sum, avg, count), and time-based grouping. Queries are executed server-side and results returned as structured data. HQL abstracts away the underlying database schema, providing a domain-specific interface for LLM observability queries.","intents":["I want to query logs for specific patterns (e.g., all requests from user X with latency > 5s)","I need to aggregate metrics across logs (e.g., average cost per model per day)","I want to export custom reports from log data for analysis or compliance"],"best_for":["data analysts and engineers needing flexible log analysis without database access","teams building custom dashboards or reports from LLM usage data","compliance and audit teams extracting specific log subsets"],"limitations":["HQL syntax and capabilities unknown; unclear what aggregations/filters are supported","Query execution performance unknown; no mention of query timeouts or result limits","HQL available Pro+ tier only; hobby tier limited to dashboard UI queries","No mention of saved queries or query scheduling for recurring reports","Query result export format unknown; unclear if results can be exported to CSV/JSON"],"requires":["Helicone Pro+ tier","API access to HQL endpoint (rate limit: 1,000 calls/min on Team tier)","Knowledge of HQL syntax and query structure"],"input_types":["HQL query strings"],"output_types":["Structured query results (JSON format assumed)","Aggregated metrics and filtered log subsets"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_7","uri":"capability://tool.use.integration.webhook.based.event.notifications.and.integrations","name":"webhook-based event notifications and integrations","description":"Helicone sends webhook notifications for configurable events (request completion, cost threshold exceeded, error occurred, etc.) to external systems. Webhooks are HTTP POST requests containing event metadata and can trigger downstream workflows in Slack, PagerDuty, or custom applications. Webhook configuration includes event filtering, retry logic, and payload customization. Webhooks enable real-time alerting and integration with external monitoring/incident management systems.","intents":["I want to be notified in Slack when LLM API errors occur in production","I need to trigger alerts when cost exceeds a threshold for a user or feature","I want to integrate Helicone events with my incident management system (PagerDuty, etc.)"],"best_for":["teams needing real-time alerts on LLM API issues or cost anomalies","organizations integrating LLM observability with existing monitoring stacks","developers building custom workflows triggered by LLM events"],"limitations":["Webhook event types and filtering options unknown; unclear what events are supported","Retry logic and delivery guarantees unknown; no mention of at-least-once or exactly-once semantics","Webhook payload schema unknown; unclear what metadata is included in events","Webhooks available Pro+ tier only; hobby tier limited to dashboard notifications","No webhook signature verification mentioned; security implications unknown"],"requires":["Helicone Pro+ tier","Public HTTPS endpoint to receive webhooks","Webhook configuration in Helicone dashboard"],"input_types":["Event configuration (event type, filters, target URL)"],"output_types":["HTTP POST requests to webhook URL with event payload"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_8","uri":"capability://memory.knowledge.prompt.management.and.versioning","name":"prompt management and versioning","description":"Helicone's Prompts feature enables storing, versioning, and managing LLM prompts in a centralized registry. Prompts can be tagged, versioned, and deployed to production with rollback capabilities. The system tracks which prompt version was used for each request, enabling analysis of prompt performance and A/B testing. Prompts are accessed via API or dashboard, with version history and metadata stored in Helicone's database.","intents":["I want to version and manage prompts separately from application code","I need to A/B test different prompt versions and measure their performance","I want to track which prompt version was used for each LLM request"],"best_for":["teams iterating on LLM prompts and needing version control","product teams running prompt experiments with performance tracking","organizations centralizing prompt management across multiple applications"],"limitations":["Prompt management feature details unknown; unclear if versioning is automatic or manual","A/B testing capabilities unknown; no mention of statistical significance testing or experiment design","Prompt access control and sharing options unknown","No mention of prompt templates or parameterization for dynamic prompts","Integration with application code unknown; unclear how prompts are fetched and used"],"requires":["Helicone account (tier requirements unknown)","API access to prompt registry","Application code to fetch and use prompts from Helicone"],"input_types":["Prompt text (string)","Metadata (tags, version info)"],"output_types":["Prompt versions with metadata","Prompt performance metrics (if A/B testing enabled)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__cap_9","uri":"capability://data.processing.analysis.dataset.management.and.evaluation.scoring","name":"dataset management and evaluation scoring","description":"Helicone's Datasets feature enables creating curated datasets of LLM inputs/outputs for evaluation and testing. Datasets can be created from production logs or manually uploaded, with support for custom evaluation metrics and scoring. The Scores feature allows attaching evaluation scores (e.g., correctness, relevance) to requests, enabling quality tracking over time. Datasets and scores are used for prompt testing and model evaluation in the Playground.","intents":["I want to create a test dataset from production logs to evaluate new prompts","I need to score LLM outputs (correct/incorrect, relevant/irrelevant) and track quality metrics","I want to evaluate new models or prompts against a consistent test dataset"],"best_for":["teams building evaluation frameworks for LLM quality assurance","data scientists creating benchmark datasets for model comparison","organizations tracking LLM output quality over time"],"limitations":["Dataset creation and management details unknown; unclear if datasets are versioned","Scoring system details unknown; unclear if scores are manual, automated, or both","Evaluation metric definitions and custom metric support unknown","Dataset size limits and storage constraints unknown","Integration with external evaluation frameworks (e.g., LangChain evaluators) unknown"],"requires":["Helicone account (tier requirements unknown)","Production logs or manual dataset upload","Evaluation scoring mechanism (manual or automated)"],"input_types":["LLM requests and responses (from logs or manual upload)","Evaluation scores (numeric or categorical)"],"output_types":["Curated datasets with metadata","Quality metrics and score aggregations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helicone__headline","uri":"capability://data.processing.analysis.llm.observability.platform","name":"llm observability platform","description":"Helicone is an open-source observability platform designed for Large Language Models, providing features like request logging, cost tracking, and user analytics to enhance LLM performance and monitoring.","intents":["best LLM observability platform","LLM observability for performance tracking","open-source tools for LLM analytics","how to monitor LLM usage effectively","cost tracking solutions for LLMs"],"best_for":["developers using LLMs","data scientists monitoring AI models"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["HTTP/HTTPS client library in application (standard in all languages)","Network access to Helicone's proxy endpoints","Valid Helicone API key for authentication","Ability to modify API endpoint URLs in application configuration","Active Helicone account with appropriate tier for retention needs","Network connectivity to Helicone logging infrastructure","LLM provider API compatibility (works with all major providers)","Helicone account (tier requirements unknown)","Web browser access to Helicone dashboard","LLM provider API keys configured in Helicone"],"failure_modes":["Proxy adds network latency (~50-200ms estimated) for each request round-trip through Helicone infrastructure","Requires network connectivity to Helicone's gateway; no offline mode available","Streaming responses may have higher latency overhead due to proxy buffering requirements","No built-in request transformation or payload modification at proxy layer","Data retention limited by tier: 7 days (hobby), 1 month (pro), 3 months (team), forever (enterprise only)","Storage quota of 1 GB free with usage-based overage charges (~$0.97/GB estimated)","Ingestion rate limits may cause log drops during traffic spikes if tier limits exceeded","No real-time streaming of logs; dashboard updates on polling interval (frequency unknown)","Playground parameter support unknown; unclear which LLM parameters are exposed","Batch testing against datasets may be slow for large datasets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"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":"2026-05-24T12:16:22.066Z","last_scraped_at":null,"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=helicone","compare_url":"https://unfragile.ai/compare?artifact=helicone"}},"signature":"q7GxI9Jx6W495y16cuuUcF/GOlnpe3PjnYUcRucDzKJEEGHyZNaVz9QwES9aD2WmfWz00Mo5dsMjgus12oMDBA==","signedAt":"2026-06-20T04:52:20.920Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/helicone","artifact":"https://unfragile.ai/helicone","verify":"https://unfragile.ai/api/v1/verify?slug=helicone","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"}}