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BingZi-233Monitor AI model API health and performance
Top 96.5% on SourcePulse
<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This project provides a real-time health dashboard for monitoring AI model API availability, latency, and error information from providers like OpenAI, Gemini, and Anthropic. It targets teams needing internal status walls, vendor SLA monitoring, or multi-model comparisons, offering a centralized view of AI service performance.
How It Works
Built on Next.js App Router and Supabase, the system employs backend polling to continuously collect health metrics. It aggregates data on availability, real-time latency, and ping latency, storing historical timelines for up to 30 days. The architecture supports grouping by provider or custom groups, maintenance modes, and official status page checks, with secure handling of API keys and multi-node deployment capabilities.
Quick Start & Requirements
pnpm install..env.example to .env.local and populate Supabase credentials and other environment variables.supabase/schema.sql or apply migrations from supabase/migrations/. Add a minimal check_configs entry.pnpm dev for local development (access at http://localhost:3000).docs/ARCHITECTURE.md), Operations (docs/OPERATIONS.md), Provider Extension (docs/EXTENDING_PROVIDERS.md).Highlighted Details
Maintenance & Community
The provided README does not detail specific contributors, community channels (e.g., Discord, Slack), or a public roadmap.
Licensing & Compatibility
Limitations & Caveats
Setup requires a Supabase project and involves database schema initialization and configuration. The README does not specify alpha/beta status or known bugs. Extending provider support necessitates consulting the documentation on provider extension.
4 days ago
Inactive