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APIParkLab/APIPark
Cloud native, ultra-high performance AI&API gateway, LLM API management, distribution system, open platform, supporting all AI APIs.🦄云原生、超高性能 AI&API网关，LLM API 管理、分发系统、开放平台，支持所有AI API，不限于OpenAI、Azure、Anthropic Claude、Google Gemini、DeepSeek、字节豆包、ChatGLM、文心一言、讯飞星火、通义千问、360 智脑、腾讯混元等主流模型，统一 API 请求和返回，API申请与审批，调用统计、负载均衡、多模型灾备。一键部署，开箱即用。
APIPark is an API gateway with a built-in LLM proxy layer — you configure your AI provider credentials once and it normalizes requests across OpenAI, Anthropic, Gemini, DeepSeek, and 100+ others behind a single endpoint. It also ships a developer portal for internal API sharing, subscription/approval workflows, and usage dashboards. The target is teams that want to centralize AI provider access rather than scatter API keys across every service.
The unified request format is genuinely useful — swapping models or rotating providers doesn't require touching application code. The provider YAML config system is well-structured and easy to extend, which matters when new models ship weekly. The subscription/approval workflow is a real feature that most homegrown proxies skip, giving platform teams actual control over who calls what. One-command Docker deployment gets you running fast without wading through Kubernetes manifests.
The Go backend is the real gateway, but the repo leads with TypeScript in the language field — that mismatch creates confusion about what you're actually deploying. The model runtime provider files are mostly frozen around late-2024 model versions (no Claude 3.5 Sonnet v2, no Gemini 2.5, outdated Bedrock listings), so you'll be patching YAML files immediately after install. The README leans heavily on screenshots and a partner list that reads like a marketing page, while actual configuration docs live on an external site — finding how to configure TLS, set up clustering, or tune the Go HTTP layer requires leaving the repo. No mention of how the 'outperforms Nginx' performance claim was benchmarked.