justlovemaki/aiclient2api

Self-hosted multi-protocol AI API proxy for Antigravity, Codex, Grok, Kiro, OpenAI, Claude, and custom providers. Supports OpenAI-compatible API, Claude...

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Summary Information

Updated 33 minutes ago
Added to GitGenius on September 7th, 2026
Created on July 20th, 2025
Open Issues & Pull Requests: 5 (+0)
GitHub issues: Enabled
Number of forks: 1,378
Total Stargazers: 8,758 (+1)
Total Subscribers: 39 (+0)

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Detailed Description

AIClient2API is a self-hosted multi-protocol AI API proxy that unifies requests across multiple AI providers into a single interface.

The tool solves the fragmentation problem of working with multiple AI APIs that each have different protocols and request formats. It acts as a proxy layer that accepts requests from various client-only AI services—including Antigravity, Codex, Grok, Kiro, OpenAI, Claude, and custom providers—translates them, and exposes them through an OpenAI-compatible API interface. This approach lets developers write against a single standardized API while the proxy handles the complexity of routing to the correct backend provider. The tool supports protocol conversion for Gemini, implements smart routing and automatic failover across provider pools, and can simulate requests to handle client-only APIs that normally cannot be accessed programmatically.

Developers should choose this tool if they need to integrate multiple AI providers into a single application without rewriting code for each API's unique interface. It suits projects that want provider flexibility, cost optimization through failover strategies, or the ability to switch between AI services without application changes. The tool is particularly valuable for those working with client-only APIs that lack traditional server-side access, as it bridges that gap through simulation and proxying.

The project maintains active development with regular updates addressing new provider integrations and protocol support. The codebase shows ongoing refinement of routing logic and failover mechanisms to improve reliability across multiple backend services. Documentation is actively maintained across multiple languages, indicating sustained effort to serve a diverse user base. The project demonstrates responsiveness to emerging AI providers and protocols, continuously expanding its supported service list.