"I Stopped Pretending Every AI Provider Was the Same"

The developer of CliGate, an AI gateway supporting multiple models like Claude Code, Codex CLI, Gemini CLI, and OpenClaw, discovered that assuming compatibility between different AI providers leads to issues such as dropped fields, broken image payloads, and silent behavior changes. This was because, despite sharing similar API shapes, these models have distinct capabilities and requirements. For instance, Claude-style payloads need translation, while Codex-compatible flows require degradation of unsupported fields.

The trend of developing AI gateways and control planes is gaining traction as companies seek to integrate multiple AI models into their applications. CliGate's experience highlights the importance of nuanced routing and capability-aware translation in ensuring reliable and predictable behavior. This development is particularly relevant in the context of the growing AI market, where companies like Anthropic, OpenAI, and Google are competing to offer the most advanced and compatible models. The need for standardized APIs, like OpenAI's, is clear, but CliGate's experience shows that API shape is not enough to guarantee compatibility.

The implications of CliGate's experience are significant, as they suggest that companies building AI gateways must prioritize capability-aware routing and translation to avoid subtle bugs and failures. This requires a deeper understanding of the capabilities and limitations of each AI model, as well as the development of explicit degradation rules to handle unsupported fields and capabilities. As the AI market continues to evolve, companies that can effectively integrate and manage multiple AI models will have a competitive advantage. The risks of not adopting capability-aware routing include unreliable behavior, inconsistent results, and difficulties in debugging and troubleshooting.