Route chat, coding, and reasoning requests to Chinese LLMs via an OpenAI-compatible AIsa API.
npx skills add https://github.com/AIsa-team/OpenClaw-Skills --skill cn-llm
Please help me install this Agent Skill. Skill: cn-llm Source: https://github.com/AIsa-team/OpenClaw-Skills/tree/main/cn-llm Command: npx skills add https://github.com/AIsa-team/OpenClaw-Skills --skill cn-llm
The CN-LLM Skill provides a single, unified interface to access multiple Chinese language models (Qwen, DeepSeek, GLM, Baichuan, and Moonshot) via the AIsa API, eliminating the need to manage separate endpoints and keys for each provider.
High-intent search queries and answers about installing and using this skill.
You can access multiple Chinese LLMs like Qwen and DeepSeek through a single API by using a unified gateway that routes requests via one API key, presenting an OpenAI-compatible interface for chat, coding, and reasoning tasks.
Yes, the unified gateway for Chinese language models provides an OpenAI-compatible interface, allowing seamless integration with existing tooling to route chat and reasoning requests to models like GLM and Baichuan.
Before routing requests to Chinese language models, you need to set the AISA_API_KEY environment variable with your API key, which authenticates your access to the unified AIsa-based API gateway.
Yes, you can switch between Moonshot and Qwen models to compare responses in one application by calling the Python client and specifying the desired model name in your chat request parameters.
The best way to manage multiple API keys for different Chinese LLM providers is to use a unified gateway that eliminates separate endpoints, requiring only one AIsa API key for routing to Qwen, DeepSeek, GLM, and Moonshot.
Model routing for coding and reasoning tasks works by sending your chat message through a Python client script that forwards the request to the specified Chinese LLM via the AIsa API, returning the model's generated response.
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