Skill: chaimengphp/openclaw-aisa-cn-llm (version 1.0.1)

--- name: cn-llm description: "China LLM Gateway - Unified interface for Chinese LLMs including Qwen, DeepSeek, GLM, Baichuan. OpenAI compatible, one API Key for all models." homepage: https://openclaw.ai metadata: {"openclaw":{"emoji":"🐉","requires":{"bins":["curl","python3"],"env":["AISA_API_KEY"]},"primaryEnv":"AISA_API_KEY"}} --- # OpenClaw CN-LLM 🐉 **China LLM Unified Gateway. Powered by AIsa.** One API Key to access all Chinese LLMs. OpenAI compatible interface. Qwen, DeepSeek, GLM, Baichuan, Moonshot, and more - unified API access. ## 🔥 What You Can Do ### Intelligent Chat ``` "Use Qwen to answer Chinese questions, use DeepSeek for coding" ``` ### Deep Reasoning ``` "Use DeepSeek-R1 for complex reasoning tasks" ``` ### Code Generation ``` "Use DeepSeek-Coder to generate Python code with explanations" ``` ### Long Text Processing ``` "Use Qwen-Long for ultra-long document summarization" ``` ### Model Comparison ``` "Compare response quality between Qwen-Max and DeepSeek-V3" ``` ## Supported Models ### Qwen (Alibaba) | Model | Input Price | Output Price | Features | |-----|---------|---------|------| | qwen3-max | $1.37/M | $5.48/M | Most powerful general model | | qwen3-max-2026-01-23 | $1.37/M | $5.48/M | Latest version | | qwen3-coder-plus | $2.86/M | $28.60/M | Enhanced code generation | | qwen3-coder-flash | $0.72/M | $3.60/M | Fast code generation | | qwen3-coder-480b-a35b-instruct | $2.15/M | $8.60/M | 480B large model | | qwen3-vl-plus | $0.43/M | $4.30/M | Vision-language model | | qwen3-vl-flash | $0.86/M | $0.86/M | Fast vision model | | qwen3-omni-flash | $4.00/M | $16.00/M | Multimodal model | | qwen-vl-max | $0.23/M | $0.57/M | Vision-language | | qwen-plus-2025-12-01 | $1.26/M | $12.60/M | Plus version | | qwen-mt-flash | $0.168/M | $0.514/M | Fast machine translation | | qwen-mt-lite | $0.13/M | $0.39/M | Lite machine translation | ### DeepSeek | Model | Input Price | Output Price | Features | |-----|---------|---------|------| | deepseek-r1 | $2.00/M | $8.00/M | Reasoning model, supports Tools | | deepseek-v3 | $1.00/M | $4.00/M | General chat, 671B parameters | | deepseek-v3-0324 | $1.20/M | $4.80/M | V3 stable version | | deepseek-v3.1 | $4.00/M | $12.00/M | Latest Terminus version | > **Note**: Prices are in M (million tokens). Model availability may change, see [marketplace.aisa.one/pricing](https://marketplace.aisa.one/pricing) for the latest list. ## Quick Start ```bash export AISA_API_KEY="your-key" ``` ## API Endpoints ### OpenAI Compatible Interface ``` POST https://api.aisa.one/v1/chat/completions ``` #### Qwen Example ```bash curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen3-max", "messages": [ {"role": "system", "content": "You are a professional Chinese assistant."}, {"role": "user", "content": "Please explain what a large language model is?"} ], "temperature": 0.7, "max_tokens": 1000 }' ``` #### DeepSeek Example ```bash # DeepSeek-V3 general chat (671B parameters) curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-v3", "messages": [{"role": "user", "content": "Write a quicksort algorithm in Python"}], "temperature": 0.3 }' # DeepSeek-R1 deep reasoning (supports Tools) curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-r1", "messages": [{"role": "user", "content": "A farmer needs to cross a river with a wolf, a sheep, and a cabbage. The boat can only carry the farmer and one item at a time. If the farmer is not present, the wolf will eat the sheep, and the sheep will eat the cabbage. How can the farmer safely cross?"}] }' # DeepSeek-V3.1 Terminus latest version curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-v3.1", "messages": [{"role": "user", "content": "Implement an LRU cache with get and put operations"}] }' ``` #### Qwen3 Code Generation Example ```bash curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen3-coder-plus", "messages": [{"role": "user", "content": "Implement a thread-safe Map in Go"}] }' ``` #### Parameter Reference | Parameter | Type | Required | Description | |-----|------|-----|------| | `model` | string | Yes | Model identifier | | `messages` | array | Yes | Message list | | `temperature` | number | No | Randomness (0-2, default 1) | | `max_tokens` | integer | No | Maximum tokens to generate | | `stream` | boolean | No | Stream output (default false) | | `top_p` | number | No | Nucleus sampling parameter (0-1) | #### Response Format ```json { "id": "chatcmpl-xxx", "object": "chat.completion", "created": 1234567890, "model": "qwen-max", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "A large language model (LLM) is a deep learning-based..." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 30, "completion_tokens": 150, "total_tokens": 180, "cost": 0.001 } } ``` ### Streaming Output ```bash curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen-plus", "messages": [{"role": "user", "content": "Tell a Chinese folk story"}], "stream": true }' ``` Returns Server-Sent Events (SSE) format: ``` data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":"Once"}}]} data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":" upon"}}]} ... data: [DONE] ``` ## Python Client ### CLI Usage ```bash # Qwen chat python3 {baseDir}/scripts/cn_llm_client.py chat --model qwen3-max --message "Hello, please introduce yourself" # Qwen3 code generation python3 {baseDir}/scripts/cn_llm_client.py chat --model qwen3-coder-plus --message "Write a binary search algorithm" # DeepSeek-R1 reasoning python3 {baseDir}/scripts/cn_llm_client.py chat --model deepseek-r1 --message "Which is larger, 9.9 or 9.11? Please reason in detail" # DeepSeek-V3 chat python3 {baseDir}/scripts/cn_llm_client.py chat --model deepseek-v3 --message "Tell a story" --stream # With system prompt python3 {baseDir}/scripts/cn_llm_client.py chat --model qwen3-max --system "You are a classical poetry expert" --message "Write a poem about plum blossoms" # Model comparison python3 {baseDir}/scripts/cn_llm_client.py compare --models "qwen3-max,deepseek-v3" --message "What is quantum computing?" # List supported models python3 {baseDir}/scripts/cn_llm_client.py models ``` ### Python SDK Usage ```python from cn_llm_client import CNLLMClient client = CNLLMClient() # Uses AISA_API_KEY environment variable # Qwen chat response = client.chat( model="qwen3-max", messages=[{"role": "user", "content": "Hello!"}] ) print(response["choices"][0]["message"]["content"]) # Qwen3 code generation response = client.chat( model="qwen3-coder-plus", messages=[ {"role": "system", "content": "You are a professional programmer."}, {"role": "user", "content": "Implement a singleton pattern in Python"} ], temperature=0.3 ) # Streaming output for chunk in client.chat_stream( model="deepseek-v3", messages=[{"role": "user", "content": "Tell a story about an idiom"}] ): print(chunk, end="", flush=True) # Model comparison results = client.compare_models( models=["qwen3-max", "deepseek-v3", "deepseek-r1"], message="Explain what machine learning is" ) for model, result in results.items(): print(f"{model}: {result['response'][:100]}...") ``` ## Use Cases ### 1. Chinese Content Generation ```python # Copywriting response = client.chat( model="qwen3-max", messages=[ {"role": "system", "content": "You are a professional copywriter."}, {"role": "user", "content": "Write a product introduction for a smart watch"} ] ) ``` ### 2. Code Development ```python # Code generation and explanation response = client.chat( model="qwen3-coder-plus", messages=[{"role": "user", "content": "Implement a thread-safe Map in Go"}] ) ``` ### 3. Complex Reasoning ```python # Mathematical reasoning response = client.chat( model="deepseek-r1", messages=[{"role": "user", "content": "Prove: For any positive integer n, n³-n is divisible by 6"}] ) ``` ### 4. Visual Understanding ```python # Image understanding response = client.chat( model="qwen3-vl-plus", messages=[ {"role": "user", "content": [ {"type": "text", "text": "Describe the content of this image"}, {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}} ]} ] ) ``` ### 5. Model Routing Strategy ```python MODEL_MAP = { "chat": "qwen3-max", # General chat "code": "qwen3-coder-plus", # Code generation "reasoning": "deepseek-r1", # Complex reasoning "vision": "qwen3-vl-plus", # Visual understanding "fast": "qwen3-coder-flash", # Fast response "translate": "qwen-mt-flash" # Machine translation } def route_by_task(task_type: str, message: str) -> str: model = MODEL_MAP.get(task_type, "qwen3-max") return client.chat(model=model, messages=[{"role": "user", "content": message}]) ``` ## Error Handling Errors return JSON with `error` field: ```json { "error": { "code": "model_not_found", "message": "Model 'xxx' is not available" } } ``` Common error codes: - `401` - Invalid or missing API Key - `402` - Insufficient balance - `404` - Model not found - `429` - Rate limit exceeded - `500` - Server error ## Pricing | Model | Input ($/M) | Output ($/M) | |-----|-----------|-----------| | qwen3-max | $1.37 | $5.48 | | qwen3-coder-plus | $2.86 | $28.60 | | qwen3-coder-flash | $0.72 | $3.60 | | qwen3-vl-plus | $0.43 | $4.30 | | deepseek-v3 | $1.00 | $4.00 | | deepseek-r1 | $2.00 | $8.00 | | deepseek-v3.1 | $4.00 | $12.00 | > Price unit: $ per Million tokens. Each response includes `usage.cost` and `usage.credits_remaining`. ## Get Started 1. Register at [aisa.one](https://aisa.one) 2. Get API Key 3. Top up (pay-as-you-go) 4. Set environment variable: `export AISA_API_KEY="your-key"` ## Full API Reference See [API Reference](https://docs.aisa.one/reference/) for complete endpoint documentation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)