Skill: 0xjordansg-yolo/openclaw-aisa-llm-gateway (version 1.0.0)

--- name: llm-router description: "Unified LLM Gateway - One API for 70+ AI models. Route to GPT, Claude, Gemini, Qwen, Deepseek, Grok and more with a single API key." homepage: https://openclaw.ai metadata: {"openclaw":{"emoji":"🧠","requires":{"bins":["curl","python3"],"env":["AISA_API_KEY"]},"primaryEnv":"AISA_API_KEY"}} --- # OpenClaw LLM Router 🧠 **Unified LLM Gateway for autonomous agents. Powered by AIsa.** One API key. 70+ models. OpenAI-compatible. Replace 100+ API keys with one. Access GPT-4, Claude-3, Gemini, Qwen, Deepseek, Grok, and more through a unified, OpenAI-compatible API. ## 🔥 What Can You Do? ### Multi-Model Chat ``` "Chat with GPT-4 for reasoning, switch to Claude for creative writing" ``` ### Model Comparison ``` "Compare responses from GPT-4, Claude, and Gemini for the same question" ``` ### Vision Analysis ``` "Analyze this image with GPT-4o - what objects are in it?" ``` ### Cost Optimization ``` "Route simple queries to fast/cheap models, complex queries to GPT-4" ``` ### Fallback Strategy ``` "If GPT-4 fails, automatically try Claude, then Gemini" ``` ## Why LLM Router? | Feature | LLM Router | Direct APIs | |---------|------------|-------------| | API Keys | 1 | 10+ | | SDK Compatibility | OpenAI SDK | Multiple SDKs | | Billing | Unified | Per-provider | | Model Switching | Change string | Code rewrite | | Fallback Routing | Built-in | DIY | | Cost Tracking | Unified | Fragmented | ## Supported Model Families | Family | Developer | Example Models | |--------|-----------|----------------| | GPT | OpenAI | gpt-4.1, gpt-4o, gpt-4o-mini, o1, o1-mini, o3-mini | | Claude | Anthropic | claude-3-5-sonnet, claude-3-opus, claude-3-sonnet | | Gemini | Google | gemini-2.0-flash, gemini-1.5-pro, gemini-1.5-flash | | Qwen | Alibaba | qwen-max, qwen-plus, qwen2.5-72b-instruct | | Deepseek | Deepseek | deepseek-chat, deepseek-coder, deepseek-v3, deepseek-r1 | | Grok | xAI | grok-2, grok-beta | > **Note**: Model availability may vary. Check [marketplace.aisa.one/pricing](https://marketplace.aisa.one/pricing) for the full list of currently available models and pricing. ## Quick Start ```bash export AISA_API_KEY="your-key" ``` ## API Endpoints ### OpenAI-Compatible Chat Completions ``` POST https://api.aisa.one/v1/chat/completions ``` #### Request ```bash curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4.1", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Explain quantum computing in simple terms."} ], "temperature": 0.7, "max_tokens": 1000 }' ``` #### Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `model` | string | Yes | Model identifier (e.g., `gpt-4.1`, `claude-3-sonnet`) | | `messages` | array | Yes | Conversation messages | | `temperature` | number | No | Randomness (0-2, default: 1) | | `max_tokens` | integer | No | Maximum response tokens | | `stream` | boolean | No | Enable streaming (default: false) | | `top_p` | number | No | Nucleus sampling (0-1) | | `frequency_penalty` | number | No | Frequency penalty (-2 to 2) | | `presence_penalty` | number | No | Presence penalty (-2 to 2) | | `stop` | string/array | No | Stop sequences | #### Message Format ```json { "role": "user|assistant|system", "content": "message text or array for multimodal" } ``` #### Response ```json { "id": "chatcmpl-xxx", "object": "chat.completion", "created": 1234567890, "model": "gpt-4.1", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Quantum computing uses..." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 50, "completion_tokens": 200, "total_tokens": 250, "cost": 0.0025 } } ``` ### Streaming Response ```bash curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "claude-3-sonnet", "messages": [{"role": "user", "content": "Write a poem about AI."}], "stream": true }' ``` Streaming returns Server-Sent Events (SSE): ``` data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":"In"}}]} data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":" circuits"}}]} ... data: [DONE] ``` ### Vision / Image Analysis Analyze images by passing image URLs or base64 data: ```bash curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4o", "messages": [ { "role": "user", "content": [ {"type": "text", "text": "What is in this image?"}, {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}} ] } ] }' ``` ### Function Calling Enable tools/functions for structured outputs: ```bash curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4.1", "messages": [{"role": "user", "content": "What is the weather in Tokyo?"}], "functions": [ { "name": "get_weather", "description": "Get current weather for a location", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "City name"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]} }, "required": ["location"] } } ], "function_call": "auto" }' ``` ### Google Gemini Format For Gemini models, you can also use the native format: ``` POST https://api.aisa.one/v1/models/{model}:generateContent ``` ```bash curl -X POST "https://api.aisa.one/v1/models/gemini-2.0-flash:generateContent" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "contents": [ { "role": "user", "parts": [{"text": "Explain machine learning."}] } ], "generationConfig": { "temperature": 0.7, "maxOutputTokens": 1000 } }' ``` ## Python Client ### Installation No installation required - uses standard library only. ### CLI Usage ```bash # Basic completion python3 {baseDir}/scripts/llm_router_client.py chat --model gpt-4.1 --message "Hello, world!" # With system prompt python3 {baseDir}/scripts/llm_router_client.py chat --model claude-3-sonnet --system "You are a poet" --message "Write about the moon" # Streaming python3 {baseDir}/scripts/llm_router_client.py chat --model gpt-4o --message "Tell me a story" --stream # Multi-turn conversation python3 {baseDir}/scripts/llm_router_client.py chat --model qwen-max --messages '[{"role":"user","content":"Hi"},{"role":"assistant","content":"Hello!"},{"role":"user","content":"How are you?"}]' # Vision analysis python3 {baseDir}/scripts/llm_router_client.py vision --model gpt-4o --image "https://example.com/image.jpg" --prompt "Describe this image" # List supported models python3 {baseDir}/scripts/llm_router_client.py models # Compare models python3 {baseDir}/scripts/llm_router_client.py compare --models "gpt-4.1,claude-3-sonnet,gemini-2.0-flash" --message "What is 2+2?" ``` ### Python SDK Usage ```python from llm_router_client import LLMRouterClient client = LLMRouterClient() # Uses AISA_API_KEY env var # Simple chat response = client.chat( model="gpt-4.1", messages=[{"role": "user", "content": "Hello!"}] ) print(response["choices"][0]["message"]["content"]) # With options response = client.chat( model="claude-3-sonnet", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Explain relativity."} ], temperature=0.7, max_tokens=500 ) # Streaming for chunk in client.chat_stream( model="gpt-4o", messages=[{"role": "user", "content": "Write a story."}] ): print(chunk, end="", flush=True) # Vision response = client.vision( model="gpt-4o", image_url="https://example.com/image.jpg", prompt="What's in this image?" ) # Compare models results = client.compare_models( models=["gpt-4.1", "claude-3-sonnet", "gemini-2.0-flash"], message="Explain quantum computing" ) for model, result in results.items(): print(f"{model}: {result['response'][:100]}...") ``` ## Use Cases ### 1. Cost-Optimized Routing Use cheaper models for simple tasks: ```python def smart_route(message: str) -> str: # Simple queries -> fast/cheap model if len(message) < 50: model = "gpt-3.5-turbo" # Complex reasoning -> powerful model else: model = "gpt-4.1" return client.chat(model=model, messages=[{"role": "user", "content": message}]) ``` ### 2. Fallback Strategy Automatic fallback on failure: ```python def chat_with_fallback(message: str) -> str: models = ["gpt-4.1", "claude-3-sonnet", "gemini-2.0-flash"] for model in models: try: return client.chat(model=model, messages=[{"role": "user", "content": message}]) except Exception: continue raise Exception("All models failed") ``` ### 3. Model A/B Testing Compare model outputs: ```python results = client.compare_models( models=["gpt-4.1", "claude-3-opus"], message="Analyze this quarterly report..." ) # Log for analysis for model, result in results.items(): log_response(model=model, latency=result["latency"], cost=result["cost"]) ``` ### 4. Specialized Model Selection Choose the best model for each task: ```python MODEL_MAP = { "code": "deepseek-coder", "creative": "claude-3-opus", "fast": "gpt-3.5-turbo", "vision": "gpt-4o", "chinese": "qwen-max", "reasoning": "gpt-4.1" } def route_by_task(task_type: str, message: str) -> str: model = MODEL_MAP.get(task_type, "gpt-4.1") 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 'xyz' is not available" } } ``` Common error codes: - `401` - Invalid or missing API key - `402` - Insufficient credits - `404` - Model not found - `429` - Rate limit exceeded - `500` - Server error ## Best Practices 1. **Use streaming** for long responses to improve UX 2. **Set max_tokens** to control costs 3. **Implement fallback** for production reliability 4. **Cache responses** for repeated queries 5. **Monitor usage** via response metadata 6. **Use appropriate models** - don't use GPT-4 for simple tasks ## OpenAI SDK Compatibility Just change the base URL and key: ```python import os from openai import OpenAI client = OpenAI( [REDACTED_SECRET]"AISA_API_KEY"], base_url="https://api.aisa.one/v1" ) response = client.chat.completions.create( model="gpt-4.1", messages=[{"role": "user", "content": "Hello!"}] ) print(response.choices[0].message.content) ``` ## Pricing Token-based pricing varies by model. Check [marketplace.aisa.one/pricing](https://marketplace.aisa.one/pricing) for current rates. | Model Family | Approximate Cost | |--------------|------------------| | GPT-4.1 / GPT-4o | ~$0.01 / 1K tokens | | Claude-3-Sonnet | ~$0.01 / 1K tokens | | Gemini-2.0-Flash | ~$0.001 / 1K tokens | | Qwen-Max | ~$0.005 / 1K tokens | | DeepSeek-V3 | ~$0.002 / 1K tokens | Every response includes `usage.cost` and `usage.credits_remaining`. ## Get Started 1. Sign up at [aisa.one](https://aisa.one) 2. Get your API key from the dashboard 3. Add credits (pay-as-you-go) 4. Set environment variable: `export AISA_API_KEY="your-key"` ## Full API Reference See [API Reference](https://aisa.mintlify.app/api-reference/introduction) for complete endpoint documentation.


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