# Send LLM Router Gateway to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "aisa-llm-router-skill", "name": "LLM Router Gateway", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/bowen-dotcom/aisa-llm-router-skill", "canonicalUrl": "https://clawhub.ai/bowen-dotcom/aisa-llm-router-skill", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/aisa-llm-router-skill", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=aisa-llm-router-skill", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "README.md", "SKILL.md", "scripts/llm_router_client.py" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/aisa-llm-router-skill" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/aisa-llm-router-skill", "downloadUrl": "https://openagent3.xyz/downloads/aisa-llm-router-skill", "agentUrl": "https://openagent3.xyz/skills/aisa-llm-router-skill/agent", "manifestUrl": "https://openagent3.xyz/skills/aisa-llm-router-skill/agent.json", "briefUrl": "https://openagent3.xyz/skills/aisa-llm-router-skill/agent.md" } } ``` ## Documentation ### 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. ### 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? FeatureLLM RouterDirect APIsAPI Keys110+SDK CompatibilityOpenAI SDKMultiple SDKsBillingUnifiedPer-providerModel SwitchingChange stringCode rewriteFallback RoutingBuilt-inDIYCost TrackingUnifiedFragmented ### Supported Model Families FamilyDeveloperExample ModelsGPTOpenAIgpt-4.1, gpt-4o, gpt-4o-mini, o1, o1-mini, o3-miniClaudeAnthropicclaude-3-5-sonnet, claude-3-opus, claude-3-sonnetGeminiGooglegemini-2.0-flash, gemini-1.5-pro, gemini-1.5-flashQwenAlibabaqwen-max, qwen-plus, qwen2.5-72b-instructDeepseekDeepseekdeepseek-chat, deepseek-coder, deepseek-v3, deepseek-r1GrokxAIgrok-2, grok-beta Note: Model availability may vary. Check marketplace.aisa.one/pricing for the full list of currently available models and pricing. ### Quick Start export AISA_API_KEY="your-key" ### OpenAI-Compatible Chat Completions POST https://api.aisa.one/v1/chat/completions Request 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 ParameterTypeRequiredDescriptionmodelstringYesModel identifier (e.g., gpt-4.1, claude-3-sonnet)messagesarrayYesConversation messagestemperaturenumberNoRandomness (0-2, default: 1)max_tokensintegerNoMaximum response tokensstreambooleanNoEnable streaming (default: false)top_pnumberNoNucleus sampling (0-1)frequency_penaltynumberNoFrequency penalty (-2 to 2)presence_penaltynumberNoPresence penalty (-2 to 2)stopstring/arrayNoStop sequences Message Format { "role": "user|assistant|system", "content": "message text or array for multimodal" } Response { "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 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: 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: 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 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 } }' ### Installation No installation required - uses standard library only. ### CLI Usage # 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 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]}...") ### 1. Cost-Optimized Routing Use cheaper models for simple tasks: 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: 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: 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: 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: { "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 Use streaming for long responses to improve UX Set max_tokens to control costs Implement fallback for production reliability Cache responses for repeated queries Monitor usage via response metadata Use appropriate models - don't use GPT-4 for simple tasks ### OpenAI SDK Compatibility Just change the base URL and key: import os from openai import OpenAI client = OpenAI( api_key=os.environ["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 for current rates. Model FamilyApproximate CostGPT-4.1 / GPT-4o~$0.01 / 1K tokensClaude-3-Sonnet~$0.01 / 1K tokensGemini-2.0-Flash~$0.001 / 1K tokensQwen-Max~$0.005 / 1K tokensDeepSeek-V3~$0.002 / 1K tokens Every response includes usage.cost and usage.credits_remaining. ### Get Started Sign up at aisa.one Get your API key from the dashboard Add credits (pay-as-you-go) Set environment variable: export AISA_API_KEY="your-key" ### Full API Reference See API Reference for complete endpoint documentation. ## Trust - Source: tencent - Verification: Indexed source record - Publisher: bowen-dotcom - Version: 1.0.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-30T16:55:25.780Z - Expires at: 2026-05-07T16:55:25.780Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/aisa-llm-router-skill) - [Send to Agent page](https://openagent3.xyz/skills/aisa-llm-router-skill/agent) - [JSON manifest](https://openagent3.xyz/skills/aisa-llm-router-skill/agent.json) - [Markdown brief](https://openagent3.xyz/skills/aisa-llm-router-skill/agent.md) - [Download page](https://openagent3.xyz/downloads/aisa-llm-router-skill)