Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
LLM Router Gateway
Unified LLM Gateway - One API for 70+ AI models. Route to GPT, Claude, Gemini, Qwen, Deepseek, Grok and more with a single API key.
skill openclawclawhub Free
Unified LLM Gateway - One API for 70+ AI models. Route to GPT, Claude, Gemini, Qwen, Deepseek, Grok and more with a single API key.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, scripts/llm_router_client.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- 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.
New install
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
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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- bowen-dotcom
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
OpenClaw LLM Router ๐ง
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.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Scripts
- SKILL.md
- README.md
- scripts/llm_router_client.py