One API key for 70+ AI models. Route to GPT, Claude, Gemini, Qwen, Deepseek, Grok and more

Unified LLM Gateway - One API for 70+ AI models. Route to GPT, Claude, Gemini, Qwen, Deepseek, Grok and more with a single API key.

Security Scan

The skill is internally coherent: its docs, CLI client, required binaries, and single required env var (AISA_API_KEY) all match the stated purpose of routing requests to the AIsa unified LLM gateway.

Purpose & Capability

Name/description, SKILL.md examples, and the included Python client all target the same API host (https://api.aisa.one) and require a single API key (AISA_API_KEY). Required binaries (curl, python3) are appropriate for a CLI/client skill. No unrelated credentials or binaries are requested.

Instruction Scope

SKILL.md and README instruct the agent to send user prompts, images, and other data to the remote AIsa endpoints. That is expected for a gateway, but it means any data you pass (including images or secrets) is transmitted to a third-party service. The instructions do not request reading local files or unrelated environment variables, nor do they instruct exfiltration beyond the intended API.

Install Mechanism

There is no install spec (instruction-only), and the shipped Python script uses only stdlib modules (urllib, json, argparse). Nothing is downloaded or written to disk by an installer here, so install-time risk is low.

Credentials

The skill requires a single API credential (AISA_API_KEY) which is proportional to a gateway client. That key grants access to the provider and may be able to consume billing/quota and see all request payloads — this is expected but sensitive. No other unrelated secrets are requested.

Persistence & Privilege

always:false and no install-time changes or modifications to other skills are requested. The skill is user-invocable and allows normal autonomous invocation (platform default) but does not request elevated or permanent platform privileges.

Assessment

This skill appears to do what it says: route requests to the AIsa / aisa.one gateway using a single API key. Before installing, confirm you trust the AIsa provider (https://api.aisa.one / marketplace.aisa.one) because any prompt, file, or image you send will be visible to that service and may consume billing. Use a key with appropriate billing limits or a dedicated account, avoid sending sensitive secrets or private data, and monitor usage after enabling the skill. If you need stronger controls, ask for more info about the provider's privacy, retention, and billing policies or test with a low-privilege key first.

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 for the full list of currently available models and pricing.

Quick Start

export AISA_API_KEY="your-key"

API Endpoints

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

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

{
  "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
    }
  }'

Python Client

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]}...")

Use Cases

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

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:

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 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
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 for complete endpoint documentation.