openclaw-aisa-affordable-llm-model-tokensUnified LLM Gateway - One API for 70+ AI models. Route to GPT, Claude, Gemini, Qwen, Deepseek, Grok and more with a single API key.
Install via ClawdBot CLI:
clawdbot install 0xjordansg-yolo/openclaw-aisa-affordable-llm-model-tokensUnified 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.
"Chat with GPT-4 for reasoning, switch to Claude for creative writing""Compare responses from GPT-4, Claude, and Gemini for the same question""Analyze this image with GPT-4o - what objects are in it?""Route simple queries to fast/cheap models, complex queries to GPT-4""If GPT-4 fails, automatically try Claude, then Gemini"| 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 |
| 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.
export AISA_API_KEY="your-key"POST https://api.aisa.one/v1/chat/completionscurl -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
}'| 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 |
{
"role": "user|assistant|system",
"content": "message text or array for multimodal"
}{
"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
}
}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]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"}}
]
}
]
}'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"
}'For Gemini models, you can also use the native format:
POST https://api.aisa.one/v1/models/{model}:generateContentcurl -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
}
}'No installation required - uses standard library only.
# 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?"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 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}])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")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"])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}])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 key402 - Insufficient credits404 - Model not found429 - Rate limit exceeded500 - Server errorJust 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)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.
export AISA_API_KEY="your-key"See API Reference for complete endpoint documentation.
Generated Mar 1, 2026
Develop a customer support chatbot that uses GPT-4 for technical troubleshooting, Claude for empathetic responses, and Gemini for multilingual queries, all through a single API integration. This reduces development time and costs by eliminating the need to manage multiple provider SDKs.
Build a platform that generates marketing copy, blog posts, and social media content by routing tasks to different models: Qwen for creative writing, Deepseek for code snippets, and GPT-4 for editing. Users can compare outputs and select the best result, streamlining content workflows.
Create an educational app that provides explanations on complex topics like quantum computing by fetching responses from GPT-4, Claude, and Gemini simultaneously. Students can view side-by-side comparisons to enhance learning, with fallback routing ensuring reliability if one model fails.
Implement a system that analyzes warehouse images using GPT-4o to identify objects and track inventory levels. The router allows easy switching to other vision-capable models for redundancy, optimizing accuracy and reducing manual inspection costs.
Develop an internal AI assistant for startups that routes simple queries to cheaper models like GPT-4o-mini and complex tasks to GPT-4.1, with built-in fallback to Claude or Gemini to maintain uptime while minimizing token expenses.
Offer a unified LLM gateway as a SaaS platform with tiered pricing based on usage volume and model access. Charge monthly or annual subscriptions, providing businesses with predictable billing and cost savings compared to managing multiple API keys.
Operate a marketplace where users pay per token for accessing various models through the router, with a markup on top of provider costs. Generate revenue from transaction fees and offer volume discounts to attract high-usage clients like enterprises.
License the LLM router technology to other companies for embedding into their products, such as CRM systems or educational platforms. Charge upfront licensing fees or revenue-sharing agreements, enabling partners to offer multi-model AI without development overhead.
💬 Integration Tip
Set the AISA_API_KEY environment variable and use the OpenAI-compatible endpoint to quickly integrate with existing codebases, minimizing changes to SDK calls.
Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
Gemini CLI for one-shot Q&A, summaries, and generation.
Research any topic from the last 30 days on Reddit + X + Web, synthesize findings, and write copy-paste-ready prompts. Use when the user wants recent social/web research on a topic, asks "what are people saying about X", or wants to learn current best practices. Requires OPENAI_API_KEY and/or XAI_API_KEY for full Reddit+X access, falls back to web search.
Check Antigravity account quotas for Claude and Gemini models. Shows remaining quota and reset times with ban detection.
Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates opencla...
Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates openclaw.json. Use when the user mentions free AI, OpenRouter, model switching, rate limits, or wants to reduce AI costs.