Local LLM Gateway Control Center

Enterprise-grade OpenAI compatible gateway for private on-premise LLMs with token metering & quota enforcement.

API Base URL https://api.one-router.ai.one-bit.net/v1
Active Models 🤖
1
Local backends connected
Customers & Projects 👥
1
Active tenant accounts
Total Tokens Processed
0
Input & Output metered
Engine Health 🛡️
Ready
SQLite WAL + FastAPI Proxy

Customer Quota Overview

Loading customers...

Quick Verification

OpenAI Compatible

Use standard OpenAI client configuration pointing to this gateway:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.one-router.ai.one-bit.net/v1",
    api_key="sk-llm-your-key-here"
)

models = client.models.list()
print([m.id for m in models.data])

Registered Local Models

Public model aliases routed securely to internal LLM engines (Ollama, vLLM, etc.)

Loading models...

Customer Accounts & API Keys

Manage tenants, allocate monthly token budgets, and issue hashed Bearer API keys.

Customer Name ID Email Status Token Budget Used Tokens API Keys Actions
Loading customer accounts...

API Test Console

Test authentication and verify model listing in real-time.

Request Parameters

Pre-filled with the active key generated during bootstrap.

Response

Awaiting test
Click "Send Request" to execute API call...

Developer Integration Guide

Connect your applications using standard OpenAI client libraries without code modifications.

🐍 Python (OpenAI Official SDK)

import os
from openai import OpenAI

# Initialize client pointing to your One-Router gateway
client = OpenAI(
    base_url="https://api.one-router.ai.one-bit.net/v1",
    api_key="sk-llm-your-api-key"
)

# 1. List Available Models
models = client.models.list()
print("Available Models:", [m.id for m in models.data])

# 2. Chat Completion
response = client.chat.completions.create(
    model="qwen3-32b",
    messages=[
        {"role": "system", "content": "You are a secure local AI."},
        {"role": "user", "content": "Explain zero trust architecture."}
    ],
    temperature=0.7
)

print(response.choices[0].message.content)

🌐 cURL / Shell Request

# 1. Check Health
curl -s https://api.one-router.ai.one-bit.net/health

# 2. Fetch Models
curl -s -X GET "https://api.one-router.ai.one-bit.net/v1/models" \
  -H "Authorization: Bearer sk-llm-your-key-here"

# 3. Chat Completion
curl -s -X POST "https://api.one-router.ai.one-bit.net/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-llm-your-key-here" \
  -d '{
    "model": "qwen3-32b",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'