Quickstart

Prerequisites

An each::labs API key. Grab one here if you don’t have one yet.

Getting Started

Get your API key

Update your code

Point any OpenAI-compatible SDK to the each::labs base URL:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_EACHLABS_API_KEY",
    base_url="https://api.eachlabs.ai/v1"
)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_EACHLABS_API_KEY",
  baseURL: "https://api.eachlabs.ai/v1",
});

Make your first request

Use the provider/model-name format to pick any model:

response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[
        {"role": "user", "content": "What is the capital of France?"}
    ]
)

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

const response = await client.chat.completions.create({
  model: "openai/gpt-4o",
  messages: [
    { role: "user", content: "What is the capital of France?" },
  ],
});

console.log(response.choices[0].message.content);

Switch Models Instantly

Just change the model parameter. That’s literally it. No config changes, no new SDKs, no drama:

# Use Anthropic Claude
response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

# Use Google Gemini
response = client.chat.completions.create(
    model="google/gemini-2.5-pro-preview-06-05",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

# Use DeepSeek
response = client.chat.completions.create(
    model="deepseek/deepseek-r1",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

Streaming

Set stream: true to receive OpenAI-compatible server-sent events as tokens arrive:

stream = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Write a haiku about coding"}],
    stream=True
)

for chunk in stream:
    content = chunk.choices[0].delta.content if chunk.choices else None
    if content:
        print(content, end="")

const stream = await client.chat.completions.create({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Write a haiku about coding" }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
}

To include streamed token usage when it is available, pass stream_options={"include_usage": True} in Python or stream_options: { include_usage: true } in TypeScript.

Advanced Features

LLM Router comes with some nice tricks out of the box:

Smart Routing automatically sends requests to the cheapest or fastest provider
Fallbacks so your app doesn’t break when a model is having a bad day
Caching to save you money and time on repeated requests
Rate Limiting per model or per user, your call
Usage Analytics to keep tabs on costs, tokens, and latency

Overview

Prerequisites

Getting Started

Switch Models Instantly

Streaming

Advanced Features

Next Steps

LLM Router Overview

Available Models

​Prerequisites

​Getting Started

​Switch Models Instantly

​Streaming

​Advanced Features

​Next Steps

LLM Router Overview

Available Models

Prerequisites

Getting Started

Switch Models Instantly

Streaming

Advanced Features

Next Steps