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Documentation Index

Fetch the complete documentation index at: https://docs.memorylake.ai/llms.txt

Use this file to discover all available pages before exploring further.

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What are MVP Examples?

These are minimal, complete, and runnable code examples that you can copy and use immediately. Each example is self-contained and shows the essential code needed to make API calls to Model Router.
Before running these examples, make sure you have:
  1. Created an API key (see Create API Key)
  2. Checked which models are available (see List Available Models)
  3. Replaced sk-demo123 with your actual API key
  4. Replaced the model name with one available to your API key

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Python Examples

Recommended: The OpenAI official SDK is the easiest way to use Model Router. You only need to change the base_url parameter - everything else works exactly the same as with OpenAI’s API.

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Installation

pip install openai

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Complete Example

from openai import OpenAI

# Initialize client with Model Router base URL
client = OpenAI(
    base_url="https://app.memorylake.ai/v1",  # Only change needed!
    api_key="sk-your-api-key-here"  # Your Model Router API key
)

# Replace with a model available to your API key
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Hello! How are you?"}
    ]
)

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

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Running the Example

  1. Save the code to a file (e.g., chat.py)
  2. Install dependencies: pip install openai
  3. Replace api_key and model with your values
  4. Run: python chat.py
Key Point: The only difference from using OpenAI’s API directly is setting base_url="https://app.memorylake.ai/v1". All other code remains exactly the same!

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Using Environment Variables

You can also use environment variables for better security: 
export OPENAI_API_KEY="sk-your-api-key-here"

from openai import OpenAI

# Client automatically reads OPENAI_API_KEY from environment
client = OpenAI(
    base_url="https://app.memorylake.ai/v1"  # Only change needed!
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Hello! How are you?"}
    ]
)

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

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Using requests Library (Alternative)

If you prefer using the requests library directly:

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Installation

pip install requests

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Complete Example

import requests

# Replace with your API key
API_KEY = "sk-your-api-key-here"
BASE_URL = "https://app.memorylake.ai/v1"

# Replace with a model available to your API key
MODEL = "gpt-4o-mini"

def chat_completion(messages):
    """Send a chat completion request"""
    url = f"{BASE_URL}/chat/completions"
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    data = {
        "model": MODEL,
        "messages": messages,
        "stream": False
    }
    
    response = requests.post(url, headers=headers, json=data)
    response.raise_for_status()
    return response.json()

# Example usage
if __name__ == "__main__":
    messages = [
        {"role": "user", "content": "Hello! How are you?"}
    ]
    
    result = chat_completion(messages)
    print(result["choices"][0]["message"]["content"])

​
Running the Example

  1. Save the code to a file (e.g., chat.py)
  2. Install dependencies: pip install requests
  3. Replace API_KEY and MODEL with your values
  4. Run: python chat.py

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TypeScript/JavaScript Examples

Recommended: The OpenAI official SDK works seamlessly with Model Router. You only need to change the baseURL parameter - everything else works exactly the same as with OpenAI’s API.

​
Installation

npm install openai
For TypeScript projects, you may also want to install type definitions: 
npm install --save-dev typescript @types/node

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Complete Example (TypeScript)

import OpenAI from "openai";

// Initialize client with Model Router base URL
const client = new OpenAI({
  baseURL: "https://app.memorylake.ai/v1",  // Only change needed!
  apiKey: "sk-your-api-key-here"  // Your Model Router API key
});

// Replace with a model available to your API key
const response = await client.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [
    { role: "user", content: "Hello! How are you?" }
  ]
});

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

​
Complete Example (JavaScript)

import OpenAI from "openai";

// Initialize client with Model Router base URL
const client = new OpenAI({
  baseURL: "https://app.memorylake.ai/v1",  // Only change needed!
  apiKey: "sk-your-api-key-here"  // Your Model Router API key
});

// Replace with a model available to your API key
const response = await client.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [
    { role: "user", content: "Hello! How are you?" }
  ]
});

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

​
Running the Example

  1. Save the code to a file (e.g., chat.ts or chat.mjs)
  2. Install dependencies: npm install openai
  3. Replace apiKey and model with your values
  4. Run:
    • TypeScript: npx ts-node chat.ts (or compile first with tsc)
    • JavaScript: node chat.mjs (or node chat.js if using CommonJS)
Key Point: The only difference from using OpenAI’s API directly is setting baseURL: "https://app.memorylake.ai/v1". All other code remains exactly the same!

​
Using Environment Variables

You can also use environment variables for better security: 
export OPENAI_API_KEY="sk-your-api-key-here"

import OpenAI from "openai";

// Client automatically reads OPENAI_API_KEY from environment
const client = new OpenAI({
  baseURL: "https://app.memorylake.ai/v1"  // Only change needed!
});

const response = await client.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [
    { role: "user", content: "Hello! How are you?" }
  ]
});

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

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Streaming Examples

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Python - Using OpenAI Official SDK

For streaming responses with the OpenAI SDK: 
from openai import OpenAI

client = OpenAI(
    base_url="https://app.memorylake.ai/v1",
    api_key="sk-your-api-key-here"
)

stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Tell me a short story"}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end='', flush=True)
print()  # New line at the end

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Python - Using requests Library

For streaming responses with the requests library: 
import requests
import json

API_KEY = "sk-your-api-key-here"
BASE_URL = "https://app.memorylake.ai/v1"
MODEL = "gpt-4o-mini"

def chat_completion_stream(messages):
    """Send a streaming chat completion request"""
    url = f"{BASE_URL}/chat/completions"
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    data = {
        "model": MODEL,
        "messages": messages,
        "stream": True
    }
    
    response = requests.post(url, headers=headers, json=data, stream=True)
    response.raise_for_status()
    
    for line in response.iter_lines():
        if line:
            line_text = line.decode('utf-8')
            if line_text.startswith('data: '):
                json_str = line_text[6:]  # Remove 'data: ' prefix
                if json_str == '[DONE]':
                    break
                try:
                    data = json.loads(json_str)
                    if 'choices' in data and len(data['choices']) > 0:
                        delta = data['choices'][0].get('delta', {})
                        if 'content' in delta:
                            print(delta['content'], end='', flush=True)
                except json.JSONDecodeError:
                    pass

# Example usage
if __name__ == "__main__":
    messages = [
        {"role": "user", "content": "Tell me a short story"}
    ]
    chat_completion_stream(messages)
    print()  # New line at the end

​
TypeScript/JavaScript - Using OpenAI Official SDK

For streaming responses with the OpenAI SDK: 
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://app.memorylake.ai/v1",
  apiKey: "sk-your-api-key-here"
});

const stream = await client.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [
    { role: "user", content: "Tell me a short story" }
  ],
  stream: true
});

for await (const chunk of stream) {
  if (chunk.choices[0]?.delta?.content) {
    process.stdout.write(chunk.choices[0].delta.content);
  }
}
console.log(); // New line at the end

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Other Official SDKs

Model Router is compatible with many official SDKs from different AI platforms. Most of them only require configuring the API_KEY and BASE_URL to work with Model Router.
Key Point: For most official SDKs, you only need to:
  1. Set the baseURL or base_url to https://app.memorylake.ai/v1 (or the corresponding endpoint)
  2. Use your Model Router API key as the apiKey or api_key
  3. Everything else works exactly the same as with the original platform’s API

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Anthropic Claude SDK

The official Anthropic Claude SDK supports Python, TypeScript/JavaScript, Java, Go, C#, Ruby, and PHP. Example (Python): 
from anthropic import Anthropic

client = Anthropic(
    api_key="sk-your-api-key-here",
    base_url="https://app.memorylake.ai/claude"  # Claude native endpoint
)

message = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=200,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
Example (TypeScript): 
import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  apiKey: 'sk-your-api-key-here',
  baseURL: 'https://app.memorylake.ai/claude'  // Claude native endpoint
});

const message = await client.messages.create({
  model: 'claude-3-opus-20240229',
  max_tokens: 200,
  messages: [
    { role: 'user', content: 'Hello!' }
  ]
});
Documentation: Anthropic Client SDKs

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Vercel AI SDK

The Vercel AI SDK provides a unified interface for multiple AI providers, including OpenAI, Anthropic, Google, and more. Example: 
import { openai } from '@ai-sdk/openai';

const client = openai({
  baseURL: 'https://app.memorylake.ai/v1',
  apiKey: 'sk-your-api-key-here'
});
Documentation: Vercel AI SDK Providers

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Google Gemini SDK

The official Google Gemini SDK supports Python, Node.js, and other languages. Example (Python): 
import google.generativeai as genai

genai.configure(
    api_key="sk-your-api-key-here",
    transport="rest",
    client_options={
        "api_endpoint": "https://app.memorylake.ai/gemini"
    }
)

model = genai.GenerativeModel('gemini-1.5-pro')
response = model.generate_content("Hello!")
Documentation: Google Gemini API Migration Guide

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General Pattern

Most official SDKs follow a similar pattern:
  1. Install the SDK: Use the official package manager (pip, npm, etc.)
  2. Configure the client: Set baseURL/base_url to Model Router’s endpoint
  3. Set API key: Use your Model Router API key
  4. Use normally: All other code remains the same
Common Endpoints:
  • OpenAI-compatible: https://app.memorylake.ai/v1
  • Claude native: https://app.memorylake.ai/claude
  • Gemini native: https://app.memorylake.ai/gemini
Before using any SDK, make sure to check which models are available to your API key by calling the model list endpoint.

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Next Steps

  1. Test Your Code: Run one of the examples above with your API key
  2. Check Your Usage: See View Usage and Billing to monitor your API calls
  3. Handle Errors: Learn about Error Handling if you encounter issues
  4. Explore More: Check out Direct API Requests for more advanced usage