Quickstart

TL;DR

Sign up at assisters.dev and get an API key (starts with ask_). 2) Install OpenAI SDK (pip install openai). 3) Set base_url to https://api.assisters.dev/v1. 4) Use model assisters-chat-v1 for chat completions. That's it—you're ready to build!

This guide will help you make your first API call in under 5 minutes.

Prerequisites

An Assisters account (sign up free)

An API key from your dashboard

Python 3.8+ or Node.js 18+ installed

Step 1: Get Your API Key

Log in to your Assisters Dashboard
Navigate to API Keys
Click Create New Key
Copy your key (it starts with ask_)

Keep your API key secure! Never expose it in client-side code or commit it to version control.

Step 2: Install the SDK

Since Assisters API is OpenAI-compatible, you can use the official OpenAI SDK:

pip install openai

npm install openai

pnpm add openai

Step 3: Make Your First Request

from openai import OpenAI

# Initialize the client with Assisters API
client = OpenAI(
    api_key="ask_your_api_key_here",
    base_url="https://api.assisters.dev/v1"
)

# Create a chat completion
response = client.chat.completions.create(
    model="assisters-chat-v1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ]
)

print(response.choices[0].message.content)
# Output: The capital of France is Paris.

import OpenAI from 'openai';

// Initialize the client with Assisters API
const client = new OpenAI({
  apiKey: 'ask_your_api_key_here',
  baseURL: 'https://api.assisters.dev/v1'
});

// Create a chat completion
const response = await client.chat.completions.create({
  model: 'assisters-chat-v1',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is the capital of France?' }
  ]
});

console.log(response.choices[0].message.content);
// Output: The capital of France is Paris.

curl https://api.assisters.dev/v1/chat/completions \
  -H "Authorization: Bearer ask_your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "assisters-chat-v1",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"}
    ]
  }'

Step 4: Use Streaming (Optional)

For real-time responses, enable streaming:

from openai import OpenAI

client = OpenAI(
    api_key="ask_your_api_key_here",
    base_url="https://api.assisters.dev/v1"
)

# Stream the response
stream = client.chat.completions.create(
    model="assisters-chat-v1",
    messages=[
        {"role": "user", "content": "Write a haiku about coding"}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'ask_your_api_key_here',
  baseURL: 'https://api.assisters.dev/v1'
});

// Stream the response
const stream = await client.chat.completions.create({
  model: 'assisters-chat-v1',
  messages: [
    { role: 'user', content: 'Write a haiku about coding' }
  ],
  stream: true
});

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

Step 5: Try Other Endpoints

Create Embeddings

response = client.embeddings.create(
    model="assisters-embed-v1",
    input="The quick brown fox jumps over the lazy dog"
)

print(f"Embedding dimensions: {len(response.data[0].embedding)}")
# Output: Embedding dimensions: 1024

Content Moderation

response = client.moderations.create(
    model="assisters-moderation-v1",
    input="Hello, how are you today?"
)

print(f"Flagged: {response.results[0].flagged}")
# Output: Flagged: False

Vision Analysis

response = client.chat.completions.create(
    model="assisters-vision-v1",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
        ]
    }]
)

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

Code Generation

response = client.chat.completions.create(
    model="assisters-code-v1",
    messages=[
        {"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}
    ]
)

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

Understanding the Response

A typical chat completion response looks like this:

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1706745600,
  "model": "assisters-chat-v1",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 8,
    "total_tokens": 33
  }
}

The usage field shows token consumption, which determines your billing. See token counting for details.

Environment Variables

For production, use environment variables instead of hardcoding your API key:

ASSISTERS_API_KEY=ask_your_api_key_here

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("ASSISTERS_API_KEY"),
    base_url="https://api.assisters.dev/v1"
)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.ASSISTERS_API_KEY,
  baseURL: 'https://api.assisters.dev/v1'
});

Quickstart

Quickstart

Prerequisites

Step 1: Get Your API Key

Step 2: Install the SDK

Step 3: Make Your First Request

Step 4: Use Streaming (Optional)

Step 5: Try Other Endpoints

Create Embeddings

Content Moderation

Vision Analysis

Code Generation

Understanding the Response

Environment Variables

Next Steps

Authentication

API Reference

Models

Rate Limits

Troubleshooting

On this page

Quickstart

Authentication

API Reference

Models

Rate Limits

401 Unauthorized Error

429 Rate Limit Error

Model Not Found

On this page