OpenAI-Compatible API

When to Use OpenAI-Compatible Endpoint

The OpenAI-compatible API is ideal when:

You’re migrating from OpenAI and want minimal code changes
You’re using frameworks built for OpenAI (LangChain, LlamaIndex, Vercel AI SDK)
You want a simple chat interface for browser automation
You need streaming responses
You prefer the familiar OpenAI SDK patterns

Choose native REST/WebSocket instead if you need:

Multi-task sessions with persistent browser state
Manual browser control and takeover
Video streaming
Fine-grained session management

The OpenAI-compatible endpoint creates a new session for each request and auto-terminates after completion. For multi-task workflows, use the native REST API.

Setup with OpenAI SDK

Installation

npm install openai

Basic Usage

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://connect.enigma.click/v1',
  apiKey: 'YOUR_API_KEY' // Your Enigma API key
});

const completion = await client.chat.completions.create({
  model: 'enigma-browser-1',
  messages: [
    { role: 'user', content: 'Go to google.com and search for Anthropic' }
  ]
});

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

Response:

{
  "id": "chatcmpl-a1b2c3d4",
  "object": "chat.completion",
  "created": 1704067200,
  "model": "enigma-browser-1",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Successfully searched for Anthropic on Google. The first result is the official Anthropic website at anthropic.com, which describes Claude as a next-generation AI assistant..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 12450,
    "completion_tokens": 3200,
    "total_tokens": 15650
  }
}

Framework Examples

LangChain

import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage } from "@langchain/core/messages";

const model = new ChatOpenAI({
  modelName: "enigma-browser-1",
  openAIApiKey: "YOUR_API_KEY",
  configuration: {
    baseURL: "https://connect.enigma.click/v1"
  }
});

const response = await model.invoke([
  new HumanMessage("Go to amazon.com and search for wireless keyboards. List the top 3 results with prices.")
]);

console.log(response.content);

With Streaming:

const stream = await model.stream([
  new HumanMessage("Search Google for Anthropic and summarize the first result")
]);

for await (const chunk of stream) {
  process.stdout.write(chunk.content);
}

With Chains:

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const model = new ChatOpenAI({
  modelName: "enigma-browser-1",
  openAIApiKey: "YOUR_API_KEY",
  configuration: {
    baseURL: "https://connect.enigma.click/v1"
  }
});

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a web research assistant. Extract structured data from websites."],
  ["human", "{task}"]
]);

const chain = prompt.pipe(model).pipe(new StringOutputParser());

const result = await chain.invoke({
  task: "Go to example.com and extract all product names and prices"
});

console.log(result);

LlamaIndex

import { OpenAI } from "llamaindex";

const llm = new OpenAI({
  model: "enigma-browser-1",
  apiKey: "YOUR_API_KEY",
  additionalChatOptions: {
    baseURL: "https://connect.enigma.click/v1"
  }
});

const response = await llm.chat({
  messages: [
    {
      role: "user",
      content: "Navigate to github.com/anthropics and list the top 5 repositories"
    }
  ]
});

console.log(response.message.content);

With Agent:

import { OpenAI, OpenAIAgent } from "llamaindex";

const llm = new OpenAI({
  model: "enigma-browser-1",
  apiKey: "YOUR_API_KEY",
  additionalChatOptions: {
    baseURL: "https://connect.enigma.click/v1"
  }
});

const agent = new OpenAIAgent({
  llm,
  systemPrompt: "You are a browser automation assistant. Help users extract data from websites."
});

const response = await agent.chat({
  message: "Go to product hunt and find the top 3 products today"
});

console.log(response.response);

Vercel AI SDK

import { openai } from '@ai-sdk/openai';
import { generateText } from 'ai';

const model = openai('enigma-browser-1', {
  baseURL: 'https://connect.enigma.click/v1',
  apiKey: 'YOUR_API_KEY'
});

const { text } = await generateText({
  model,
  prompt: 'Go to news.ycombinator.com and summarize the top 5 stories'
});

console.log(text);

With Streaming:

import { openai } from '@ai-sdk/openai';
import { streamText } from 'ai';

const model = openai('enigma-browser-1', {
  baseURL: 'https://connect.enigma.click/v1',
  apiKey: 'YOUR_API_KEY'
});

const { textStream } = await streamText({
  model,
  prompt: 'Search Google for "AI browser automation" and summarize the results'
});

for await (const textPart of textStream) {
  process.stdout.write(textPart);
}

In Next.js Route Handler:

// app/api/research/route.ts
import { openai } from '@ai-sdk/openai';
import { streamText } from 'ai';

export async function POST(req: Request) {
  const { task } = await req.json();

  const model = openai('enigma-browser-1', {
    baseURL: 'https://connect.enigma.click/v1',
    apiKey: process.env.ENIGMA_API_KEY
  });

  const result = await streamText({
    model,
    prompt: task
  });

  return result.toAIStreamResponse();
}

// Usage in component:
// const response = await fetch('/api/research', {
//   method: 'POST',
//   body: JSON.stringify({ task: 'Go to example.com and extract all links' })
// });

Chat Completions Format

Request Format

POST https://connect.enigma.click/v1/chat/completions
Content-Type: application/json
Authorization: Bearer YOUR_API_KEY

{
  "model": "enigma-browser-1",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful browser automation assistant."
    },
    {
      "role": "user",
      "content": "Go to example.com and extract all headings"
    }
  ],
  "stream": false,
  "max_tokens": 2000
}

Parameters:

Parameter	Type	Required	Description
`model`	string	Yes	Must be `"enigma-browser-1"`
`messages`	array	Yes	Array of message objects with `role` and `content`
`stream`	boolean	No	Enable streaming responses (default: false)
`max_tokens`	number	No	Maximum tokens in response (default: 2000)
`temperature`	number	No	Not used (included for compatibility)

Non-Streaming Response

{
  "id": "chatcmpl-a1b2c3d4e5f6",
  "object": "chat.completion",
  "created": 1704067200,
  "model": "enigma-browser-1",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I found 5 headings on example.com:\n1. Example Domain\n2. More Information\n3. Contact\n4. About\n5. Privacy Policy"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 8450,
    "completion_tokens": 2100,
    "total_tokens": 10550
  },
  "enigma": {
    "sessionId": "a1b2c3d4e5f6",
    "taskId": "x9y8z7w6v5u4",
    "cost": 0.0089
  }
}

Streaming Response

When stream: true, responses use Server-Sent Events (SSE):

data: {"id":"chatcmpl-a1b2c3","object":"chat.completion.chunk","created":1704067200,"model":"enigma-browser-1","choices":[{"index":0,"delta":{"role":"assistant","content":"I"},"finish_reason":null}]}

data: {"id":"chatcmpl-a1b2c3","object":"chat.completion.chunk","created":1704067200,"model":"enigma-browser-1","choices":[{"index":0,"delta":{"content":" found"},"finish_reason":null}]}

data: {"id":"chatcmpl-a1b2c3","object":"chat.completion.chunk","created":1704067200,"model":"enigma-browser-1","choices":[{"index":0,"delta":{"content":" 5"},"finish_reason":null}]}

...

data: {"id":"chatcmpl-a1b2c3","object":"chat.completion.chunk","created":1704067200,"model":"enigma-browser-1","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]

Handling Streams:

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://connect.enigma.click/v1',
  apiKey: 'YOUR_API_KEY'
});

const stream = await client.chat.completions.create({
  model: 'enigma-browser-1',
  messages: [
    { role: 'user', content: 'Go to news.ycombinator.com and summarize top stories' }
  ],
  stream: true
});

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

Available Models

Model Name	Description
`enigma-browser-1`	Default browser automation model (Claude Sonnet 3.5)

Currently, only enigma-browser-1 is available. Additional models may be added in the future.

Differences from Standard OpenAI API

✅ Supported Features

Chat completions endpoint
Streaming responses
Message history
System messages
Token usage reporting

❌ Not Supported

Function calling (use native REST API for manual control)
Vision (images in messages)
Tool use (use native REST API for multi-task workflows)
Fine-tuning
Embeddings
Moderation
Audio/Speech
Temperature/top_p (task execution is deterministic)

🔄 Different Behavior

1. Session Management

OpenAI: Stateless, each request is independent
Enigma: Each request creates a new browser session that auto-terminates

2. Response Time

OpenAI: Typically 1-5 seconds
Enigma: Typically 10-50 seconds (real browser automation)

3. Context Window

OpenAI: Based on model (e.g., 128k tokens)
Enigma: Task-focused, less emphasis on large context

4. Pricing

OpenAI: Per-token pricing
Enigma: Per-task pricing with token usage included in response

Limitations

Multi-Task Workflows

The OpenAI-compatible endpoint creates a new session per request. For multi-step workflows, use the native REST API:

// ❌ OpenAI-compatible - Each request is a new session
const completion1 = await client.chat.completions.create({
  model: 'enigma-browser-1',
  messages: [{ role: 'user', content: 'Go to amazon.com' }]
});

const completion2 = await client.chat.completions.create({
  model: 'enigma-browser-1',
  messages: [{ role: 'user', content: 'Search for keyboards' }] // New session, not on Amazon
});

// ✅ Native REST API - Persistent session
const session = await fetch('https://connect.enigma.click/start/start-session', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
  },
  body: JSON.stringify({ taskDetails: 'Go to amazon.com' })
}).then(r => r.json());

await fetch('https://connect.enigma.click/start/send-message', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
  },
  body: JSON.stringify({
    sessionId: session.sessionId,
    message: {
      actionType: 'newTask',
      newState: 'start',
      taskDetails: 'Search for keyboards' // Same session, still on Amazon
    }
  })
});

Guardrails

Guardrails (human-in-the-loop) are not supported via OpenAI-compatible endpoint. If a guardrail triggers, the request will fail:

{
  "error": {
    "message": "Guardrail triggered: I need login credentials to proceed",
    "type": "guardrail_error",
    "code": "guardrail_triggered"
  }
}

Solution: Use native REST or WebSocket API for guardrail handling.

Video Streaming

Live video streaming is not available via OpenAI-compatible endpoint. Use native REST API to get webRTCURL and webViewURL.

Complete Example: Research Assistant

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://connect.enigma.click/v1',
  apiKey: process.env.ENIGMA_API_KEY
});

async function researchTopic(topic) {
  console.log(`Researching: ${topic}`);

  const completion = await client.chat.completions.create({
    model: 'enigma-browser-1',
    messages: [
      {
        role: 'system',
        content: 'You are a research assistant. Search the web and provide concise, factual summaries.'
      },
      {
        role: 'user',
        content: `Search Google for "${topic}" and summarize the top 3 results in a structured format.`
      }
    ],
    stream: false
  });

  const result = completion.choices[0].message.content;
  const usage = completion.usage;
  const cost = completion.enigma?.cost || 0;

  console.log('\n--- Results ---');
  console.log(result);
  console.log('\n--- Usage ---');
  console.log(`Tokens: ${usage.total_tokens} (${usage.prompt_tokens} prompt + ${usage.completion_tokens} completion)`);
  console.log(`Cost: $${cost}`);

  return result;
}

// Usage
await researchTopic('Claude AI capabilities');
await researchTopic('Browser automation with AI agents');

Error Handling

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://connect.enigma.click/v1',
  apiKey: 'YOUR_API_KEY'
});

async function safeCompletion(prompt) {
  try {
    const completion = await client.chat.completions.create({
      model: 'enigma-browser-1',
      messages: [{ role: 'user', content: prompt }]
    });

    return completion.choices[0].message.content;
  } catch (error) {
    if (error.status === 401) {
      console.error('Invalid API key');
    } else if (error.status === 402) {
      console.error('Insufficient balance');
    } else if (error.status === 429) {
      console.error('Rate limit exceeded');
    } else if (error.status === 503) {
      console.error('No browser instances available');
    } else if (error.code === 'guardrail_triggered') {
      console.error('Guardrail triggered:', error.message);
      // Use native API for guardrail handling
    } else {
      console.error('Unexpected error:', error.message);
    }

    throw error;
  }
}

Next Steps

REST API Integration

Full control with native REST API

WebSocket Integration

Real-time events and live updates

Multi-Task Workflows

Build complex automation sequences

API Reference

Complete endpoint documentation

Getting Started

Concepts

Usage Guides

Integrations

API Reference

Troubleshooting

When to Use OpenAI-Compatible Endpoint

Setup with OpenAI SDK

Installation

Basic Usage

Framework Examples

LangChain

LlamaIndex

Vercel AI SDK

Chat Completions Format

Request Format

Non-Streaming Response

Streaming Response

Available Models

Differences from Standard OpenAI API

✅ Supported Features

❌ Not Supported

🔄 Different Behavior

Limitations

Multi-Task Workflows

Guardrails

Video Streaming

Complete Example: Research Assistant

Error Handling

Next Steps

REST API Integration

WebSocket Integration

Multi-Task Workflows

API Reference

Getting Started

Concepts

Usage Guides

Integrations

API Reference

Troubleshooting

​When to Use OpenAI-Compatible Endpoint

​Setup with OpenAI SDK

​Installation

​Basic Usage

​Framework Examples

​LangChain

​LlamaIndex

​Vercel AI SDK

​Chat Completions Format

​Request Format

​Non-Streaming Response

​Streaming Response

​Available Models

​Differences from Standard OpenAI API

​✅ Supported Features

​❌ Not Supported

​🔄 Different Behavior

​Limitations

​Multi-Task Workflows

​Guardrails

​Video Streaming

​Complete Example: Research Assistant

​Error Handling

​Next Steps

REST API Integration

WebSocket Integration

Multi-Task Workflows

API Reference

When to Use OpenAI-Compatible Endpoint

Setup with OpenAI SDK

Installation

Basic Usage

Framework Examples

LangChain

LlamaIndex

Vercel AI SDK

Chat Completions Format

Request Format

Non-Streaming Response

Streaming Response

Available Models

Differences from Standard OpenAI API

✅ Supported Features

❌ Not Supported

🔄 Different Behavior

Limitations

Multi-Task Workflows

Guardrails

Video Streaming

Complete Example: Research Assistant

Error Handling

Next Steps