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Nebula · OpenAI compatible

Nebula API Docs (OpenAI-Compatible)

Use Nebula in your own application through an OpenAI-compatible API. Start with your API key, then follow the examples below.

ModelsAvailable models and pricingAuthAPI key setupToolsFunction calling guideMCPModel Context Protocol
Model family

Available models, priced per 1M tokens.

One family, one flat rate. Choose the surface that fits the request.

TextDefault

Nebula 4.5

nebula-4.5

Adaptive intelligence. Every request is routed across the engine matrix by meaning, cost and learned reputation. You do not pick a tier.

  • Chat
  • Reasoning
  • Tool Calling
  • Security Analysis
Input
£10.00 /1M
Output
£20.00 /1M
Context
256K tokens
Speed
Adaptive
Multimodal

Nebula V

nebula-v

Vision, image and audio understanding. Screenshots, documents, video frames and speech in one context.

  • Vision
  • Image Analysis
  • Transcription
  • Audio
Input
£10.00 /1M
Output
£20.00 /1M
Context
1M tokens
Speed
Fast
Realtime

Nebula Realtime

nebula-realtime

Low-latency bidirectional voice, vision and screen over a WebSocket.

  • Voice
  • Vision
  • Screen
  • Audio
Input
£10.00 /1M
Output
£20.00 /1M
Context
1M tokens
Speed
Realtime
Embeddings

Nebula Embed

nebula-embed

Text embeddings for retrieval and RAG.

  • Embeddings
  • Retrieval
Input
£10.00 /1M
Output
£20.00 /1M
Context
8K tokens
Speed
Ultra Fast
Image

Nebula Image

nebula-image

Text-to-image generation.

  • Image Generation
Input
£10.00 /1M
Output
£20.00 /1M
Context
n/a
Speed
Fast
Security

Authentication, with scoped API keys.

Secure your API requests with API keys.

Getting an API key

  1. 1Log in to your BreachLine dashboard and go to Settings
  2. 2Navigate to API Keys section
  3. 3Click "Create API Key"
  4. 4Select "LLM API" scope (llm:*)
  5. 5Copy and securely store your key

Using your API key

Include your API key in the X-API-Key header:

X-API-Key: bl_live_xxxxxxxxxxxx
Security: Never expose your API key in client-side code or public repositories.
Getting started

Quick start, in three languages.

Get started with Nebula in minutes.

cURL

curl -X POST https://api.breachline.io/api/v1/llm/v1/chat/completions \
  -H "X-API-Key: bl_live_xxxxxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nebula-4.5",
    "messages": [
      {"role": "system", "content": "You are a security analyst."},
      {"role": "user", "content": "Analyze this SQL injection: SELECT * FROM users WHERE id = \'" + input + "\'"}
    ],
    "max_tokens": 2048,
    "temperature": 0.3
  }'

Python (OpenAI SDK)

from openai import OpenAI

# Initialize client with Nebula endpoint
client = OpenAI(
    api_key="bl_live_xxxxxxxxxxxx",
    base_url="https://api.breachline.io/api/v1/llm/v1"
)

# Chat completion
response = client.chat.completions.create(
    model="nebula-4.5",
    messages=[
        {"role": "system", "content": "You are a security expert."},
        {"role": "user", "content": "Analyze this vulnerability report..."}
    ],
    max_tokens=2048,
    temperature=0.3
)

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

JavaScript / TypeScript

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'bl_live_xxxxxxxxxxxx',
  baseURL: 'https://api.breachline.io/api/v1/llm/v1'
});

async function analyzeVulnerability(finding: string) {
  const response = await client.chat.completions.create({
    model: 'nebula-4.5',
    messages: [
      { role: 'system', content: 'You are a security analyst.' },
      { role: 'user', content: `Analyze: ${finding}` }
    ]
  });

  return response.choices[0].message.content;
}
Functions

Tool calling, with your own functions.

Enable Nebula to execute functions and interact with external systems.

Supported models

Tool calling is available on nebula-4.5, and is routed to a tool-capable engine automatically.

Tool calling example

from openai import OpenAI

client = OpenAI(
    api_key="bl_live_xxxxxxxxxxxx",
    base_url="https://api.breachline.io/api/v1/llm/v1"
)

# Define security tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "scan_target",
            "description": "Perform a security scan on a target",
            "parameters": {
                "type": "object",
                "properties": {
                    "target": {"type": "string", "description": "URL or IP inside written approved scope"},
                    "scope": {"type": "string", "description": "Optional note of bounds already agreed in writing"}
                },
                "required": ["target"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "lookup_cve",
            "description": "Look up CVE details",
            "parameters": {
                "type": "object",
                "properties": {
                    "cve_id": {"type": "string", "description": "CVE ID (e.g., CVE-2024-1234)"}
                },
                "required": ["cve_id"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="nebula-4.5",
    messages=[{"role": "user", "content": "Test example.com within the agreed written scope"}],
    tools=tools,
    tool_choice="auto"
)

# Handle tool calls
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        print(f"Tool: {tool_call.function.name}")
        print(f"Args: {tool_call.function.arguments}")
Real-time

Streaming, token by token.

Server-sent events on the standard endpoint: set stream=True and tokens arrive as they are generated.

Streaming example

from openai import OpenAI

# Streaming is server-sent events on the standard endpoint. Set stream=True.
# (There is no separate socket for chat; the only WebSocket we expose is the
# realtime voice/vision plane at /api/v1/llm/v1/realtime.)
client = OpenAI(
    api_key="bl_live_xxxxxxxxxxxx",
    base_url="https://api.breachline.io/api/v1/llm/v1",
)

stream = client.chat.completions.create(
    model="nebula-4.5",
    messages=[
        {"role": "user", "content": "Write a security audit report for example.com"}
    ],
    stream=True,
)

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

MCP, for your own tools.

Connect public security review tools to your agent.

What is MCP?

Connect Cursor or another compatible client to BreachLine's hosted MCP endpoint for deterministic security reviews. The example below uses Cursor's configuration format. This public tool service sends supplied content to BreachLine for processing; it does not expose the Nebula chat API.

Cursor hosted MCP configuration

{
  "mcpServers": {
    "breachline": {
      "url": "https://breachline.io/mcp"
    }
  }
}

No account or API key is needed. Shared usage and input limits apply. Follow the MCP setup guide for Claude Code commands, tool names and data handling.

Usage

Rate limits, per API key.

60
req/min
Requests
1K
req/hour
Hourly
10K
req/day
Daily
100K
tokens/min
Tokens

Need higher limits? Contact us for enterprise plans.

Reference

Endpoints, at a glance.

POST/api/v1/llm/v1/chat/completions

Create a chat completion (OpenAI compatible)

POST/api/v1/llm/v1/completions

Simple text completion endpoint

GET/api/v1/llm/v1/models

List available models and pricing

GET/api/v1/llm/v1/usage/current

Get current usage statistics

Get started

Ready to build?

Create an API key and start building with Nebula.

Request accessPlatform API docs