Nebula API

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 to scan"},
                    "scan_type": {"type": "string", "enum": ["quick", "full", "stealth"]}
                },
                "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": "Scan example.com for vulnerabilities"}],
    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}")

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)

// MCP client configuration (add to your MCP client's config file)
{
  "mcpServers": {
    "nebula": {
      "command": "npx",
      "args": ["-y", "@breachline/mcp-server"],
      "env": {
        "NEBULA_API_KEY": "bl_live_xxxxxxxxxxxx",
        "NEBULA_BASE_URL": "https://api.breachline.io/api/v1/llm/v1"
      }
    }
  }
}

// Use with MCP SDK
import { Client } from "@modelcontextprotocol/sdk/client/index.js";

const client = new Client({ name: "my-app", version: "1.0.0" });
await client.connect(transport);

const result = await client.callTool({
  name: "nebula_chat",
  arguments: {
    model: "nebula-4.5",
    message: "Analyze security headers for example.com"
  }
});