Make Your First Request

This guide walks you through your first chat request to Agumbe AI Gateway using an OpenAI-compatible SDK.

Agumbe AI Gateway base URL:

https://api.agumbe.ai/api/v1/llm

Step 1: Create an API key

Create a gateway API key from the Agumbe Console. Use this key from your backend service, worker, or local development environment.

Set it as an environment variable:

export AGUMBE_API_KEY="your_agumbe_gateway_api_key"

Step 2: Install the SDK

Agumbe AI Gateway is compatible with every major LLM provider out there, but for this example we will use the official OpenAI SDK.

For Node.js:

npm install openai

For Python:

pip install openai

Step 3: Send a chat request

cURL

curl https://api.agumbe.ai/api/v1/llm/chat/completions \
  -H "Authorization: Bearer $AGUMBE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.2",
    "messages": [
      {
        "role": "system",
        "content": "You are a concise assistant."
      },
      {
        "role": "user",
        "content": "Explain what an AI Gateway does in one sentence."
      }
    ],
    "max_completion_tokens": 200
  }'

Typescript

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.AGUMBE_API_KEY,
  baseURL: "https://api.agumbe.ai/api/v1/llm",
});

const response = await client.chat.completions.create({
  model: "gpt-5.2",
  messages: [
    {
      role: "system",
      content: "You are a concise assistant.",
    },
    {
      role: "user",
      content: "Explain what an AI Gateway does in one sentence.",
    },
  ],
  max_completion_tokens: 200,
});

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

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AGUMBE_API_KEY"],
    base_url="https://api.agumbe.ai/api/v1/llm",
)

response = client.chat.completions.create(
    model="gpt-5.2",
    messages=[
        {
            "role": "system",
            "content": "You are a concise assistant.",
        },
        {
            "role": "user",
            "content": "Explain what an AI Gateway does in one sentence.",
        },
    ],
    max_completion_tokens=200,
)

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

Step 5: Choose a model

You can use a supported model ID or an Agumbe alias.

Examples:

gpt-5.2
smart-default
cheap-fast
reasoning

Use smart-default when you want Agumbe to choose the default general-purpose model for your application.

Step 6: Add guardrails when needed

If your API key is tenant-scoped, you can choose which app’s guardrails apply by passing agumbe_guardrails_app_id.

const response = await client.chat.completions.create({
  model: "smart-default",
  messages: [
    {
      role: "user",
      content: "Draft a safe response to this customer message.",
    },
  ],
  max_completion_tokens: 200,
  agumbe_guardrails_app_id: "app_support",
} as any);

If your API key is app-scoped, the gateway applies the bound app’s guardrails automatically.

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That’s it. Your application is now sending LLM traffic through Agumbe AI Gateway, with one endpoint for model access, policy enforcement, usage tracking, and observability.