RouterBase Model Gateway

RouterBase Model Gateway

Overview

Use routerbase when an application needs one OpenAI-compatible API surface for model routing across GPT, Claude, Gemini, image, video, audio, and embedding workloads. This skill helps agents migrate existing OpenAI SDK calls, document model-selection tradeoffs, and produce safe implementation snippets without exposing credentials.

RouterBase model availability, pricing, and provider capabilities can change, so treat examples as starting points and verify current catalog data before production recommendations.

When to Use This Skill

• Use when migrating an OpenAI-compatible client to RouterBase by changing the base URL and model ID.
• Use when selecting primary and fallback models for chat, reasoning, vision, media generation, audio, or embeddings.
• Use when debugging RouterBase request setup, headers, environment variables, streaming, tool calls, JSON mode, or multimodal payloads.
• Use when documenting an internal model-routing plan that balances cost, latency, quality, and provider redundancy.

How It Works

Step 1: Classify the Workload

Identify the modality and hard constraints before choosing a model:

• Modality: chat, vision, image, video, audio, embeddings, or mixed.
• Quality target: draft, production, high-stakes review, or automated background task.
• Runtime constraints: latency budget, context length, streaming, JSON mode, tool calling, and retry tolerance.
• Business constraints: price ceiling, provider preference, regional requirements, and fallback rules.

Step 2: Configure the OpenAI-Compatible Client

Keep the RouterBase API key server-side in an environment variable such as ROUTERBASE_API_KEY. Do not put keys in browser, mobile, or public repository code.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ROUTERBASE_API_KEY"],
    base_url="https://routerbase.com/v1",
)

response = client.chat.completions.create(