Backend Frameworks – ModelRiver Docs

Overview

Backend frameworks are the foundation of AI-powered web applications. Whether you're building a chatbot API, a document processing pipeline, or a real-time streaming interface, ModelRiver integrates with your backend in minutes.

Why use ModelRiver with your backend framework?

One endpoint for all providers: No per-provider SDK configuration
Streaming support: SSE and WebSocket out of the box
Background processing: Async tasks and webhook-driven workflows
Full observability: Track every request across your application

Supported frameworks

Framework	Language	Highlights	Difficulty	Guide
Phoenix	Elixir	LiveView streaming, GenServer integration	⭐⭐ Medium	View guide →
Next.js	TypeScript	Vercel AI SDK, server actions, edge functions	⭐⭐ Medium	View guide →
FastAPI	Python	Async REST, WebSocket, SSE, background tasks	⭐⭐ Medium	View guide →
Django	Python	DRF, Celery, Django Channels	⭐⭐ Medium	View guide →

Phoenix (Elixir)

Build real-time AI features with Phoenix LiveView. Stream responses directly to the browser with built-in WebSocket support.

ELIXIR

1config :my_app, :openai,
2  base_url: "https://api.modelriver.com/v1",
3  api_key: System.get_env("MODELRIVER_API_KEY")

Full Phoenix guide →

Next.js

Full-stack AI applications with the Vercel AI SDK. Server actions, edge functions, and React Server Components for the fastest possible UX.

TYPESCRIPT

1import OpenAI from "openai";
2 
3const client = new OpenAI({
4  baseURL: "https://api.modelriver.com/v1",
5  apiKey: process.env.MODELRIVER_API_KEY,
6});

Full Next.js guide →

FastAPI

High-performance async Python APIs. REST endpoints, WebSocket streaming, SSE, and background task processing.

PYTHON

1from openai import AsyncOpenAI
2 
3client = AsyncOpenAI(
4    base_url="https://api.modelriver.com/v1",
5    api_key=os.environ["MODELRIVER_API_KEY"],
6)

Full FastAPI guide →

Django

Python's batteries-included framework. DRF views, Celery background tasks, and Django Channels for real-time streaming.