Learn how to integrate XRoute seamlessly with official OpenAI, Anthropic, Google GenAI, and Byteplus Ark SDKs, as well as open-source frameworks.
The XRoute gateway provides native compatibility with official client libraries from OpenAI and Anthropic, supports provider native SDKs (such as Google GenAI, Byteplus Ark, and DashScope), and works out-of-the-box with popular orchestration frameworks like LangChain and LlamaIndex.You only need to configure the base_url (or baseURL) and provide your api_key to transition existing applications to XRoute.
Modality Calling Rules:
Text Models: Support OpenAI SDK, Anthropic SDK, and provider native SDKs.
Image Models: Native Format Only (OpenAI Images API, Google GenAI Image module, Byteplus Ark Images).
Video Models: Native Asynchronous Task SDKs Only.
1. Text Output Models SDK Invocation
OpenAI Official SDK
Compatible with all text output models (e.g. OpenAI/gpt-5.6-sol, OpenAI-Pro/gpt-6-astra, Google/gemini-3.1-pro-preview, Byteplus/seed-2-0-code-preview-260328, etc.).
Python
Node.js / TypeScript
from openai import OpenAIclient = OpenAI( base_url="https://api.xroute.ai/v1", api_key="your_xroute_api_key",)response = client.chat.completions.create( model="OpenAI/gpt-5.6-sol", messages=[ {"role": "system", "content": "You are an experienced system architect."}, {"role": "user", "content": "Briefly contrast microservices vs monolithic architecture."} ], temperature=0.7,)print(response.choices[0].message.content)
import OpenAI from 'openai';const openai = new OpenAI({ baseURL: 'https://api.xroute.ai/v1', apiKey: process.env.XROUTE_API_KEY,});const completion = await openai.chat.completions.create({ model: 'OpenAI/gpt-5.6-sol', messages: [{ role: 'user', content: 'Briefly contrast microservices vs monolithic architecture.' }],});console.log(completion.choices[0].message.content);
Anthropic Official SDK
Compatible with Anthropic standard message payloads (e.g. Anthropic/claude-fable-5, Anthropic-Pro/claude-opus-4-6, etc.).
Python
TypeScript
from anthropic import Anthropicclient = Anthropic( base_url="https://api.xroute.ai/v1", api_key="your_xroute_api_key",)message = client.messages.create( model="Anthropic/claude-fable-5", max_tokens=1024, messages=[ {"role": "user", "content": "Summarize Rust memory safety principles in one paragraph."} ],)print(message.content[0].text)
import Anthropic from '@anthropic-ai/sdk';const client = new Anthropic({ baseURL: 'https://api.xroute.ai/v1', apiKey: process.env.XROUTE_API_KEY,});const message = await client.messages.create({ model: 'Anthropic/claude-fable-5', max_tokens: 1024, messages: [{ role: 'user', content: 'Summarize Rust memory safety principles in one paragraph.' }],});console.log(message.content[0].text);
Provider Native SDKs
For providers with specialized client libraries, point the SDK’s base_url to the vendor path on XRoute:
Byteplus Ark SDK
Google GenAI SDK
from byteplus_sdk_ark_runtime import Arkclient = Ark( base_url="https://api.xroute.ai/Byteplus", api_key="your_xroute_api_key",)completion = client.chat.completions.create( model="Byteplus/seed-2-0-code-preview-260328", messages=[{"role": "user", "content": "Implement an LRU Cache in Python."}],)print(completion.choices[0].message.content)
from google import genaiclient = genai.Client( api_key="your_xroute_api_key", http_options={"base_url": "https://api.xroute.ai/Google"},)response = client.models.generate_content( model="gemini-3.1-pro-preview", contents="Explain practical applications of quantum annealing.",)print(response.text)
2. Image Output Models SDK Invocation
Image models only support native format. Use provider native SDK image modules or native endpoints directly.
from byteplus_sdk_ark_runtime import Arkclient = Ark( base_url="https://api.xroute.ai/Byteplus", api_key="your_xroute_api_key",)response = client.images.generate( model="Byteplus/seedream-5-0-260128", prompt="Golden sunset over a mountain lake, cinematic lighting, 8k photography", size="1024x1024",)print("Image URL:", response.data[0].url)
3. Video Output Models SDK Invocation
Video models follow an asynchronous task workflow (create task -> poll status until completion):
import timefrom byteplus_sdk_ark_runtime import Arkclient = Ark( base_url="https://api.xroute.ai/Byteplus", api_key="your_xroute_api_key",)# 1. Create tasktask = client.content_generation.tasks.create( model="Byteplus/seedance-1-5-pro-251215", content=[{ "type": "text", "text": "Cinematic aerial drone flight gliding over snowy canyon peaks, 4k resolution" }],)task_id = task.idprint(f"Task created: {task_id}")# 2. Poll for video outputwhile True: status = client.content_generation.tasks.get(task_id=task_id) if status.status == "succeeded": print("Generated Video URL:", status.content.video_url) break elif status.status in ["failed", "cancelled"]: print(f"Task stopped: {status.status}") break time.sleep(5)
4. Popular Open-Source Frameworks
LangChain
from langchain_openai import ChatOpenAIllm = ChatOpenAI( model="OpenAI/gpt-5.6-sol", openai_api_base="https://api.xroute.ai/v1", openai_api_key="your_xroute_api_key", temperature=0.7,)response = llm.invoke("Write a single inspiring motto for early-stage startup founders.")print(response.content)
LlamaIndex
from llama_index.llms.openai import OpenAIllm = OpenAI( model="OpenAI/gpt-5.6-sol", api_base="https://api.xroute.ai/v1", api_key="your_xroute_api_key",)response = llm.complete("Name 3 fundamental engineering trade-offs in distributed storage.")print(response.text)
