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LangChain’s ChatOpenAI is OpenAI-compatible — point it at the Orbitrage gateway and every call is routed and traced. Tools, managed tools, and streaming all work unchanged.

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Install

pip install -U orbitrage langchain langchain-openai

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Basic chat

Wrapper clients build their own HTTP client, so set the base URL and key explicitly, and pass your end-user id via default_headers. 
import os, orbitrage
orbitrage.init(os.environ["ORBITRAGE_API_KEY"], user_id="customer_42")

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="claude-sonnet-4-6",                       # direct model — or "grok-4-fast"; "auto" to route
    base_url="https://api.orbitrage.ai/v1",
    api_key=os.environ["ORBITRAGE_API_KEY"],
    default_headers={"x-orbitrage-end-user-id": "customer_42"},
    max_tokens=512,
)
print(llm.invoke("Explain routing in one line.").content)

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Managed tools (web search, scrape, calculator — no keys)

Reference a managed tool by name and Orbitrage runs it server-side, then loops the result back to the model. 
# bind passes the managed tool straight through to the API
out = llm.bind(tools=["calculator_orbitrage"]).invoke(
    "Use the calculator to compute 1234*5678. Only the number."
)
print(out.content)   # 7006652
Your own LangChain tools (@tool / bind_tools) keep working alongside managed tools in the same call.

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Streaming

for chunk in llm.stream("Count from 1 to 5."):
    print(chunk.content, end="", flush=True)
Pin a direct model (claude-sonnet-4-6, grok-4-fast) for predictable behavior. Use model="auto" to let Orbitrage pick the cheapest capable model — and keep max_tokens ≥ 512 so reasoning models aren’t truncated.