Integrations-Anleitung
LangChain + OrcaRouter
LangChain's ChatOpenAI class takes a base_url parameter. Pointing it at OrcaRouter gives every chain, agent, and retriever automatic failover and zero-markup pricing without touching the rest of your graph.
Einrichtungsschritte
In 5 Minuten an OrcaRouter anbinden
- 1.Install: pip install langchain-openai
- 2.Import ChatOpenAI from langchain_openai
- 3.Construct with base_url='https://api.orcarouter.ai/v1' and api_key='sk-orca-…'
- 4.Set the model to any OrcaRouter-supported model ID.
- 5.Use it anywhere LangChain expects a chat model — chains, agents, tools.
Beispielkonfiguration
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="sk-orca-...",
model="claude-sonnet-4",
temperature=0,
)
response = llm.invoke("Explain retrieval-augmented generation in one paragraph.")
print(response.content)Warum LangChain über OrcaRouter routen?
LangChain agents do lots of short, bursty calls that are sensitive to rate limits. OrcaRouter spreads those calls across healthy providers automatically and gives you one cost-attribution view for the whole chain.
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