Integrazione · Configurazione in 60 secondi · Zero markup
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.
Configurazione
Pronto in cinque passi.
- 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.
Configurazione
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)Perché instradare LangChain attraverso OrcaRouter?
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.
Altre integrazioni
Instrada subito LangChain attraverso OrcaRouter.
Registrati in un minuto, ottieni una chiave sk-orca-… e incollala in LangChain. Zero markup sui token, failover automatico tra tutti i provider.
