Guida all’integrazione
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.
Passi di configurazione
Collega OrcaRouter in 5 minuti
- 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 di esempio
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
Pronto a instradare LangChain tramite OrcaRouter?
Ottieni una chiave API e instrada LangChain attraverso OrcaRouter verso 200+ modelli — ricarico zero.
