Skip to main content

LangChain

LangChain is a framework for building LLM-powered applications — chains, agents, RAG pipelines, and more. Both the Python and JavaScript versions use the OpenAI or Anthropic client under the hood, so they work with Routerly without any framework-specific changes.

Install​

# Python
pip install langchain langchain-openai langchain-anthropic

# JavaScript / TypeScript
npm install langchain @langchain/openai

Configure​

Pass the Routerly base URL and project token when initialising the ChatOpenAI model:

Python
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5-mini",
    base_url="http://localhost:3000/v1",
    api_key="sk-rt-YOUR_PROJECT_TOKEN",
)
JavaScript / TypeScript
import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-5-mini",
  configuration: {
    baseURL: "http://localhost:3000/v1",
    apiKey: "sk-rt-YOUR_PROJECT_TOKEN",
  },
});

To use Anthropic models via the Anthropic SDK + LangChain:

Python (Anthropic)
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-haiku-4-5",
    anthropic_api_url="http://localhost:3000",
    api_key="sk-rt-YOUR_PROJECT_TOKEN",
)

Usage​

Use llm in any LangChain chain, agent, or LCEL expression as you normally would:

from langchain_core.messages import HumanMessage

response = llm.invoke([HumanMessage(content="Explain LangChain in one sentence.")])
print(response.content)

Every call goes through Routerly. Retries, failover, and cost tracking are handled transparently.