LangChain

Reviewed by Proof · Updated

Definition

LangChain is a framework for building LLM apps whose ChatOpenAI class can call luv13 by setting base_url.

Key takeaways

  • Install the langchain-openai package and use its ChatOpenAI class.
  • Pass base_url="https://api.luv13.ai/v1", your luv13 key as api_key, and a luv13 model id such as luv13/glm-5.3-flash.
  • ChatOpenAI also reads the OPENAI_API_BASE environment variable if you don't pass base_url.
  • Use it for chat. OpenAIEmbeddings won't work, because luv13 doesn't serve embeddings.
  • These names come from the langchain-openai source on GitHub.

Install

pip install langchain-openai

Example

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="luv13/glm-5.3-flash",
    base_url="https://api.luv13.ai/v1",
    api_key=os.environ["LUV13_API_KEY"],
)

reply = llm.invoke("Explain a context window in one sentence.")
print(reply.content)

Where the base URL comes from

LangChain picks the base URL in this order:

  1. The base_url argument (also accepted as openai_api_base).
  2. The OPENAI_API_BASE environment variable.
  3. The OPENAI_BASE_URL environment variable, read by the underlying OpenAI SDK.

Passing base_url directly is the clearest option, and it avoids sending requests to OpenAI by accident.

Notes

  • ChatOpenAI is built on the OpenAI SDKs, so the same luv13 rules apply: chat completions and model listing are the endpoints to use. See Endpoints.
  • For retrieval apps, pair a luv13 chat model with embeddings from another service. See Retrieval-Augmented Generation.
  • Tool calling through LangChain depends on luv13's tool support. See Tool Calling on luv13.