LangChain setup with LaoZhang API

LangChain connects to LaoZhang API through langchain-openai: point the chat and embedding clients at the LaoZhang API base URL and pass your key. GPT-6, Gemini, DeepSeek, and other text models all work with the same client.

Item	Details
Package	`langchain-openai`
`base_url`	`https://api.laozhang.ai/v1`
Chat models	`gpt-6-sol`, `gemini-3.8-flash`, `deepseek-v4-flash`, and others
Embedding models	`text-embedding-3-small` and others
Last verified	October 5, 2026, langchain-openai 1.6.7

Install and configure

pip install langchain-openai
export LAOZHANG_API_KEY="YOUR_LAOZHANG_API_KEY"

All the examples below read the key from that variable:

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-6-sol",
    base_url="https://api.laozhang.ai/v1",
    api_key=os.environ["LAOZHANG_API_KEY"],
)

print(llm.invoke("Describe LangChain in one sentence").content)

To switch models, change only model, for example to gemini-3.8-flash or deepseek-v4-flash. Prices are in the model catalog. You can also set the OPENAI_API_KEY and OPENAI_BASE_URL environment variables and leave api_key and base_url out of your code.

Call tools

from langchain_core.tools import tool


@tool
def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return f"Sunny in {city}"


ai_message = llm.bind_tools([get_weather]).invoke("What's the weather in Paris? Use the tool.")
print(ai_message.tool_calls)

tool_calls returns the tool name and arguments, such as {'name': 'get_weather', 'args': {'city': 'Paris'}}. After running the tool, send the result back as a ToolMessage, or let a LangGraph agent handle the loop.

Stream the output

for chunk in llm.stream("Count from 1 to 5"):
    print(chunk.content, end="", flush=True)

Use the Responses API

When you need features of OpenAI’s Responses API, add use_responses_api=True:

llm = ChatOpenAI(
    model="gpt-6-sol",
    base_url="https://api.laozhang.ai/v1",
    api_key=os.environ["LAOZHANG_API_KEY"],
    use_responses_api=True,
)
print(llm.invoke("Describe the Responses API in one sentence").text)

Create embeddings

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://api.laozhang.ai/v1",
    api_key=os.environ["LAOZHANG_API_KEY"],
)
vectors = embeddings.embed_documents(["hello", "world"])
print(len(vectors), len(vectors[0]))  # 2 1536

Available embedding models and dimensions are in the Embeddings API.

FAQ

I get “Unsupported parameter: ‘max_tokens’”

GPT-6 models don’t accept max_tokens. Setting the output limit through ChatOpenAI’s own argument usually works; if you added that field by hand in extra request parameters, rename it to max_completion_tokens. See max_tokens.

I get a 401 or 404

Make sure base_url is https://api.laozhang.ai/v1 and isn’t overridden by an environment variable such as OPENAI_BASE_URL. See Invalid API key or 404.

Overview

Coding tools

Chat clients

Frameworks

LangChain setup with LaoZhang API

Install and configure

Call tools

Stream the output

Use the Responses API

Create embeddings

FAQ

I get “Unsupported parameter: ‘max_tokens’”

I get a 401 or 404

​Install and configure

​Call tools

​Stream the output

​Use the Responses API

​Create embeddings

​FAQ

​I get “Unsupported parameter: ‘max_tokens’”

​I get a 401 or 404

​Related pages

Install and configure

Call tools

Stream the output

Use the Responses API

Create embeddings

FAQ

I get “Unsupported parameter: ‘max_tokens’”

I get a 401 or 404

Related pages