LangChain integration

Connect LangChain to Modellane through its OpenAI-compatible chat model class, with streaming and tool calling configured.

LangChain talks to Modellane through its OpenAI-compatible chat model. Configure the base URL and key once and use it in chains and agents.

LangChain reaches Modellane through its OpenAI chat model class, which calls the Chat Completions API (POST /v1/chat/completions). Set the base URL and key once, then use the model in chains, tools and agents.

Install#

pip install langchain-openai

npm install @langchain/openai @langchain/core

Create the chat model#

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="lane-1",
    api_key=os.environ["MODELLANE_API_KEY"],
    base_url="https://usemodellane.com/v1",
    stream_usage=True,
)

print(llm.invoke("Explain recursion in one sentence.").content)

import { ChatOpenAI } from "@langchain/openai"

const llm = new ChatOpenAI({
  model: "lane-1",
  apiKey: process.env.MODELLANE_API_KEY,
  configuration: { baseURL: "https://usemodellane.com/v1" },
  streamUsage: true,
})

const reply = await llm.invoke("Explain recursion in one sentence.")
console.log(reply.content)

stream_usage (Python) and streamUsage (Node.js) ask for the usage chunk on streamed calls, so LangChain callbacks and tracing see real token counts.

Streaming#

for chunk in llm.stream("Write a short poem about rain."):
    print(chunk.content, end="", flush=True)

for await (const chunk of await llm.stream("Write a short poem about rain.")) {
  process.stdout.write(String(chunk.content))
}

Tool calling#

On models that list Tool calls in Models & pricing, bind_tools works as with any OpenAI model.

from langchain_core.tools import tool

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

reply = llm.bind_tools([get_weather]).invoke("What is the weather in Paris?")
print(reply.tool_calls)

Your code runs the tool and sends the result back in a tool message; see Tool calls.

Things to keep in mind#

• Stay on Chat Completions. Leave use_responses_api (Python) and useResponsesApi (Node.js) off, and do not bind OpenAI's built-in tools such as web search or file search: they run on OpenAI's servers, and LangChain can switch to the Responses API when they are used.
• Reasoning text is not kept. LangChain's OpenAI class does not keep non-standard response fields such as reasoning_content, so a model's reasoning does not show up in the message. The answer and the token usage are not affected.
• Context length. Trim long histories so they fit the context length shown in Models & pricing.

Verify#

Run the invoke example above. A printed reply means the base URL, key and model id are right; an authentication error points at the key, and model_not_found at the model id.

Images#