MyIP

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LangChain

Pointing `ChatOpenAI` at our endpoint

LangChain has no MyIP-specific integration, and does not need one. Give ChatOpenAI our base URL and key — we implement the OpenAI-compatible shape it already speaks.

ItemValue
Base URLhttps://openrouter.myip.co.kr/api/v1
Environment variableMYIP_API_KEY
Example modelsgoogle/gemma-4-26b-a4b, lgai/exaone-4.0-32b

Python

bash
pip install langchain langchain-openai
export MYIP_API_KEY="sk-mo-v1-..."
python
import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(
    model="google/gemma-4-26b-a4b",
    base_url="https://openrouter.myip.co.kr/api/v1",
    api_key=os.environ["MYIP_API_KEY"],
    temperature=0.7,
    default_headers={
        "HTTP-Referer": "https://example.com",
        "X-Title": "My LangChain App",
    },
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a concise assistant."),
    ("human", "{question}"),
])

chain = prompt | llm | StrOutputParser()
print(chain.invoke({"question": "What is the capital of South Korea?"}))

JavaScript / TypeScript

bash
npm install @langchain/openai @langchain/core
export MYIP_API_KEY="sk-mo-v1-..."
typescript
import { ChatOpenAI } from '@langchain/openai';
import { ChatPromptTemplate } from '@langchain/core/prompts';
import { StringOutputParser } from '@langchain/core/output_parsers';

const llm = new ChatOpenAI({
  model: 'google/gemma-4-26b-a4b',
  apiKey: process.env.MYIP_API_KEY,
  temperature: 0.7,
  configuration: {
    baseURL: 'https://openrouter.myip.co.kr/api/v1',
    defaultHeaders: {
      'HTTP-Referer': 'https://example.com',
      'X-Title': 'My LangChain App',
    },
  },
});

const prompt = ChatPromptTemplate.fromMessages([
  ['system', 'You are a concise assistant.'],
  ['human', '{question}'],
]);

const chain = prompt.pipe(llm).pipe(new StringOutputParser());
console.log(await chain.invoke({ question: 'What is the capital of South Korea?' }));

Streaming

python
for chunk in llm.stream("Write a short poem."):
    print(chunk.content, end="", flush=True)
typescript
const stream = await llm.stream('Write a short poem.');
for await (const chunk of stream) {
  process.stdout.write(String(chunk.content));
}

You do not need to enable stream_options.include_usage — we always request it. To have LangChain fold the final usage chunk into its own totals, though, turn on stream usage collection:

python
llm = ChatOpenAI(
    model="lgai/exaone-4.0-32b",
    base_url="https://openrouter.myip.co.kr/api/v1",
    api_key=os.environ["MYIP_API_KEY"],
    stream_usage=True,
)

Reading cost and the generation id

LangChain hides response headers, so the reliable route to cost is the generation id in the response metadata.

python
msg = llm.invoke("Hello")

print(msg.usage_metadata)                  # token counts
gen_id = msg.response_metadata.get("id")   # 'gen-...'
python
import requests

def cost_krw(generation_id: str) -> float:
    res = requests.get(
        "https://openrouter.myip.co.kr/api/v1/generation",
        params={"id": generation_id},
        headers={"Authorization": f"Bearer {os.environ['MYIP_API_KEY']}"},
        timeout=10,
    )
    return res.json()["data"]["total_cost"]   # KRW

print(cost_krw(gen_id), "KRW")

Settlement finishes asynchronously, so a lookup immediately after a stream ends can be early; wait a moment and retry. All amounts are in Korean won (KRW).

Tool calling

python
from langchain_core.tools import tool

@tool
def get_weather(city: str) -> str:
    """Look up the current weather for a city."""
    return f"{city}: clear, 21C"

agent_llm = llm.bind_tools([get_weather])
msg = agent_llm.invoke("What is the weather in Seoul?")
print(msg.tool_calls)

The model has to support tool calling. Check that supported_parameters in the GET /api/v1/models response contains tools. See Tool calling.

What you cannot use

  • OpenAIEmbeddings — there is no embeddings endpoint; you get 404 not_supported.
  • LangChain's image and audio integrations — those endpoints do not exist here.

For a RAG pipeline, generate embeddings elsewhere (a local embedding model, for instance) and send only the generation step through our gateway. The full list is in Unsupported endpoints.

Related

Última atualização: 5 de set. de 2026