Connect LangChain

Updated 2026-11-11

Python framework for LLM apps; point ChatOpenAI at the platform.

Use cases

Good for Python projects that build chains, agents or retrieval flows with LangChain or LangGraph. ChatOpenAI speaks the OpenAI-compatible API, so base_url and api_key are all that change.

Tips

Read the key from a server-side environment variable. Use llm.stream() for streaming and llm.bind_tools() for tool calling.

If it cannot connect

1. Use https://modelportal.ai/v1 as the API host, without /chat/completions.
2. The key starts with mp- (older keys: key_) and is not paused or revoked; invalid_api_key means the key cannot be used.
3. The model ID matches the catalog and is in the key's allowed models.
4. insufficient_balance means the balance is too low; see common errors for more.

Setup

1. Install the dependency: pip install langchain-openai.
2. Create ChatOpenAI with base_url set to the API host below, your own key as api_key, and the model ID as model.
3. Use the model object in chains, agents, or LangGraph as usual.

Settings

• API host：https://modelportal.ai/v1
• API key：YOUR_API_KEY
• Model：MODEL_ID

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="MODEL_ID",
    base_url="https://modelportal.ai/v1",
    api_key="YOUR_API_KEY",
)
print(llm.invoke("你好").content)