LangChain

Use TensorX models with LangChain, the popular framework for building LLM-powered applications.

Installation

pip install -U langchain-openai

Quick Start

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="z-ai/glm-5.1",
    base_url="https://api.tensorx.ai/v1",
    api_key="your-tensorx-api-key",
)

response = llm.invoke("Hello, how are you?")
print(response.content)

Configuration

LangChain's ChatOpenAI class supports OpenAI-compatible APIs through the base_url parameter.

Parameter	Value
base_url	https://api.tensorx.ai/v1
api_key	Your TensorX API key
model	Any TensorX model ID

Examples

Basic Chat

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="z-ai/glm-5.1",
    base_url="https://api.tensorx.ai/v1",
    api_key="your-tensorx-api-key",
    temperature=0.7,
)

messages = [
    ("system", "You are a helpful assistant."),
    ("human", "What is the capital of France?"),
]

response = llm.invoke(messages)
print(response.content)

Streaming

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="minimax/minimax-m2",
    base_url="https://api.tensorx.ai/v1",
    api_key="your-tensorx-api-key",
)

for chunk in llm.stream("Tell me a short story"):
    print(chunk.content, end="", flush=True)

Tool Calling

from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field

class GetWeather(BaseModel):
    """Get current weather for a location"""
    location: str = Field(description="City and state, e.g. San Francisco, CA")

llm = ChatOpenAI(
    model="z-ai/glm-5.1",
    base_url="https://api.tensorx.ai/v1",
    api_key="your-tensorx-api-key",
)

llm_with_tools = llm.bind_tools([GetWeather])
response = llm_with_tools.invoke("What's the weather in Tokyo?")
print(response.tool_calls)

Structured Output

from langchain_openai import ChatOpenAI
from pydantic import BaseModel

class Joke(BaseModel):
    setup: str
    punchline: str

llm = ChatOpenAI(
    model="minimax/minimax-m2",
    base_url="https://api.tensorx.ai/v1",
    api_key="your-tensorx-api-key",
)

structured_llm = llm.with_structured_output(Joke)
result = structured_llm.invoke("Tell me a programming joke")
print(f"Setup: {result.setup}")
print(f"Punchline: {result.punchline}")

Async Usage

import asyncio
from langchain_openai import ChatOpenAI

async def main():
    llm = ChatOpenAI(
        model="z-ai/glm-5.1",
        base_url="https://api.tensorx.ai/v1",
        api_key="your-tensorx-api-key",
    )
    
    response = await llm.ainvoke("Hello!")
    print(response.content)

asyncio.run(main())

Available Models

Model	ID	Best For
GLM-5.1 ⭐	z-ai/glm-5.1	Coding, reasoning, functions
MiniMax-M2.5	minimax/minimax-m2.5	Reasoning, functions
Kimi-K2.5	moonshotai/kimi-k2.5	Vision, functions, long context
MiniMax-M2	minimax/minimax-m2	Coding, fast responses

Tips

• Use z-ai/glm-5.1 for applications that require tool/function calling
• Use minimax/minimax-m2 for reasoning tasks and long conversations
• Set temperature=0 for deterministic outputs

Resources

• LangChain ChatOpenAI Docs
• TensorX API Reference