> ## Documentation Index
> Fetch the complete documentation index at: https://developers.telnyx.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain Integration

> Use Telnyx Inference as an LLM provider in LangChain. Configure ChatOpenAI with the Telnyx base URL to power chains, agents, and retrieval pipelines.

OpenAI-compatible. Use `ChatOpenAI` with a `base_url` swap.

## Setup

```shell theme={null}
pip install langchain-openai
```

## Usage

```python theme={null}
import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.telnyx.com/v2/ai/openai",
    api_key=os.getenv("TELNYX_API_KEY"),
    model="zai-org/GLM-5.1-FP8",
)

for chunk in llm.stream("Help me plan my vacation"):
    print(chunk.content, end="", flush=True)
```

## Function Calling

```python theme={null}
import os
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

@tool
def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"The weather in {location} is sunny and 72°F."

llm_with_tools = ChatOpenAI(
    base_url="https://api.telnyx.com/v2/ai/openai",
    api_key=os.getenv("TELNYX_API_KEY"),
    model="zai-org/GLM-5.1-FP8",
).bind_tools([get_weather])

result = llm_with_tools.invoke("What's the weather in Chicago?")
print(result.tool_calls)
```

## Streaming

```python theme={null}
from langchain_core.messages import HumanMessage

messages = [HumanMessage(content="Explain quantum computing in 3 sentences")]
for chunk in llm.stream(messages):
    print(chunk.content, end="", flush=True)
```