How to beginner · 3 min read

How to use Llama with LangChain

Q: How to use Llama with LangChain

To use Llama with LangChain, use an OpenAI-compatible provider like Groq or Together AI by configuring the OpenAI client with the provider's base_url and API key. Then instantiate ChatOpenAI from langchain_openai with the provider model name to run chat completions seamlessly.

Quick answer

To use Llama with LangChain, use an OpenAI-compatible provider like Groq or Together AI by configuring the OpenAI client with the provider's base_url and API key. Then instantiate ChatOpenAI from langchain_openai with the provider model name to run chat completions seamlessly.

PREREQUISITES

Python 3.8+
OpenAI-compatible API key from a Llama provider (e.g., Groq, Together AI)
pip install openai>=1.0 langchain_openai

Setup

Install the required Python packages and set your environment variables for the Llama provider API key. Use an OpenAI-compatible Llama API endpoint such as Groq or Together AI.

bash

pip install openai langchain_openai

Step by step

This example shows how to use LangChain with a Llama model hosted by Groq. Replace GROQ_API_KEY with your actual API key and set the base_url to Groq's OpenAI-compatible endpoint.

python

import os
from openai import OpenAI
from langchain_openai import ChatOpenAI

# Set environment variable GROQ_API_KEY before running
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")

# Initialize LangChain ChatOpenAI with Groq Llama model
chat = ChatOpenAI(
    client=client,
    model_name="llama-3.3-70b-versatile",
    temperature=0.7
)

# Run a chat completion
response = chat.invoke([{"role": "user", "content": "Explain LangChain integration with Llama."}])
print(response.content)

output

Explain LangChain integration with Llama.

LangChain can use Llama models by configuring the OpenAI-compatible client with the Llama provider's API endpoint and model name, enabling seamless chat completions.

Common variations

Use Together AI by changing base_url to https://api.together.xyz/v1 and model to meta-llama/Llama-3.3-70B-Instruct-Turbo.
For async calls, use await chat.ainvoke([...]) inside an async function.
Adjust temperature or max_tokens in ChatOpenAI for different output styles.

python

import asyncio

async def async_example():
    response = await chat.ainvoke([{"role": "user", "content": "What is LangChain?"}])
    print(response.content)

asyncio.run(async_example())

output

LangChain is a framework for building applications with language models, enabling chaining of prompts and integration with various AI providers.

Troubleshooting

If you get authentication errors, verify your API key is set correctly in os.environ.
For model not found errors, confirm the model name matches the provider's current offerings.
Timeouts may require increasing client timeout settings or checking network connectivity.

✅

Key Takeaways

Use OpenAI-compatible clients with provider-specific base URLs to access Llama models in LangChain.
Configure ChatOpenAI with the provider's model name for seamless integration.
Async and streaming calls are supported by LangChain's ChatOpenAI interface.
Always set API keys securely via environment variables to avoid authentication issues.
Check provider documentation for up-to-date model names and endpoints.

Verified 2026-04 · llama-3.3-70b-versatile, meta-llama/Llama-3.3-70B-Instruct-Turbo

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