Skip to main content
This page makes reference to Groq, an AI hardware and software company. For information on how to use Grok models (provided by xAI), see the xAI provider page.
API ReferenceFor detailed documentation of all features and configuration options, head to the ChatGroq API reference.
For a list of all Groq models, visit their docs.

​
Overview

​
Integration details

ClassPackageSerializableJS supportDownloadsVersion
ChatGroqlangchain-groqbeta✅PyPI - DownloadsPyPI - Version

​
Model features

Tool callingStructured outputImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
✅✅✅❌❌✅✅✅✅

​
Setup

To access Groq models you’ll need to create a Groq account, get an API key, and install the langchain-groq integration package.

​
Credentials

Head to the Groq console to sign up to Groq and generate an API key. Once you’ve done this set the GROQ_API_KEY environment variable: 
import getpass
import os

if "GROQ_API_KEY" not in os.environ:
    os.environ["GROQ_API_KEY"] = getpass.getpass("Enter your Groq API key: ")
To enable automated tracing of your model calls, set your LangSmith API key: 
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

​
Installation

The LangChain Groq integration lives in the langchain-groq package: 
pip install -qU langchain-groq

​
Instantiation

Now we can instantiate our model object and generate chat completions.
Reasoning FormatIf you choose to set a reasoning_format, you must ensure that the model you are using supports it. You can find a list of supported models in the Groq documentation.
from langchain_groq import ChatGroq

llm = ChatGroq(
    model="qwen/qwen3-32b",
    temperature=0,
    max_tokens=None,
    reasoning_format="parsed",
    timeout=None,
    max_retries=2,
    # other params...
)

​
Invocation

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="J'aime la programmation.", additional_kwargs={'reasoning_content': 'Okay, so I need to translate the sentence "I love programming." into French. Let me think about how to approach this. \n\nFirst, I know that "I" in French is "Je." That\'s straightforward. Now, the verb "love" in French is "aime" when referring to oneself. So, "I love" would be "J\'aime." \n\nNext, the word "programming." In French, programming is "la programmation." But wait, in French, when you talk about loving an activity, you often use the definite article. So, it would be "la programmation." \n\nPutting it all together, "I love programming" becomes "J\'aime la programmation." That sounds right. I think that\'s the correct translation. \n\nI should double-check to make sure I\'m not missing anything. Maybe I can think of similar phrases. For example, "I love reading" is "J\'aime lire," but when it\'s a noun, like "I love music," it\'s "J\'aime la musique." So, yes, using "la programmation" makes sense here. \n\nI don\'t think I need to change anything else. The sentence structure in French is Subject-Verb-Object, just like in English, so "J\'aime la programmation" should be correct. \n\nI guess another way to say it could be "J\'adore la programmation," using "adore" instead of "aime," but "aime" is more commonly used in this context. So, sticking with "J\'aime la programmation" is probably the best choice.\n'}, response_metadata={'token_usage': {'completion_tokens': 346, 'prompt_tokens': 23, 'total_tokens': 369, 'completion_time': 1.447541218, 'prompt_time': 0.000983386, 'queue_time': 0.009673684, 'total_time': 1.448524604}, 'model_name': 'deepseek-r1-distill-llama-70b', 'system_fingerprint': 'fp_e98d30d035', 'finish_reason': 'stop', 'logprobs': None}, id='run--5679ae4f-f4e8-4931-bcd5-7304223832c0-0', usage_metadata={'input_tokens': 23, 'output_tokens': 346, 'total_tokens': 369})

print(ai_msg.content)

J'aime la programmation.

​
Vision

Groq supports vision capabilities with select models, allowing you to send images along with text prompts.
Vision-capable models
  • meta-llama/llama-4-scout-17b-16e-instruct
  • meta-llama/llama-4-maverick-17b-128e-instruct
For the latest list of vision-capable models, check the Groq documentation.
from langchain_groq import ChatGroq
from langchain.messages import HumanMessage

llm = ChatGroq(model="meta-llama/llama-4-scout-17b-16e-instruct")

message = HumanMessage(
    content=[
        {"type": "text", "text": "Describe this image in detail."},
        {
            "type": "image_url",
            "image_url": {"url": "https://example.com/image.jpg"},
        },
    ]
)

response = llm.invoke([message])
print(response.content)
Image URL requirementsGroq fetches images from URLs directly. Ensure your image URLs:
  • Are publicly accessible (no authentication required)
  • Return the image directly (no redirects)
  • Maximum image size: 20MB per request

​
API reference

For detailed documentation of all ChatGroq features and configurations head to the API reference.
Edit this page on GitHub or file an issue.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.