ChatGroq

Groq LLM provider for Browser Use with high-speed inference

Overview

ChatGroq provides integration with Groq’s lightning-fast inference platform, supporting models like Llama, Qwen, GPT-OSS, and Kimi K2.

Basic Usage

from browser_use import Agent, ChatGroq
import asyncio

async def main():
    llm = ChatGroq(
        model='meta-llama/llama-4-maverick-17b-128e-instruct',
        api_key='your_groq_api_key'
    )
    agent = Agent(
        task="Find the number 1 post on Show HN",
        llm=llm,
    )
    await agent.run()

if __name__ == "__main__":
    asyncio.run(main())

Configuration

Required Parameters

model · str · required

Groq model to use. Verified options:

• meta-llama/llama-4-maverick-17b-128e-instruct: Latest Llama 4 with 128 experts
• meta-llama/llama-4-scout-17b-16e-instruct: Llama 4 Scout variant
• qwen/qwen3-32b: Qwen 3 32B model
• moonshotai/kimi-k2-instruct: Kimi K2 instruction model
• openai/gpt-oss-20b: GPT-OSS 20B
• openai/gpt-oss-120b: GPT-OSS 120B

Model Parameters

temperature · float · default: "None"

Sampling temperature (0.0 to 2.0). Controls randomness in responses.

service_tier · str · default: "None"

Service tier for request routing: auto, on_demand, or flex.

top_p · float · default: "None"

Nucleus sampling parameter (0.0 to 1.0).

seed · int · default: "None"

Random seed for deterministic output.

Client Parameters

api_key · str · default: "None"

Groq API key. Required for authentication.

Get your free API key at console.groq.com

base_url · str · default: "None"

Custom base URL for Groq API or compatible endpoints.

timeout · float · default: "None"

Request timeout in seconds or httpx.Timeout object.

max_retries · int · default: "10"

Maximum number of retries for failed requests. Increased default for automation reliability.

Advanced Usage

Structured Output with JSON Schema

Groq supports structured output through JSON schema (for compatible models) or tool calling:

from browser_use import Agent, ChatGroq
from pydantic import BaseModel

class SearchResult(BaseModel):
    title: str
    url: str
    description: str

llm = ChatGroq(
    model='meta-llama/llama-4-maverick-17b-128e-instruct',
    api_key='your_groq_api_key',
)

agent = Agent(
    task="Extract search results",
    llm=llm,
    output_model_schema=SearchResult,
)

result = await agent.run()
print(result.structured_output)  # SearchResult instance

JSON schema mode is supported by: llama-4-maverick, llama-4-scout, gpt-oss-20b, and gpt-oss-120b. Tool calling mode is used for kimi-k2-instruct.

Custom Base URL

from browser_use import Agent, ChatGroq

llm = ChatGroq(
    model='custom-model',
    base_url='https://custom-groq-compatible.com',
    api_key='your_api_key',
)

agent = Agent(task="Your task", llm=llm)

Service Tiers

from browser_use import Agent, ChatGroq

# Use on-demand service tier for higher priority
llm = ChatGroq(
    model='meta-llama/llama-4-maverick-17b-128e-instruct',
    api_key='your_groq_api_key',
    service_tier='on_demand',
)

agent = Agent(task="Your task", llm=llm)

Environment Setup

.env

GROQ_API_KEY=your_api_key_here

Error Handling

from browser_use import Agent, ChatGroq
from browser_use.llm.exceptions import (
    ModelProviderError,
    ModelRateLimitError
)

try:
    llm = ChatGroq(
        model='meta-llama/llama-4-maverick-17b-128e-instruct',
        api_key='your_groq_api_key',
    )
    agent = Agent(task="Your task", llm=llm)
    result = await agent.run()
except ModelRateLimitError as e:
    print(f"Rate limit exceeded: {e.message}")
except ModelProviderError as e:
    print(f"API error: {e.message}")
    print(f"Status code: {e.status_code}")

Properties

provider

Returns the provider name: "groq"

llm = ChatGroq(
    model='meta-llama/llama-4-maverick-17b-128e-instruct',
    api_key='your_groq_api_key',
)
print(llm.provider)  # "groq"

name

Returns the model name.

llm = ChatGroq(
    model='meta-llama/llama-4-maverick-17b-128e-instruct',
    api_key='your_groq_api_key',
)
print(llm.name)  # "meta-llama/llama-4-maverick-17b-128e-instruct"

Methods

get_client()

Returns an AsyncGroq client instance.

llm = ChatGroq(
    model='meta-llama/llama-4-maverick-17b-128e-instruct',
    api_key='your_groq_api_key',
)
client = llm.get_client()
# Use client directly for advanced operations

ainvoke()

Asynchronously invoke the model with messages.

from browser_use.llm.messages import SystemMessage, UserMessage

llm = ChatGroq(
    model='meta-llama/llama-4-maverick-17b-128e-instruct',
    api_key='your_groq_api_key',
)

messages = [
    SystemMessage(content="You are a helpful assistant"),
    UserMessage(content="What is Browser Use?")
]

response = await llm.ainvoke(messages)
print(response.completion)     # String response
print(response.usage)          # Token usage
print(response.stop_reason)    # Why generation stopped

Parameters

• messages (list[BaseMessage]): List of messages
• output_format (type[T] | None): Optional Pydantic model for structured output

Returns

ChatInvokeCompletion[T] | ChatInvokeCompletion[str] with:

• completion: Response content (string or structured output)
• usage: Token usage including:
  • prompt_tokens: Input tokens
  • completion_tokens: Output tokens
  • total_tokens: Total tokens used
• stop_reason: Not available from Groq API

Groq doesn’t support cached tokens - prompt_cached_tokens is always None.

Model Capabilities

JSON Schema Models

These models support native JSON schema for structured output:

• meta-llama/llama-4-maverick-17b-128e-instruct
• meta-llama/llama-4-scout-17b-16e-instruct
• openai/gpt-oss-20b
• openai/gpt-oss-120b

Tool Calling Models

These models use tool calling for structured output:

• moonshotai/kimi-k2-instruct

General Purpose

All models support regular text completion for general tasks.

Performance

Groq is known for extremely fast inference speeds:

• Lightning-fast token generation
• High throughput for production workloads
• Excellent for real-time applications
• Generous free tier for testing

Related

• ChatBrowserUse - Recommended provider
• ChatOpenAI
• ChatAnthropic