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Overview

GrokLLMService provides access to Grok’s language models through an OpenAI-compatible interface. It inherits from OpenAILLMService and supports streaming responses, function calling, and context management with Grok’s unique reasoning capabilities.

Grok LLM API Reference

Pipecat’s API methods for Grok integration

Example Implementation

Complete example with function calling

Grok Documentation

Official Grok API documentation and features

X.AI Platform

Access Grok models and manage API keys

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Installation

To use Grok services, install the required dependencies: 
pip install "pipecat-ai[grok]"

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Prerequisites

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Grok Account Setup

Before using Grok LLM services, you need:
  1. X.AI Account: Sign up at X.AI Console
  2. API Key: Generate an API key from your console dashboard
  3. Model Selection: Choose from available Grok models

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Required Environment Variables

  • XAI_API_KEY: Your X.AI API key for authentication

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Configuration

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api_key
str
required
X.AI API key for authentication.
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base_url
str
default:"https://api.x.ai/v1"
Base URL for Grok API endpoint.
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model
str
default:"grok-3-beta"
Model identifier to use.

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InputParams

This service uses the same input parameters as OpenAILLMService. See OpenAI LLM for details.

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Usage

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Basic Setup

import os
from pipecat.services.grok import GrokLLMService

llm = GrokLLMService(
    api_key=os.getenv("XAI_API_KEY"),
    model="grok-3-beta",
)

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With Custom Parameters

from pipecat.services.grok import GrokLLMService

llm = GrokLLMService(
    api_key=os.getenv("XAI_API_KEY"),
    model="grok-3-beta",
    params=GrokLLMService.InputParams(
        temperature=0.7,
        top_p=0.9,
        max_completion_tokens=1024,
    ),
)

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Notes

  • Grok uses incremental token reporting. The service accumulates token usage metrics during processing and reports the final totals at the end of each request.
  • Grok supports prompt caching and reasoning tokens, which are tracked in usage metrics when available.