Fireworks AI LLM

Overview

FireworksLLMService provides access to Fireworks AI’s language models through an OpenAI-compatible interface. It inherits from OpenAILLMService and supports streaming responses, function calling, and context management with optimized inference infrastructure.

Fireworks LLM API Reference

Pipecat’s API methods for Fireworks AI integration

Example Implementation

Complete example with function calling

Fireworks Documentation

Official Fireworks AI API documentation and features

Fireworks Platform

Access models and manage API keys

Installation

To use Fireworks AI services, install the required dependency:

uv add "pipecat-ai[fireworks]"

Prerequisites

Fireworks AI Account Setup

Before using Fireworks AI LLM services, you need:

Fireworks Account: Sign up at Fireworks AI
API Key: Generate an API key from your account dashboard
Model Selection: Choose from available open-source and proprietary models

Required Environment Variables

FIREWORKS_API_KEY: Your Fireworks AI API key for authentication

Configuration

str

required

Fireworks AI API key for authentication. Raises ValueError when empty.

str

default:"None"

deprecated

Deprecated since 0.0.105, removed in 2.0.0. Use settings=FireworksLLMService.Settings(model=...) instead.

FireworksLLMService.Settings

default:"None"

Runtime-configurable settings. See Settings below.

str

default:"https://api.fireworks.ai/inference/v1"

Base URL for Fireworks API endpoint.

Settings

Runtime-configurable settings passed via the settings constructor argument using FireworksLLMService.Settings(...). These can be updated mid-conversation with LLMUpdateSettingsFrame. See Service Settings for details. This service uses the same settings as OpenAILLMService. See OpenAI LLM Settings for the full parameter reference. The service defaults to model="accounts/fireworks/models/nemotron-3-ultra-nvfp4" when no model is specified.

Usage

Basic Setup

import os
from pipecat.services.fireworks.llm import FireworksLLMService

llm = FireworksLLMService(
    api_key=os.getenv("FIREWORKS_API_KEY"),
    settings=FireworksLLMService.Settings(
        model="accounts/fireworks/models/nemotron-3-ultra-nvfp4"
    ),
)

With Custom Settings

from pipecat.services.fireworks.llm import FireworksLLMService

llm = FireworksLLMService(
    api_key=os.getenv("FIREWORKS_API_KEY"),
    settings=FireworksLLMService.Settings(
        model="accounts/fireworks/models/nemotron-3-ultra-nvfp4",
        temperature=0.7,
        top_p=0.9,
        max_tokens=1024,
    ),
)

Notes

seed and max_completion_tokens are not sent by this service, so they have no effect. Use max_tokens instead.
Model identifiers use the accounts/fireworks/models/ prefix format.

Get Started

Pipecat Server

Client SDKs

Pipecat Flows

Pipecat Cloud

CLI

Pipecat Context Hub

Fireworks AI LLM

Overview

Fireworks LLM API Reference

Example Implementation

Fireworks Documentation

Fireworks Platform

Installation

Prerequisites

Fireworks AI Account Setup

Required Environment Variables

Configuration

Settings

Usage

Basic Setup

With Custom Settings

Notes

​Overview

Fireworks LLM API Reference

Example Implementation

Fireworks Documentation

Fireworks Platform

​Installation

​Prerequisites

​Fireworks AI Account Setup

​Required Environment Variables

​Configuration

​Settings

​Usage

​Basic Setup

​With Custom Settings

​Notes

Overview

Installation

Prerequisites

Fireworks AI Account Setup

Required Environment Variables

Configuration

Settings

Usage

Basic Setup

With Custom Settings

Notes