OpenPipe

Overview

OpenPipeLLMService extends the BaseOpenAILLMService to provide integration with OpenPipe, enabling request logging, model fine-tuning, and performance monitoring. It maintains compatibility with OpenAI’s API while adding OpenPipe’s logging and optimization capabilities.

OpenPipe LLM API Reference

Pipecat’s API methods for OpenPipe integration

Example Implementation

Browse examples using OpenPipe logging

OpenPipe Documentation

Official OpenPipe API documentation and features

OpenPipe Platform

Access logging and fine-tuning features

Installation

To use OpenPipe services, install the required dependencies:

pip install "pipecat-ai[openpipe]"

Prerequisites

OpenPipe Account Setup

Before using OpenPipe LLM services, you need:

OpenPipe Account: Sign up at OpenPipe
API Keys: Generate both OpenPipe and OpenAI API keys
Project Setup: Configure logging and fine-tuning projects

Required Environment Variables

OPENPIPE_API_KEY: Your OpenPipe API key for logging and fine-tuning
OPENAI_API_KEY: Your OpenAI API key for underlying model access

Configuration

str

default:"None"

deprecated

Deprecated in v0.0.105. Use settings=OpenPipeLLMService.Settings(model=...) instead.

str

default:"None"

OpenAI API key for authentication. If not provided, reads from environment.

str

default:"None"

Custom OpenAI API endpoint URL. Uses the default OpenAI URL if not provided.

str

default:"None"

OpenPipe API key for request logging and fine-tuning features. If not provided, reads from environment.

str

default:"https://app.openpipe.ai/api/v1"

OpenPipe API endpoint URL.

OpenPipeLLMService.Settings

default:"None"

Runtime-configurable settings. See Settings below.

Dict[str, str]

default:"None"

Dictionary of tags to apply to all requests for tracking and filtering in the OpenPipe dashboard.

Settings

Runtime-configurable settings passed via the settings constructor argument using OpenPipeLLMService.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.

Usage

Basic Setup

import os
from pipecat.services.openpipe import OpenPipeLLMService

llm = OpenPipeLLMService(
    api_key=os.getenv("OPENAI_API_KEY"),
    openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
    model="gpt-4.1",
)

With Tags for Tracking

from pipecat.services.openpipe import OpenPipeLLMService

llm = OpenPipeLLMService(
    api_key=os.getenv("OPENAI_API_KEY"),
    openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
    model="gpt-4.1",
    tags={
        "environment": "production",
        "project": "voice-assistant",
    },
)

Notes

All requests are automatically logged to OpenPipe for monitoring and fine-tuning purposes.
Tags are included with every request and can be used to filter and organize requests in the OpenPipe dashboard.
OpenPipe uses its own client (openpipe.AsyncOpenAI) instead of the standard OpenAI client to enable transparent request logging.

The InputParams / params= pattern is deprecated as of v0.0.105. Use Settings / settings= instead. See the Service Settings guide for migration details.

Pipecat Server

Pipecat Flows

Pipecat Cloud

CLI

Overview

OpenPipe LLM API Reference

Example Implementation

OpenPipe Documentation

OpenPipe Platform

Installation

Prerequisites

OpenPipe Account Setup

Required Environment Variables

Configuration

Settings

Usage

Basic Setup

With Tags for Tracking

Notes

​Overview

OpenPipe LLM API Reference

Example Implementation

OpenPipe Documentation

OpenPipe Platform

​Installation

​Prerequisites

​OpenPipe Account Setup

​Required Environment Variables

​Configuration

​Settings

​Usage

​Basic Setup

​With Tags for Tracking

​Notes

Overview

Installation

Prerequisites

OpenPipe Account Setup

Required Environment Variables

Configuration

Settings

Usage

Basic Setup

With Tags for Tracking

Notes