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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

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Installation

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

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Prerequisites

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OpenPipe Account Setup

Before using OpenPipe LLM services, you need:
  1. OpenPipe Account: Sign up at OpenPipe
  2. API Keys: Generate both OpenPipe and OpenAI API keys
  3. Project Setup: Configure logging and fine-tuning projects

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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

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Configuration

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model
str
default:"gpt-4.1"
The model name to use for completions.
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api_key
str
default:"None"
OpenAI API key for authentication. If not provided, reads from environment.
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base_url
str
default:"None"
Custom OpenAI API endpoint URL. Uses the default OpenAI URL if not provided.
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openpipe_api_key
str
default:"None"
OpenPipe API key for request logging and fine-tuning features. If not provided, reads from environment.
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openpipe_base_url
str
default:"https://app.openpipe.ai/api/v1"
OpenPipe API endpoint URL.
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tags
Dict[str, str]
default:"None"
Dictionary of tags to apply to all requests for tracking and filtering in the OpenPipe dashboard.

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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.openpipe import OpenPipeLLMService

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

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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",
    },
)

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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.