> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pipecat.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAI

> Large Language Model services using OpenAI's chat completion API

## Overview

`OpenAILLMService` provides chat completion capabilities using OpenAI's API, supporting streaming responses, function calling, vision input, and advanced context management for conversational AI applications with state-of-the-art language models.

<CardGroup cols={2}>
  <Card title="OpenAI LLM API Reference" icon="code" href="https://reference-server.pipecat.ai/en/latest/api/pipecat.services.openai.base_llm.html">
    Pipecat's API methods for OpenAI integration
  </Card>

  <Card title="Example Implementation" icon="play" href="https://github.com/pipecat-ai/pipecat/blob/main/examples/getting-started/07-function-calling.py">
    Function calling example with weather API
  </Card>

  <Card title="OpenAI Documentation" icon="book" href="https://platform.openai.com/docs/api-reference/chat">
    Official OpenAI API documentation
  </Card>

  <Card title="OpenAI Platform" icon="microphone" href="https://platform.openai.com/api-keys">
    Access models and manage API keys
  </Card>
</CardGroup>

## Installation

To use OpenAI services, install the required dependencies:

```bash theme={null}
uv add "pipecat-ai[openai]"
```

## Prerequisites

### OpenAI Account Setup

Before using OpenAI LLM services, you need:

1. **OpenAI Account**: Sign up at [OpenAI Platform](https://platform.openai.com/)
2. **API Key**: Generate an API key from your account dashboard
3. **Model Selection**: Choose from available models (GPT-4.1, GPT-4o, GPT-4o-mini, etc.)
4. **Usage Limits**: Set up billing and usage limits as needed

### Required Environment Variables

* `OPENAI_API_KEY`: Your OpenAI API key for authentication

## Configuration

<ParamField path="model" type="str" default="None" deprecated>
  OpenAI model name to use (e.g., `"gpt-4.1"`, `"gpt-4o"`, `"gpt-4o-mini"`).
  *Deprecated in v0.0.105. Use `settings=OpenAILLMService.Settings(...)`
  instead.*
</ParamField>

<ParamField path="api_key" type="str" default="None">
  OpenAI API key. If `None`, uses the `OPENAI_API_KEY` environment variable.
</ParamField>

<ParamField path="base_url" type="str" default="None">
  Custom base URL for the OpenAI API. Override for proxied or self-hosted
  deployments.
</ParamField>

<ParamField path="organization" type="str" default="None">
  OpenAI organization ID.
</ParamField>

<ParamField path="project" type="str" default="None">
  OpenAI project ID.
</ParamField>

<ParamField path="default_headers" type="Mapping[str, str]" default="None">
  Additional HTTP headers to include in every request.
</ParamField>

<ParamField path="settings" type="OpenAILLMService.Settings" default="None">
  Runtime-configurable model settings. See [Settings](#settings) below.
</ParamField>

<ParamField path="params" type="InputParams" default="None" deprecated>
  Runtime-configurable model settings. See [Settings](#settings) below.
  *Deprecated in v0.0.105. Use `settings=OpenAILLMService.Settings(...)`
  instead.*
</ParamField>

<ParamField path="retry_timeout_secs" type="float" default="5.0">
  Request timeout in seconds. Used when `retry_on_timeout` is enabled to
  determine when to retry.
</ParamField>

<ParamField path="retry_on_timeout" type="bool" default="False">
  Whether to retry the request once if it times out. The retry attempt has no
  timeout limit.
</ParamField>

### Settings

Runtime-configurable settings passed via the `settings` constructor argument using `OpenAILLMService.Settings(...)`. These can be updated mid-conversation with `LLMUpdateSettingsFrame`. See [Service Settings](/pipecat/fundamentals/service-settings) for details.

| Parameter               | Type    | Default     | Description                                                                                         |
| ----------------------- | ------- | ----------- | --------------------------------------------------------------------------------------------------- |
| `model`                 | `str`   | `"gpt-4.1"` | OpenAI model identifier. *(Inherited from base settings.)*                                          |
| `system_instruction`    | `str`   | `None`      | System instruction/prompt for the model. *(Inherited from base settings.)*                          |
| `temperature`           | `float` | `NOT_GIVEN` | Sampling temperature (0.0 to 2.0). Lower values are more focused, higher values are more creative.  |
| `max_tokens`            | `int`   | `NOT_GIVEN` | Maximum tokens to generate.                                                                         |
| `top_p`                 | `float` | `NOT_GIVEN` | Top-p (nucleus) sampling (0.0 to 1.0). Controls diversity of output.                                |
| `top_k`                 | `int`   | `NOT_GIVEN` | Top-k sampling parameter.                                                                           |
| `frequency_penalty`     | `float` | `NOT_GIVEN` | Penalty for frequent tokens (-2.0 to 2.0). Positive values discourage repetition.                   |
| `presence_penalty`      | `float` | `NOT_GIVEN` | Penalty for new topics (-2.0 to 2.0). Positive values encourage the model to talk about new topics. |
| `seed`                  | `int`   | `NOT_GIVEN` | Random seed for deterministic outputs.                                                              |
| `max_completion_tokens` | `int`   | `NOT_GIVEN` | Maximum completion tokens to generate.                                                              |

<Note>
  `NOT_GIVEN` values are omitted from the API request entirely, letting the
  OpenAI API use its own defaults. This is different from `None`, which would be
  sent explicitly.
</Note>

## Usage

### Basic Setup

```python theme={null}
from pipecat.services.openai import OpenAILLMService

llm = OpenAILLMService(
    api_key=os.getenv("OPENAI_API_KEY"),
    model="gpt-4o",
)
```

### With Custom Settings

```python theme={null}
from pipecat.services.openai import OpenAILLMService

llm = OpenAILLMService(
    api_key=os.getenv("OPENAI_API_KEY"),
    settings=OpenAILLMService.Settings(
        model="gpt-4.1",
        temperature=0.7,
        max_completion_tokens=1000,
        frequency_penalty=0.5,
    ),
)
```

### Updating Settings at Runtime

Model settings can be changed mid-conversation using `LLMUpdateSettingsFrame`:

```python theme={null}
from pipecat.frames.frames import LLMUpdateSettingsFrame
from pipecat.services.openai.base_llm import OpenAILLMSettings

await task.queue_frame(
    LLMUpdateSettingsFrame(
        delta=OpenAILLMSettings(
            temperature=0.3,
            max_completion_tokens=500,
        )
    )
)
```

<Tip>
  The `InputParams` / `params=` pattern is deprecated as of v0.0.105. Use
  `Settings` / `settings=` instead. See the [Service Settings
  guide](/pipecat/fundamentals/service-settings) for migration details.
</Tip>

## Notes

* **OpenAI-compatible providers**: Many third-party LLM providers offer OpenAI-compatible APIs. You can use `OpenAILLMService` with them by setting `base_url` to the provider's endpoint.
* **Retry behavior**: When `retry_on_timeout=True`, the first attempt uses the `retry_timeout_secs` timeout. If it times out, a second attempt is made with no timeout limit.
* **Function calling**: Supports OpenAI's tool/function calling format. Register function handlers on the pipeline task to handle tool calls automatically.
* **System instruction precedence**: If both `system_instruction` (from the constructor) and a system message in the context are set, the constructor's `system_instruction` takes precedence and a warning is logged.

## Event Handlers

`OpenAILLMService` supports the following event handlers, inherited from [LLMService](/api-reference/server/events/service-events):

| Event                       | Description                                                             |
| --------------------------- | ----------------------------------------------------------------------- |
| `on_completion_timeout`     | Called when an LLM completion request times out                         |
| `on_function_calls_started` | Called when function calls are received and execution is about to start |

```python theme={null}
@llm.event_handler("on_completion_timeout")
async def on_completion_timeout(service):
    print("LLM completion timed out")
```