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

# OpenAI

> OpenAI is a leading artificial intelligence research and deployment company committed to ensuring that artificial general intelligence(AGI) benefits all of humanity. It develops cutting-edge AI models, such as GPT-4, to assist in various applications, from natural language processing to complex problem-solving. OpenAI focuses on safety, scalability, and ethical considerations in AI advancements.

Using Langtrace to monitor your OPENAI LLM apps is quick and easy. Follow these steps:

## Setup

1. Install the Langtrace and OpenAI SDKs.

*Note: You'll need API keys from Langtrace and OpenAI. Sign up for [Langtrace](https://langtrace.ai) and/or [OpenAI](https://openai.com/) if you haven't done so already.*

```bash Python theme={null}
# Install the SDK
pip install -U langtrace-python-sdk openai
```

2. Setup environment variables:

```bash Shell theme={null}
export LANGTRACE_API_KEY=YOUR_LANGTRACE_API_KEY
export OPENAI_API_KEY=YOUR_OPEN_AI_API_KEY
```

## Usage

Generate a simple output with your deployment's model:

<CodeGroup>
  ```python Python theme={null}
    import os
  from langtrace_python_sdk import langtrace # Must precede any llm module imports
  from openai import OpenAI
  langtrace.init(api_key = os.environ['LANGTRACE_API_KEY'])
  client = OpenAI(
      # This is the default and can be omitted
      api_key=os.environ.get("OPENAI_API_KEY"),
  )

  # Generate a simple output with OPEN AI'S GPT 3.5 MODEL
  chat_completion = client.chat.completions.create(
      messages=[
          {
              "role": "user",
              "content": "What is LangChain?",
          }
      ],
      model="gpt-3.5-turbo",
  )
  print(chat_completion.choices[0].message.content)

  # Lets also create some embeddings
  response = client.embeddings.create(
      input="Your text string goes here",
      model="text-embedding-3-small"
  )

  print(response.data[0].embedding)
  ```
</CodeGroup>

You can now view your traces on the Langtrace dashboard.

<img src="https://mintcdn.com/langtraceai-2/m7BVnnhK3Cx2yRxS/images/openai.png?fit=max&auto=format&n=m7BVnnhK3Cx2yRxS&q=85&s=9350b3dbb11b66174f407416c3c912ff" alt="traces" width="1992" height="794" data-path="images/openai.png" />

Want to see more supported methods? Checkout the sample code in the [Langtrace OpenAI Python Example](https://github.com/Scale3-Labs/langtrace-recipes/blob/main/integrations/language-model/openai/starter.ipynb) repository.