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

# LLM provider integrations

> Auto-instrument OpenAI, Anthropic, Cohere, Mistral, Groq, AWS Bedrock, and 20+ more LLM providers with zero code changes

OpenLIT auto-instruments every supported LLM provider you have installed. Install the OpenLIT SDK, call `openlit.init()` once at startup, and all LLM calls your application makes are automatically traced with token counts, latency, cost, and model metadata.

## Get started

<Steps>
  <Step title="Install OpenLIT">
    <Tabs>
      <Tab title="Python">
        ```shell theme={null}
        pip install openlit
        ```
      </Tab>

      <Tab title="TypeScript">
        ```shell theme={null}
        npm install openlit
        ```
      </Tab>
    </Tabs>
  </Step>

  <Step title="Install your LLM provider SDK">
    Install the SDK for the provider you use. OpenLIT detects it automatically.

    <Tabs>
      <Tab title="Python">
        ```shell theme={null}
        # Example: OpenAI
        pip install openai

        # Example: Anthropic
        pip install anthropic
        ```
      </Tab>

      <Tab title="TypeScript">
        ```shell theme={null}
        # Example: OpenAI
        npm install openai
        ```
      </Tab>
    </Tabs>
  </Step>

  <Step title="Initialize OpenLIT in your application">
    Add `openlit.init()` once at the top of your application entry point.

    <Tabs>
      <Tab title="Python">
        ```python theme={null}
        import openlit
        import openai

        openlit.init(otlp_endpoint="http://127.0.0.1:4318")

        client = openai.OpenAI()

        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": "What is observability?"}]
        )
        print(response.choices[0].message.content)
        ```
      </Tab>

      <Tab title="TypeScript">
        ```typescript theme={null}
        import Openlit from 'openlit';
        import OpenAI from 'openai';

        Openlit.init({ otlpEndpoint: 'http://127.0.0.1:4318' });

        const client = new OpenAI();

        const response = await client.chat.completions.create({
            model: 'gpt-4o',
            messages: [{ role: 'user', content: 'What is observability?' }]
        });
        console.log(response.choices[0].message.content);
        ```
      </Tab>
    </Tabs>
  </Step>
</Steps>

## Supported LLM providers

| Provider                 | Python SDK       | TypeScript SDK | Instrumentor key     |
| ------------------------ | ---------------- | -------------- | -------------------- |
| OpenAI                   | ✅ `openai`       | ✅              | `openai`             |
| Anthropic                | ✅ `anthropic`    | ✅              | `anthropic`          |
| Cohere                   | ✅ `cohere`       | —              | `cohere`             |
| Mistral                  | ✅ `mistralai`    | —              | `mistral`            |
| Groq                     | ✅ `groq`         | —              | `groq`               |
| AWS Bedrock              | ✅ `boto3`        | —              | `bedrock`            |
| Vertex AI                | ✅ `vertexai`     | —              | `vertexai`           |
| Google AI Studio         | ✅ `google-genai` | —              | `google-ai-studio`   |
| Azure OpenAI             | ✅ (via `openai`) | ✅              | `azure-ai-inference` |
| Ollama                   | ✅ `ollama`       | ✅              | `ollama`             |
| vLLM                     | ✅ `vllm`         | —              | `vllm`               |
| HuggingFace Transformers | ✅ `transformers` | —              | `transformers`       |
| DeepSeek                 | ✅ (via `openai`) | —              | `openai`             |
| AI21                     | ✅ `ai21`         | —              | `ai21`               |
| Reka                     | ✅ `reka`         | —              | `reka-api`           |
| LiteLLM                  | ✅ `litellm`      | —              | `litellm`            |
| GPT4All                  | ✅ `gpt4all`      | —              | `gpt4all`            |
| Together AI              | ✅ `together`     | ✅              | `together`           |
| NVIDIA NIM               | ✅ (via `openai`) | —              | `openai`             |
| xAI                      | ✅ (via `openai`) | —              | `openai`             |
| Sarvam                   | ✅ `sarvamai`     | —              | `sarvam`             |
| PremAI                   | ✅ `premai`       | —              | `premai`             |
| Julep                    | ✅ `julep`        | —              | `julep`              |
| MultiOn                  | ✅ `multion`      | —              | `multion`            |
| ElevenLabs               | ✅ `elevenlabs`   | —              | `elevenlabs`         |
| AssemblyAI               | ✅ `assemblyai`   | —              | `assemblyai`         |

## Span attributes captured

Every LLM call trace includes the following attributes:

| Attribute                       | Description                                              |
| ------------------------------- | -------------------------------------------------------- |
| `gen_ai.system`                 | LLM provider name (e.g., `openai`)                       |
| `gen_ai.request.model`          | Model name requested (e.g., `gpt-4o`)                    |
| `gen_ai.response.model`         | Model name returned by the API                           |
| `gen_ai.usage.input_tokens`     | Number of prompt tokens consumed                         |
| `gen_ai.usage.output_tokens`    | Number of completion tokens generated                    |
| `gen_ai.usage.total_tokens`     | Total tokens used                                        |
| `gen_ai.request.temperature`    | Temperature parameter                                    |
| `gen_ai.request.max_tokens`     | Max tokens parameter                                     |
| `gen_ai.operation.name`         | Operation type (e.g., `chat`, `embeddings`)              |
| `gen_ai.request.finish_reasons` | Stop reason returned by the model                        |
| `gen_ai.prompt`                 | Prompt content (when `capture_message_content=True`)     |
| `gen_ai.completion`             | Completion content (when `capture_message_content=True`) |
| `db.cost`                       | Estimated cost in USD based on token usage               |

## Disabling specific providers

Pass a list of instrumentor keys to skip specific providers:

```python theme={null}
import openlit

# Instrument everything except Cohere and LiteLLM
openlit.init(
    otlp_endpoint="http://127.0.0.1:4318",
    disabled_instrumentors=["cohere", "litellm"]
)
```

```typescript theme={null}
import Openlit from 'openlit';

Openlit.init({
    otlpEndpoint: 'http://127.0.0.1:4318',
    disabledInstrumentations: ['cohere', 'litellm']
});
```

You can also use the environment variable:

```shell theme={null}
export OPENLIT_DISABLED_INSTRUMENTORS="cohere,litellm"
```

***

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