> ## 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.

# Quickstart

> Get LLM observability running in under 5 minutes

This guide walks you through deploying OpenLIT and sending your first LLM trace.

<Steps>
  <Step title="Deploy OpenLIT">
    Clone the repository and start the full stack with Docker Compose:

    ```bash theme={null}
    git clone git@github.com:openlit/openlit.git
    cd openlit
    docker compose up -d
    ```

    This starts:

    * OpenLIT UI at [http://127.0.0.1:3000](http://127.0.0.1:3000)
    * OpenTelemetry Collector on ports 4317 (gRPC) and 4318 (HTTP)
    * ClickHouse database on port 8123
  </Step>

  <Step title="Install the OpenLIT SDK">
    <CodeGroup>
      ```bash Python theme={null}
      pip install openlit
      ```

      ```bash TypeScript theme={null}
      npm install openlit
      ```
    </CodeGroup>
  </Step>

  <Step title="Initialize OpenLIT in your application">
    Add these two lines at the start of your application, before any LLM calls:

    <CodeGroup>
      ```python Python theme={null}
      import openlit

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

      ```typescript TypeScript theme={null}
      import OpenLIT from "openlit";

      OpenLIT.init({ otlpEndpoint: "http://127.0.0.1:4318" });
      ```
    </CodeGroup>

    <Tip>
      If you omit `otlp_endpoint`, OpenLIT prints traces to the console — useful during development without a running collector.
    </Tip>
  </Step>

  <Step title="Make an LLM call">
    OpenLIT auto-instruments any supported LLM library you have installed. Here's an example using OpenAI:

    <CodeGroup>
      ```python Python theme={null}
      import openlit
      from openai import OpenAI

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

      client = OpenAI()

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

      ```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 OpenTelemetry?" }],
      });
      console.log(response.choices[0].message.content);
      ```
    </CodeGroup>
  </Step>

  <Step title="View your traces">
    Open [http://127.0.0.1:3000](http://127.0.0.1:3000) and log in with the default credentials:

    | Field    | Value             |
    | -------- | ----------------- |
    | Email    | `user@openlit.io` |
    | Password | `openlituser`     |

    Navigate to **Requests** to see your LLM traces, including model name, token usage, cost, and latency.
  </Step>
</Steps>

## What gets traced automatically

OpenLIT auto-detects and instruments any supported library installed in your environment:

* **LLM calls** — model, prompt, completion, tokens, cost, latency, finish reason
* **Agent traces** — tool calls, chain steps, memory operations
* **Vector DB operations** — collection, operation type, latency
* **GPU metrics** — utilization, memory, temperature (when `collect_gpu_stats=True`)

No code changes are needed beyond `openlit.init()`.

## Next steps

<CardGroup cols={2}>
  <Card title="SDK configuration" icon="sliders" href="/sdk/configuration">
    Full list of openlit.init() parameters for customizing instrumentation
  </Card>

  <Card title="Integrations" icon="plug" href="/sdk/integrations/overview">
    Browse all 50+ supported LLM providers, frameworks, and vector databases
  </Card>

  <Card title="Evaluations" icon="ruler" href="/openlit/evaluations/overview">
    Set up automatic LLM response quality evaluation
  </Card>

  <Card title="Dashboards" icon="grid" href="/openlit/dashboards/overview">
    Build custom dashboards for your AI telemetry
  </Card>
</CardGroup>