comet-ml--opik/apps/opik-documentation/documentation/fern/docs-v2/observability/getting-started.mdx

---
headline: Getting Started with Observability | Opik Documentation
og:description: Add observability to your LLM application with Opik tracing using
  AI-powered skills or manual SDK integration
og:site_name: Opik Documentation
og:title: Getting Started with Observability — Opik
title: Getting started with Observability
---

Opik makes it easy to add observability to your existing LLM application. The fastest way is to let
your coding agent do it — install the Opik skill in Claude Code, Cursor, Codex, or any other
coding agent and it will instrument your code for you. If you'd rather stay inside Opik, use Opik
Connect to have [Ollie](/ollie) set up tracing from the dashboard. You can also add
tracing manually with the SDK.

<video
  src="/img/v2/observability/getting-started.mp4"
  width="854"
  height="480"
  autoPlay
  muted
  loop
  playsInline
  controls
  preload="auto"
/>

## Adding observability to your code

<Tabs>
  <Tab title="AI coding agent">
    The fastest way to add observability is to install the Opik skill in your coding agent and let
    it instrument your code for you. The skill is compatible with Claude Code, Codex, Cursor,
    OpenCode and any other agent that supports skills.

    <Steps>
      <Step title="Install the Opik skill">
        ```bash
        npx skills add comet-ml/opik-skills
        ```
      </Step>
      <Step title="Run the integration">
        Ask your coding agent to instrument your code:
        ```
        Instrument my agent with Opik using the /instrument command.
        ```

        The agent will read your code, pick the right Opik integration, and add tracing.
      </Step>
    </Steps>
  </Tab>
  <Tab title="Opik Connect">
    Opik Connect links your local repository to Opik so that [Ollie](/ollie), Opik's
    built-in AI coding assistant, can inspect your code and add tracing from the dashboard — no
    local agent setup required.

    <Steps>
      <Step title="Install Opik">
        ```bash
        pip install opik
        ```
      </Step>
      <Step title="Set your environment variables">
        <Tabs>
          <Tab title="Opik Cloud">
            ```bash
            export OPIK_API_KEY="<YOUR_API_KEY>"
            export OPIK_WORKSPACE="<YOUR_WORKSPACE>"
            ```

            You can find your API key and workspace name in the [Opik dashboard](https://www.comet.com/opik).
          </Tab>
          <Tab title="Self-hosted">
            ```bash
            export OPIK_URL_OVERRIDE="http://localhost:5173/api"
            ```

            Replace the URL with your Opik instance address if it differs from the default.
          </Tab>
        </Tabs>
      </Step>
      <Step title="Connect your repository">
        Run this command in the repository you want Ollie to work in:

        ```bash
        opik connect --project "<YOUR_PROJECT_NAME>"
        ```

        This creates a local connection between Opik and your machine so Ollie can inspect your
        code and help add tracing.
      </Step>
    </Steps>

    Once connected, open Opik and Ollie will help you instrument your code and set up tracing.
    See the [Ollie documentation](/ollie) for more details.
  </Tab>
  <Tab title="Manual integration">
    Opik has integrations with all the popular Agent frameworks in both Python and TypeScript as well as
    first-class support for OpenTelemetry:

    <CardGroup cols={3}>
      <Card title="LangChain" href="/integrations/langchain" icon={<img src="/img/tracing/langchain.svg" />} iconPosition="left"/>
      <Card title="LlamaIndex" href="/integrations/llama_index" icon={<img src="/img/tracing/llamaindex.svg" />} iconPosition="left"/>
      <Card title="Anthropic" href="/integrations/anthropic" icon={<img src="/img/tracing/anthropic.svg" />} iconPosition="left"/>
      <Card title="AWS Bedrock" href="/integrations/bedrock" icon={<img src="/img/tracing/bedrock.svg" />} iconPosition="left"/>
      <Card title="Google Gemini" href="/integrations/gemini" icon={<img src="/img/tracing/gemini.svg" />} iconPosition="left"/>
      <Card title="CrewAI" href="/integrations/crewai" icon={<img src="/img/tracing/crewai.svg" />} iconPosition="left"/>
    </CardGroup>

    **[View all 30+ integrations →](/integrations/overview)**

    If your framework is not listed, you can use the `@track` decorator (Python) or `track` wrapper
    (TypeScript) to manually instrument your code:

    <CodeBlocks>
      ```python title="Python"
      import opik

      opik.configure()

      @opik.track
      def my_llm_call(user_message):
          # Your LLM call here
          response = call_llm(user_message)
          return response

      @opik.track(name="my-agent")
      def my_agent(user_message):
          context = retrieve_context(user_message)
          response = my_llm_call(user_message)
          return response
      ```

      ```ts title="Typescript"
      import { Opik } from "opik";

      const client = new Opik();

      const myLlmCall = client.track({
        name: "my_llm_call",
        fn: async (userMessage: string) => {
          // Your LLM call here
          const response = await callLlm(userMessage);
          return response;
        },
      });

      const myAgent = client.track({
        name: "my-agent",
        fn: async (userMessage: string) => {
          const context = await retrieveContext(userMessage);
          const response = await myLlmCall(userMessage);
          return response;
        },
      });
      ```
    </CodeBlocks>
  </Tab>
</Tabs>

## Viewing your traces

After running your application, traces will appear in the Opik dashboard. Each trace captures the
full execution path of a request, including all nested spans, inputs, outputs, and timing information.

<Frame>
  <img src="/img/v2/observability/traces-page.png" alt="Opik traces page showing trace details with span tree, outputs, and feedback scores" />
</Frame>

You can use [Ollie](/ollie) to analyze your traces, identify issues in your agent's
behavior, and get actionable suggestions for improvement.

## Next steps

- [Concepts](/tracing/concepts) — Learn about traces, spans, threads, and feedback scores
- [Log traces](/tracing/advanced/log_traces) — In-depth guide on customizing what gets logged
- [Cost tracking](/tracing/advanced/cost_tracking) — Monitor token usage and spending