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

# Tracing with Lambda

### Introduction

A Trace comprises a series of events executed within an LLM chain(workflow). Tracing enables Baserun to capture and display the LLM chain's entire lifecycle, whether synchronous or asynchronous.

Tracing LLM chains allows you to debug your application, monitor your LLM chains' performance, and also collect user feedback.

<Frame>
  <img src="https://mintcdn.com/baserun/sfThaUjl8v9XCswy/images/monitor-trace-details.png?fit=max&auto=format&n=sfThaUjl8v9XCswy&q=85&s=f41ab40d22d04e324b9853961f85d9e6" alt="Trace a LLM chain" width="2560" height="1440" data-path="images/monitor-trace-details.png" />
</Frame>

### Use cases

Please reference the [Monitoring Overview](/monitoring/overview) to learn why logging LLM chain is critical for LLM feature development.

### Features

* Is model and framework agnostic
* UI to show sequence of events
* Provides token usage, estimated cost, duration, input, and output
* Support automatic evaluation
* Supports evaluation
* Supports annotation
* Supports user feedback
* Supports async functions
* Option to add custom trace name
* Option to log custom metadata
* Option to set trace result

### Instruction

The first 3 steps are the same as the [Logging LLM requests tutorial](/). So, if you have already done this before, jump to step 4.

<Steps>
  <Step title="Install Baserun SDK">
    <CodeGroup>
      ```bash typescript theme={null}
      npm install baserun
      # or
      yarn add baserun
      ```
    </CodeGroup>
  </Step>

  <Step title="Generate an API key">
    Create an account at [https://app.baserun.ai/sign-up](https://app.baserun.ai/sign-up). Then generate an API key for your project in the [settings](https://app.baserun.ai/settings) tab. Set it as an environment variable:

    ```bash theme={null}
    export BASERUN_API_KEY="your_api_key_here"
    ```

    Alternatively set the Baserun API key when initializing the SDK

    <CodeGroup>
      ```typescript typescript theme={null}
      import { baserun } from "baserun";

      // init needs to be awaited. If top-level await is not available, wrap it in an async function,
      // but make sure it is called before instantiating OpenAI, Anthropic or Replicate
      await baserun.init({
        apiKey: "br-...",
      });
      ```
    </CodeGroup>
  </Step>

  <Step title=" Initialize Baserun">
    At your application's startup, define the environment in which you'd like to run Baserun. You can use Baserun in the development environment while iterating on your features, utilizing it for debugging and analysis, or in the production environment to monitor your application.

    <CodeGroup>
      ```typescript typescript theme={null}
      import { baserun } from "baserun";

      // in your main function

      // init needs to be awaited. If top-level await is not available, wrap it in an async function,
      // but make sure it is called before instantiating OpenAI, Anthropic or Replicate
      await baserun.init();
      ```
    </CodeGroup>
  </Step>

  <Step title="Decide what to trace">
    The function(s) to trace are ultimately dependent on your app. It could be a main() function, or it could be a handler for an API call.

    Note for TS/JS: Make sure to always call `await baserun.init()` before you instantiate OpenAI, Anthropic or Replicate.

    <CodeGroup>
      ```typescript typescript theme={null}
      import { baserun } from "baserun"

      const getResponse = baserun.trace((message: string) => {
        ...
      }, "getResponse")

      ```
    </CodeGroup>
  </Step>
</Steps>

### Full Example

Follow the example below to trace a Lambda function.

<CodeGroup>
  ```typescript typescript theme={null}

  const { baserun } = require("baserun");
  const { streamifyResponse } = require("lambda-stream")
  ...
  const handler = baserun.trace(async (event, responseStream, context) => {
      // lambda function logic, with streaming responses, OpenAI calls, etc
  }, "Lambda-ai-chat");

  const wrapper = async (event, responseStream, context) => {
    await baserun.init({
      apiKey: YOUR_BASERUN_API_KEY_HERE,
    });
    await handler(event, responseStream, context);
  }

  exports.handler = streamifyResponse(wrapper);

  ```
</CodeGroup>

Congrats, you are done! Now, you can navigate to the monitoring tab. Here is what you will see interact with your application:

<Frame>
  <img src="https://mintcdn.com/baserun/sfThaUjl8v9XCswy/images/monitor_trace_list.png?fit=max&auto=format&n=sfThaUjl8v9XCswy&q=85&s=627923d9ed75309234311bed5ba3b567" alt="Trace list" width="1280" height="800" data-path="images/monitor_trace_list.png" />
</Frame>

Optionally, you can add metadata like trace name, user ID, and session ID to aid in debugging. Read [Logging > Advanced tracing features](/monitoring/advanced-tracing) for more details.

## Demo projects

[Python example repo](https://github.com/baserun-ai/testing-agent/), <br />
[Typescript example repo](https://github.com/baserun-ai/testing-agent-js)

If you have any questions or feature requests, join our [Discord channel](https://discord.com/invite/xEPFsvSmkb) or send us an email at [hello@baserun.ai](mailto:hello@baserun.ai)