---
catalog_title: Datadog
catalog_description: Develop, evaluate, and monitor LLM applications
catalog_icon: /integrations/assets/datadog.png
catalog_tags: ["observability"]
---

# Datadog Observability for ADK

<div class="language-support-tag">
    <span class="lst-supported">Supported in ADK</span><span class="lst-python">Python</span>
</div>

[Datadog LLM
Observability](https://www.datadoghq.com/product/llm-observability/) helps AI
engineers, data scientists, and application developers quickly develop,
evaluate, and monitor LLM applications. Confidently improve output quality,
performance, costs, and overall risk with structured experiments, end-to-end
tracing across AI agents, and evaluations.

## Overview

Datadog LLM Observability can [automatically instrument and trace your agents
built on Google
ADK](https://docs.datadoghq.com/llm_observability/instrumentation/auto_instrumentation?tab=python#google-adk),
allowing you to:

- **Observe agent executions and interactions** - Automatically capture every
  agent run, tool call, and code execution within your agents
- **Capture LLM calls and responses** made with the underlying Google GenAI SDK
- **Debug issues** by providing error rates, token usage and cost, and
  out-of-the-box evaluations on your LLM calls and tool usage

## Prerequisites

Sign up for a [Datadog account](https://www.datadoghq.com/) if you do not have
one and [get your API
key](https://docs.datadoghq.com/account_management/api-app-keys/#api-keys).

## Installation

Install the required packages:

```bash
pip install ddtrace
```

## Setup

### Create an Application using ADK

If you do not have an application using ADK, follow the steps in the [ADK
Getting Started Guide](https://adk.dev/get-started/) to create a sample ADK
agent.

### Configure Environment Variables

You will need to specify an ML Application name in the following environment
variables. An ML Application is a grouping of LLM Observability traces
associated with a specific LLM-based application. See [ML Application Naming
Guidelines](https://docs.datadoghq.com/llm_observability/instrumentation/sdk?tab=python#application-naming-guidelines)
for more information on limitations with ML Application names.

```shell
export DD_API_KEY=<YOUR_DD_API_KEY>
export DD_SITE=<YOUR_DD_SITE>
export DD_LLMOBS_ENABLED=true
export DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME>
export DD_LLMOBS_AGENTLESS_ENABLED=true
export DD_APM_TRACING_ENABLED=false  # Only set this if you are not using Datadog APM
```

These variables must be exported before running your application so the
following `ddtrace-run` command can use them, as opposed to putting them in the
agent's `.env` file.

### Run Your Application

Once you have configured your environment variables, you can run your
application and start observing your LLM-based applications.

```shell
ddtrace-run adk run my_agent
```

## Observe

Navigate to the [Datadog LLM Observability Traces
View](https://app.datadoghq.com/llm/traces) to see the traces generated by your
application.

![datadog-observability.png](./assets/datadog-observability.png)

## Support and Resources
- [Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/)
- [Datadog Support](https://docs.datadoghq.com/help/)
