Add in Datadog LLM Observability Documentation

docs/observability/datadog.md

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+# Agent Observability with Datadog
+
+[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
+
+### Configure Environment Variables
+
+You will also 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
+```
+
+Additionally, configure any LLM provider API keys:
+
+```shell
+export GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>
+#  or
+export GOOGLE_API_KEY=<YOUR_GOOGLE_API_KEY>
+...
+```
+
+### 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 python your_application.py
+```
+
+## 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/)

mkdocs.yml

@@ -262,6 +262,7 @@ nav:
       - BigQuery Agent Analytics: observability/bigquery-agent-analytics.md
       - AgentOps: observability/agentops.md
       - Arize AX: observability/arize-ax.md
+      - Datadog: observability/datadog.md
       - Freeplay: observability/freeplay.md
       - MLflow: observability/mlflow.md
       - Monocle: observability/monocle.md