This repository demonstrates how to build a secure, enterprise-grade AI agent is incapsulated within a simply Python application built using streamlit, NVIDIA NeMo Agent Toolkit and NVIDIA NeMo Guardrails. All of the observability telemetry of traces, logs, and metrics are collected using the Dynatrace distribution of the OpenTelemetry Collector for analysis within Dynatrace.

This repo and related guides assume Mac OS/Linux, but you can adapt as required for Windows.

Setup

This diagram below depicts the setup consisting of:

• Sample Python app - Used to generate prompts and send telemetry data to and OpenTelemetry Collector
• OpenTelemetry Collector - Configured to send telemetry data to Dynatrace OTLP APIs
• NVIDIA Build - free to use LLM models accessed via APIs and a Build API key
• Tavily - Uses as Agentic tool to search the internet and accessed via APIs and a Build API key
• Dynatrace - View and analyze OpenTelemetry metrics

🚀 Quick Start

Prerequisites

1. Local software
   • Python 3.11, 3.12, or 3.13
   • Python package and project manager, uv
   • Docker or Podman for containerized deployment of a OpenTelemetry Collector
2. NVIDIA Build Account on build.nvidia.com
3. Tavily Developer Account on tavily.com
4. Dynatrace Tenant. For a Trial, visit Dynatrace signup page

Installation

1. Clone the repository:

   git clone git@github.com:dynatrace-ace/perform-2026-nvidia-workshop.git
   
   cd perform-2026-nvidia-workshop

2. Create Environment Variables file

   Make an environment file using the provided environment variable template:

   cp .env-app-template .env

3. Set Tavily API Key

   • Create a Tavily API KEY API Key on tavily.com
   • Adjust .env with your Tavily API Key for TAVILY_API_KEY
   • Once set, you can review your API usage with this command.

     curl --request GET \
         --url https://api.tavily.com/usage  \
         --header "Authorization: Bearer $TAVILY_API_KEY" | jq .

4. Set NVIDIA API Key

   • Create a NVIDIA API Key on build.nvidia.com
   • Adjust .env with your NVIDIA API Key for NVIDIA_API_KEY

5. Create Dynatrace API Key

   • Make a Dynatrace API Token with the required scopes for the OTLP API:
     • openTelemetryTrace.ingest
     • metrics.ingest
     • logs.ingest
   • Adjust .env with your Dynatrace environment DT_BASE_URL and DT_API_TOKEN

6. Start an OpenTelemetry Collector configured to send observability data to Dynatrace. For this, follow the OTLP receiver only setup guide

🚀 Run the Application locally using Python

1. Create virtual environment

   uv venv --python 3.13 .venv
   source .venv/bin/activate
   

2. Install dependencies

   # Using uv (recommended)
   uv pip install -r requirements.txt

3. Update the NVIDIA configuration files

   This script will create the guardrails_config/config.yml and src/configs/config.yml files from the provided template for NVIDIA build API usage required for NVIDIA NAT and Guardrail usage.

   source .env
   python update_config.py build

4. Start sample App

   This will start a web app on port 8501 for example http://localhost:8501

   streamlit run app.py

5. Open App

   Start app which will open the web UI in a local browser at http://localhost:5801

📚 Setup Details

See the RESOURCES.md guide for details.

📚 Reference

• Dynatrace AI and LLM Observability
• NVIDIA NeMo Agent Toolkit
• NeMo Guardrails
• NeMo Guardrails Documentation
• NVIDIA NIM
• NVIDIA AI Endpoints