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Agentic AI > Develop agentic workflows > Quickstart

Quickstart

This guide covers creating, deploying, and testing an agentic application using DataRobot's pre-built templates, including setting up a development environment, configuring an agent framework, and deploying your workflow to interact with DataRobot's chat interface.

Prerequisites

Before proceeding, install the required components.

Installation process

Before starting, complete all installation and setup steps in agentic-install. Skipping this process can cause errors and prevent your agentic application from running correctly. Do not proceed until you have finished the installation guide.

Get started

Run the following command to start the local development environment:

dr start

This command starts an interactive wizard to guide you through configuring your application. It will automatically clone the application repository and create a .env file in the root directory populated with environment variables you specify. The wizard provides guidance and context for each step, but for more details, click the dropdown below.

Tip
  1. Specify whether you wish to use the "low-code" agent template:
    • Press y to use the YAML-based NeMo Agent Toolkit template.
    • Press n to choose from a list of available agent templates.
  2. After a few moments, the wizard opens a web browser window to automatically configure your API endpoint and key. Click Proceed to continue.
    • If the browser doesn't open automatically, look for a URL in the terminal output and open it manually.
    • Click Proceed in the browser to continue.
    • If you encounter authentication issues, ensure you're logged into DataRobot in your browser.
  3. Specify the port for the local web application and press Enter. The default is 8842.
  4. If desired, specify the default execution environment for your agent and press Enter. The default is [DataRobot] Python 3.11 GenAI Agents.
  5. Provide a secret key to sign cookies for your session and press Enter. If you do not provide a value, a randomly-generated one will be used.
  6. Enter the URI for a database to use for the application and press Enter. The default is sqlite+aiosqlite:///.data/database.sqlite.
  7. Select your backend OAuth provider and press Enter.
  8. Specify your authorization server by selecting it from the list and pressing Space. Press Enter to confirm.
  9. Enter a passphrase (or leave blank if you don't want to use a passphrase) for your Pulumi stack and press Enter.
  10. Specify the ID of a DataRobot Use Case (e.g., 69331fad5e07469e7c4f5c6f), if one is available, and press Enter.
    • You can find your Use Case ID by navigating to the Use Case in the DataRobot UI and copying the ID from the URL.
    • If left blank, a new Use Case will be created automatically.
  11. Specify your LLM integration and press Enter.
  12. Specify the port for the MCP server and press Enter. The default is 9000.
  13. Review the .env configuration summary displayed and press Enter to confirm.
    • NOTE: This step will take several minutes to complete.
  14. Once the configuration finishes, choose a Pulumi stack to use for your application and press Enter. If you wish to create a new stack, press Enter and you will be prompted to enter a name for it. The name cannot match any existing stack name.

First-time initialization

When run for the first time, the dr start command prepares your development environment to develop and deploy your application. This includes both environment and agent component configuration. After this first initialization, future dr start operations will only set up your local environment. For subsequent updates to the configuration of your agent component, please run the dr component update command.

After dr start completes successfully, you should see:

  • A .env file in your project root
  • Your application directory created (typically named datarobot-agent-application or based on your application name)

Now that your application is configured, proceed to the next section.

Run your agent

Running your agent

Do not proceed to this section until you have run dr start, detailed in the previous section.

Navigate to the application directory created during dr start:

cd datarobot-agent-application # or the custom directory name you specified during the wizard, if different

Then, run the following command to start all components of the application:

task dev

This starts four processes, running in parallel:

  • Application frontend
  • Application backend
  • Agent
  • MCP server

Once all services are running:

  1. Open your web browser and navigate to http://localhost:5173
  2. You should see the agent application interface
  3. Try sending a test message to verify everything is working

From here, you can start customizing your agent by adding your own logic and functionality. See the Develop your agent section for more details.

Starting individual services

You can also start individual services in separate terminal windows; for example, task agent:dev will start just the agent.

Develop your agent

Now that your agent has been built and tested, you are ready to customize it by adding your own logic and functionality. See the following documentation for more details:

Deploy your agent

Testing your agent

Ensure that you have tested your agent locally before deploying.

Next, deploy your agent to DataRobot, which requires a Pulumi login.

Run the following command to deploy your agent:

dr task run deploy

Deployment process

The deployment process will take several minutes to complete.

Once deployment is complete, the script displays the deployment details, as shown in the example below. Note that the deployment details will vary based on your configuration.

Outputs:
    AGENT_DEPLOYMENT_ID                               : "69331fad5e07469e7c4f5c6f"
    Agent Custom Model Chat Endpoint [apptest] [agent]: "https://datarobot.com/api/v2/genai/agents/fromCustomModel/69331f816e1bf9f1890d5d1d/chat/"
    Agent Deployment Chat Endpoint [apptest] [agent]  : "https://datarobot.com/api/v2/deployments/69331fad5e07469e7c4f5c6f/chat/completions"
    Agent Execution Environment ID [apptest] [agent]  : "680fe4949604e9eba46b1775"
    Agent Playground URL [apptest] [agent]            : "https://datarobot.com/usecases/69331e4c3be0efe3b95a7be0/agentic-playgrounds/69331e4d1c036307186c9b16/comparison/chats"
    Agentic Application Starter [apptest]             : "https://datarobot.com/custom_applications/6933204a9e21e9b59b5a7bee/"
    DATABASE_URI                                      : "sqlite+aiosqlite:////tmp/agent_app/.data/agent_app.db"
    DATAROBOT_APPLICATION_ID                          : "6933204a9e21e9b59b5a7bee"
    DATAROBOT_OAUTH_PROVIDERS                         : (json) []

    LLM_DEFAULT_MODEL                                 : "azure/gpt-4o-2024-11-20"
    SESSION_SECRET_KEY                                : "secretkey123"
    USE_DATAROBOT_LLM_GATEWAY                         : "1"
    [apptest] [mcp_server] Custom Model Id            : "69331eebb49131d3d5430ac7"
    [apptest] [mcp_server] Deployment Id              : "69331f1f30548f83b668d9dc"
    [apptest] [mcp_server] MCP Server Base Endpoint   : "https://datarobot.com/api/v2/deployments/69331f1f30548f83b668d9dc/directAccess/"
    [apptest] [mcp_server] MCP Server MCP Endpoint    : "https://datarobot.com/api/v2/deployments/69331f1f30548f83b668d9dc/directAccess/mcp"