# Quickstart
URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-quickstart.html

> Learn how to build, deploy, and test agentic workflows using DataRobot's pre-built templates for popular AI agent frameworks.

# 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](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-install.html).

> [!WARNING] Installation process
> Before starting, complete all installation and setup steps in [agentic-install](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-install.html). 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 the DataRobot CLI's interactive wizard ( [dr start](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/start.html) in the [CLI command reference](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html)). 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.

> [!NOTE] 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

> [!WARNING] 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](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-quickstart.html#develop-your-agent) section for more details.

> [!NOTE] 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:

- Customize your agent
- Add tools to your agent
- Configure LLM providers
- Add Python requirements
- Manage prompts

## Deploy your agent

> [!WARNING] 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
```

For more on the `task` and `run` commands, see the [CLI task command](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/task.html) and [CLI run command](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/run.html).

> [!NOTE] 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 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"
```

> [!NOTE] Note
> The sample output above reflects an agent using the LLM gateway ( `USE_DATAROBOT_LLM_GATEWAY` is `"1"`). If you use the DataRobot Deployed LLM option instead, `USE_DATAROBOT_LLM_GATEWAY` is automatically set to `0`.

---
