quickstart

name	quickstart
description	Set up TypeScript LangChain agent development environment. Use when: (1) First time setup, (2) Configuring Databricks authentication, (3) User says 'quickstart', 'set up', 'authenticate', or 'configure databricks', (4) No .env file exists.

Quickstart & Authentication

Prerequisites

• Node.js 18+
• npm (comes with Node.js)
• Databricks CLI v0.283.0+

Check CLI version:

databricks -v  # Must be v0.283.0 or above
brew upgrade databricks  # If version is too old

Run Quickstart

npm run quickstart

This interactive wizard will:

1. Detect existing Databricks CLI authentication
2. Configure model endpoint
3. Create MLflow experiment
4. Set up MCP tools (optional)
5. Install dependencies
6. Create .env file

What Quickstart Configures

Creates/updates .env with:

• DATABRICKS_HOST - Workspace URL
• DATABRICKS_TOKEN - Personal access token
• DATABRICKS_MODEL - Model serving endpoint name
• MLFLOW_TRACKING_URI - Set to databricks
• MLFLOW_EXPERIMENT_ID - Auto-created experiment ID
• ENABLE_SQL_MCP - SQL MCP tools enabled/disabled

Manual Authentication (Fallback)

If quickstart fails:

# Create new profile
databricks auth login --host https://your-workspace.cloud.databricks.com

# Verify
databricks auth profiles

Then manually create .env (copy from .env.example):

# Databricks Authentication
DATABRICKS_HOST=https://your-workspace.cloud.databricks.com
DATABRICKS_TOKEN=dapi...

# Model Configuration
DATABRICKS_MODEL=databricks-claude-sonnet-4-5
USE_RESPONSES_API=false
TEMPERATURE=0.1
MAX_TOKENS=2000

# MLflow Tracing
MLFLOW_TRACKING_URI=databricks
MLFLOW_EXPERIMENT_ID=<your-experiment-id>

# Server Configuration
PORT=8000

# MCP Configuration (Optional)
ENABLE_SQL_MCP=false

TypeScript-Specific Setup

Install Dependencies

npm install

Build

npm run build

This compiles TypeScript to JavaScript in the dist/ directory.

Next Steps

After quickstart completes:

1. Run npm run dev to start the development server (see run-locally skill)
2. Test the agent with curl http://localhost:8000/health
3. Deploy to Databricks with databricks bundle deploy -t dev (see deploy skill)

Available Models

Common Databricks foundation models:

• databricks-claude-sonnet-4-5 (Claude Sonnet 4.5)
• databricks-gpt-5-2 (GPT-5.2)
• databricks-meta-llama-3-3-70b-instruct (Llama 3.3 70B)

Or use your own custom model serving endpoint.

Troubleshooting

"Databricks CLI not found"

Install the Databricks CLI:

brew install databricks
# OR
curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh

"Cannot find experiment"

Create the experiment manually:

databricks experiments create \
  --experiment-name "/Users/$(databricks current-user me --output json | jq -r .userName)/agent-langchain-ts"

"Module not found" errors

Ensure dependencies are installed:

npm install