databricks-agent-app-builder

name	databricks-agent-app-builder
description	Generate complete Databricks App projects with MLflow agent server for LangGraph, OpenAI Agents SDK, or non-conversational agents. Use when creating new AI agent applications for Databricks Apps, building conversational or stateless API agents, setting up MLflow tracing for agent observability, or deploying agents to Databricks workspace.

Databricks Agent App Builder

Generate production-ready Databricks App projects with MLflow AgentServer for AI agents.

When to Use This Skill

Use this skill when:

• Creating a new AI agent application for Databricks Apps
• Building conversational agents with LangGraph or OpenAI Agents SDK
• Building non-conversational (stateless) API agents
• Setting up MLflow tracing for agent observability
• Deploying agents to Databricks workspace

Skill Workflow

Step 1: Gather Requirements

Ask the user these questions to determine the project configuration:

1. Agent Type: "What type of agent are you building?"

   • Conversational: Chat-based agents with streaming support
   • Non-Conversational: Stateless API agents for discrete tasks

2. Framework (if conversational): "Which framework do you prefer?"

   • LangGraph: For complex multi-step workflows, state machines, conditional routing
   • OpenAI Agents SDK: For simpler conversational agents with direct MCP integration

3. Project Name: "What should I name the project?"

   • Use kebab-case (e.g., my-agent-app)

4. Agent Description: "Briefly describe what your agent does"

   • Used for instructions and documentation

5. Frontend (if conversational): "Do you want a production chat UI frontend?"

   • Yes: Will sparse checkout e2e-chatbot-app-next from app-templates repo
   • No: API-only deployment (can add frontend later)

Step 2: Generate Project Structure

Based on user selections, generate the following structure:

{project-name}/
├── agent_server/
│   ├── __init__.py
│   ├── agent.py              # Framework-specific agent logic
│   ├── start_server.py       # MLflow AgentServer setup
│   ├── utils.py              # Auth & stream utilities (conversational only)
│   └── evaluate_agent.py     # MLflow evaluation script
├── scripts/
│   ├── quickstart.sh         # Local setup wizard
│   └── deploy.sh             # Parameterized deployment script
├── app.yaml                  # Databricks Apps configuration
├── pyproject.toml            # Python dependencies (uv)
├── .env.example              # Environment template
├── .gitignore
└── README.md                 # Project documentation

Step 3: Generate Files

Use the templates in templates/ directory as reference. Customize based on:

• Framework selection (LangGraph / OpenAI Agents SDK / Non-Conversational)
• Agent description and purpose
• Project name

Step 4: Setup Frontend (Optional)

If the user requested a frontend for conversational agents, set it up via sparse checkout:

4.1 Sparse Checkout Frontend

# Create a temporary clone with sparse checkout
git clone --filter=blob:none --sparse https://github.com/databricks/app-templates.git temp-app-templates
cd temp-app-templates
git sparse-checkout set agent-langgraph/e2e-chatbot-app-next

# Move frontend to sibling directory
mv agent-langgraph/e2e-chatbot-app-next ../{project-name}-frontend
cd ..
rm -rf temp-app-templates

4.2 Configure Frontend

After checkout, configure the frontend to connect to the agent:

cd {project-name}-frontend

# Create .env.local with agent endpoint
cat > .env.local << 'EOF'
# Databricks Authentication
DATABRICKS_CONFIG_PROFILE=DEFAULT

# Agent Serving Endpoint (created by deploying the backend agent)
DATABRICKS_SERVING_ENDPOINT={agent-endpoint-name}

# Database (optional - for persistent chat history)
# Uncomment and configure if using Lakebase
# PGUSER=your-databricks-username
# PGHOST=your-lakebase-host
# PGDATABASE=databricks_postgres
# PGPORT=5432
EOF

4.3 Frontend Project Structure

The frontend is a production-ready full-stack application:

{project-name}-frontend/
├── client/                 # React + Vite frontend
├── server/                 # Express.js backend (BFF)
├── packages/               # Shared libraries
│   ├── core/              # Domain types, errors
│   ├── auth/              # Authentication utilities
│   ├── ai-sdk-providers/  # Databricks AI SDK integration
│   ├── db/                # Database layer (Drizzle ORM)
│   └── utils/             # Shared utilities
├── scripts/
│   ├── quickstart.sh      # Interactive setup wizard
│   └── start-app.sh       # Start development server
├── databricks.yml         # Asset Bundle configuration
└── package.json           # npm workspaces monorepo

4.4 Frontend Setup Options

Option A: Interactive Setup (Recommended)

cd {project-name}-frontend
./scripts/quickstart.sh

The quickstart script will:

• Install prerequisites (Node.js 20, Databricks CLI)
• Configure authentication
• Set up serving endpoint
• Optionally configure database for persistent chat history
• Deploy to Databricks (optional)

Option B: Manual Setup

cd {project-name}-frontend
npm install
npm run dev  # Starts on localhost:3000 (frontend) and localhost:3001 (backend)

4.5 Frontend Deployment

The frontend deploys separately via Databricks Asset Bundle:

cd {project-name}-frontend

# Update databricks.yml with your endpoint name
# Then deploy:
databricks bundle validate
databricks bundle deploy
databricks bundle run databricks_chatbot

4.6 Final Directory Structure

After setup, you'll have two sibling directories:

workspace/
├── {project-name}/           # Backend agent (Python/MLflow)
│   ├── agent_server/
│   ├── app.yaml
│   └── pyproject.toml
│
└── {project-name}-frontend/  # Chat UI (TypeScript/React)
    ├── client/
    ├── server/
    ├── databricks.yml
    └── package.json

This separation allows:

• Independent deployment cycles
• Different tech stacks (Python vs Node.js)
• Separate scaling and resource management

Code Generation Rules

Common Files (All Frameworks)

app.yaml

command: ["uv", "run", "start-server"]
# For conversational with frontend: ["uv", "run", "start-app"]

env:
  - name: MLFLOW_TRACKING_URI
    value: "databricks"
  - name: MLFLOW_REGISTRY_URI
    value: "databricks-uc"
  - name: MLFLOW_EXPERIMENT_ID
    valueFrom: experiment

start_server.py

import logging
from dotenv import load_dotenv
from mlflow.genai.agent_server import AgentServer, setup_mlflow_git_based_version_tracking

load_dotenv(dotenv_path=".env.local", override=True)

import agent_server.agent  # noqa: E402

# For conversational agents:
agent_server = AgentServer("ResponsesAgent", enable_chat_proxy=True)
# For non-conversational agents:
# agent_server = AgentServer()

app = agent_server.app

try:
    setup_mlflow_git_based_version_tracking()
except Exception as e:
    logging.warning(f"Git-based version tracking not available: {e}")

def main():
    agent_server.run(app_import_string="agent_server.start_server:app")

Framework-Specific: LangGraph

Dependencies (pyproject.toml)

dependencies = [
    "fastapi>=0.115.12",
    "uvicorn>=0.34.2",
    "databricks-langchain>=0.12.0",
    "mlflow>=3.8.0rc0",
    "langgraph>=1.0.1",
    "langchain-mcp-adapters>=0.1.11",
    "python-dotenv",
]

agent.py Pattern

from typing import AsyncGenerator
import mlflow
from databricks.sdk import WorkspaceClient
from databricks_langchain import ChatDatabricks, DatabricksMCPServer, DatabricksMultiServerMCPClient
from langchain.agents import create_agent
from mlflow.genai.agent_server import invoke, stream
from mlflow.types.responses import (
    ResponsesAgentRequest, ResponsesAgentResponse, ResponsesAgentStreamEvent,
    to_chat_completions_input,
)
from agent_server.utils import get_databricks_host_from_env, process_agent_astream_events

mlflow.langchain.autolog()

def init_mcp_client(workspace_client: WorkspaceClient) -> DatabricksMultiServerMCPClient:
    host_name = get_databricks_host_from_env()
    return DatabricksMultiServerMCPClient([
        DatabricksMCPServer(
            name="system-ai",
            url=f"{host_name}/api/2.0/mcp/functions/system/ai",
        ),
    ])

async def init_agent(workspace_client=None):
    mcp_client = init_mcp_client(workspace_client or WorkspaceClient())
    tools = await mcp_client.get_tools()
    return create_agent(tools=tools, model=ChatDatabricks(endpoint="databricks-claude-3-7-sonnet"))

@invoke()
async def non_streaming(request: ResponsesAgentRequest) -> ResponsesAgentResponse:
    outputs = [event.item async for event in streaming(request) if event.type == "response.output_item.done"]
    return ResponsesAgentResponse(output=outputs)

@stream()
async def streaming(request: ResponsesAgentRequest) -> AsyncGenerator[ResponsesAgentStreamEvent, None]:
    agent = await init_agent()
    messages = {"messages": to_chat_completions_input([i.model_dump() for i in request.input])}
    async for event in process_agent_astream_events(agent.astream(input=messages, stream_mode=["updates", "messages"])):
        yield event

Framework-Specific: OpenAI Agents SDK

Dependencies (pyproject.toml)

dependencies = [
    "fastapi>=0.115.12",
    "uvicorn>=0.34.2",
    "databricks-openai>=0.8.0",
    "mlflow>=3.8.0rc0",
    "openai-agents>=0.4.1",
    "python-dotenv",
]

agent.py Pattern

from typing import AsyncGenerator
import mlflow
from agents import Agent, Runner, set_default_openai_api, set_default_openai_client
from agents.tracing import set_trace_processors
from databricks_openai import AsyncDatabricksOpenAI
from databricks_openai.agents import McpServer
from mlflow.genai.agent_server import invoke, stream
from mlflow.types.responses import (
    ResponsesAgentRequest, ResponsesAgentResponse, ResponsesAgentStreamEvent,
)
from agent_server.utils import get_databricks_host_from_env, process_agent_stream_events

set_default_openai_client(AsyncDatabricksOpenAI())
set_default_openai_api("chat_completions")
set_trace_processors([])
mlflow.openai.autolog()

async def init_mcp_server():
    return McpServer(
        url=f"{get_databricks_host_from_env()}/api/2.0/mcp/functions/system/ai",
        name="system.ai uc function mcp server",
    )

def create_agent(mcp_server: McpServer) -> Agent:
    return Agent(
        name="agent-name",
        instructions="Your agent instructions here",
        model="databricks-claude-3-7-sonnet",
        mcp_servers=[mcp_server],
    )

@invoke()
async def invoke(request: ResponsesAgentRequest) -> ResponsesAgentResponse:
    async with await init_mcp_server() as mcp_server:
        agent = create_agent(mcp_server)
        messages = [i.model_dump() for i in request.input]
        result = await Runner.run(agent, messages)
        return ResponsesAgentResponse(output=[item.to_input_item() for item in result.new_items])

@stream()
async def stream(request: dict) -> AsyncGenerator[ResponsesAgentStreamEvent, None]:
    async with await init_mcp_server() as mcp_server:
        agent = create_agent(mcp_server)
        messages = [i.model_dump() for i in request.input]
        result = Runner.run_streamed(agent, input=messages)
        async for event in process_agent_stream_events(result.stream_events()):
            yield event

Framework-Specific: Non-Conversational

Dependencies (pyproject.toml)

dependencies = [
    "fastapi>=0.115.12",
    "uvicorn[standard]>=0.34.2",
    "mlflow>=3.7.0",
    "databricks-sdk>=0.63.0",
    "pydantic>=2.11",
    "python-dotenv",
]

agent.py Pattern

import json
import os
from databricks.sdk import WorkspaceClient
from mlflow.genai.agent_server import invoke
from pydantic import BaseModel, Field

w = WorkspaceClient()
openai_client = w.serving_endpoints.get_open_ai_client()

class AgentInput(BaseModel):
    # Define your input schema
    document_text: str = Field(..., description="Input text to process")

class AgentOutput(BaseModel):
    # Define your output schema
    result: str = Field(..., description="Processing result")

@invoke()
async def invoke(data: dict) -> dict:
    input_data = AgentInput(**data)

    # Your processing logic here
    llm_response = openai_client.chat.completions.create(
        model=os.getenv("LLM_MODEL", "databricks-claude-3-7-sonnet"),
        messages=[{"role": "user", "content": input_data.document_text}],
    )

    result = llm_response.choices[0].message.content
    return AgentOutput(result=result).model_dump()

Deployment Guide

Local Development

# First-time setup
./scripts/quickstart.sh

# Start server
uv run start-server

# Test API
curl -X POST http://localhost:8000/invocations \
  -H "Content-Type: application/json" \
  -d '{"input": [{"role": "user", "content": "hello"}]}'

Deploy to Databricks Apps

# Option 1: Use deploy.sh script
./scripts/deploy.sh my-app-name --create

# Option 2: Manual steps
databricks apps create my-app-name
DATABRICKS_USERNAME=$(databricks current-user me | jq -r .userName)
databricks sync . "/Users/$DATABRICKS_USERNAME/my-app-name"
databricks apps deploy my-app-name --source-code-path /Workspace/Users/$DATABRICKS_USERNAME/my-app-name

Query Deployed App

# Get OAuth token (PATs not supported)
databricks auth token

# Query
curl -X POST <app-url>/invocations \
  -H "Authorization: Bearer <oauth-token>" \
  -H "Content-Type: application/json" \
  -d '{"input": [{"role": "user", "content": "hello"}]}'

Frontend Integration

For conversational agents that need a chat UI, see Step 4: Setup Frontend (Optional) above.

Quick Summary:

• Source: https://github.com/databricks/app-templates → agent-langgraph/e2e-chatbot-app-next/
• Tech stack: React + Vite + Express.js + TypeScript + Tailwind CSS
• Requires: Node.js 20+, npm 8+
• Features:
  • Persistent chat history (optional, requires Lakebase)
  • Databricks authentication
  • Streaming responses via Vercel AI SDK
  • Databricks Asset Bundle deployment

Adding Frontend Later:

If the user initially chose API-only and wants to add frontend later:

# Navigate to parent of your agent project
cd /path/to/workspace

# Sparse checkout the frontend
git clone --filter=blob:none --sparse https://github.com/databricks/app-templates.git temp-clone
cd temp-clone
git sparse-checkout set agent-langgraph/e2e-chatbot-app-next
mv agent-langgraph/e2e-chatbot-app-next ../my-agent-frontend
cd .. && rm -rf temp-clone

# Configure and run
cd my-agent-frontend
./scripts/quickstart.sh

Reference Documentation

See the references/ directory for detailed patterns:

• mlflow-agent-server.md - AgentServer setup and decorators
• langgraph-patterns.md - LangGraph-specific patterns
• openai-agents-patterns.md - OpenAI Agents SDK patterns
• non-conversational-patterns.md - Stateless agent patterns
• deployment-guide.md - Complete deployment instructions