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SKILL.md
npx skills add databricks/databricks-agent-skills --skill databricks-apps-python
Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.
npx skills add databricks/databricks-agent-skills --skill databricks-apps-python
First, confirm this skill is the right one. The default for new Databricks Apps is databricks-apps (AppKit — Node.js + TypeScript + React SDK). Load that skill first unless the user explicitly asks for a Python backend, is extending an existing Python app, or the team is Python-only. Everything below is the Python-backend alternative.
Config() for authentication (never hardcode tokens)app.yaml valueFrom for resources (never hardcode resource IDs)dash-bootstrap-components for Dash app layout and styling@st.cache_resource for Streamlit database connectionsCopy this checklist and verify each item:
| Framework | Best For | app.yaml Command |
|---|---|---|
| FastAPI (default) | Any Python backend by default — async APIs, auto-generated OpenAPI docs, JSON-serving apps | ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"] |
| Flask | Custom REST APIs, lightweight apps, webhooks | ["gunicorn", "app:app", "-w", "4", "-b", "0.0.0.0:8000"] |
| Dash | Production dashboards, BI tools, complex interactivity | ["python", "app.py"] |
| Streamlit | Rapid prototyping, data science apps, internal tools where the UI is a series of Python widgets | ["streamlit", "run", "app.py"] |
| Gradio | ML demos, model interfaces, chat UIs | ["python", "app.py"] |
| Reflex | Full-stack Python apps without JavaScript | ["reflex", "run", "--env", "prod"] |
Default: FastAPI. Reach for FastAPI unless the user explicitly asks for Streamlit-style widget prototyping (Streamlit), a heavy dashboard grid (Dash), or a Gradio-style ML demo. FastAPI pairs naturally with a JS/HTML frontend or a JSON-consuming caller — the same posture databricks-apps uses on the Node side.
| Concept | Details |
|---|---|
| Runtime | Python 3.11, Ubuntu 22.04, 2 vCPU, 6 GB RAM |
| Pre-installed | Dash 2.18.1, Streamlit 1.38.0, Gradio 4.44.0, Flask 3.0.3, FastAPI 0.115.0 |
| Auth (app) | Service principal via Config() — auto-injected DATABRICKS_CLIENT_ID/DATABRICKS_CLIENT_SECRET |
| Auth (user) | x-forwarded-access-token header — see references/1-authorization.md |
| Resources | valueFrom in app.yaml — see references/2-app-resources.md |
| SDK / Foundation Models / Vector Search / Model Serving | Use the databricks-python-sdk skill — same WorkspaceClient and OpenAI-compatible foundation-model patterns work inside a Databricks App. For UC model services (system.ai.*), see the model-service note in references/2-app-resources.md and the databricks-unity-gateway skill |
| Docs | https://docs.databricks.com/dev-tools/databricks-apps/ |
Authorization: Use references/1-authorization.md when configuring app or user authorization — covers service principal auth, on-behalf-of user tokens, OAuth scopes, and per-framework code examples. (Keywords: OAuth, service principal, user auth, on-behalf-of, access token, scopes)
App resources: Use references/2-app-resources.md when connecting your app to Databricks resources — covers SQL warehouses, Lakebase, model serving, secrets, volumes, and the valueFrom pattern. (Keywords: resources, valueFrom, SQL warehouse, model serving, secrets, volumes, connections)
Frameworks: See references/3-frameworks.md for Databricks-specific patterns per framework — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex — with auth integration and deployment commands. (Keywords: FastAPI, Flask, Dash, Streamlit, Gradio, Reflex, framework selection)
Deployment: Use references/4-deployment.md when deploying your app — covers Databricks CLI, Asset Bundles (DABs), app.yaml configuration, and post-deployment verification. (Keywords: deploy, CLI, DABs, asset bundles, app.yaml, logs)
Lakebase: Use references/5-lakebase.md when using Lakebase (PostgreSQL) as your app's data layer — covers auto-injected env vars, psycopg2/asyncpg patterns, and when to choose Lakebase vs SQL warehouse. (Keywords: Lakebase, PostgreSQL, psycopg2, asyncpg, transactional, PGHOST)
CLI commands: Use references/6-cli-approach.md for managing app lifecycle via CLI — covers creating, deploying, monitoring, and deleting apps. (Keywords: CLI, create app, deploy app, app logs)
Foundation Models / SDK / Vector Search / Model Serving: Use the databricks-python-sdk skill for the OpenAI-compatible foundation-model client, WorkspaceClient calls, Vector Search, and model-serving invocation — the same patterns apply inside a Databricks App. The examples in this skill's examples/ folder (fm-minimal-chat.py, fm-parallel-calls.py, fm-structured-outputs.py, llm_config.py) show the App-side wiring only. For UC model services (system.ai.*, called on /ai-gateway/mlflow/v1), see the model-service note in references/2-app-resources.md and the databricks-unity-gateway skill.
Determine the task type:
New app from scratch? → Load databricks-apps first (AppKit / Node). Only stay in this skill if the user explicitly asks for a Python backend.
Python-backend confirmed? → Python Framework Selection — default to FastAPI.
Setting up authorization? → Read references/1-authorization.md
Connecting to data/resources? → Read references/2-app-resources.md
Using Lakebase (PostgreSQL)? → Read references/5-lakebase.md
Deploying to Databricks? → Read references/4-deployment.md
Using CLI for app lifecycle? → Read references/6-cli-approach.md
Calling foundation model / LLM APIs, Vector Search, or model-serving endpoints? → Load the databricks-python-sdk skill. This skill's examples/ folder shows only the App-side wiring on top of those SDK patterns.
Follow the instructions in the relevant guide.
All Python Databricks apps follow this pattern:
| Issue | Solution |
|---|---|
| Connection exhausted | Use @st.cache_resource (Streamlit) or connection pooling |
| Auth token not found | Check x-forwarded-access-token header — only available when deployed, not locally |
| App won't start | Check app.yaml command matches framework; check databricks apps logs <name> |
| Resource not accessible | Add resource via UI, verify SP has permissions, use valueFrom in app.yaml |
| Import error on deploy | Add missing packages to requirements.txt (pre-installed packages don't need listing) |
| Lakebase app crashes on start | psycopg2/asyncpg are NOT pre-installed — MUST add to requirements.txt |
| Port conflict | Apps must bind to DATABRICKS_APP_PORT env var (defaults to 8000). Never use 8080. Streamlit is auto-configured; for others, read the env var in code or use 8000 in app.yaml command |
| Streamlit: set_page_config error | st.set_page_config() must be the first Streamlit command |
| Dash: unstyled layout | Add dash-bootstrap-components; use dbc.themes.BOOTSTRAP |
| Slow queries | Use Lakebase for transactional/low-latency; SQL warehouse for analytical queries |
| Constraint | Details |
|---|---|
| Runtime | Python 3.11, Ubuntu 22.04 LTS |
| Compute | 2 vCPUs, 6 GB memory (default) |
| Pre-installed frameworks | Dash, Streamlit, Gradio, Flask, FastAPI, Shiny |
| Custom packages | Add to requirements.txt in app root |
| Network | Apps can reach Databricks APIs; external access depends on workspace config |
| User auth | GA, on by default — no admin enablement required; declare user_api_scopes to add scopes |
WorkspaceClient, OpenAI-compatible foundation-model client, Vector Search, model-serving invocation; the same patterns work inside a Databricks Appimport os
from databricks.sdk.core import Config
USE_MOCK = os.getenv("USE_MOCK_BACKEND", "true").lower() == "true"
if USE_MOCK:
from backend_mock import MockBackend as Backend
else:
from backend_real import RealBackend as Backend
backend = Backend()from databricks.sdk.core import Config
from databricks import sql
cfg = Config() # Auto-detects credentials from environment
conn = sql.connect(
server_hostname=cfg.host,
http_path=f"/sql/1.0/warehouses/{os.getenv('DATABRICKS_WAREHOUSE_ID')}",
credentials_provider=lambda: cfg.authenticate,
)from pydantic import BaseModel, Field
from datetime import datetime
from enum import Enum
class Status(str, Enum):
ACTIVE = "active"
PENDING = "pending"
class EntityOut(BaseModel):
id: str
name: str
status: Status
created_at: datetime
class EntityIn(BaseModel):
name: str = Field(..., min_length=1)
status: Status = Status.PENDING