Coding Agents on Databricks Apps

Run Claude Code, Codex, Gemini CLI, and OpenCode in your browser — zero setup, wired to your Databricks workspace.

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Screenshots

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What's Inside

🟠 Claude Code — Anthropic's coding agent with 39 Databricks skills + 2 MCP servers

🟣 Codex — OpenAI's coding agent, pre-configured for Databricks

🔵 Gemini CLI — Google's coding agent with shared skills

🟢 OpenCode — Open-source agent with multi-provider support

Every agent installs at boot and connects to your Databricks AI Gateway — on first terminal session, paste a short-lived PAT and all CLIs are configured automatically. Token auto-rotates every 10 minutes.

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Why Databricks

This isn't just a terminal in the cloud. Running coding agents on Databricks gives you enterprise-grade infrastructure out of the box:

	Benefit	What you get
🔐	Unity Catalog Integration	All data access governed by UC permissions — agents can only touch what your identity allows
🤖	AI Gateway	Route all LLM calls through a single control plane — swap models, set rate limits, and manage API keys centrally
🔀	Multi-AI & Multi-Agent	Switch between Claude, GPT, Gemini, and open-source models on the fly — change the model or agent without redeploying
📊	Consumption Monitoring	Track token usage, cost, and latency per user and per model via the AI Gateway control center dashboard
🔍	MLflow Tracing	Every Claude Code session is automatically traced — review prompts, tool calls, and outputs in your MLflow experiment
🧬	Assess Traces with Genie	Point Genie at your MLflow traces to ask natural-language questions about agent behavior, cost patterns, and session quality
📝	App Logs to Delta	Optionally route application logs to Delta tables for long-term retention, querying, and dashboarding

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Terminal Features

🎨 8 Themes	Dracula, Nord, Solarized, Monokai, GitHub Dark, and more
✂️ Split Panes	Run two sessions side by side with a draggable divider
🌐 WebSocket I/O	Real-time terminal output over WebSocket — zero-latency, eliminates polling delay
🔁 HTTP Polling Fallback	Automatic fallback via Web Worker when WebSocket is unavailable
🚀 Parallel Setup	6 agent setups run in parallel (~5x faster startup)
🔍 Search	Find anything in your terminal history (Ctrl+Shift+F)
🎤 Voice Input	Dictate commands with your mic (Option+V)
📋 Image Paste	Paste or drag-and-drop images into the terminal — saved to ~/uploads/, path inserted automatically
⌨️ Customizable	Fonts, font sizes, themes — all persisted across sessions
🐍 Loading Screen	Play snake while setup steps run in parallel
🔄 Workspace Sync	Every git commit auto-syncs to /Workspace/Users/{you}/projects/
✏️ Micro Editor	Modern terminal editor, pre-installed
⚙️ Databricks CLI	Installed at boot, configured interactively on first session
📊 MLflow Tracing	Every Claude Code session is automatically traced to your Databricks MLflow experiment

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MLflow Tracing

Every Claude Code session is automatically traced to a Databricks MLflow experiment — zero configuration required.

How it works

Claude Code session starts
        │
        ▼
   Environment vars set automatically:
   MLFLOW_TRACKING_URI=databricks
   MLFLOW_EXPERIMENT_NAME=/Users/{you}/{app-name}
        │
        ▼
   You work normally — code, debug, deploy
        │
        ▼
   Session ends → Stop hook fires
        │
        ▼
   Full session transcript logged as an MLflow trace
   at /Users/{you}/{app-name} in your workspace

What gets traced

When a Claude Code session ends, the Stop hook automatically calls mlflow.claude_code.hooks.stop_hook_handler(), which captures the full session transcript — your prompts, agent actions, tool calls, and outputs — and logs it as an MLflow trace.

Where traces live

Traces are stored in a Databricks MLflow experiment at:

/Users/{your-email}/{app-name}

For example, if you're jane@company.com and your app is named coding-agents:

/Users/jane@company.com/coding-agents

View them in the Databricks UI: Workspace > Machine Learning > Experiments.

Configuration

Tracing is configured during app startup by setup_mlflow.py, which merges the following into ~/.claude/settings.json:

Setting	Value	Purpose
MLFLOW_CLAUDE_TRACING_ENABLED	true	Enables Claude Code tracing
MLFLOW_TRACKING_URI	databricks	Routes traces to Databricks backend
MLFLOW_EXPERIMENT_NAME	/Users/{owner}/{app}	Target experiment path
OTEL_EXPORTER_OTLP_ENDPOINT	""	Overrides container OTEL to prevent trace loss
Stop hook	uv run python -c "from mlflow.claude_code.hooks import stop_hook_handler; stop_hook_handler()"	Fires on session end

Tracing is skipped gracefully if APP_OWNER is not set (e.g., local dev without Databricks).

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Quick Start

Deploy to Databricks Apps

1. Click Use this template to create your own repo
2. Go to Databricks → Apps → Create App
3. Choose Custom App and connect your new repo
4. Deploy
5. Open the app — paste a short-lived PAT when prompted on first terminal session

That's it. No secrets to configure, no pre-deployment setup.

→ Full deployment guide — environment variables, gateway config, and advanced options.

Run locally

1. Click Use this template to create your own repo
2. Clone your new repo and run:

git clone https://github.com/<you>/<your-repo>.git
cd <your-repo>
uv run python app.py

Open http://localhost:8000 — type claude, codex, gemini, or opencode to start coding.

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Why This Exists

On Jan 26, 2026, Andrej Karpathy made this viral tweet about the future of coding. Boris Cherny, the creator of Claude Code, responded:

This template repo opens that vision up for every Databricks user — no IDE setup, no local installs. Click "Use this template", deploy to Databricks Apps, and start coding with AI in your browser.

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🧠 All 39 Skills

Databricks Skills (25) — ai-dev-kit

Category	Skills
AI & Agents	agent-bricks, genie, mlflow-eval, model-serving
Analytics	aibi-dashboards, unity-catalog, metric-views
Data Engineering	declarative-pipelines, jobs, structured-streaming, synthetic-data, zerobus-ingest
Development	asset-bundles, app-apx, app-python, python-sdk, config, spark-python-data-source
Storage	lakebase-autoscale, lakebase-provisioned, vector-search
Reference	docs, dbsql, pdf-generation
Meta	refresh-databricks-skills

Superpowers Skills (14) — obra/superpowers

Category	Skills
Build	brainstorming, writing-plans, executing-plans
Code	test-driven-dev, subagent-driven-dev
Debug	systematic-debugging, verification
Review	requesting-review, receiving-review
Ship	finishing-branch, git-worktrees
Meta	dispatching-agents, writing-skills, using-superpowers

🔌 2 MCP Servers

Server	What it does
DeepWiki	Ask questions about any GitHub repo — gets AI-powered answers from the codebase
Exa	Web search and code context retrieval for up-to-date information

🏗️ Architecture

┌─────────────────────┐  WebSocket    ┌─────────────────────┐
│   Browser Client    │◄═══════════►│   Gunicorn + Flask   │
│   (xterm.js)        │  (primary)    │   + Flask-SocketIO   │
│                     │───────────►│   (PTY Manager)      │
│                     │  HTTP Poll    │                     │
│                     │  (fallback)   │                     │
└─────────────────────┘               └─────────────────────┘
         │                                     │
         │ on first load                       │ on startup
         ▼                                     ▼
┌─────────────────────┐               ┌─────────────────────┐
│   Loading Screen    │               │   Background Setup  │
│   (snake game)      │               │   (8 steps, 6 ║)    │
└─────────────────────┘               └─────────────────────┘
                                               │
                                               ▼
                                      ┌─────────────────────┐
                                      │   Shell Process     │
                                      │   (/bin/bash)       │
                                      └─────────────────────┘

Startup Flow

1. Gunicorn starts, calls initialize_app() via post_worker_init hook
2. App immediately serves the loading screen (snake game)
3. Background thread runs setup: git config and micro editor run sequentially, then 6 agent setups (Claude, Codex, OpenCode, Gemini, Databricks CLI, MLflow) run in parallel via ThreadPoolExecutor
4. /api/setup-status endpoint reports progress to the loading screen
5. Once complete, the loading screen transitions to the terminal UI

API Endpoints

Endpoint	Method	Description
/	GET	Loading screen (during setup) or terminal UI
/health	GET	Health check with session count and setup status
/api/setup-status	GET	Setup progress for loading screen
/api/version	GET	App version
/api/session	POST	Create new terminal session
/api/input	POST	Send input to terminal
/api/output	POST	Poll for terminal output (single session)
/api/output-batch	POST	Batch poll output for multiple sessions
/api/heartbeat	POST	Lightweight keepalive (no buffer drain)
/api/resize	POST	Resize terminal dimensions
/api/upload	POST	Upload file (clipboard image paste)
/api/session/close	POST	Close terminal session

WebSocket Events (Socket.IO)

Event	Direction	Description
join_session	Client → Server	Join session room for output delivery
leave_session	Client → Server	Leave session room
terminal_input	Client → Server	Send keystrokes to PTY
terminal_resize	Client → Server	Resize terminal
heartbeat	Client → Server	Keepalive for idle sessions
terminal_output	Server → Client	Push PTY output in real time
session_exited	Server → Client	Shell process exited
session_closed	Server → Client	Session terminated by server
shutting_down	Server → Client	Server restarting (SIGTERM)

⚙️ Configuration

Environment Variables

Variable	Required	Description
DATABRICKS_TOKEN	No	Optional. If not set, the app prompts for a token on first session. Auto-rotated every 10 minutes
HOME	Yes	Set to /app/python/source_code in app.yaml
ANTHROPIC_MODEL	No	Claude model name (default: databricks-claude-opus-4-6)
CODEX_MODEL	No	Codex model name (default: databricks-gpt-5-2)
GEMINI_MODEL	No	Gemini model name (default: databricks-gemini-3-1-pro)
DATABRICKS_GATEWAY_HOST	No	AI Gateway URL (recommended)

Security Model

Single-user app — the owner is resolved via the app's service principal and Apps API (app.creator), with no PAT required at deploy time. Authorization checks X-Forwarded-Email against app.creator. On first terminal session, the user pastes a short-lived PAT interactively. Tokens auto-rotate every 10 minutes (15-minute lifetime), with old tokens proactively revoked. On restart, the user re-pastes (no persistence by design).

Gunicorn

Production uses workers=1 (PTY state is process-local), threads=16 (concurrent polling + WebSocket), gthread worker class, timeout=60 (long-lived WebSocket connections).

📁 Project Structure

coding-agents-in-databricks/
├── app.py                   # Flask backend + PTY management + setup orchestration
├── app.yaml.template        # Databricks Apps deployment config template
├── gunicorn.conf.py         # Gunicorn production server config
├── requirements.txt         # Python dependencies
├── setup_claude.py          # Claude Code CLI + MCP configuration
├── setup_codex.py           # Codex CLI configuration
├── setup_gemini.py          # Gemini CLI configuration
├── setup_opencode.py        # OpenCode configuration
├── setup_databricks.py      # Databricks CLI configuration
├── setup_mlflow.py          # MLflow tracing auto-configuration
├── sync_to_workspace.py     # Post-commit hook: sync to Workspace
├── install_micro.sh         # Micro editor installer
├── utils.py                 # Utility functions (ensure_https)
├── static/
│   ├── index.html           # Terminal UI (xterm.js + split panes + WebSocket)
│   ├── loading.html         # Loading screen with snake game
│   ├── poll-worker.js       # Web Worker for HTTP polling fallback
│   └── lib/
│       ├── xterm.js         # xterm.js terminal emulator
│       └── socket.io.min.js # Vendored Socket.IO client
├── .claude/
│   └── skills/              # 39 pre-installed skills
└── docs/
    ├── deployment.md        # Full Databricks Apps deployment guide
    ├── prd/                 # Product requirement documents
    └── plans/               # Design documentation

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Technologies

Flask · Flask-SocketIO · Socket.IO · Gunicorn · xterm.js · Python PTY · Databricks SDK · Databricks AI Gateway · MLflow