An open-source AI full-stack app platform built on Tencent CloudBase — conversational code generation, live preview, one-click deployment.

🔥 Open-source alternative to OpenAI Codex Sites
Self-hosted · Multi-framework · Multi-agent · Your code, your cloud, your data.

Quick Start ◆ Architecture ◆ Deployment ◆ Community ◆ 中文

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Overview

An open-source alternative to OpenAI Codex Sites / Lovable / v0 / bolt.new — an AI full-stack app development platform built on Tencent CloudBase. Describe what you want, the agent writes the code, you preview it live, and deploy with one click. Dual Agent runtimes (CodeBuddy / OpenCode), three-tier environment isolation, and full self-hosting on your own cloud.

Why this matters now: OpenAI's Codex Sites (June 2026) lets ChatGPT Business / Enterprise users describe a site and have Codex host it on OpenAI-managed Cloudflare Workers infrastructure. Great for closed-ecosystem productivity, but — closed source, framework-locked (Workers ES modules only), agent-locked (OpenAI only), data lives at OpenAI, requires a paid ChatGPT seat. This project gives you the same conversational create → preview → deploy loop, but fully open-source, on your own cloud, with any framework and any agent.

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News

Date	Player	What shipped
2026-06	This repo	Open-source self-hostable platform — same conversational create → preview → deploy on your cloud
2026-06	OpenAI	Codex Sites — describe → host on OpenAI-managed Cloudflare Workers (D1 + R2). Closed-source.
2025-08	Vercel	v0.dev rebranded to v0.app — AI builder positioned for non-developers as well
2024-11	Lovable	Public launch (pivoted from GPT-Engineer); Supabase integration
2024-10	StackBlitz	bolt.new launched — in-browser WebContainer dev loop
2024-09	Replit	Replit Agent launched (full-stack scaffold + deploy)
2024-06	Anthropic	Claude Artifacts shipped with Claude 3.5 Sonnet
2023-10	Vercel	v0.dev launched — generative UI from prompt

How we read this

According to Codex Sites' public materials: users invoke it via @Sites inside the Codex app to turn a natural-language description into a deployable website, web app, or game, hosted by OpenAI on a Cloudflare Workers-compatible runtime; D1 (database), R2 (object storage), and workspace-authenticated identity are available as optional bindings; the workflow is create → save a reviewable version → deploy to production; environment variables and access modes (admins_only / workspace_all / custom) are managed through the Sites panel in the sidebar.

This project implements: CodeBuddy / OpenCode dual agent runtimes, with CloudBase providing the database, object storage, Functions, domain, and CDN; MCP wires up tool calls; the sandbox runs on SCF + TCR container images (a stronger Agent Sandbox variant lives on the feature/stateful-infra branch); the main loop is create → live preview → one-click deploy, all running inside your own Tencent Cloud account.

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AI generation process

generate.mp4

Application showcase

preview.mp4

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Why this project

vs OpenAI Codex Sites

Codex Sites is closed-source, so we can only describe it from its public docs. The table below compares what each system openly states, not behind-the-scenes capability — feature parity is not the goal here.

	Codex Sites (per public docs)	This project (verifiable in repo)
Source code	Closed-source	Apache 2.0, full source in this repo
Hosting target	OpenAI-managed Cloudflare Workers	Your own Tencent CloudBase account
Data residency	OpenAI / Cloudflare (D1 + R2)	Your account — DB / Storage / Functions are yours
Build output	Workers-compatible ES modules	Any container-runnable stack (Next, Vite, Python, Go, …)
Agent runtime	OpenAI Codex	CodeBuddy SDK + OpenCode (ACP) — both swappable
Access requirement	ChatGPT Business / Enterprise seat	Self-hosted, no external subscription
WeChat Mini Program	Not advertised	Built-in deploy target with QR preview
Plugin / tool model	OpenAI plugin system	MCP — bring any MCP server

Things Codex Sites has that we don't yet: save-version-then-deploy two-stage flow, in-thread annotations, role-specific plugin packs, dedicated env / access-control settings UI. See News › How we read this for the honest take.

vs Lovable / v0 / bolt.new

These are closed SaaS products; the comparison below is at the level of how the platform itself is delivered, not feature-by-feature UX.

	Lovable / v0 / bolt.new	This project
Distribution	Hosted SaaS only	Source available, self-hostable (Apache 2.0)
Cost model	Usage-based / subscription	You pay your cloud bill directly
Infrastructure	Vendor's own cloud	Tencent CloudBase (DB / Storage / Functions / CDN)
Agent engine	Single built-in	CodeBuddy + OpenCode, swap from the UI
Sandbox	Platform-managed	CloudBase SCF + TCR container images, customize the runtime image
Deploy targets	Vendor-hosted only	Web CDN / WeChat Mini Program / custom domain
Extensibility	UI-only	Monorepo, decoupled FE/BE, MCP for tools

We're not claiming our UX is better than these — they've had years and a lot of polish. The point is shape: same conversational create → preview → deploy loop, but in a form you can read, fork, and run yourself.

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Feature highlights

Capability	Highlights
Dual Agent engines	Choose between CodeBuddy and OpenCode, each with its own model list, one-click switch from the UI
Three-tier isolation	shared / isolated (per user) / task (per-task subaccount), hot-switchable from Admin without restart
Environment pool	Pre-created CloudBase env + CAM + Policy; acquisition latency drops from minutes to milliseconds; fallback on miss
Coding sandbox	SCF container cold start → PTY terminal → Vite dev server with dynamic port; progress split into pull / ready / init
Live preview	Embedded browser toolbar (address bar / nav / refresh); HMR; auto-feedback loop on preview errors
Sub-workspaces	Multiple isolated scopes per session, independent dev servers, ports 5173–5199 dynamically allocated
CloudBase MCP	50+ tools covering DB, Storage, Functions, domains, security rules — Agent operates cloud resources directly
Human-in-Loop	Four-value tool confirmation (allow / always / deny / exit); inline AskUser form without breaking chat context
Plan mode	Auto-intercepts write operations; three-button decision (execute / refine / reject); cross-component state sharing
Tool rendering	10 dedicated renderers (Bash / Read / Write / Edit / Grep / Glob, etc.); Edit ships with built-in git-diff view
One-click deploy	Web static hosting → CDN; async WeChat Mini Program deploy; unified artifact aggregated in Deployments tab
Image generation	AI-generated images auto-uploaded to CloudBase hosting; CDN URL returned; rendered inline as Markdown
Git archive	Auto-push to remote on task end; branch by envId + directory by conversationId; in-memory credentials, no token leak
Resource dashboard	Embedded DB / Storage / SQL / Functions management inside the task detail page
Admin console	User management, env pool monitoring, provision mode config, audit logs
Scheduled tasks	Cron scheduling + distributed lock to prevent re-entry
Credential security	AES-256-CBC encrypted storage; STS scoped temporary credentials; logs restricted to static strings only

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Screenshots

Create a task, pick agent and model

Coding mode: chat on the left, live preview on the right

Chat UI: tool-call cards, phase indicator

Human-in-Loop: tool confirmation & asking the user

ToolConfirm	AskUserQuestion

Embedded CloudBase Dashboard

Deployment complete, view artifact

Artifact in chat	Deployments tab

Admin: environment pool management

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Quick Start

Prerequisites

• Node.js >= 18
• Docker
• A Tencent Cloud account (CloudBase environment + API credentials)
• A CodeBuddy API Key or OAuth config

One-shot init

git clone https://github.com/TencentCloudBase/OpenVibeCoding.git
cd OpenVibeCoding

# macOS / Linux / Git Bash / WSL
./init.sh

# Windows (make sure Node.js >= 18 and pnpm are installed first)
node scripts/init.mjs

The init script runs: Node.js check → pnpm install → .env.local generation → Docker check → CloudBase setup → dependency install → CodeBuddy auth → TCR setup → database init.

For detailed steps and troubleshooting, see docs/setup.md.

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Development

pnpm dev          # Start web (localhost:5174) and server (localhost:3001) together
pnpm dev:web      # Frontend only
pnpm dev:server   # Backend only

Production

pnpm build        # Build all packages
pnpm start        # Start prod server (port 3001, serves API and static files)

Deploy to CloudRun

This project supports one-click deployment to CloudBase CloudRun (container service). No local Docker required — the script uploads source code and Dockerfile to the cloud for building.

Prerequisites

• Completed ./init.sh initialization (TCB_ENV_ID, TCB_SECRET_ID, TCB_SECRET_KEY configured)
• CloudBase CLI installed: npm i -g @cloudbase/cli

One-click deploy

pnpm deploy:cloud

The script will:

1. Upload source + Dockerfile to CloudBase for cloud-side image building
2. Deploy as a CloudRun container service (service name: vibecoding-platform, port: 80)
3. Query and display the service access URL

After deployment

• Access URL format: https://{serviceName}-{id}.{region}.run.tcloudbase.com
• Build progress can be viewed in CloudBase Console → CloudRun → Service Details → Deploy Records
• Environment variables should be configured in the console's service settings

Common commands

# Code quality
pnpm type-check   # TypeScript type-check
pnpm lint         # ESLint
pnpm format       # Prettier

# Database
pnpm db:generate  # Generate migrations
pnpm db:push      # Push schema
pnpm db:studio    # Open Drizzle Studio

# TCR image registry
pnpm setup:tcr
pnpm setup:tcr --namespace my-app --local-image node:20

# OpenCode
pnpm opencode:setup   # Configure OpenCode provider and models

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Project structure

├── docs/
│   ├── setup.md                  # Setup walkthrough & troubleshooting
│   ├── architecture.md           # System architecture
│   └── scf-session-sharing.md    # SCF session sharing design
├── packages/
│   ├── web/                      # React 19 + Vite frontend
│   ├── server/                   # Hono backend: Auth, Agent orchestration, Sandbox
│   ├── dashboard/                # CloudBase resource UI (DB / Storage / Functions)
│   └── shared/                   # ACP protocol types, task / message schemas
├── scripts/
│   ├── init.mjs                  # Interactive init script
│   └── setup-tcr.mjs             # TCR image registry setup
└── init.sh                       # Quick entry

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Tech stack

Layer	Stack
Frontend	React 19, Vite, Tailwind CSS 4, shadcn/ui, Jotai
Backend	Hono, Node.js, Drizzle ORM
Database	CloudBase DB (primary), SQLite (local fallback)
AI	@tencent-ai/agent-sdk (CodeBuddy), OpenCode ACP
Sandbox	CloudBase SCF, TCR container images
Auth	JWE session, bcrypt, Arctic (OAuth)
Storage	CloudBase DB, local .jsonl, Git archive
Protocol	ACP (JSON-RPC 2.0 + SSE), MCP (Model Context Protocol)

Full module design, data flow, and API routes are in docs/architecture.md.

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Environment variables

Full variable reference is in docs/setup.md. Core variables:

# Encryption keys (auto-generated by init script)
JWE_SECRET=
ENCRYPTION_KEY=

# Auth
NEXT_PUBLIC_AUTH_PROVIDERS=local   # local | github | cloudbase

# CloudBase
TCB_SECRET_ID=
TCB_SECRET_KEY=
TENCENTCLOUD_ACCOUNT_ID=
TCB_ENV_ID=
TCB_PROVISION_MODE=shared          # shared | isolated | task

# TCR
TCR_NAMESPACE=
TCR_PASSWORD=
TCR_IMAGE=

# Optional
MAX_MESSAGES_PER_DAY=50
MAX_SANDBOX_DURATION=300
ANTHROPIC_API_KEY=
OPENAI_API_KEY=
GEMINI_API_KEY=
GIT_PERSONAL_AUTH=

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OpenCode model configuration

The project ships with an OpenCode ACP runtime. To use the OpenCode agent from the frontend, configure at least one model provider first.

Prerequisite: install the opencode CLI

npm i -g opencode-ai
# verify
opencode --version

One-shot setup

pnpm opencode:setup

The command will:

1. Call the Tencent CloudBase AI+ endpoint DescribeAIModels to fetch models
2. Walk you through configuring the Tencent CloudBase API Key
3. Take the complete config from the catalog and write it to .opencode/opencode.json (including npm / baseURL / models)
4. Append the API Key to packages/server/.env

Example output

// .opencode/opencode.json (auto-generated; fields pulled from models.dev)
{
  "$schema": "https://opencode.ai/config.json",
  "model": "cloudbase/deepseek-v4-flash",
  "provider": {
    "cloudbase": {
      "options": {
        "baseURL": "https://envId-xxxxxxx.api.tcloudbasegateway.com/v1/ai/cloudbase",
        "apiKey": "{env:CLOUDBASE_API_KEY}"
      },
      "models": {
        "glm-5": {
          "name": "glm-5"
        }
      }
    }
  }
}

# packages/server/.env gets the API Key appended
CLOUDBASE_API_KEY=eyJhbGciOiJS.xxxxxxxx

Why write the full fields instead of an empty object? The opencode child process also needs these settings on startup. With just {}, the child would have to fetch the catalog from models.dev itself to learn npm / baseURL / models, and a network failure would break it. Writing the full fields makes the config self-contained, with no runtime network dependency.

Advanced: custom provider / overrides

If you need to:

• Use a provider not in the built-in catalog (e.g. an internal LLM gateway, local Ollama)
• Override the catalog's default baseURL / headers (e.g. route through a regional mirror)
• Restrict which models are exposed via whitelist / blacklist
• Configure variants (e.g. Anthropic thinking budget)

Refer to .opencode/opencode.example.json and the OpenCode providers docs and edit .opencode/opencode.json manually.

Tip: the $schema field at the top of opencode.json enables auto-completion and hover docs in VS Code / Cursor — press Ctrl+Space while editing to inspect all available fields.

Re-running / adding providers

pnpm opencode:setup is idempotent and can be run multiple times:

• Existing providers are not overwritten (to preserve manual tweaks)
• Already-set env keys are not asked for again
• Providers with missing env are flagged at startup

CodeBuddy model configuration

By default the project uses CodeBuddy's (@tencent-ai/agent-sdk) official model service. To use custom AI models on CloudBase (e.g. DeepSeek, Hunyuan), configure as below.

One-shot setup

pnpm codebuddy:setup

The command will:

1. Call the Tencent CloudBase AI+ endpoint DescribeAIModels to fetch models enabled in the current environment
2. Check for CLOUDBASE_API_KEY; if missing, prompt for input and write it to packages/server/.env
3. Also set CODEBUDDY_USE_CUSTOM_MODELS=true
4. Generate packages/server/.config/.codebuddy/models.json for the SDK to read

Example output

// packages/server/.config/.codebuddy/models.json (auto-generated)
{
  "models": [
    {
      "id": "deepseek-v4-flash",
      "name": "deepseek-v4-flash",
      "vendor": "cloudbase",
      "apiKey": "${CLOUDBASE_API_KEY}",
      "url": "https://envId-xxxxxxx.api.tcloudbasegateway.com/v1/ai/cloudbase",
      "supportsToolCall": true,
      "supportsImages": true
    }
  ],
  "availableModels": ["deepseek-v4-flash"]
}

# packages/server/.env gets auto-appended
CLOUDBASE_API_KEY=eyJhbGciOiJS.xxxxxxxx
CODEBUDDY_USE_CUSTOM_MODELS=true

About the ${CLOUDBASE_API_KEY} placeholder: the apiKey field in models.json uses ${VAR_NAME} syntax, resolved at runtime by @tencent-ai/agent-sdk to the corresponding env value — avoids hard-coding secrets in config files.

Syncing & custom models

pnpm codebuddy:setup is idempotent:

• CloudBase models follow the API — if you add or remove models in the CloudBase console, re-running the script syncs models.json
• Already-set env keys are not asked for again

Manually adding custom models

To plug in non-CloudBase models (e.g. local Ollama, private LLM gateway), edit:

packages/server/.config/.codebuddy/models.json

Append a custom entry to the models array (note: do not set vendor to cloudbase, or the sync will overwrite it):

{
  "id": "my-custom-model",
  "name": "My Custom Model",
  "vendor": "custom",
  "apiKey": "${MY_API_KEY}",
  "url": "https://my-llm-gateway.example.com/v1/chat/completions",
  "supportsToolCall": true,
  "supportsImages": false
}

Make sure the matching env variable is defined in packages/server/.env, and set:

CODEBUDDY_USE_CUSTOM_MODELS=true

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Further reading

• Setup guide — init flow, env variables, verification checklist, troubleshooting
• Architecture — system layers, module design, key data flows
• SCF session sharing — sandbox session reuse design

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Contributing

1. Fork and create a feature branch (git checkout -b feature/xxx)
2. Before submitting, make sure these pass: pnpm type-check && pnpm lint && pnpm format
3. Open a Pull Request

Logging safety rule: every logger.* / console.* call must use static strings only — no ${dynamic values}. See AGENTS.md.

Acknowledgments

• coding-agent-template by Vercel
• CloudBase — cloud development infrastructure
• CodeBuddy — AI Agent
• Hono — lightweight web framework

Join the community

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License

Derived from coding-agent-template (Copyright 2025 Vercel, Inc.) under Apache License 2.0. See LICENSE and NOTICE.