SuperDev
SuperDev is a local-first desktop app + MCP server that gives AI coding agents (Claude Code, Codex, Cursor — anything MCP-compatible) access to the runtime: live and historical logs across services, breakpoints in already-running processes (same PID, no restart), real browser control, and DAG deploy pipelines — with human approval and an audit trail on protected writes.
README
<p align="center"> <img src="./docs/assets/readme/superdev-logo-v5-launch.svg" width="96" alt="SuperDev logo" /> </p>
<h1 align="center">SuperDev</h1>
<p align="center"> <strong>Your AI writes code — but in the real environment it's blind and handcuffed.</strong><br /> <strong>SuperDev gives it the full workbench: See (runtime + logs), Inspect (breakpoint debugging), Operate (browser + deploys).</strong><br /> Across every local and remote project, in one place. </p>
<p align="center"> <a href="https://gosuper.dev/"><strong>gosuper.dev</strong></a> · <a href="#why-superdev">Why</a> · <a href="#see-inspect-operate">See · Inspect · Operate</a> · <a href="#quick-start">Quick start</a> · <a href="#architecture">Architecture</a> · <a href="./README.zh-CN.md">简体中文</a> </p>
<p align="center"> <a href="https://gosuper.dev/"> <img alt="SuperDev runtime console" src="./docs/assets/readme/screenshot-en.png" width="760" /> </a> </p>
Watch the workflow: choose an AI agent, install MCP, add a host, let AI create the project, services, environments, pipeline and deployment, approve operations, and arrive at the shared runtime overview: gosuper.dev/#demo.
<p align="center"> <img alt="Platform: macOS first" src="https://img.shields.io/badge/platform-macOS%20first-111827" /> <img alt="Tauri" src="https://img.shields.io/badge/Tauri-2.x-24C8DB" /> <img alt="Vue" src="https://img.shields.io/badge/Vue-3-42b883" /> <img alt="Go" src="https://img.shields.io/badge/Go-agent-00ADD8" /> <img alt="MCP" src="https://img.shields.io/badge/MCP-ready-7C3AED" /> <img alt="Local first" src="https://img.shields.io/badge/local--first-yes-16A34A" /> <a href="https://github.com/Xsxdot/super-dev/actions/workflows/ci.yml"><img alt="CI" src="https://github.com/Xsxdot/super-dev/actions/workflows/ci.yml/badge.svg" /></a> </p>
Why SuperDev
AI coding tools can read, edit, and run code. But that only answers "what's in the repo." Real development happens in the runtime: which services are up, which ports are taken, which logs belong to this feature, where a bug actually is, and whether the frontend you just changed even renders.
A human developer has a full toolbox for this — a process list, a log viewer, a debugger that stops on a line, a browser to click through the UI. Your AI has almost none of it. It writes the code, then goes blind: it can't see the running service, can't step through the bug, can't tell if the page broke. So it guesses, restarts services it can't see, and asks you to paste logs.
SuperDev gives AI the same workbench you use — See, Inspect, Operate — over one source of runtime truth that the desktop app and AI share, across every local and remote project.
See, Inspect, Operate
A human developer doesn't just read code — they watch it run, stop it mid-flight to inspect state, and drive the UI to confirm it works. SuperDev gives AI all three.
👁 See — runtime and logs
AI sees the services already running (no shadow copies, no port contention), and reads the same live/historical logs you do — cross-service search, context lookup, folding, bookmarks — instead of asking you to paste fragments. Diagnostics give deterministic evidence; AI owns the root-cause reasoning.
🔬 Inspect — breakpoint debugging
When logs aren't enough, AI attaches to a running managed process (no restart, same pid), stops on a source line, and reads the call stack, scopes, and variables in one call. Default support covers Go, Python, Rust, C/C++; experimental support covers Node and Java/Kotlin (bring your own / configured adapter). Exposed over MCP as list_code_debug_targets and debug_capture_at.
🎮 Operate — browser and deploys
AI changes the frontend, then verifies it itself: navigate, click, type, screenshot, read console logs and network requests, and (when authorized) evaluate JS in the page — driven through Playwright. For shipping, AI runs the same DAG pipelines you do. Every real-environment action stays behind preflight, approval, one-time tokens, and audit.
Highlights
See / Inspect / Operate is what AI does. Two things make it trustworthy and continuous — one shared source of runtime truth and safe operations. Everything below serves those.
🤝 Shared runtime, no competing services
- AI observes existing services, ports, logs, and deployments before deciding whether it needs to request an action.
- Avoid shadow environments, port contention, duplicate processes, and split runtime state.
- Follow one feature across local services, log changes, pipeline runs, remote deployments, and ingress.
- Treat production errors as shared runtime context, not pasted text detached from the system that produced it.
🔒 Safe AI operations
superdev-mcpships with the desktop app and connects to the local agent athttp://127.0.0.1:57017by default.- The bundled SuperDev skill teaches AI to build a global view first, collect evidence, reason explicitly, and only then execute safely.
- Runtime writes call
start/stop/restartdirectly; when approval is required, MCP waits for desktop approval by default and resumes with a one-time token. - Approval tokens are bound to an operation fingerprint, expire quickly, are single-use, and cannot be reused for a different target.
- Approvals, rejections, executions, and failures are recorded locally for audit.
- Browser control (navigate / click / evaluate) runs under the same model:
openrequests approval,evaluateis gated by a trust toggle rather than per-call prompts, and control actions are redacted in the audit log.
Capabilities built around those two
| Capability | What it does |
|---|---|
| Unified runtime console | See local processes, Launchd jobs, systemd services, Docker containers, and remote-host deployments in one model; choose managed control or monitor-only; desktop UI and MCP share one source of truth. |
| Logs & diagnostics | Live / historical logs, cross-service search, context lookup, filter rules, split panels, synchronized recording, bookmark ranges, repeated-log folding. Diagnostics give deterministic evidence; AI owns the root-cause reasoning. |
| AI breakpoint debugging | When logs aren't enough, AI attaches to a running managed process (no restart, same pid), stops on a source line, and reads call stack / scopes / variables in one call. Default support: Go, Python, Rust, C/C++. Experimental (bring/configure adapter): Node, Java/Kotlin. Exposed over MCP as list_code_debug_targets / debug_capture_at. |
| Browser control | AI drives the running frontend through Playwright: browser_navigate / browser_click / browser_type / browser_screenshot / browser_set_viewport / browser_console_logs / browser_network_requests / browser_evaluate, plus snapshot, reload, wait-for-selector, press-key, select-option. Verifies its own UI changes; control actions stay under approval + redacted audit. |
| Production-minded pipelines | DAG pipelines, reusable templates, variables, artifacts, run history, replayable run logs; built-in Go / Node / Python / Java / Rust / PHP / Vue+Go templates; systemd uses a release/current layout, rollback reuses the same path. |
| Declarative ingress | Pipelines deliver artifacts repeatedly; Ingress converges long-lived edge state: domains, DNS, reverse proxy, HTTPS, managed certs. Supports nginx, manual DNS, Cloudflare, Aliyun, ACME DNS-01, and orphan detection. |
Zero-touch onboarding
- Choose any detected built-in Connector on first launch. Eight verified built-ins are available: Claude Code, Codex, Cursor, OpenCode, OpenClaw, Hermes, Kimi Code, and Grok.
- Full support (Claude Code, Codex, Cursor, Hermes, Grok): automatic MCP + Skill + Session Hook.
- Standard support (OpenCode, OpenClaw, Kimi Code): automatic MCP + Skill, with a manual Session Hook step.
- Unknown compatible local Agents can still use Connector/manual materials without a hard-coded whitelist.
- Pi is deferred from this wave and is not registered as a built-in Connector.
- Other local MCP-capable Agents can be connected manually with the standard stdio instructions.
- Cloud or isolated-sandbox Agents cannot reach the local
127.0.0.1endpoint; they require a future authenticated Remote MCP Gateway and are not presented with a misleading local configuration. - Install the MCP connection and the SuperDev guide skill from the desktop app.
- Seed a local
superdev-sampleproject automatically. - Copy one prompt to AI and watch the full loop: inspect logs, request approval, approve in the desktop app, and let MCP continue automatically.
Quick Start
Run from source
git clone https://github.com/Xsxdot/super-dev.git
cd super-dev
cd desktop
pnpm install
pnpm tauri dev
macOS app download instructions will be added after the first public release package is verified. This README intentionally avoids unverified release links.
Try the AI safety demo
- Open SuperDev.
- Pick a detected Connector during onboarding, or open the manual setup instructions for another local MCP Agent.
- Install the MCP connection.
- Copy the generated prompt into your AI coding agent.
- When AI asks to restart the sample service, approve it in SuperDev Operation Approvals.
- MCP fetches the one-time approval token and continues the restart; AI reads logs again and explains the WARN/ERROR lines.
Architecture
flowchart TB
AI["Built-in or manually configured local Agent"] --> MCP["superdev-mcp"]
MCP --> Agent["Local SuperDev Agent (Go)"]
Desktop["Desktop UI (Tauri + Vue)"] --> Agent
Agent --> Runtime["Runtime Control"]
Agent --> Logs["Logs & Diagnostics"]
Agent --> Pipelines["Pipelines & Artifacts"]
Agent --> Ingress["Ingress / DNS / HTTPS"]
The important boundary is the local agent. It is the runtime gateway and source of truth. MCP does not bypass the agent to edit config files, SQLite, processes, or remote hosts. The safety gate is enforced in the agent layer, not merely suggested by prompts.
Examples and Templates
examples/ contains small projects used to validate built-in pipeline templates:
| Example | Template | Runtime |
|---|---|---|
go-http |
go-binary-build |
Go binary |
node-http |
node-standard-build |
Node |
python-http |
python-standard-build |
Python |
java-springboot |
java-maven-build |
Java Spring Boot |
rust-http |
rust-cargo-build |
Rust binary |
php-http |
php-standard-build |
PHP built-in server |
vue-go-combined |
vue-go-combined-build |
Go serving Vue dist |
mcp-log-lab |
runtime/log diagnostics fixture | Go command services |
Ingress examples live in examples/ingress/ and cover manual DNS, Cloudflare, Aliyun, nginx, and TLS declarations.
Status and Roadmap
SuperDev is approaching its first open-source release. The current focus is macOS desktop usage and local-first workflows.
- Available: Tauri desktop app, Go local agent, MCP server, SuperDev skill, multi-service logs, breakpoint debugging (Go/Python/Rust/C++ by default; Node and Java/Kotlin experimental), browser control (Playwright-driven), operation approvals, pipeline templates, ingress, and zero-touch onboarding.
- Near term: verified release packaging, final README screenshots, more pipeline templates, a smoother remote agent / tunnel experience, and a richer release walkthrough.
- Principle: local-first by default. AI can participate in operations, but writes must remain preflighted, approved, token-bound, and auditable.
Platform Support
| Platform | Local desktop app | Remote agent install target | Go/Python/Rust/C/C++ debugging | Node debugging | JVM debugging |
|---|---|---|---|---|---|
macOS (darwin) |
Supported with Tauri sidecars. | superdev-agent-darwin-amd64 and superdev-agent-darwin-arm64. |
Supported by the default attach flow when the language debugger is installed. | Experimental attach via SIGUSR1 inspector activation. |
Experimental; bring or configure the JVM adapter. |
| Linux | Covered by desktop-linux CI packaging. |
superdev-agent-linux-amd64 and superdev-agent-linux-arm64. |
Supported by the default attach flow when the language debugger is installed. | Experimental attach via SIGUSR1 inspector activation. |
Experimental; bring or configure the JVM adapter. |
| Windows | Covered by desktop-windows CI packaging with .exe sidecars. |
superdev-agent-windows-amd64.exe. |
Supported by the default attach flow when the matching debugger toolchain is available. | Experimental prearm flow using --inspect=0 instead of Unix signals. |
Experimental; bring or configure the JVM adapter. |
Development
# Desktop
cd desktop
pnpm install --frozen-lockfile
pnpm build # Frontend type-check + vite build (does not package the desktop app)
pnpm test # Frontend unit tests (vitest)
pnpm tauri build # Package the macOS desktop app (produces an installer, does not launch it)
# Agent
cd agent
go test ./...
pnpm buildonly builds frontend assets. To package the full macOS desktop app usepnpm tauri build; to just run it and see it live usepnpm tauri dev.
Tauri builds package sidecar binaries through desktop/scripts/build-agent.sh: superdev-agent, superdev-mcp, and superdev-sample.
Open Source Governance
- Contribution guide: CONTRIBUTING.md
- Security reports: SECURITY.md
- Versioning and releases: docs/release.md
- Changelog: CHANGELOG.md
Contributing
Contributions are welcome: pipeline templates, runtime adapters, log diagnostics, ingress providers, documentation, and example projects. Please include reproducible verification commands in PRs when possible. For changes that touch runtime writes, preserve the preview, approval, and audit boundaries.
License
See LICENSE.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。