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PrimeIntellect-ai/prime-agent

A self-improving RLM agent for coding workflows and long-running autonomous tasks.

Prime Agent: A Self-Improving RLM Agent

Documentation • Verifiers • PRIME-RL • pi-mono

Prime Agent is an open-source coding and research agent for general and long-running work. It is designed around two core abstractions:

The Recursive Language Model (RLM) treats context as variables (prompt-as-a-variable) and tools like recursive subagents as function calls (programmatic tool /sub-agent calling) inside a persistent REPL.
The Continual Harness stores supplemental prompts, memories, skill descriptions, and reusable subagent specifications as durable state that Prime Agent can refine through small, evidence-backed updates, local to the session by default.

Prime Agent combines a persistent Python control environment with durable harness state, so useful working context and reusable operating patterns can outlive a single chat window.

Everything is programmatic: persistent IPython is the built-in model tool; file operations, shell commands, tool use, subagents, and context management happen through code.
Subagents are built in: rlm(...) spawns real child agents for parallel or background work and returns their results programmatically.
The harness can improve: /refine reviews the current trajectory and can apply small, evidence-backed updates to supplemental harness state. It never rewrites the immutable base system prompt, and recorded snapshots support rollback.
Skills are executable: skills are importable Python packages, and the built-in skill creator can turn recurring workflows into project or personal skills.
Sessions run in the background: daemon-backed agents keep running when the terminal disconnects and can be reattached later.
Agents communicate directly: running agents can exchange messages and orchestrate one another without routing everything through the user.
Long tasks keep moving: automatic compaction, persistent goals, heartbeats, schedules, autonomous mode, and retained subagents preserve progress across turns and terminal sessions.

Getting Started

Install the latest stable release on macOS or Linux:

curl -fsSL https://app.primeintellect.ai/prime-agent/install.sh | sh

The installer downloads a versioned release, verifies its SHA-256 checksum, installs the prime-agent command, and can prepare the IPython runtime used by the agent.

Start Prime Agent from the repository or directory you want it to work in:

cd /path/to/project
prime-agent

On first launch, run /login to choose a subscription or API-key provider. Prime Agent works in the current directory and can run commands and modify files there. Use a disposable clone, clean worktree, or another checkpoint you can inspect and restore.

Warning

Prime Agent executes model-generated Python and project commands with your user permissions. Its worker and kernel processes improve lifecycle isolation and recovery; they are not a security sandbox. Review changes and use trusted repositories, instructions, skills, and extensions only. Run untrusted code or instructions in an external sandbox or restricted environment.

Useful commands:

prime-agent agents                   # Browse running, idle, and saved sessions
prime-agent attach <agent>           # Reattach to a running session
prime-agent --resume <path|id>       # Resume a saved session
prime-agent status                   # Inspect background service state
prime-agent doctor [--fix]           # Inspect or repair background services
prime-agent update [--force]         # Update Prime Agent
prime-agent shutdown [--force]       # Stop every agent, worker, and background service

Built for Long-Running Work

Prime Agent is built for long-running work, especially for evaluations in research. These features are available in the TUI, and when run autonomously.

Continual Harness: /refine can persist focused, reviewable lessons as supplemental prompts, memories, reusable skill descriptions, or subagent specifications, with recorded refinement history. It does not replace packaging and reviewing new executable skills.
Direct agent-to-agent communication: running agents and retained subagents can discover one another, exchange messages, and steer active work.
Daemon-backed continuity: active sessions, IPython state, schedules, and subagents keep running when the terminal detaches and can be reattached later.
Heartbeats and schedules: /heartbeat, rlm_heartbeat, and prime-agent schedule can re-enter a session periodically or at a specific time.
Persistent goals: /goal keeps an objective and its progress active across turns until it is completed, paused, or cleared.
Bounded autonomous mode: /autonomous continues within configured turn, token, and time budgets and can run user-defined quality gates. A passed gate checks only what that gate verifies; reaching a limit does not imply task success.

Documentation

Quickstart — install, authenticate, and run a first session
Usage and CLI reference — commands, sessions, autonomous limits, and output modes
Long-running and background agents — detach and reattach, goals, heartbeats, and schedules
RLM programming model — persistent IPython, subagents, skills, and the trust model
JSON mode and RPC mode — headless automation and integrations
Skills — install and create reusable capabilities
Provider setup — subscription and API-key providers
Architecture overview — daemon, worker, kernel, and persistence boundaries
Development — build and run from source

Acknowledgements

Our agent and TUI is built on top of pi. We thank the authors of pi for their valuable work.

License

Prime Agent is fully open source and released under the MIT License.

pingdotgg/t3code

T3 Code

T3 Code is an "agent harness control surface". It enables control of the agents on your machine with a best-in-class mobile app (iOS, Android), web app and Electron-based desktop app.

Works with your subscriptions on Claude Code, Codex, Cursor, Grok Build, and OpenCode. If they're set up on your computer, T3 Code can control them.

"Wait, what are you selling me?"

Nothing. We built T3 Code because we wanted the best possible development experience with agents. We were inspired by existing solutions like the Codex desktop app, Conductor, Claude Desktop and Cursor Glass, but none met our bar.

We wanted something performant, remote-ready, and truly open. If we ever go the wrong direction, we want you to have everything you need to fork and build the editor that you want.

Installation

Warning

T3 Code currently supports Codex, Claude, Cursor, Grok Build and OpenCode. Install and authenticate at least one provider before use:

Codex: install Codex CLI and run codex login
Claude: install Claude Code and run claude auth login
Cursor: install Cursor CLI and run agent login
Grok Build: install Grok Build CLI and run grok login
OpenCode: install OpenCode and run opencode auth login

Try it out (install-free)

The easiest way to test T3 Code is to run the server in your terminal (requires Node.js 22.16+, 23.11+, or 24.10+):

npx t3@latest

This will launch T3 Code's backend on your machine as well as the local web app to control your agents.

Tip: Use npx t3@latest --help for the full CLI reference.

Desktop app

Install the latest version of the desktop app from GitHub Releases, or from your favorite package registry:

Windows (`winget`)

winget install T3Tools.T3Code

macOS (Homebrew)

brew install --cask t3-code

Arch Linux (AUR)

yay -S t3code-bin

Some notes

We are very very early in this project. Expect bugs.

We are (mostly) not accepting contributions yet. Small fixes may be considered. Big features will not be.

Documentation

Full docs live in docs/. There's no docs site yet.

Install and first run
Permission modes
Keyboard shortcuts
Customize a project icon
Remote access from a phone or another machine
Keeping app and server in sync
Source control integrations
Multiple accounts: Codex · Claude
Linux: run T3 Code as a background service

Building from source? Start at docs/internals/overview.md.

If you REALLY want to contribute still.... read this first

Install `vp`

T3 Code uses Vite+ so you'll need to install the global vp command-line tool.

macOS / Linux

curl -fsSL https://vite.plus | bash

Windows

irm https://vite.plus/ps1 | iex

Checkout their getting started guide for more information: https://viteplus.dev/guide/

Install dependencies

vp i

Read CONTRIBUTING.md before opening an issue or PR.

Need support? Join the Discord.

diegosouzapw/OmniRoute

Never stop coding. Free MIT AI gateway: one endpoint, 290+ providers (90+ free), 500+ models — Kimi, Claude, GPT, OpenAI, Gemini, GLM, DeepSeek, MiniMax. Works with Claude Code, Codex, Cursor, OpenCode, Cline & Copilot. Quota-aware auto-fallback, RTK+Caveman compression saves 15-95% tokens, MCP/A2A, Desktop/PWA. Built by 500+ contributors

🚀 OmniRoute — The Free AI Gateway

💰 ~1.53B Free Tokens / Month

Stacking free tiers by hand is painful — dozens of SDKs, dozens of rate limits, and no idea how much you actually have. OmniRoute aggregates the documented free tiers of 43 provider pools / 516 models into one honest number and shows it live on the dashboard (/dashboard/free-tiers).

Animated summary of the live /dashboard/free-tiers page. Full methodology (pool dedupe, credit tiers, provider terms): docs/reference/FREE_TIERS.md.

_{These figures are re-audited every two weeks against the live catalog and move both ways — a provider ends a free tier and the number drops; a new one lands and it climbs. We publish what the catalog actually computes, never a rounded-up best case.}

⭐ Star the repo if OMNIROUTE helped you save money and make your work easier.

💬 Join the community

👋 Follow the maintainer — get new providers, releases & tips first:

Questions, provider tips, roadmap & support → Discord · Telegram · WhatsApp 🌍 Global / 🇧🇷 Brasil

🧩 Available

🚀 Start	🚀 Quick Start	📦 Install	🆓 Zero-config
💡 Learn	💥 The Promise	🤔 Why OmniRoute	🏆 What Sets Apart
⚙️ Features	🎯 Combos	🌐 Providers	🔌 CLI & MCP
	🗜️ Compression	🖥️ Where It Runs	🔒 Private
👀 See it	🎬 In Action	✨ What's New	🤖 Compatible CLIs
💚 Support	💚 Support / Donate	💬 Community	💖 Sponsors
📦 Project	🛠️ Tech Stack	📖 Docs	👥 Contributors

🌐 In 43 languages

🆓 Works the second you install it — no keys, no config

# Fresh install, zero credentials — `auto` already works:
curl http://localhost:20128/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"auto","messages":[{"role":"user","content":"Hello!"}]}'

_{Prefer a specific free backend? Call it directly, e.g. oc/… (OpenCode Free) or felo/… (Felo). Then graduate to auto and let OmniRoute pick.}

_{📦 Copy-paste quickstart scripts for Python, Node.js, PHP, and cURL → examples/quickstart/}

💥 The Promise

🤔 Why OmniRoute?

🤝 Supported by our Open Source Friends

Want to join as an Open Source Friend? These are the companies that back open source and help keep OmniRoute moving — and we say publicly where every token they give us goes. Reach out: diegosouza.pw@outlook.com

Kimi
_{Moonshot AI} Thanks to Kimi (Moonshot AI), our founding Open Source Friend, for backing this project! Kimi is the AI lab behind the open-weight K2 and K3 model families — Kimi K3 delivers a 1M-token context window, native vision and frontier-level coding at a fraction of closed-model prices, and works out of the box with Claude Code, Codex and every coding tool OmniRoute serves.

What Kimi's support powers: Kimi's API credits power OmniRoute's AI-validated release pipeline — the merge validation powered by Kimi K3 stage that reviews every pull request before it ships — plus day-to-day feature development. First-class Kimi support ships on both rails: the direct Kimi API (kimi-k3) and the Kimi Code coding plan (OAuth and API key). OmniRoute is also the first Brazilian open-source project in Kimi's support program. Get a Kimi API key →

Cheaper Inference
_{cheaperinference.com} Thanks to Cheaper Inference, an OmniRoute Open Source Friend, for backing this project! Cheaper Inference is a cost-ranked gateway that resells 42 frontier models — Claude, GPT-5.x, Gemini, Kimi K3, GLM, DeepSeek, Grok and MiniMax — behind one OpenAI-compatible endpoint, routing each request to the cheapest eligible provider without ever charging above the model maker's list price.

First-class support in OmniRoute: Chat Completions, the native /v1/responses endpoint, vision, tool calling and 3 image models (grok-imagine, nano-banana-pro, nano-banana-2, reachable as cheaperinference/<model>). Get an API key →

_{Links tagged aff=omniroute are partner links. They fund the project at no extra cost to you.}

_{🎟️ Affiliates Promo — free signup coupons from providers we don't sponsor (click to expand)}

_{This section is for referral/coupon codes only. Sponsored partnerships live in 🤝 Supported by our Open Source Friends above. OmniRoute has no sponsorship or partnership with the providers listed here — these are public coupons anyone can use.}

_AgentRouter
_{agentrouter.org} _{AgentRouter — affiliate signup · $100 free credits on signup (free server, expect higher latency — best for testing, not production). First-class support in OmniRoute since v3.8.50: Chat Completions, the Anthropic-compatible wire format and the OpenAI-compatible path. Available models include claude-opus-4-8, claude-opus-5, gpt-5.6-sol and more. Grab your $100 →}

_{⚠️ Affiliate link — OmniRoute has no sponsorship or partnership with this provider.}

_{Know another provider with a generous free signup coupon that benefits OmniRoute users? Open an issue and we'll add it here.}

🎯 Combos — The Flagship

A combo is a chain of models OmniRoute routes across automatically. Quota runs out, a provider fails, or costs spike — the combo silently slides to the next model. This is what makes OmniRoute unbreakable. 🛡️

⚡ Zero-config — just use `auto`

No combo to create. Set your model to auto (or a variant) and OmniRoute builds a virtual combo from your connected providers, scored live:

Model ID	What it optimizes for
`auto`	🎯 Balanced default (LKGP — sticks to your last good provider)
`auto/coding`	🧑‍💻 Quality-first weights for code generation
`auto/fast`	⚡ Lowest latency first
`auto/cheap`	💰 Cheapest per token first
`auto/offline`	🔋 Most quota / rate-limit headroom first
`auto/smart`	🔭 Quality-first + 10% exploration to discover better models

🔀 Or build your own — 19 routing strategies

All 19 strategies — mix & match per combo step:

#	Strategy	What it does
1	`priority`	First-target ordered list — drain each before the next 🥇
2	`fill-first`	Fill each target's quota fully before moving on
3	`weighted`	Weighted random by per-target weight
4	`round-robin`	Cycle through targets in order
5	`p2c`	Power-of-two-choices random load balancing
6	`least-used`	Pick the target with the lowest current load
7	`random`	Uniform random pick (deduplicated)
8	`strict-random`	Random without de-duplicating repeats 🎲
9	`cost-optimized`	Minimize $ per request from live catalog pricing 💸
10	`headroom`	Pick the target with the most remaining quota
11	`reset-window`	Prefer the target whose quota window resets soonest
12	`reset-aware`	Rank by quota reset time — short windows first 📊
13	`context-relay`	Hand off context across targets for long conversations 🧠
14	`context-optimized`	Pick the best fit for the current context size
15	`cache-optimized`	Pin each reusable prompt prefix to the same account — maximize prompt-cache hits 🎯
16	`lkgp`	Last-Known-Good Path — sticky to the last successful target
17	`auto`	12-factor live scoring across every connection 🤖
18	`fusion`	Fan out to a panel of models + a judge synthesizes one answer 🧬
19	`pipeline`	Chain steps — each target's output feeds the next one 🔗

_{The Auto-Combo engine scores every candidate on 12 factors (health, quota, cost, latency, success rate, freshness…) — see docs/routing/AUTO-COMBO.md.}

🧱 Resilience is built in (3 independent layers)

_{📖 Auto-Combo Engine · Resilience Guide}

🏆 What Sets OmniRoute Apart

_{📊 Full methodology & per-feature detail vs 9router, OpenRouter, CLIProxyAPI & LiteLLM → docs/comparison/OMNIROUTE_VS_ALTERNATIVES.md}

💚 Support OmniRoute

OmniRoute is MIT-licensed and maintained in the open. If it saves you time or money, here's how to keep it independent — pick whatever fits you. Sponsorship never affects routing priority; it buys visibility, not ranking.

⭐ Star the repo	Free — genuinely helps visibility	Star OmniRoute
🐙 GitHub Sponsors	One-off or monthly · zero platform fee	github.com/sponsors/diegosouzapw
☕ Ko-fi	Quick one-off tip, no signup for the donor	ko-fi.com/diegosouzapw
🧋 Buy Me a Coffee	Small, informal gesture	buymeacoffee.com/diegosouzapw
🖐 Liberapay	Recurring · non-profit · open source	liberapay.com/diegosouzapw
🇧🇷 PIX (Brazil)	Instant, no fees	key & QR below
₿ Crypto	BTC · ETH · USDT-TRC20 · USDC-Solana	addresses below

🇧🇷 PIX — instant, no fees (Brazil)

Key (random): 5d865059-bc44-483a-962d-43ceb80126eb

Pix copia-e-cola:

00020101021126580014br.gov.bcb.pix01365d865059-bc44-483a-962d-43ceb80126eb5204000053039865802BR5922OMNIROUTE CONTRIBUICAO6006BRASIL62070503***630475DD

₿ Crypto — BTC · ETH · USDT-TRC20 · USDC-Solana (click to expand)

₿ BTC	Bitcoin (SegWit)	`bc1qh00smz004sy85wyl28v77tenkt3ckl6eaep7fd`
Ξ ETH	Ethereum (ERC20)	`0x64Cf6B68A6Ff34288e89172950a2d00102337a84`
₮ USDT	Tron (TRC20)	`TKAF41JpuQrHbKTnsQa9svJE2T192Hvsc2`
$ USDC	Solana	`2emNNZzVVWQc3FQ2wk9M6qXUQmW8AKdjjL174fXR28Tu`

_{⚠️ Send each coin only on the network shown — sending on the wrong network can lose the funds.}

🐛 Found a bug or have feedback? Open a Discussion.

✨ What's New

Recent highlights from v3.8.20 → v3.8.49. Full history in CHANGELOG.md.

🗜️ Compression hardening — default-on inflation guard, Caveman packs for DE / FR / JA + Chinese (wényán), RTK filters for Gradle & .NET. → Compression
💸 Honest flat-rate cost — subscription / coding-plan providers read $0 in cost analytics; budget, quota & routing keep estimating. → API Reference
⚖️ Quota-Share routing — split a shared account's quota fairly across pooled keys, work-conserving so idle slices are lent out. → Resilience Guide
🤖 One-command CLI/agent setup — setup-* configures 12+ coding tools; omniroute launch / launch-codex are zero-config. → CLI Integrations
🛰️ Remote mode — drive a remote OmniRoute with scoped tokens (connect / contexts / tokens) + an antigravity OAuth helper for VPS installs. → Remote Mode
🧭 Smarter auto-routing — auto/<category>:<tier> combos, Fusion (model panel + judge), task-aware routing, per-request model / mode / USD-budget overrides. → Auto-Combo
🗜️ Pluggable compression — 12 composable engines + Compression Studios: LLMLingua-2, two-tier Ultra, omniglyph, per-step fidelity gate, GCF v3.2, drag-reorder editor. → Compression
🕵️ Transparent MITM decrypt (TPROXY) — capture CLIs that ignore proxy env vars, with a per-SNI CA + trust-store installer. → MITM/TPROXY
💸 Cost telemetry everywhere — X-OmniRoute-* cost/usage headers on every endpoint, cache-HIT savings header, per-key USD spend quotas. → API Reference
🧠 Memory you control — off by default, opt-in int8 vector quantization + typed decay, per-request x-omniroute-no-memory. → Memory
🛡️ Security — prompt-injection guard on every LLM route (red-team suite), opt-in credential-masking guardrail (redacts leaked API keys/secrets in both directions), free DuckDuckGo last-resort web search, and an optional OIDC login gate for the dashboard (password login always stays available). → Guardrails
🖼️ New endpoints — /v1/ocr (Mistral OCR) and /v1/audio/translations (Whisper-style) round out the media surface. → API Reference
🎨 Image / video / audio generation — one API for media: xAI Grok Imagine & Novita AI video, ComfyUI, Freepik, Adobe Firefly, Microsoft Designer, Google Imagen, Segmind, EdgeTTS. → API Reference
🌍 Deployment & ops — reverse-proxy basePath, browser-language auto-detect, per-key device tracking, root-less MITM trust, zh-TW localization. → Environment
🤝 More providers & agents — Cursor Cloud Agent, Grok Build (xAI) with browser + OAuth login, Ollama first-class card, Claude Opus 5 & Sonnet 5, Kimi official partnership (Code/Web/Moonshot), Zed, Requesty, SenseNova, Yuanbao, Agnes AI… and a refreshed 291-provider catalog. → Providers
📡 Routing transparency — every response carries an X-OmniRoute-Decision header naming the strategy/provider/latency that served it, a new cache-optimized combo strategy + Auto-Combo cacheAffinity factor route repeat requests back to the connection holding the cached prefix, and a read-only /v1/auto-combo/{channel}/candidates endpoint exposes an auto/* channel's live candidate pool. → Auto-Combo
⚡ Local performance & infra — one-click local Redis, Cloudflare Workers / Deno Deploy relay deployers, Bifrost & Mux as supervised embedded services. → Embedded Services

🤖 Compatible CLIs & Coding Agents

One config — http://localhost:20128/v1 — and every AI IDE or CLI runs on free & low-cost models.

_{Claude Code}	_{Codex CLI}	_Cline	_{Kilo Code}	_{Zoo Code}	_Continue
_Aider	_ForgeCode	_jcode	_{DeepSeek TUI}	_CodeWhale	_OpenCode
_{Factory Droid}	_{Copilot CLI}	_{Cursor CLI}	_Smelt	_Pi	_{Grok Build}
_{Hermes Agent}	_OpenClaw	_Goose	_{Open Interpreter}	_{Warp AI}	_{Agent Deck}

＋ also works with · Kiro · Command Code · Antigravity · Windsurf · AMP · any OpenAI-compatible tool

_{📖 Per-tool setup for all 33 tools (25 CLI Code's + 8 CLI Agents) → docs/reference/CLI-TOOLS.md · 🧩 OpenCode plugin → @omniroute/opencode-provider}

🌐 291 AI Providers — 90+ Free

The most complete catalog of any open-source router: 291 providers, 90+ with a free tier, 40+ free forever.

🏢 Every major lab — through one endpoint

_OpenAI	_Anthropic	_Gemini	_{xAI Grok}	_DeepSeek	_Mistral
_Qwen	_{Meta Llama}	_Groq	_NVIDIA	_MiniMax	_Cohere
_Perplexity	_HuggingFace	_Together	_Fireworks	_Cloudflare	_Baidu

_{…and 220+ more — every icon resolves live from the dashboard's provider catalog. 📖 Provider Reference}

🆓 Free Forever — $0, no card

OpenCode Zen _{DeepSeek V4, Nemotron 3 No token cap}	Kilo Code _{Auto-router, Tencent Hy3 Free forever}	Requesty _{GPT-OSS 120B, Nemotron Free forever}	SiliconFlow _{DeepSeek V3.2 / R1 Free tier}	Z.AI GLM _{GLM-4.7 / 4.5-Flash Free forever}	Baidu ERNIE _{ERNIE 4.0 Free forever}
Qoder AI _{Qwen3-Max, Kimi-K2 Unlimited FREE}	Pollinations _{GPT, Llama, Claude No key needed}	Cloudflare AI _{50+ models 10K neurons/day}	NVIDIA NIM _{GLM, MiniMax ~40 RPM free}	Cerebras _{GLM 4.7, GPT-OSS 1M tokens/day}	OpenRouter _{:free models +$10 → higher RPM}

📖 Full machine-readable catalog → docs/reference/PROVIDER_REFERENCE.md

🖥️ Where OmniRoute Runs — Anywhere

Same app, your machine, your rules. From a global npm install to your phone via Termux.

Platform	Install	Highlights
📦 npm (global)	`npm install -g omniroute`	One command, any OS
🐳 Docker	`docker run … diegosouzapw/omniroute`	Multi-arch AMD64 + ARM64
🖥️ Desktop (Electron)	`npm run electron:build`	Native window + system tray — Windows / macOS / Linux
💪 ARM	native `arm64`	Raspberry Pi, ARM servers, Apple Silicon
📱 Android (Termux)	`pkg install nodejs && npx -y omniroute`	Runs on your phone, 24/7, no root
📲 PWA	"Add to Home Screen"	Fullscreen, offline, installable from browser
🧩 OpenCode plugin	`@omniroute/opencode-provider`	Native OpenCode integration
🛠️ From source	`npm install && npm run dev`	Hack on it, contribute

_{📖 Docker Guide · Desktop · Termux · PWA · OpenCode}

🔒 Private & Local-First

_{📖 Authorization · Guardrails · Compliance}

🔌 Full CLI + A2A & MCP

Beyond the server, OmniRoute is a full command-line cockpit with 80+ commands, plus open agent protocols so an AI agent can drive it on its own.

⌨️ A real CLI (not just `start`)

omniroute               # serve gateway + dashboard (port 20128)
omniroute chat          # interactive TUI chat client (slash: /model /combo /skill /memory)
omniroute setup         # guided first-run wizard
omniroute doctor        # diagnose providers, ports, native deps

🛰️ Remote mode — run the CLI here, OmniRoute on a VPS

OmniRoute on a server? Drive it from your laptop with the same CLI. Log in once with a scoped access token; every command then targets the remote.

omniroute connect 192.168.0.15            # password → scoped token, saved as a context
omniroute models list                     # ← runs against the REMOTE server
omniroute configure codex                 # ← picks a remote model, writes a local Codex profile
omniroute tokens create --name ci --scope read   # mint narrower tokens for other machines
omniroute contexts use default            # ← switch back to the local server

Tokens are scoped read / write / admin; process-spawning routes stay loopback-only. _{📖 Remote Mode}

🤝 Connect an agent — and it controls OmniRoute itself

Expose OmniRoute over MCP, A2A, a REST API, webhooks or a remote CLI — any capable agent (or your own code) gets the keys to the whole gateway: routing, providers, combos, cache, compression, memory — autonomously. HTTP endpoints below are served under http://localhost:20128.

Interface	Endpoint / command	Use it for
🧰 MCP (stdio)	`omniroute --mcp`	Plug into Claude Desktop, Cursor, any MCP client
🌊 MCP (HTTP)	`/api/mcp/stream`	Remote MCP — 105 tools, 31 scopes, full audit trail
📡 MCP (SSE)	`/api/mcp/sse`	Streaming MCP transport
🤝 A2A	`/.well-known/agent.json`	Agent-to-agent, JSON-RPC 2.0 + SSE, 6 skills
🌐 REST API	`/v1/*`	OpenAI-compatible — chat, embeddings, images, audio, OCR
🔔 Webhooks	`/api/webhooks`	Push events (usage, quota, errors, routing) to your URL
🛰️ Remote CLI	`omniroute connect`	Drive a remote instance with scoped access tokens

# Give Claude Code the full OmniRoute toolset over MCP:
claude mcp add-server omniroute --type http --url http://localhost:20128/api/mcp/stream

_{📖 MCP Server · A2A Server · Agent Protocols}

🗜️ Save 15–95% Tokens — Automatically

📖 How it works — pipeline, architecture & savings math

Default stacked combo runs RTK → Caveman. When both act on the same tool/context payload, savings compound:

combined = 1 − (1 − RTK) × (1 − Caveman_input)
average  = 1 − (1 − 0.80) × (1 − 0.46) = 89.2%
range    = 78.4 – 94.6%

Code blocks, URLs, JSON and structured data are always protected by the preservation engine.

Why use many tokens when few tokens do the trick? Every request passes through OmniRoute's compression pipeline transparently — no client changes. It's now a stack of 12 composable engines that run in order and mix & match per routing combo — building on ideas from RTK, Caveman (⭐ 90K+), LLMLingua-2, and Troglodita (PT-BR).

🧱 The 12-engine stack

Engines run in pipeline order; each is independently toggleable and configurable per combo:

#	Engine	What it does
1	Session-Dedup	Drops content repeated across turns (content-addressed, cross-turn)
2	CCR	Archives large blocks behind retrieve markers, fetched on demand
3	Lite	Whitespace + image-URL trimming (latency-light baseline)
4	RTK	Smart tool-result filtering, dedup & truncation (command-aware)
5	Responses Tool Output	Lossless-first JSON + bounded diagnostic compression for shell/patch/search/build outputs (Responses API)
6	Headroom	Lossless tabular compaction of JSON arrays (~30%) via a vendored GCF codec
7	Relevance	Extractive sentence scoring against the last user query
8	Caveman	Rule-based prose compression (~65–75% on output)
9	Aggressive	Summarization + progressive aging of old turns
10	LLMLingua-2	ML semantic pruning via MobileBERT ONNX — code-safe, async
11	Ultra	Heuristic token pruning with an optional small-model (SLM) tier
12	OmniGlyph	Experimental context-as-image encoding routed to Claude Fable 5 (most aggressive; opt-in)

Code blocks, URLs and structured data are always preserved byte-perfect. One-click presets combine the engines:

Mode	Savings	Best for
🪶 Lite	~15%	Always-on safe default
🪨 Standard (Caveman)	~30%	Daily coding
⚡ Aggressive	~50%	Long tool-heavy sessions
🔥 Ultra	~75%	Maximum savings
🧰 RTK	60–90%	Shell/test/build/git output
🔗 Stacked (RTK → Caveman)	78–95%	Mixed prompts + tool logs

Real example — Standard mode:

Before (69 tokens): "The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re-render. I would recommend using useMemo to memoize the object."

After (19 tokens): "New object ref each render. Inline object prop = new ref = re-render. Wrap in useMemo."

Same answer. 72% fewer tokens. Zero accuracy loss. ✅

PT-BR example — Troglodita mode:

Antes (42 tokens): "O problema é que o componente está re-renderizando porque uma nova referência de objeto está sendo criada em cada ciclo de renderização. Eu recomendaria usar useMemo."

Depois (12 tokens): "Re-render: ref nova cada ciclo (objeto inline recriado). Usar useMemo."

Mesma resposta. ~70% menos tokens. Precisão técnica intacta. ✅

🎚️ Beyond the engines — output styles, the adaptive dial & per-request control

The 12 engines above shrink what goes in. Three more layers shape how, when, and what comes out:

🪄 Output Styles (output-axis steering) — inject deterministic, cache-safe response-shaping instructions; combinable, each at lite / full / ultra intensity. Adding a style is a one-line registry entry:
- Terse prose — drop filler / articles / hedging; keep technical substance exact.
- Less code — "lazy senior dev" YAGNI: smallest working change, no unrequested scaffolding.
- Terse CJK (文言) — classical-Chinese ultra-terse style (locale-gated to zh).
🎯 Adaptive context-budget (the dial) — instead of one on/off token threshold, escalate the cheapest, most-lossless engines only as far as needed to fit the model's context window. Policy: reserve-output (default, model-aware) · percentage · absolute. Mode: floor (guarantee fit) · replace-autotrigger (your explicit choice wins) · off (legacy threshold).
🎛️ Where compression is decided (precedence, high → low) — per-request x-omniroute-compression header › routing-combo override › active named profile › adaptive / auto-trigger › panel default › off. The applied plan echoes back in the X-OmniRoute-Compression: <mode>; source=<source> response header.

Auto-trigger by token threshold, flip on the adaptive dial, pin a named profile, set a one-off per request, or assign a pipeline per routing combo — whichever fits the workload. An opt-in offline eval harness (npm run eval:compression) scores fidelity vs. savings on a pinned corpus before you promote a change.

📖 COMPRESSION_GUIDE.md · RTK_COMPRESSION.md · COMPRESSION_ENGINES.md

⚡ Quick Start

1) Install & run

npm install -g omniroute
omniroute

💡 See npm warn ERESOLVE or peer-dep warnings? They're harmless.

Dashboard at http://localhost:20128 · API at http://localhost:20128/v1.

2) Connect a FREE provider (no signup)

Dashboard → Providers → connect Kiro AI (free Claude, ~50 credits/month per account) or OpenCode Free (no auth) → done.

3) Point your coding tool

Base URL: http://localhost:20128/v1
API Key:  [copy from Dashboard → Endpoints]
Model:    auto            (zero-config smart routing — or any provider/model)

4) Verify it's working

curl http://localhost:20128/v1/models -H "Authorization: Bearer YOUR_KEY"

You should see your connected models listed. 🎉 That's it — start coding, and OmniRoute auto-routes & falls back for you.

If your client cannot send custom headers, OmniRoute also exposes tokenized compatibility aliases:

OpenAI catalog:   http://localhost:20128/vscode/YOUR_KEY/
OpenAI models:    http://localhost:20128/vscode/YOUR_KEY/models
OpenAI chat:      http://localhost:20128/vscode/YOUR_KEY/chat/completions
OpenAI responses: http://localhost:20128/vscode/YOUR_KEY/responses
Ollama chat:      http://localhost:20128/vscode/YOUR_KEY/api/chat
Ollama tags:      http://localhost:20128/vscode/YOUR_KEY/api/tags

Use these only for clients that cannot attach Authorization: Bearer .... Header auth remains the preferred mode.

📦 More install methods — Docker, source, pnpm, Arch

🐳 Docker

docker run -d --name omniroute --restart unless-stopped --stop-timeout 40 \
  -p 127.0.0.1:20128:20128 -v omniroute-data:/app/data diegosouzapw/omniroute:latest

Pre-release Docker channel: diegosouzapw/omniroute:next and diegosouzapw/omniroute:next-web follow the current default release/v* branch. These mutable tags are intended only for testing unreleased fixes and are not supported for production. See Docker Release Channels.

🛠️ From source

cp .env.example .env && npm install
PORT=20128 npm run dev

📦 pnpm

pnpm add -g omniroute@latest --allow-build=better-sqlite3 --allow-build=@swc/core && omniroute

🐧 Arch Linux (AUR)

yay -S omniroute-bin && systemctl --user enable --now omniroute.service

🔧 Nix (Flake)

# Using Nix flakes
nix develop
npm run dev

# Or using devbox
devbox run npm run dev

📖 Docker Guide — Compose profiles, Caddy HTTPS, Cloudflare tunnels.

🦭 Podman

# 1. Prepare the bind-mounted data directory
mkdir -p data

# 2. Linux + local rootless Podman only (never a remote Podman Machine client):
podman unshare chown 1000:1000 ./data

# 3. Set the runtime hint, build the local Compose image, and start
echo "CONTAINER_HOST=podman" >> .env
podman compose --profile base up -d --build

On macOS or Windows, Podman uses a remote Podman Machine: skip podman unshare and follow the topology-specific data directory guidance.

📖 Podman Guide — Compose builds, Podman Machine, and Linux/systemd Quadlet setup.

⚡ Faster / leaner install (skip the native build)

The native SQLite engine (better-sqlite3) is an optional dependency, so a global install never blocks on compiling from source: it uses a prebuilt binary when one matches your platform/Node, and otherwise falls back transparently to a pure-JS engine (node:sqlite on Node 22+, else the bundled sql.js WASM) — no build tools required.

To skip the post-install native warm-up entirely (CI, headless, or slow machines):

OMNIROUTE_SKIP_POSTINSTALL=1 npm install -g omniroute   # CI=1 also skips it

For the fastest installs prefer pnpm (content-addressed store + hard links — see above). For a dashboard-free, headless runtime use the Docker base profile (above) or the Termux guide. The CLI and the web dashboard are served by the same process on one port, so there is no separate CLI-only package today.

🎬 OmniRoute in Action

🇧🇷 Português
_{Guia completo}

🇺🇸 English
_{Complete walkthrough}

🇷🇺 Русский
_{Полное руководство}

🎬 Made a video about OmniRoute? Open an issue or discussion with the link — we'll feature it here.

📧 Community & Help

Everything in one place — follow the maintainer, chat with the community, or open an issue.

Channel	Where / how
💼 LinkedIn — follow the maintainer	linkedin.com/in/diegosouzapw
🐙 GitHub — follow for releases & tips	@diegosouzapw
💬 Discord	discord.gg/U47eFqAXCn
✈️ Telegram	t.me/omnirouteOficial
🟢 WhatsApp — 🌍 Global	join the group
🟢 WhatsApp — 🇧🇷 Brasil	entrar no grupo
🌍 Website	omniroute.online
📦 Source code	github.com/diegosouzapw/OmniRoute
🐛 Report a bug	open an issue — attach `npm run system-info` output
🤝 Contribute	CONTRIBUTING.md · Branching & Release Model · pick a `good first issue`
💚 Support the project	Ways to support ↑ · GitHub Sponsors

🛠️ Tech Stack

Layer	Technology
Runtime	Node.js 22.x / 24.x LTS — `>=22.22.2 <23 \|\| >=24.0.0 <27`
Language	TypeScript 6.0 — 100% TypeScript across `src/` and `open-sse/` (zero `any` in core since v2.0)
Framework	Next.js 16 + React 19 + Tailwind CSS 4
Database	better-sqlite3 (SQLite, WAL journaling) + LowDB (JSON legacy) — 95 domain modules, 110 migrations
Memory	SQLite FTS5 full-text + int8-quantized vector embeddings, typed decay
Schemas	Zod 4 — MCP tool I/O validation + API contracts
Protocols	MCP (stdio / HTTP / SSE) + A2A v0.3 (JSON-RPC 2.0 + SSE)
Streaming	Server-Sent Events (SSE) + WebSocket bridge (`/v1/ws`)
Compression	12-engine pipeline — RTK, Caveman, LLMLingua-2 (MobileBERT ONNX), GCF, OmniGlyph
Auth & security	OAuth 2.0 (PKCE) + JWT + API Keys + MCP scoped auth · AES-256-GCM at rest · DOMPurify
Stealth	wreq-js — JA3 / JA4 TLS fingerprint impersonation, 3-level proxy
Resilience	Circuit breaker, exponential backoff, anti-thundering-herd, auto-combo self-healing
Logging	pino — structured JSON logs with request context
Testing	Node.js test runner + Vitest — 25,000+ test cases across 3,300+ files (unit, integration, E2E, security, ecosystem)
Platforms	Desktop (Electron) · Android (Termux) · PWA (any browser)
CI/CD	GitHub Actions — auto npm publish + Docker Hub on release
Links	Website · npm · Docker Hub

📖 Documentation

📘 Getting Started

Document	Description
User Guide	Providers, combos, CLI integration, deployment
Setup Guide	Full install methods, CLI tool configs, protocol setup, timeout tuning
CLI Tools Guide	Per-tool setup for Claude Code, Codex, Cursor, Cline, OpenClaw, Kilo, Copilot
Remote Mode	Drive a remote OmniRoute (VPS) from your laptop CLI via scoped access tokens
Claude Code Config	Point Claude Code at OmniRoute (local/remote) with `launch` + per-model profiles
Quick Start	3-step install → connect → configure

🔧 Operations & Deployment

Document	Description
Docker Guide	Docker run, Compose profiles, Caddy HTTPS, tunnels, image tags
Podman Guide	Quadlet systemd integration, podman-compose, SELinux
VM Deployment	Complete guide: VM + nginx + Cloudflare setup
Fly.io Deployment	Deploy to Fly.io with persistent storage
Termux Guide	Run OmniRoute on Android via Termux
PWA Guide	Progressive Web App install, caching, architecture
Uninstall Guide	Clean removal for all install methods
Environment Config	Complete `.env` variables and references

🧠 Features & Architecture

Document	Description
Architecture	System architecture, data flow, and internals
Compression Guide	7-option pipeline: off / lite / standard / aggressive / ultra / RTK / stacked
RTK Compression	Command-output compression, filters, trust, verify, raw-output recovery
Compression Engines	Caveman, RTK, stacked pipelines, dashboard/API/MCP surfaces
Compression Rules Format	JSON rule-pack schemas for Caveman and RTK filters
Compression Language Packs	Language detection and Caveman rule-pack authoring
Resilience Guide	Circuit breakers, cooldowns, queue, anti-thundering herd, TLS spoofing
Auto-Combo Engine	12-factor scoring, mode packs, self-healing
Proxy Guide	3-level proxy system, 1proxy marketplace, registry CRUD
Free Tiers	25+ free API providers consolidated directory
Features Gallery	Visual dashboard tour with screenshots
Codebase Documentation	Beginner-friendly codebase walkthrough

🤖 Protocols & APIs

Document	Description
API Reference	All endpoints with examples
OpenAPI Spec	OpenAPI 3.0 specification
MCP Server	104 MCP tools, IDE configs, Python/TS/Go clients
MCP Server Guide	MCP installation, transports, and tool reference
A2A Server	JSON-RPC 2.0 protocol, skills, streaming, task mgmt
A2A Server Guide	A2A agent card, tasks, skills, and streaming

📋 Project & Quality

Document	Description
Contributing	Development setup and guidelines
Branching & Release Model	Where PRs target (`release/*`), what `main` and tags mean
Changelog	Full per-version release history
Security Policy	Vulnerability reporting and security practices
i18n Guide	40+ language support, translation workflow, RTL
Release Checklist	Pre-release validation steps
Coverage Plan	Test coverage strategy and 25,000+ test suite

⭐ Top Contributors

OmniRoute is shaped by a passionate open-source community. These individuals have made exceptional contributions that directly impact the quality, stability, and reach of the project. Thank you.

oyi77 _{🥇 213 commits • +114K lines} _{Analytics engine, SQL aggregations, proxy marketplace, test coverage}	R.D. & Randi _{🥈 108 commits • +38K lines} _{Endpoints page, tunnel integrations, Docker workflows, A2A status, compression UI}	Chris Staley _{🥉 70 commits • +1.8K lines} _{SSE stream hardening, Responses API, Gemini pagination, test regression fixes}	zenobit _{🏅 62 commits • +22K lines} _{CI/CD pipeline, i18n for 33 languages, Void Linux package, platform fixes}	Jan Leon _{🏅 58 commits • +22K lines} _{Reasoning-effort routing, proxy controls, quota visibility, Live Zone compression}
backryun _{🏅 53 commits • +70K lines} _{Provider catalog curation — Perplexity, Kimi, Cerebras, Copilot, LMArena refreshes}	Chirag Singhal _{🏅 46 commits • +4.8K lines} _{Error sanitization, MITM prefill fix, fusion judge, breaker/429 correctness}	kfiramar _{🏅 38 commits • +1.7K lines} _{Codex websocket + passthrough, auth/onboarding, Electron hardening, DB migrations}	Benson K B _{🏅 28 commits • +9.2K lines} _{Electron desktop app, auto-updater, release build workflows, cross-platform CI}	Hernan J. Ardila _{🏅 25 commits • +174K lines} _{Zero-latency combos, vision-bridge auto-routing, catalog context-length, resilience 429 hints}

🙏 These contributors' features, bug fixes, and infrastructure improvements are a core part of what makes OmniRoute reliable and feature-rich. Every pull request, every test case, and every i18n translation file matters. Open source is built by people like them.

💖 Sponsors

A heartfelt thank-you to the people who fund OmniRoute out of their own pocket — every contribution keeps the project free, independent and moving.

Prof. Igor Morais
_{💛 Sponsor}

longtao
_{💛 Sponsor}

_{… and others who prefer to stay private 💛}

💖 Become a sponsor → — every dollar keeps OmniRoute free and independent.

👥 500+ Contributors

How to Contribute

Fork the repository
Branch from the active release/vX.Y.Z tip (not main) — see Branching & Release Model
Create your feature branch (git checkout -b feat/amazing-feature)
Commit your changes (git commit -m 'feat: add amazing feature')
Push to the branch (git push origin feat/amazing-feature)
Open a Pull Request with base = that release/vX.Y.Z branch

See CONTRIBUTING.md for detailed guidelines.

Releasing a New Version

# Create a release — npm publish happens automatically
gh release create v3.8.2 --title "v3.8.2" --generate-notes

📊 Stars

🌍 StarMapper

🙏 Acknowledgments

OmniRoute stands on the shoulders of giants. It started as a fork of 9router and a TypeScript port of the Go project CLIProxyAPI — and from there, every subsystem below was inspired by an open-source project that got there first. Each one shaped a concrete piece of OmniRoute. This is our thank-you to all of them. 🙏

⭐ star counts as of July 2026 — go give these projects a star.

🧬 Lineage & gateway

Project	⭐	How it inspired OmniRoute
9router	22.7k	The original project this fork is built on — extended here with multi-modal APIs and a full TypeScript rewrite.
CLIProxyAPI	43.6k	The Go implementation that inspired this JavaScript / TypeScript port.
LiteLLM	54.0k	The AI gateway whose public pricing dataset feeds our cost-tracking sync and whose provider-normalization model informed our routing.

🗜️ Context & token compression — engines

Project	⭐	How it inspired OmniRoute
Caveman	90.8k	The viral "why use many token when few token do trick" project — its caveman-speak philosophy powers our standard compression mode and 30+ filler/condensation rules.
RTK – Rust Token Killer	71.8k	High-performance command-output compression — inspired our RTK engine, JSON filter DSL, raw-output recovery and the stacked RTK → Caveman pipeline.
headroom	60.1k	Reversible context-compression (SmartCrusher) — inspired our `headroom` engine and the `ccr` retrieve-marker pattern.
LLMLingua	6.5k	Prompt-compression research (LLMLingua / LLMLingua-2) — inspired our async, code-safe, fail-open `llmlingua` engine.
llmlingua-2-js	30	The JS/ONNX port (MobileBERT / XLM-RoBERTa) used as the worker-thread backend for our LLMLingua engine.
Troglodita	26	PT-BR token compression — powers our pt-BR language pack: pleonasm reduction and filler removal tuned for Brazilian-Portuguese grammar.
ponytail	86.0k	The viral "lazy senior dev" YAGNI-coder skill — inspired our less-code Output Style: smallest-working-change steering that cuts _generated_ code (the output-axis sibling to Caveman's terse prose).

🧩 Compact formats, token research & code-aware tooling

Project	⭐	How it inspired OmniRoute
TOON	24.9k	Token-Oriented Object Notation — its columnar, header-plus-rows model shaped our tabular compaction stage.
GCF – Graph Compact Format	22	First inspired our tabular compaction stage; now its zero-dependency, lossless generic-profile encoder is vendored directly as the Headroom codec (MIT, SPDX-marked), current with GCF spec v3.2.
token-optimizer-mcp	444	Brotli/SQLite cache + per-session context-delta — inspired our `session-dedup` engine.
token-savior	1.1k	Bash-output compaction + MCP profiles — inspired our compression bail-out discipline and MCP tool-manifest reduction.
token-saver	117	Content-aware, per-file-type output compression with failure-aware bail-out — validated our per-type dispatch and minimum-gain skip.
token-optimizer	1.7k	"Find the ghost tokens" — its offload + recoverable-handle pattern informed our CCR offload thinking.
TokenMizer	16	A session-graph + cross-turn line-dedup blueprint that informed our session-dedup design.
OmniCompress	3	Rust columnar-JSON + content-addressed retrieve + cross-message dedup — validated our `headroom`/`ccr`/`session-dedup` engine design and the cache-stable "compressed form is position-independent" invariant.
mcp-compressor	98	MCP tool-schema/description compression — informed our MCP tool-manifest cardinality reduction.
RepoMapper	187	Aider-style repo-map ranking — informed our repo-map / retrieval-ranking exploration.
quiet-shell-mcp	4	Declarative shell-output reduction over MCP — validated our declarative bash-output compaction.
ts-morph	6.1k	TypeScript Compiler API toolkit — inspired our parser-based comment removal that preserves string, template and regex literals.

🧠 Memory & RAG

Project	⭐	How it inspired OmniRoute
Mem0	61.2k	Universal memory layer — its proxy-as-write/read-boundary model shaped our memory architecture.
Letta (MemGPT)	23.9k	Stateful agents with tiered memory — inspired our Context Control & Recovery (CCR) tiered model.
WFGY	1.8k	The ProblemMap taxonomy of 16 recurring RAG/LLM failure modes — the shared vocabulary in our troubleshooting guide.

🛰️ Traffic inspection, MITM & transparent proxy

Project	⭐	How it inspired OmniRoute
llm-interceptor	49	MITM interception/analysis of coding-assistant ↔ LLM traffic — our Traffic Inspector ports its SSE merge, conversation normalization, host passthrough and secret masking (MIT).
ProxyBridge	5.5k	Transparent per-process proxy routing — inspired our crash-safe MITM teardown, socket idle-timeouts, `/proc` process attribution and TPROXY capture.

📚 Model data, observability & UI

Project	⭐	How it inspired OmniRoute
models.dev	6.0k	Open database of AI model specs, pricing and capabilities — synced natively into our model catalog.
React Flow / xyflow	37.7k	The node-based graph library powering our real-time Compression Studio and Combo/Routing Studio.
LangGraph	37.6k	LangGraph Studio's live workflow-graph visualization inspired our Studios' real-time cascade view.
Langfuse	31.4k	Its trace → span → generation observability model shaped our Compression Studio waterfall.
Kiali	3.6k	Istio service-mesh observability — inspired our circuit-breaker badges and error-edge visuals in the Routing/Combo Studio.
lobe-icons	2.2k	AI/LLM brand logos that render the provider icons across our dashboard.

🛡️ Security

Project	⭐	How it inspired OmniRoute
awesome-secure-defaults	710	A curated list of secure-by-default libraries that guides our security choices (Helmet.js, DOMPurify, ssrf-req-filter, safe-regex, Google Tink).

🧭 Complementary tools

Project	⭐	How it inspired OmniRoute

📄 License

MIT License - see LICENSE for details.

⬆ Back to top · Built with ❤️ for the open-source AI community.

_{OmniRoute v3.8.49 · Node ≥22.22.2 · MIT License · omniroute.online}

openclaw/openclaw

Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

OpenClaw 🦞 — Your assistant, on your devices, in your chats

OpenClaw is a personal AI assistant that runs on your devices and meets you in the channels you already use. It is designed for a single operator and connects models, tools, messaging channels, and optional companion apps through one Gateway.

Website · Docs · Getting started · Showcase · FAQ · Vision · DeepWiki

Install

The installer supports macOS, Linux, and Windows. It provisions a supported Node.js runtime when needed.

# macOS / Linux / WSL2
curl -fsSL https://openclaw.ai/install.sh | bash

# Windows PowerShell
iwr -useb https://openclaw.ai/install.ps1 | iex

Already manage Node.js? Install the published package instead (Node 22.22.3+, 24.15+, or 25.9+):

npm install -g openclaw@latest

See the installation guide for npm 12 lifecycle-script requirements, Docker, Nix, and other deployment paths.

Quick start

openclaw onboard --install-daemon
openclaw gateway status
openclaw dashboard

Onboarding verifies model access, creates the workspace, and configures the Gateway. The last command opens the Control UI; send a message there to confirm the assistant is working. See the getting started guide for channel setup and troubleshooting.

How it fits together

The Gateway is the local control plane for sessions, tools, events, and channel connections.
The Control UI, CLI, and TUI connect to the Gateway.
Channels bring the assistant to WhatsApp, Telegram, Slack, Discord, Google Chat, Signal, iMessage, and other messaging services.
Companion apps and nodes add voice, Canvas, camera, screen, and device-local actions on supported platforms.

OpenClaw works with hosted and local model providers. Its tools, skills, and plugins extend what an assistant can do.

Security

Treat inbound messages as untrusted input. DM-capable channels pair unknown senders by default; approve a pairing request with openclaw pairing approve <channel> <code>.

Tools run on the host for the main session unless you configure sandboxing. Read the security guide, exposure runbook, and sandboxing guide before connecting other users or exposing the Gateway remotely.

Documentation

Goal	Start here
Configure models and auth	Models · Model providers
Connect a messaging service	Channels
Add tools, skills, and plugins	Tools · Skills · Plugins · ClawHub
Run apps and device nodes	Platforms · Nodes
Use the CLI and chat commands	CLI reference · Slash commands
Configure or operate the Gateway	Configuration · Architecture · Updating · Release channels

Development

The repository is a pnpm workspace. Plain npm install at the repository root is not supported.

git clone https://github.com/openclaw/openclaw.git
cd openclaw
pnpm install
pnpm build
pnpm ui:build

See CONTRIBUTING.md for the contribution workflow and the source setup guide for the development loop.

Community

OpenClaw is developed in the open by the OpenClaw Foundation, a non-profit. See CONTRIBUTING.md for maintainers and contribution guidelines; AI-assisted PRs are welcome.

Use the issue chooser for bugs and feature requests, ask setup questions in Discord, and report vulnerabilities through SECURITY.md. New capabilities usually belong in plugins built on the plugin SDK and shared through ClawHub.

OpenClaw was built for Molty, a space lobster AI assistant, by Peter Steinberger and the community. Explore the project lore, soul.md, Peter's site, Star History, and @openclaw.

Special thanks to Mario Zechner for his support and for pi, and to Adam Doppelt for the lobster.bot domain.

Contributors

Thanks to all clawtributors:

License

MIT © OpenClaw Foundation. See THIRD_PARTY_NOTICES.md for incorporated or adapted code.

cloudflare/computer

Give your agent a computer 👾

Cloudflare Computer

Cloudflare Computer is a virtual filesystem that lives inside a Durable Object. The Durable Object holds the authoritative state in SQLite and exposes one pluggable execution surface through workspace.runtime. Three backends ship today:

Container projects the SQLite state into a sandbox container as a real FUSE mount. A sandbox-side daemon (computerd) mounts the state as a filesystem and syncs changes back over a capnweb RPC channel. Full Linux userland, real binaries, real network.
Isolate shell runs just-bash in a Dynamic Worker. It reaches the authoritative Workspace over Workers RPC, so there is no second store or sync round trip.
Isolate JavaScript runs an ECMAScript module in a fresh Dynamic Worker with structured input/results, durable relative imports, configured libraries, Workspace-backed node:fs/promises, and trusted ws:git and ws:artifacts modules.

A Workspace may register multiple backends under stable IDs. workspace.runtime.exec(source, { backend }) is the single execution entry point; the selected backend defines whether source is a shell command or an ECMAScript module. Backends connect lazily on first use.

Workspace can also be constructed without a backend at all, giving callers the filesystem on its own.

Important

PREVIEW ONLY This package is provided as a preview for feedback only. APIs are unstable and the design is subject to change.

Suitable for experiments, exploration and prototypes. It is NOT suitable for production use at this time.

The specification under docs/ is forward-looking — read it for intent, not as description of the code today.

Using it

If you want to build on Cloudflare Computer, install @cloudflare/computer and follow that package's README — it has the installation steps, the entrypoint map, and worked examples of the fs and runtime surfaces.

To contribute feedback, see CONTRIBUTING.md. Approved collaborators should follow COLLABORATORS.md for setup, build, and test instructions.

Examples

The examples/ directory holds runnable consumers of the public surface. Each is a Worker workspace with its own README.

examples/container — runs computerd inside a container, mounts a workspace, and talks to a Durable Object over capnweb. A write / read / exec HTTP surface.
examples/worker-shell — same HTTP surface as the container example, but the shell runs just-bash in a Dynamic Worker loaded through env.LOADER. No container.
examples/worker-javascript — mirrors worker-shell, but exec evaluates an ECMAScript module in a Dynamic Worker instead of running a shell command.
examples/think — a @cloudflare/think chat agent that uses the workspace as its working directory, reachable from a terminal.
examples/think-compare-runtimes — a web UI that runs the same agent task against the container and worker runtimes side by side.
examples/tutorial — a step-by-step build: one endpoint, one agent that writes a markdown recipe card on the host and runs pandoc on it in the container to produce a PDF.
examples/artifacts — generates a Worker project in a workspace and publishes it to Cloudflare Artifacts as a clone-ready repo.
examples/assets — turns a prompt into an image with Workers AI, writes it to the workspace, and returns a shareable link through @cloudflare/computer/assets.

Repository layout

The repo is a small monorepo. Each package has its own README with package-specific status and usage notes.

packages/dofs (@cloudflare/dofs) — Durable Object SQLite-backed virtual filesystem, sync protocol building blocks, and a @platformatic/vfs provider for Node.
packages/rpc (@cloudflare/computer-rpc) — capnweb wire types and server/client helpers shared between the Durable Object and computerd.
packages/computerd (@cloudflare/computerd) — the computerd daemon: a FUSE mount plus HTTP/WebSocket RPC server that runs inside the sandbox container.
packages/computer (@cloudflare/computer) — the top-level Computer package consumed by Durable Objects. Work in progress.
packages/computer-computerd-linux-x64 — private Docker image context for the prebuilt computerd linux-x64 binary. The image, not an npm package, is the release artifact.

Performance

computerd's FUSE mount beats real disk on metadata-heavy work and trails it on large sequential I/O. See docs/19_performance.md for the full fs-bench numbers, a cloudflare/sandbox-sdk npm install comparison, and how to reproduce them.

Documentation

docs/ — design specification. Forward-looking; treat as intent.
docs/19_performance.md — filesystem benchmarks.

Contributing

We accept bug reports, fix proposals, feature requests, and design proposals through issues and discussions. We do not accept unsolicited pull requests. See CONTRIBUTING.md for the public contribution paths.

Approved collaborators should follow COLLABORATORS.md for setup, formatting, testing, commit message, and pull request conventions.

If you're working in this repo as an agent, start with AGENTS.md and the skills under .agents/skills/.

License

MIT. See LICENSE.

nitrojs/nitro

Next Generation Server Toolkit. Create web servers with everything you need and deploy them wherever you prefer.

Nitro

Note

You’re viewing the v3 branch. For the current stable release, see Nitro v2.

Nitro extends your Vite app with a production-ready server, designed to run anywhere. Add server routes, deploy across multiple platforms, and enjoy a zero-config experience.

📘 Docs: https://nitro.build

Contributing

Check out the Contribution Guide to get started.

License

Released under the MIT License.

github/docs

The open-source repo for docs.github.com

GitHub Docs

Welcome to GitHub Docs! GitHub’s documentation is open source, meaning anyone from inside or outside the company can contribute. For full contributing guidelines, visit our contributing guide.

Quick links by contributor type

Hubbers (GitHub employees): See CONTRIBUTING.md in the docs-content repository for GitHub-specific processes.
Open source contributors: See CONTRIBUTING.md in the docs repository for a quick-start summary.

How we sync changes across Docs repositories

There are two GitHub Docs repositories:

github/docs (public): Open to external contributions
github/docs-internal (private): For GitHub employee contributions.

The two repositories sync frequently. Content changes in one are reflected in the other. Hubbers might prefer to post in docs when working with a customer, but docs has limitations on the types of contributions it accepts to safeguard the site and our workflows. Internal contributions should usually go to docs-internal.

Important: The docs repository accepts contributions to content files (.md files in /content and select /data sections like reusables only). Infrastructure files, workflows, and site-building code are not open for external modification.

New to contributing

Here are some resources to help you get started with open source contributions:

License

This project is dual-licensed under:

Creative Commons Attribution 4.0 - for documentation and content in the assets, content, and data folders (see LICENSE)
MIT License - for code (see LICENSE-CODE)

fosrl/pangolin

Identity-aware VPN and tunneled reverse proxy for remote access based on WireGuard®.

Website | Documentation | Contact Us

Get started with Pangolin at app.pangolin.net

Pangolin is an open-source, identity-based remote access platform built on WireGuard® that enables secure connectivity to infrastructure anywhere. It combines reverse-proxy and VPN capabilities into one platform, providing browser-based access to web applications and client-based access to private resources with NAT traversal, all with granular access control.

Installation

Get started for free with Pangolin Cloud.
Or, check out the quick install guide for how to self-host Pangolin.
- Install from the DigitalOcean marketplace for a one-click pre-configured installer.

Deployment Options

Pangolin Cloud - Fully managed service - no infrastructure required.
Self-Host: Community Edition - Free, open source, and licensed under AGPL-3.
Self-Host: Enterprise Edition - Licensed under Fossorial Commercial License. Free for personal and hobbyist use, and for businesses making less than $100K USD gross annual revenue.

Key Features

Connect remote networks with sites and NAT traversal

Pangolin's site connectors provide gateways into networks so you can access any networked resources. Sites use outbound tunnels and intelligent NAT traversal to make networks behind restrictive firewalls available for authorized access without public IPs or open ports. Easily deploy a site as a binary or container on any platform.

Lightweight user-space connector runs anywhere
Punches through any firewall
Doesn't require open ports or a public IP
Strict network segmentation
WireGuard-based
Get alerts when a device or network resource goes down

Browser-based reverse proxy access

Expose HTTPS web applications and connect to VNC, RDP, and SSH entirely in the browser through identity and context-aware tunneled reverse proxies. Users access resources with authentication and granular access control without installing a client. Pangolin handles routing, load balancing, health checking, and automatic SSL certificates without exposing your network directly to the internet.

Expose a web panel anywhere
Access via any web browser
Single sign-on across all resources
HTTPS resources
Remote desktop in the browser with VNC and RDP
In-browser SSH terminal with privileged access management (PAM)
PIN codes, passcodes, email OTP, geoblocking, allow-lists, and more

Client-based private resource access

Access private resources like SSH servers, databases, RDP, and entire network ranges through Pangolin clients. Intelligent NAT traversal enables connections even through restrictive firewalls, while DNS aliases provide friendly names and fast connections to resources across all your sites. Add redundancy by routing traffic through multiple connectors in your network.

Peer-to-peer with intelligent NAT traversal
Hosts/IPs and port ranges
Network ranges/CIDRs
Friendly DNS aliases for network addresses
Privileged access management (PAM) with SSH resources
Private HTTPS resources only accessible on the private network

Give users and roles access to resources

Use Pangolin's built-in users or bring your own identity provider and set up role-based access control (RBAC). Grant users access to specific resources, not entire networks. Unlike traditional VPNs that expose full network access, Pangolin's zero-trust model ensures users can only reach the applications, services, and routes you explicitly define.

Bring your existing identity provider (IdP) or use Pangolin identities
Sync users and roles from your IdP
User- and role-based access control
Full network audit and access logs

Find and launch resources from a personalized home page

Give users a landing page to quickly find and open the resources they can access. Resources are grouped by site or label, searchable, and filterable, with grid or list views. Saved views capture filters, grouping, and layout as personal or organization-wide defaults.

Single place for admins and non-admins to see accessible resources
Create reusable views for common access patterns

Download Clients

Download the Pangolin client for your platform:

Get Started

Sign up now

Create a free account at app.pangolin.net to get started with Pangolin Cloud.

Check out the docs

We encourage everyone to read the full documentation first, which is available at docs.pangolin.net. This README provides only a very brief subset of the docs to illustrate some basic ideas.

Licensing

Pangolin is dual licensed under the AGPL-3 and the Fossorial Commercial License. For inquiries about commercial licensing, please contact us at contact@pangolin.net.

Contributions

Please see CONTRIBUTING in the repository for guidelines and best practices.

amruthpillai/reactive-resume

A one-of-a-kind resume builder that keeps your privacy in mind. Completely secure, customizable, portable, open-source and free forever. Try it out today!

Reactive Resume

Reactive Resume is a free and open-source resume builder that simplifies the process of creating, updating, and sharing your resume.

Get Started · Learn More

Reactive Resume makes building resumes straightforward. Pick a template, fill in your details, and export to PDF—no account required for basic use. For those who want more control, the entire application can be self-hosted on your own infrastructure.

Built with privacy as a core principle, Reactive Resume gives you complete ownership of your data. The codebase is fully open-source under the MIT license, with no tracking, no ads, and no hidden costs.

Features

Resume Building

Real-time preview as you type
Multiple export formats (PDF, JSON, DOCX)
Drag-and-drop section ordering
Custom sections for any content type
Rich text editor with formatting support

Templates

Professionally designed templates
A4 and Letter size support
Customizable colors, fonts, and spacing
Structured Style Rules for section and text styling

Privacy & Control

Self-host on your own infrastructure
No tracking or analytics by default
Full data export at any time
Delete your data permanently with one click

Extras

AI integration (OpenAI, Google Gemini, Anthropic Claude)
Multi-language support
Share resumes via unique links
Import from JSON Resume format
Dark mode support
Passkey and two-factor authentication

Templates

_Azurill	_Bronzor	_Chikorita	_Ditto
_Gengar	_Glalie	_Kakuna	_Lapras
_Leafish	_Onyx	_Pikachu	_Rhyhorn
_Ditgar	_Meowth	_Scizor

Quick Start

The quickest way to run Reactive Resume locally:

# Clone the repository
git clone --depth=1  https://github.com/amruthpillai/reactive-resume.git
cd reactive-resume

# Start all services
docker compose up -d

# Access the app
open http://localhost:3000

For detailed setup instructions, environment configuration, and self-hosting guides, see the documentation.

Tech Stack

Category	Technology
Framework	TanStack Start (React 19, Vite)
Runtime	Node.js
Language	TypeScript
Database	PostgreSQL with Drizzle ORM
API	ORPC (Type-safe RPC)
Auth	Better Auth
Styling	Tailwind CSS
UI Components	Base UI + shadcn-style package
State Management	Zustand + TanStack Query

Documentation

Comprehensive guides are available at docs.rxresu.me:

Guide	Description
Getting Started	First-time setup and basic usage
Self-Hosting	Deploy on your own server
Development Setup	Local development environment
Project Architecture	Codebase structure and patterns
Exporting Your Resume	PDF and JSON export options

Self-Hosting

Reactive Resume can be self-hosted using Docker. The stack includes:

PostgreSQL — Database for storing user data and resumes
SeaweedFS (optional) — S3-compatible storage for file uploads

From v5.1.0 onwards — PDF generation now runs entirely client-side via @react-pdf/renderer. New deployments no longer require Browserless, Chromium, or any external print service as a dependency. The PRINTER_* and BROWSERLESS_* environment variables are no longer read and can be removed from your .env.

Pull the latest image from Docker Hub or GitHub Container Registry:

# Docker Hub
docker pull amruthpillai/reactive-resume:latest

# GitHub Container Registry
docker pull ghcr.io/amruthpillai/reactive-resume:latest

See the self-hosting guide for complete instructions.

Support

Reactive Resume is and always will be free and open-source. If it has helped you land a job or saved you time, please consider supporting continued development:

Other ways to support:

Star this repository
Report bugs and suggest features
Improve documentation
Help with translations

Star History

Contributing

Contributions make open-source thrive. Whether fixing a typo or adding a feature, all contributions are welcome.

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

See the development setup guide for detailed instructions on how to set up the project locally.

License

MIT — do whatever you want with it.

browseros-ai/BrowserOS

🌐 The open-source Agentic browser; alternative to ChatGPT Atlas, Perplexity Comet, Dia.

Two browsers: one for your agents, one for you.

Free · Open source · Everything runs on your machine

BrowserOS neo: the browser for your agents

What is BrowserOS neo? A second browser, just for your AI agents. Import your logins from Chrome in one click, connect Claude Code, Codex, or any MCP agent, and hand off your web tasks. Agents run in parallel in their own tabs. You watch live, or replay any session like a video.

Website · Docs

Get started

Install BrowserOS neo and import from Chrome in one click: logins, bookmarks, extensions.
It finds your agents. Claude Code, Codex, Cursor, VS Code, OpenClaw, Hermes; connect with one click.
Give it a task from your agent. "Book me the cheapest flight to London." Watch it live from your new tab, replay it later.

To be clear, BrowserOS neo is NOT a Chrome replacement. It's a secondary browser that sits next to Chrome, and we’ve made it agent friendly.

What can your agents do?

Anything that needs a logged-in browser:

Post content to your social media (LinkedIn, Twitter/X), queue posts, pull engagement numbers
Clear your inbox, unsubscribe from junk email
Update your CRM, file expenses, pull reports from internal tools

Key features

Live dashboard Your new tab shows every agent working right now: which site it's on, what it's doing, how far along. Docs
One-click connect Automatically connects to every harness. We built tools optimized for web use! Docs
Replay every session Every session is saved as a scrubbable video on your disk with a step-by-step action timeline. Rewind and see exactly what happened. Docs

Your logins. Agents automate your real work using your logged-in accounts, not a blank sandbox. How it works
Parallel agents. Fire off several tasks at once. Each agent works in its own tab while you keep browsing.
Fewer tokens. For the same task, BrowserOS neo consumes significantly less tokens compare to other solutions (like Claude's chrome extension, Codex browser).
Local-only, privacy-first. Sessions, screenshots, and history live under ~/.browserclaw/ and never leave your machine. Privacy

Why BrowserOS neo over the alternatives?

Not a headless driver. Playwright and agent-browser spin up a fresh Chrome subprocess with no logins. Great for CI, useless for real work which requires your logged-in state like "read my inbox." BrowserOS neo imports your logins with one click and persists it across sessions.
Not a cloud browser. Cloud browsers (like browser-use, browserbase) run in a datacenter, so logging into your accounts is a pain, and sites like Twitter and LinkedIn block you because you are on a datacenter IP. BrowserOS neo runs on your machine, on 127.0.0.1.
Not a locked-in AI browser. Atlas, Comet, and Dia only work with their own AI. BrowserOS neo works with the agents you already use and pay for -- Claude Code, Cowork, Codex, Cursor, etc.

BrowserOS: the AI browser for humans

What is BrowserOS? BrowserOS is a free, open-source Chromium fork with an AI agent built into every new tab. Ask it to summarise a page, click through a flow, extract data, or run a scheduled task, and it uses 20+ built-in tools plus 40+ app integrations to get the work done. Bring your own AI keys or run everything locally with Ollama.

Every AI browser today asks you to sign into their cloud and hand over your data. BrowserOS is the one that doesn't. Same daily browser you already use, with a helpful agent one keystroke away.

Website · Docs

Get started

Download and install BrowserOS: macOS · Windows · Linux (AppImage) · Linux (Debian).
Import from Chrome in one click. Bookmarks, passwords, extensions all carry over.
Connect your AI provider. Claude, OpenAI, Gemini, ChatGPT Pro via OAuth, or local models via Ollama or LM Studio.

Key features

BrowserOS agent in action Ask it in plain English. 20+ built-in tools plus 40+ app integrations (Gmail, Slack, GitHub, Linear, Notion, and more). Docs
Install as MCP and control from claude-code Turn BrowserOS into an MCP server and drive it from Claude Code, Cursor, or any MCP client. Docs
Use BrowserOS to chat Chat about the current page from the side panel. Summarise, ask questions, transform what you're reading. Docs
Use BrowserOS to scrape data Point the agent at a page, tell it what to pull, and get structured data back. Docs

Cowork with files. Combine browser automation with local file operations in one session. Docs
Scheduled tasks. Run agents on autopilot: daily, hourly, or every few minutes. Docs
Bring your own AI. 11+ providers, or fully local with Ollama and LM Studio. Provider list
Real ad blocking. uBlock Origin with full Manifest V2 support. Docs

Why BrowserOS over the alternatives?

Not Chrome with an AI extension. Extensions can't touch the browser chrome, can't run scheduled background tasks, can't ship the 20+ built-in tools that the agent uses natively. BrowserOS builds the agent into Chromium itself.
Not Comet, Atlas, or Dia. Those AI browsers route your prompts through their cloud with their model. BrowserOS runs on your machine with your AI keys. Your data stays yours.

How BrowserOS compares

	BrowserOS	Chrome	Brave	Dia	Comet	Atlas
Open Source	✅	❌	✅	❌	❌	❌
AI Agent	✅	❌	❌	❌	✅	✅
MCP Server	✅	❌	❌	❌	❌	❌
Cowork (files + browser)	✅	❌	❌	❌	❌	❌
Scheduled Tasks	✅	❌	❌	❌	❌	❌
Bring Your Own Keys	✅	❌	✅	❌	❌	❌
Local Models (Ollama)	✅	❌	✅	❌	❌	❌
Local-first Privacy	✅	❌	✅	❌	❌	❌
Ad Blocking (MV2)	✅	❌	✅	❌	✅	❌

FAQ

What's the difference between BrowserOS neo and BrowserOS? BrowserOS neo is a browser your AI drives. BrowserOS is a browser you drive, with an AI agent built in. Both ship from this repo and run side by side. Keep your daily browser, and let agents work in neo.

Which AI tools work with BrowserOS neo? Any AI that speaks MCP. Claude Code, Codex, Cursor, VS Code, Zed, OpenCode, Hermes, OpenClaw and Antigravity connect with one click.

Does anything leave my machine? Your sessions, screenshots, history, and settings live under ~/.browserclaw/ and never upload. BrowserOS neo sends anonymous product-usage events (agent connect/disconnect, version, OS) to help us improve the app; it never sends URLs, page content, prompts, tool results, or screenshots. Off with one toggle in Settings. Full policy.

Do my Chrome extensions and bookmarks work? Yes. Both browsers are Chromium forks, so Chrome extensions work and your bookmarks, passwords, and settings import in one click.

What platforms are supported? BrowserOS neo runs on macOS and Windows. BrowserOS runs on macOS, Windows, and Linux. System requirements match Google Chrome.

Get help

Architecture

Both products ship from this monorepo. Two main subsystems: the browser (Chromium fork) and the agent platform (TypeScript/Go).

BrowserOS/
├── packages/browseros/              # Chromium fork + build system (Python)
│   ├── chromium_patches/            # Patches applied to Chromium source
│   ├── build/                       # Build CLI and modules
│   └── resources/                   # Icons, entitlements, signing
│
├── packages/browseros-agent/        # Agent platform (Rust/TypeScript/Go)
│   ├── apps/
│   │   ├── claw-server-rust/        # BrowserOS neo backend: MCP endpoint + JSON API (Rust)
│   │   ├── claw-app/                # BrowserOS neo dashboard extension (WXT + React)
│   │   ├── claw-onboard/            # BrowserOS neo onboarding flow
│   │   ├── server/                  # BrowserOS MCP server + AI agent loop (Bun)
│   │   ├── app/                     # BrowserOS extension UI (WXT + React)
│   │   └── cli/                     # CLI tool (Go)
│   │
│   └── packages/
│       ├── cdp-protocol/            # CDP type bindings
│       └── shared/                  # Shared constants

Package	What it does
`packages/browseros`	Chromium fork: patches, build system, signing
`apps/claw-server-rust`	BrowserOS neo backend: MCP endpoint agents connect to, plus the API behind the dashboard
`apps/claw-app`	BrowserOS neo new-tab dashboard: watch, replay, and manage agent sessions
`apps/server`	Bun server exposing the browser MCP tools and running the BrowserOS AI agent loop
`apps/app`	BrowserOS extension: new tab, side panel chat, onboarding, settings
`apps/cli`	Go CLI: control BrowserOS from the terminal or AI coding agents
`cdp-protocol`	Type-safe Chrome DevTools Protocol bindings

Contributing

We'd love your help making BrowserOS and BrowserOS neo better. See the Contributing Guide for details.

Agent development (TypeScript/Go): see the agent monorepo README for setup.
Browser development (C++/Python): requires ~100GB disk space. See packages/browseros for build instructions.

Credits

ungoogled-chromium: we use some of its patches for enhanced privacy. Thanks to everyone behind this project.
The Chromium Project: at the core of both browsers, making it possible for them to exist in the first place.

Citation

If you use BrowserOS or BrowserOS neo in your research or project, please cite:

@software{browseros2025,
  author = {Nithin Sonti and Nikhil Sonti and {BrowserOS-team}},
  title = {BrowserOS: The open-source Agentic browser},
  url = {https://github.com/browseros-ai/BrowserOS},
  year = {2025},
  publisher = {GitHub},
  license = {AGPL-3.0},
}

License

BrowserOS and BrowserOS neo are open source under the AGPL-3.0 license.

Stargazers

Thank you to all our supporters.

Nikhil	Nithin	Dani

Built with ❤️ from San Francisco

danielmiessler/LifeOS

⛰️A General Hill-climbing AI harness that helps you move from Current State to Ideal State in both Life and Work.

The AI-Powered Life Operating System

Website · Install · Walkthrough · Docs · Releases

https://github.com/user-attachments/assets/329897f5-828b-4c23-b607-1cf9c71cb4ec

LifeOS is a General Purpose AI Harness for doing anything you want to do in life and work with AI. It captures who you are, what you care about, and where you're trying to go, then uses AI that knows you to help you get there.

And because it has your full context, it makes everything you do more efficient and effective, from building apps, to starting a business, to creative projects…basically anything.

Note

The whole system works on one central concept: moving from your Current State to your Ideal State — in pursuit of Euphoric Surprise.

⭐ If LifeOS is useful to you, star the repo

Stars help more people find the project and keep it moving. It takes one click.

Install

Give it to your AI. LifeOS is installed by an AI, so the install is just a prompt. Paste this into your AI coding harness — Claude Code, Cursor, Codex, Hermes, or any capable agent — and it does the whole setup for you:

Read https://ourlifeos.ai/install and install LifeOS for me.

Your AI reads the install page and walks the setup, asking permission before it touches anything.

Prefer the terminal? There's a one-line shortcut for Claude Code on macOS/Linux:

curl -fsSL https://ourlifeos.ai/install.sh | bash

Either path needs a capable AI coding harness — we build and run on Claude Code — and bun.

Core Components

The unique features — the parts you won't find anywhere else. Anything a good harness already provides on its own (built-in subagents, for example) isn't listed; this is only what LifeOS adds. See them live and click through on ourlifeos.ai.

🧩 Skills

LifeOS installs as one self-contained skill that bundles the whole library — research, security, writing, art, and more. Browse them all on the site.

Browse all skills →

❓ FAQ

How is LifeOS different from using an AI harness on its own?

Your harness gives you raw capability. LifeOS is the layer on top that makes it yours — a system that knows your goals, people, and context, and keeps working toward them:

Persistent memory — Your DA remembers past sessions, decisions, and learnings
Custom skills — Specialized capabilities for the things you do most
Your context — Goals, contacts, preferences—all available without re-explaining
Intelligent routing — Say "research this" and the right workflow triggers automatically
Self-improvement — The system modifies itself based on what it learns

Your harness is the engine. LifeOS is everything else that makes it your car.

What harness does LifeOS run on?

Any high-end one. LifeOS is harness-agnostic by design — it's built on universal primitives (hooks, skills, context files, agentic routing), not one vendor's features. The code is TypeScript and Bash, and the core ideas — TELOS, the Algorithm, skills, memory — port to any capable agent.

Daniel builds and runs it on Claude Code, so that's the most-tested path today. But LifeOS isn't locked to it, and it's designed to run wherever your AI does.

How is this different from fabric?

Fabric is a collection of AI prompts (patterns) for specific tasks. It's focused on what to ask AI.

LifeOS is infrastructure for how your DA operates—memory, skills, routing, context, self-improvement. They're complementary. Many LifeOS users integrate Fabric patterns into their skills.

What if I break something?

Recovery is straightforward:

Back up first — Before any upgrade: cp -r ~/.claude ~/.claude-backup-$(date +%Y%m%d)
USER/ is safe — Your customizations in USER/ are never touched by the installer or upgrades
Settings merge, not overwrite — The installer only updates identity and version fields; your hooks, statusline, and custom config are preserved
Git-backed — Version control everything, roll back when needed
History is preserved — Your DA's memory survives mistakes
DA can fix it — Your DA helped build it, it can help repair it
Re-install — Run the installer again; it detects existing installations and merges intelligently

🎯 Roadmap

Feature	Description
Local Model Support	Run LifeOS with local models (Ollama, llama.cpp) for privacy and cost control
Granular Model Routing	Route different tasks to different models based on complexity
Remote Access	Access your LifeOS from anywhere—mobile, web, other devices
Outbound Phone Calling	Voice capabilities for outbound calls
External Notifications	Robust notification system for Email, Discord, Telegram, Slack

🌐 Community

GitHub Discussions: Join the conversation

Community Discord: LifeOS is discussed in the community Discord along with other AI projects

Twitter/X: @danielmiessler

Blog: danielmiessler.com

Star History

🤝 Contributing

We welcome contributions! See our GitHub Issues for open tasks.

Fork the repository
Make your changes — Bug fixes, new skills, documentation improvements
Test thoroughly — Install in a fresh system to verify
Submit a PR with examples and testing evidence

📜 License

MIT License - see LICENSE for details.

🙏 Credits

Anthropic and the Claude Code team — First and foremost. You are moving AI further and faster than anyone right now. Claude Code is the foundation that makes all of this possible.

IndyDevDan — For great videos on meta-prompting and custom agents that have inspired parts of LifeOS.

Contributors

LifeOS is built in the open, and the community's pull requests, forensic bug reports, and fresh-install writeups directly shape every release. The public repo is generated from a private source tree, so community PRs are ported into source with credit rather than merged directly — same fix, durable across releases.

^{The 28 highest-commit contributors — see all on the contributors graph. Avatars are committers only, so the lists below carry everyone the graph can't see.}

fayerman-source — Google Cloud TTS provider integration and Linux audio support for the voice system.

Matt Espinoza — Extensive testing, ideas, and feedback, plus roadmap contributions.

Code contributions (merged or ported PRs): adamlevoy · anikinsasha · asdf8675309 · atabisz · chrisglick · christauff · erf1nd0r · HotSauceHacker · imrathion · jbmml · jnpkr · justinkatz94-glitch · ksylvan · m4nt0de4 · m8ryx · MarvinDontPanic · maxolasersquad · Mutdogus · neilinger · neilsoult · pybe · qozle · rathko · rpriven · runnerr0 · salmanmkc · sauldataman · Seadubb · Spirotot · StarksLabs · thatsjet · tzioup

Bug reports, fresh-install forensics, and design feedback: badosanjos · benoitdepaire · bnkath2o · brycemagera · catchingknives · DAESA24 · deleyva · DennisTraub · docxology · DolphusCY · donovan-sec · DonovanJonesUK · eccentricnode · fjp-veo · harryf · hjbrandt · HyggeHacker · ichoosetoaccept · infinitelyloopy-bt · jacobo-ortiz · JElliottMiller · jdrolls · jlacour-git · jmmarkiewicz · karlwaldman · klausagnoletti · lexilexikon · lgangitano · loudoguno · luccomo · MatiasBarboza · MHoroszowski · michaelaye · mygirleatsmayo · nbost130 · NodarDavituri · NorthwoodsSentinel · packetsherpa · ricklesgibson · rikitikitavi2012-debug · Riskjuggler · sbusc · simeonzickert · Steffen025 · stratofax · tzioup · vanvonlj · vichong · virtualian · vpzed · waveman2020-sudo · wojteksbt · xmasyx

^{Refreshed with each release. If your contribution is missing, open an issue — that's a bug too.}

💜 Support This Project

LifeOS is free and open-source forever. If you find it valuable, you can sponsor the project.

📚 Related Reading

The Real Internet of Things — The vision behind LifeOS
AI's Predictable Path: 7 Components — Visual walkthrough of where AI is heading
Building a Personal AI Infrastructure — Full walkthrough with examples

📜 Update History

v7.28.3 — Cortex, Hermes, and hardened releases

Cortex — the memory system, named: hot-layer memory, a typed Knowledge Archive (People, Companies, Ideas, Research), learnings, and work history, consolidated across sessions so each one starts smarter than the last.
Hermes sidecar — an optional second front door: talk to your LifeOS as an agent from a terminal, with the same constitution, identity, and skills. One brain, another way in.
Named subsystems — Synapse (the input router), Conduit and Feed (the internal and external senses), Atlas (the live asset graph), and Ledger (change tracking), each a product within the product.
Release-security hardening — three new deterministic gates (sanitizer-residue, licensed-font, dead-legacy-doc-pointer); previously-shipped commercially licensed fonts removed.
Full release notes

v7.1.1 — Install Awareness

Doctor — bun LIFEOS/TOOLS/Doctor.ts reports the real state of every optional capability (live, broken, declined, stale) and reconciles hooks on disk against what's registered.
Capability-aware install — the installer asks now/later/never per optional capability and records the choice; failure-aware nudges surface the exact fix command when a broken capability trips.
System Health in Pulse — capability states, fix commands, and heartbeats on the hooks page.
Algorithm v8.4.0 — plain-language depth steering registers before a run opens; nudges bounded to a single line.
Community fixes — #1450, #1456, #1463, #1470, #1471. Thanks to the contributors.
Full release notes

v7.0.0 — The Bitter Pill Release

Bitter Pill Engineering — scaffolding cut across the whole system on one test: would a smarter model make this rule unnecessary? Reasoning choreography, self-scores, and duplicated routing are gone; the verification kernel stays.
Context roughly two-thirds smaller — the always-loaded doctrine plus its old capabilities file went from ~88KB to ~28KB, so every turn is faster and sharper.
Modes and tiers retired — one adaptive response format and one Algorithm loop that scales spend to what the work reveals, discovered not predicted.
The Algorithm v8.3.0 — the ISA is both the hill and the instrument; claims close only on tool evidence, and a single deterministic AlgorithmNudge layer keeps a run on track.
Hook layer consolidated — ~16 hooks folded into per-event dispatchers, plus community fixes for a work-events race, DA-name de-hardcoding, and config-driven identity.
Full release notes

v6.0.3 (2026-07-04) — Comprehensive AI-native install

Comprehensive component menu — INSTALL.md lays out the full two-tier model: Core plus à-la-carte enhancements (hooks, statusline, tooltips, spinner verbs, agents, Pulse, background jobs)
The lifeos launch alias wires --append-system-prompt-file so the constitutional layer actually loads
One canonical INSTALL.md rendered at ourlifeos.ai/install for humans and AIs
Full release notes

v6.0.2 (2026-07-03) — .md-first AI-native install

Skill-only distribution with an AI-native, .md-first install
The shipped install.sh pins v6.0.2 and points at danielmiessler/LifeOS
Full release notes

v6.0.0 (2026-07-02) — One Skill, One Install

Skill-based distribution — the whole system now ships as a single self-contained skill (LifeOS/): the orchestrator (SKILL.md + Workflows + Tools) plus a complete install payload (system prompt, Algorithm, 49 skills, hooks, agents, Pulse, statusline, USER + MEMORY scaffolds). One directory, one install.
First release under the LifeOS name — the project was PAI (Personal AI Infrastructure); this is the same system, renamed.
Install by prompt — hand it to your AI ("Read https://ourlifeos.ai/install and install LifeOS for me"); a curl … | bash shortcut lays it down from a terminal too.
Full Pulse on first boot — the installer stands up the Life Dashboard and menu-bar app, ships generic TELOS templates so the dashboard renders on a fresh install, and runs the setup interview to seed it.
Algorithm v6.23.0 — Current State → Ideal State across seven phases, classifier-driven mode + tier, cross-vendor verification at E4/E5.
Clean by construction — nothing personal ships; the USER tree is a blank template you populate. Release gates + a cross-vendor audit run before every publish.
Full release notes

v5.0.0 (2026-04-30) — Life Operating System

Pulse — unified daemon (port 31337): voice, hooks, observability, cron, Life Dashboard, wiki API, optional Telegram/iMessage bridges. Replaces every previous loose service.
The DA — Digital Assistant identity layer. PRINCIPAL_IDENTITY + DA_IDENTITY pair, loaded at session start. /interview walks you through naming your DA, picking a voice, capturing TELOS.
Algorithm v6.3.0 — seven-phase loop (OBSERVE → THINK → PLAN → BUILD → EXECUTE → VERIFY → LEARN). Classifier picks MINIMAL/NATIVE/ALGORITHM and tier (E1–E5) per prompt. Verification doctrine (live-probe, advisor calls, cross-vendor audit at E4/E5).
ISA System — Ideal State Artifact primitive. One document, twelve sections, five identities. Owned by the ISA skill.
Containment + release tooling — privacy is structural. Security gates run on every public release; two-stage release (stage → publish) never auto-chains.
Memory v7.6 — structured by purpose: WORK, KNOWLEDGE (typed graph), LEARNING, RELATIONSHIP, OBSERVABILITY, STATE.
Full release notes + migration guide

v4.0.3 (2026-03-01) — Community PR Patch

JSON array parsing fix in Inference.ts, 29 dead references removed, portability fixes, user context migration
Release Notes

v4.0.0 (2026-02-27) — Lean and Mean

38 flat skill directories → 12 hierarchical categories, dead systems removed, CLAUDE.md template system, comprehensive security sanitization
Release Notes

v3.0.0 (2026-02-15) — The Algorithm Matures

Algorithm v1.4.0, persistent PRDs and parallel loop execution, full installer with GUI wizard, agent teams/swarm, voice personality system
Release Notes

v2.4.0 (2026-01-23) — The Algorithm

Universal problem-solving system with ISC tracking, Euphoric Surprise as the outcome metric
Release Notes

v2.0.0 (2025-12-28) — v2 Launch

Modular architecture with independent skills, Claude Code native design

Built with ❤️ by Daniel Miessler and the LifeOS community

Augment yourself.

yikart/AiToEarn

Let's use AI to Earn!

Aitoearn：OPC（一人公司）的AI内容营销智能体

简体中文 | English | 日本語

Monetize · Publish · Engage · Create —— 一站式平台。

AiToEarn 通过 AI Agent自动化，帮助 OPC（一人公司）、创作者、品牌与企业在全球主流平台上构建、分发并变现内容。

支持渠道：抖音、小红书（Rednote）、快手、哔哩哔哩、视频号、TikTok、YouTube、Facebook、Instagram、Threads、Twitter（X）、Pinterest、LinkedIn

🚀 快速使用 AiToEarn（5 种方式）

方式	适合谁	需要部署吗
① 打开网站直接用	所有用户	❌ 不需要
② 在龙虾 OpenClaw 中用	龙虾用户	❌ 不需要
③ 在 Claude / Cursor 等 AI 助手中用	AI 工具用户	❌ 不需要
④ Docker 一键部署	想私有化部署的团队	✅ 需要服务器
⑤ 源码开发	开发者	✅ 需要开发环境

💡 方式 ②③④ 都需要先获取 API Key，请先看如何获取 API Key。

核心功能

AiToEarn 围绕内容创作者的完整变现链路，提供四大 Agent 能力：

Monetize · Publish · Engage · Create

💰 Monetize —— 内容赚钱

AiToEarn 最核心的目标：帮助每一位创作者赚钱。

创作者可以在平台出售内容以完成商家的推广任务。所有结算以结果为导向，我们提供三种结算模式：

结算模式	全称	含义
CPS	Cost Per Sale	按成交额结算
CPE	Cost Per Engagement	按互动量结算
CPM	Cost Per Mille	按播放量结算

📢 Publish —— 内容发布 Agent

一键将内容分发到全球 10+ 主流平台，告别逐个平台手动发布。

全网分发：覆盖抖音、快手、B站、小红书、视频号、微信公众号、TikTok、YouTube、Facebook、Instagram、Threads、X（Twitter）、Pinterest、LinkedIn
日历排期：像排日程一样统一规划所有平台的内容发布时间

▶ 观看演示视频

💬 Engage —— 内容互动 Agent

通过 AiToEarn 浏览器插件，在上述所有平台上实现自动化互动运营。

自动化操作：自动点赞、收藏、关注，批量高效运营
AI 智能回复：调用大模型为每条评论生成针对性回复，精准互动
评论挖掘：识别"求链接""怎么购买"等高转化信号，快速响应
品牌监测：实时追踪关于你品牌的讨论，主动参与热点话题

▶ 观看演示视频

🎨 Create —— 内容创作 Agent

我们用 Agent 的方式重构了内容制作流程。只需告诉 Agent 你的内容需求，它会自动完成从创意到成品的全部工作。

视频内容：Agent 自动调用视频生成模型（Grok、Veo、Seedance 等）、视频翻译模块、视频剪辑模块，一站式完成视频制作。

图文内容：支持调用 Nano Banana 等顶级图片模型，自动生成高质量图文内容。

批量生成：支持批量下发创作任务，Agent 可并行生成多条内容，快速铺量，适合矩阵账号运营和大规模内容分发场景。

▶ 观看演示视频

① 打开网站直接用

最简单的方式，打开浏览器即可使用，无需任何配置：

🇨🇳 中国用户访问：aitoearn.cn
🌍 国际用户访问：aitoearn.ai

🔑 如何获取 API Key（后续步骤的前置条件）

以下的方式 ②③④ 都需要 API Key。只需获取一次，所有方式通用。

3 步获取：

打开 aitoearn.cn（中国用户）或 aitoearn.ai（国际用户），注册并登录
点击左侧菜单设置
在 API Key 中点击创建，复制生成的 Key

② 在龙虾 OpenClaw 中使用

前置条件：已获取 API Key

请在服务器终端输入以下命令！请在服务器终端输入以下命令！请在服务器终端输入以下命令！

安装插件

npx -y @aitoearn/openclaw-plugin-cli

首次运行后会先让你选择环境并输入 API Key。请确保环境与 Key 匹配：

中国版：使用 aitoearn.cn 获取的 API Key
国际版：使用 aitoearn.ai 获取的 API Key

环境和 Key 不匹配会导致 401。

安装完成后，你就可以在 OpenClaw 中直接接收并执行 AiToEarn 的赚钱任务：

③ 在 Claude / Cursor / 其他 AI 助手中使用

前置条件：已获取 API Key

AiToEarn 支持所有兼容 MCP 协议的 AI 助手。以下是常见工具的配置方式：

请根据 API Key 来源选择地址，环境和 Key 不匹配会导致 401：

环境	MCP 地址	SSE 地址
中国版	`https://aitoearn.cn/api/unified/mcp`	`https://aitoearn.cn/api/unified/sse`
国际版	`https://aitoearn.ai/api/unified/mcp`	`https://aitoearn.ai/api/unified/sse`

Claude Desktop

找到并编辑 claude_desktop_config.json，添加：

{
  "mcpServers": {
    "aitoearn": {
      "type": "http",
      "url": "https://aitoearn.ai/api/unified/mcp",
      "headers": {
        "x-api-key": "你的API-Key"
      }
    }
  }
}

Cursor

在 Cursor 的 MCP 设置中添加：

MCP 地址：https://aitoearn.ai/api/unified/mcp
认证 Header：x-api-key: 你的API-Key

其他 AI 助手（通用配置）

任何支持 MCP 协议的工具，只需要两个信息：

配置项	值
MCP 地址	`https://aitoearn.ai/api/unified/mcp`
认证 Header	`x-api-key: 你的API-Key`

也支持 SSE 长连接方式：https://aitoearn.ai/api/unified/sse

💡 如果你是自部署用户，将 aitoearn.ai 替换为你自己的地址（如 localhost:8080）。

④ Docker 一键部署

前置条件：已安装 Docker

适合想把 AiToEarn 部署在自己服务器上的团队。3 条命令搞定，无需手动安装数据库：

git clone https://github.com/yikart/AiToEarn.git
cd AiToEarn
docker compose up -d

启动后打开 http://localhost:8080 即可使用。

配置 Relay（强烈推荐）

为什么要配 Relay？ 发布内容需要登录社交媒体账号（抖音、小红书、TikTok 等），而这些平台的 OAuth 登录需要开发者凭据。配置 Relay 后，你可以直接借用官方 aitoearn.ai 的凭据完成授权，不需要自己去各平台申请开发者账号。

在浏览器打开部署后的界面，进入 配置管理，按需分别配置：

Server → Relay 中转：用于内容发布和社交平台 OAuth 授权。
AI → Relay 中转：用于使用平台提供的 AI 模型。

OpenAI、Gemini、Anthropic 等模型服务商也可以在 AI → 模型服务商 中填写平台提供的 API Key 和 API 地址。

API Key 获取方式见上方说明。中国版 Key 搭配 https://aitoearn.cn/api，国际版 Key 搭配 https://aitoearn.ai/api；环境和 Key 不匹配会导致 401。

保存后点击 保存并重启，让对应服务重新加载配置。

📖 完整部署指南（生产环境配置、AI 服务、OAuth、存储等）请参阅 DOCKER_DEPLOYMENT_CN.md。

⑤ 源码开发

🧪 手动运行后端和前端（开发模式）

此模式主要用于本地开发和调试。您仍然可以使用 Docker 运行 MongoDB/Redis，或在配置文件中指向您自己的服务。

1. 启动后端服务

cd project/aitoearn-backend
pnpm install
# 复制配置文件用于本地开发
cp apps/aitoearn-ai/config/config.yaml apps/aitoearn-ai/config/local.config.yaml
cp apps/aitoearn-server/config/config.yaml apps/aitoearn-server/config/local.config.yaml
pnpm nx serve aitoearn-ai
# 在另一个终端
pnpm nx serve aitoearn-server

2. 启动前端 `aitoearn-web`

pnpm install
pnpm run dev

🖥️ 启动 Electron 桌面项目

# 克隆仓库
git clone https://github.com/yikart/AttAiToEarn.git

# 进入目录
cd AttAiToEarn

# 安装依赖
npm install

# 编译 sqlite（better-sqlite3 需要 node-gyp 和本地 Python）
npm run rebuild

# 启动开发
npm run dev

Electron 项目为 AiToEarn 提供桌面客户端。

贡献指南

请查看贡献指南开始参与。

联系

如果你在使用过程中遇到困难、使用问题或异常情况，优先通过 GitHub Issues 提交反馈，方便我们统一跟进和处理。

Telegram: https://t.me/harryyyy2025
微信：扫码添加

InsForge/InsForge

The all-in-one, open-source backend platform for agentic coding. InsForge gives your coding agent database, auth, storage, compute, hosting, and AI gateway to ship full-stack apps end-to-end.

The all-in-one, open-source backend platform for agentic coding.

⭐ Help us reach more developers and grow the InsForge community. Star this repo!

InsForge

The all-in-one, open-source backend platform for agentic coding. InsForge gives your coding agent database, auth, storage, compute, hosting, and AI gateway to ship full-stack apps end-to-end.

https://github.com/user-attachments/assets/345efbc6-ca63-4189-bde0-12ef3bda561b

How it works

Coding agents interact with InsForge through one of two interfaces:

MCP Server (self-hosted and cloud): exposes InsForge's operations as tools any MCP-compatible agent can call.
CLI + Skills (cloud only): a command-line interface paired with Skills that agents invoke directly from the terminal.

Both interfaces let coding agents operate the backend like backend engineers:

Read backend context and state: Pull documentation, schemas, metadata (deployed functions, bucket contents, auth config), and runtime logs, so the agent has what it needs to write code, verify what it built, and debug when something breaks.
Configure primitives: Deploy edge functions, run database migrations, create storage buckets, set up auth providers, and configure other backend resources directly.

graph TB

    subgraph TOP[" "]
        AG[AI Coding Agents]
    end

    subgraph MID[" "]
        SL[InsForge]
    end

    AG --> SL

    SL --> AUTH[Authentication]
    SL --> DB[Database]
    SL --> ST[Storage]
    SL --> EF[Edge Functions]
    SL --> MG[Model Gateway]
    SL --> CP[Compute]
    SL --> DEP[Deployment]

    classDef bar fill:#0b0f14,stroke:#30363d,stroke-width:1px,color:#ffffff
    classDef card fill:#161b22,stroke:#30363d,stroke-width:1px,color:#ffffff

    class AG,SL bar
    class AUTH,DB,ST,EF,MG,CP,DEP card

    style TOP fill:transparent,stroke:transparent
    style MID fill:transparent,stroke:transparent

    linkStyle default stroke:#30363d,stroke-width:1px

Core Products:

Authentication: User management, authentication, and sessions
Database: Postgres relational database
Storage: S3 compatible file storage
Model Gateway: OpenAI compatible API across multiple LLM providers
Edge Functions: Serverless code running on the edge
Compute (private preview): Long-running container services
Site Deployment: Site build and deployment

⭐️ Star the Repository

If you find InsForge useful or interesting, a GitHub Star ⭐️ would be greatly appreciated.

Quickstart

Cloud-hosted: insforge.dev

Self-hosted: Docker Compose

Prerequisites: Docker with Compose v2.

1. Setup

curl -fsSL https://raw.githubusercontent.com/InsForge/InsForge/main/deploy/setup.sh | sh -s ~/insforge

Fetches the files the stack reads and generates JWT_SECRET, ENCRYPTION_KEY, POSTGRES_PASSWORD, ROOT_ADMIN_PASSWORD, and the two access keys into ~/insforge/.env (mode 600). Nothing is started. Re-running refreshes the files and keeps every value you have set — it only ever adds COMPOSE_FILE, or points it at this checkout's compose file if it still names the development one.

cd ~/insforge
$EDITOR .env          # API_BASE_URL, VITE_API_BASE_URL — the URL browsers will use
docker compose up -d

.env sets COMPOSE_FILE, so plain docker compose commands work from that directory — no -f flags to remember.

Building from source instead

For working on InsForge itself. docker-compose.prod.yml reads the same variables but generates nothing, so set the secrets in .env yourself before starting anything you expose.

git clone https://github.com/InsForge/InsForge.git
cd InsForge
cp .env.example .env
$EDITOR .env
docker compose -f docker-compose.prod.yml up

Set JWT_SECRET, ENCRYPTION_KEY, POSTGRES_PASSWORD, and ROOT_ADMIN_PASSWORD — .env.example ships placeholders for them, and the compose file falls back to published defaults for any you leave unset. Set ACCESS_API_KEY and ACCESS_ANON_KEY too if you want to know your own keys; left empty, the backend generates a pair only it knows.

This path passes -f explicitly, which overrides COMPOSE_FILE. Add overlays as further -f flags rather than editing that variable.

2. Connect InsForge MCP

Open http://localhost:7130

Follow the steps to connect InsForge MCP Server

3. Verify installation

To verify the connection, send the following prompt to your agent:

I'm using InsForge as my backend platform, call InsForge MCP's fetch-docs tool to learn about InsForge instructions.

4. Running Multiple Projects

Give each project its own directory:

curl -fsSL https://raw.githubusercontent.com/InsForge/InsForge/main/deploy/setup.sh | sh -s ~/project1
curl -fsSL https://raw.githubusercontent.com/InsForge/InsForge/main/deploy/setup.sh | sh -s ~/project2

Then give each a project name and its own ports. Both .env files start with COMPOSE_PROJECT_NAME=insforge, and two directories sharing that name share containers — the second up -d adopts the first's, rebuilt with the second's config. Set it before starting anything.

~/project1/.env keeps the default ports — which collide with the ~/insforge instance from the quickstart above if it is still running. Stop that one, or give project1 its own ports the way project2 has:

COMPOSE_PROJECT_NAME=project1

~/project2/.env:

COMPOSE_PROJECT_NAME=project2
POSTGRES_PORT=5442
POSTGREST_PORT=5440
APP_PORT=7230
AUTH_PORT=7231
DENO_PORT=7233

Now each directory is a separate instance with its own containers, volumes, database, and secrets:

cd ~/project1 && docker compose up -d
cd ~/project2 && docker compose up -d

docker compose ps, logs -f, and down operate on whichever directory you run them from.

5. Storage Backends (Optional)

InsForge stores files on the local filesystem by default. Backing storage with an S3-compatible store also enables the S3-compatible gateway at /storage/v1/s3 (use aws CLI, rclone, or any AWS SDK against your InsForge Storage).

Append one overlay to COMPOSE_FILE in .env. Bundled MinIO, whose store stays internal to the Docker network:

COMPOSE_FILE=deploy/docker-compose/docker-compose.yml:docker-compose.minio.yml

Or RustFS, an Apache-2.0 licensed alternative:

COMPOSE_FILE=deploy/docker-compose/docker-compose.yml:docker-compose.rustfs.yml

Keep one — the file is read as shell assignments, so a second line replaces the first. Then docker compose up -d as usual.

The overlays ship with default store credentials — set MINIO_ROOT_USER/MINIO_ROOT_PASSWORD (or RUSTFS_ACCESS_KEY/RUSTFS_SECRET_KEY) in .env before production use.

Or bring your own S3-compatible store (AWS S3, MinIO, RustFS, Wasabi, R2, Tencent COS, Aliyun OSS ...) by setting S3_BUCKET, S3_REGION, S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY — plus S3_ENDPOINT_URL for non-AWS providers — in .env. If the endpoint is not reachable by browsers (private network), also set S3_USE_PRESIGNED_URLS=false to enable proxy mode.

See the self-hosted storage guide for provider notes, presigned vs. proxy mode, and upgrade tips.

One-click Deployment

In addition to running InsForge locally, you can also launch InsForge using a pre-configured setup. This allows you to get up and running quickly with InsForge without installing Docker on your local machine.

Railway	Zeabur	Sealos

Contributing

Contributing: If you're interested in contributing, you can check our guide here CONTRIBUTING.md. We truly appreciate pull requests, all types of help are appreciated!

Support: If you need any help or support, we're responsive on our Discord channel, and also feel free to email us info@insforge.dev too!

Documentation & Support

Documentation

Official Docs - Comprehensive guides and API references

Community

Discord - Join our vibrant community
Twitter - Follow for updates and tips

Contact

Email: info@insforge.dev

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Badges

Show your project is built with InsForge.

Made with InsForge

Markdown:

[![Made with InsForge](https://insforge.dev/badge-made-with-insforge.svg)](https://insforge.dev)

HTML:

<a href="https://insforge.dev">
  <img
    width="168"
    height="30"
    src="https://insforge.dev/badge-made-with-insforge.svg"
    alt="Made with InsForge"
  />
</a>

Made with InsForge (dark)

Markdown:

[![Made with InsForge](https://insforge.dev/badge-made-with-insforge-dark.svg)](https://insforge.dev)

HTML:

<a href="https://insforge.dev">
  <img
    width="168"
    height="30"
    src="https://insforge.dev/badge-made-with-insforge-dark.svg"
    alt="Made with InsForge"
  />
</a>

⭐ Star us on GitHub to get notified about new releases!

KunAgent/Kun

Local-first AI agent workspace for coding, writing, design, research, and automation — one runtime for desktop GUI and TUI.

Kun — 本地优先的 AI Agent 工作台（GUI + TUI）

在一个工作台中完成代码、写作、设计、研究与自动化。
一个共享运行时连接桌面 GUI 和终端 TUI，让任务从澄清、创作、执行到审查和交付始终可见、可控、可回溯。

下载并体验 · 阅读文档 · 在 GitHub 上 Star · English

Kun 是什么

Kun 是一款本地优先的 AI Agent 工作台，面向需要把想法真正推进到可验收结果的人。它把 Code、Write、Design、研究和自动化放到同一个产品中，并让桌面 GUI、终端 TUI、后台任务和连接手机通过同一个 kun serve 运行时共享线程、计划、审批、模型连接和任务记录。

它不是另一个只会生成回答的聊天框：Kun 帮你把需求、上下文、计划、文件改动、测试、审查和最终交付放在一条连续工作流中。

1 分钟了解 Kun

你关心的问题	Kun 的答案
适合谁	开发者、写作者、设计师、研究人员，以及需要把重复工作交给 AI 的个人和团队。
能做什么	AI 编程与代码审查、AI 写作与文档导出、AI 设计与交互原型、PDF/图片研究、多 Agent 自动化。
如何使用	在桌面 GUI 中观察任务全过程；也可以在终端用 TUI 保持手不离键盘。两者共享同一运行时和任务。
如何处理复杂任务	简单任务使用 Direct 模式；跨文件、跨阶段的任务可使用实验性的 Agent Graph 分工、监督和验收。
模型是否受限	不绑定单一模型，支持多模型选择；可接入订阅登录、Coding Plan、Token Plan、API、OpenAI/Anthropic 兼容服务和自托管模型。
支持哪些系统	macOS（Apple Silicon / Intel）、Windows x64 和 Linux x64。
数据在哪里	会话、偏好、日志和运行时数据默认保存在本机；若选择云端模型，提示、附件和任务上下文会发送给该 Provider。

5 分钟开始

下载桌面版

从 GitHub Releases 下载最新版：

平台	安装包	架构
macOS	`.dmg` / `.zip`	Apple Silicon / Intel
Windows	`.exe`	x64
Linux	`.AppImage` / `.deb`	x64

首次启动只需三步：

选择界面语言。
登录模型订阅，或配置 API Key、Token Plan 或自定义 Provider。
打开本地项目或新建工作区，发送一个目标清楚、范围有限且可以验证的任务。

桌面安装包已内置 TUI。在项目目录中打开终端并运行：

kun

GUI 和 TUI 会自动连接同一个本地运行时。服务器或无桌面环境也可以从同一 Release 下载独立 TUI 压缩包；完整使用方法见 Kun TUI 文档。

选择你的工作方式

场景	你带来什么	Kun 如何协作	可交付结果
Code：AI 编程与审查	真实代码库、Bug、功能目标或 Review 任务	搜索代码、编辑文件、执行命令、管理 Plan/Todo、查看 Diff 和测试	代码改动、测试结果、实施计划、Review findings
Write：AI 写作与文档交付	提纲、资料、草稿或选中文本	写作、润色、资料整理、内联补全和编辑	Markdown、HTML、PDF、DOCX、可编辑 PPTX
Design：AI 设计与原型	需求、参考图或现有界面	探索视觉方向、生成交互原型、沉淀设计系统，并交给 Code 实现	HTML 原型、设计画布、设计流程、`DESIGN_SYSTEM.md`
Research：多模态研究	PDF、图片、网页线索或问题	阅读资料、提取证据、组织结论、形成可继续的工作上下文	研究笔记、结构化结论、方案和后续任务
Automate：任务与 Agent Graph	重复流程、定时任务或复杂目标	使用 Schedule、Loop、Hook、MCP、Skills 和受限子代理持续执行	自动化记录、任务状态、证据和可恢复的执行历史

当前界面演示

以下界面基于当前版本，并使用虚构的演示工作区、任务和文件名，不包含真实用户数据。

从需求到验收

Kun 将“和 AI 对话”变成一条可以回到原始目标检查的工作流：

需求澄清 → 设计 / 写作 / 编码 → 计划与执行 → 审查与测试 → 验收与交付

阶段	Kun 如何参与
1. 澄清需求	建立需求草稿，结合项目内容补问题、整理边界和验收标准。
2. 探索方案	在 Design、Write 或 Research 中形成视觉方向、原型、资料与方案。
3. 形成计划	使用 `/plan` 将目标拆成可执行步骤，并与需求和 Todo 对齐。
4. 执行任务	Agent 搜索、修改、调用工具、运行命令；长任务可以继续、恢复或交给子代理。
5. 回到验收	检查 Diff、测试、浏览器和 `/review` findings，对照原始验收标准确认结果。

需求和计划默认保存在项目内，便于版本化、复盘和继续工作。需求变化时，Kun 鼓励重新检查计划和已完成步骤，而不是让旧计划静默继续执行。

Agent Graph：让复杂任务真正分工

实验性的 Agent Graph 适合跨文件、跨阶段、可以明确验收的复杂任务。Lead Agent 先建立任务依赖图，再按依赖派发受限子代理，持续查看进度、要求补充证据、触发返工，并在必要节点验证通过后交付结果。

Graph 不是第二套运行时，也不会扩大权限：

GUI 和 TUI 通过同一个 Kun 运行时读取 Graph 状态。
子代理只能使用父任务授权范围内的文件、工具、网络、Skills 和 MCP。
节点只有经过真实校验和 Lead 明确验收后，才能向下游交接结果。
可暂停、恢复、重试、修改任务图或停止；历史执行记录不会被伪装成成功。

简单问答和单点修改使用 Direct 模式更快。详细工作方式和边界见 Graph Mode 文档。

关键能力

能力	说明
真实项目工作台	本地工作区、文件搜索与编辑、Terminal、Browser、Git / Worktree、内联 Diff 和 Changes 面板。
长任务与上下文	Plan、Todo、持久目标、会话压缩、分叉、归档、旁支问题、后台 Shell 和子代理。
模型与额度	在一个入口管理订阅、套餐与 API；按默认 Agent、线程、Design、Write、Schedule 或子代理选择模型。
Agent 与知识	Agent Profile、长期记忆、项目级 `AGENTS.md`、Skills、MCP 和 Extensions。
自动化与开放扩展	一次性或周期性 Schedule、可视化 Loop、Hook、本地运行 API，以及可安装或侧载的 `.kunx` 扩展。
多模态与安全	图片和 PDF 输入、视觉理解、媒体生成、Sandbox、工具审批、Computer Use 权限和敏感操作确认。

为什么选择 Kun

真实工作中的问题	普通聊天框或分散工具	Kun 的做法
从想法推进到可交付结果	在聊天、编辑器、文档和终端之间手动搬运上下文	Code、Write、Design、Research 和自动化在同一工作台衔接，任务记录可继续。
判断 Agent 是否真的完成	通常只看到最终回答	将计划、文件 Diff、工具结果、测试、浏览器操作和审查证据留在任务旁。
处理跨阶段复杂任务	依赖人工逐个拆分和跟进	用 Direct 处理轻量任务；用 Agent Graph 建立依赖、分工、监督和验收。
选择合适的模型与接入方式	受限于单一产品或需要分别配置	将订阅、Coding Plan、Token Plan、API、兼容服务和自托管模型统一到 Provider 入口。
在桌面与终端间切换	会话和任务状态容易断开	GUI 和 TUI 共享一个 `kun serve` 运行时，可同时打开并继续同一线程。

订阅、Provider 与模型

Kun 不绑定某一家模型服务。你可以使用受支持的订阅登录和 Agent SDK，也可以接入 Coding Plan、Token Plan、按量 API、OpenAI Chat Completions / Responses、Anthropic Messages 兼容服务或自托管模型。

预设和接入方式覆盖 ChatGPT / Codex、Claude、Gemini、Cursor、Ollama、DeepSeek、Kimi、GLM、Qwen、MiniMax、Xiaomi MiMo 等生态；具体登录方式、可用模型、地区和额度以当前版本及服务商规则为准。完整 Provider 说明见模型 Provider 文档。

模型、媒体和高权限能力是否可用，取决于当前版本、操作系统、Provider、模型能力和你的授权。预设是配置起点，不代表账号天然拥有对应模型或额度。

常见问题

Kun 是什么？

Kun 是本地优先的 AI Agent 工作台，用同一个运行时提供桌面 GUI、终端 TUI、代码、写作、设计、研究和自动化能力。

Kun 适合哪些人？

适合需要把任务从想法推进到真实交付的人：开发者、写作者、设计师、研究人员，以及需要自动化重复工作的小团队。

Kun 只能用于 AI 编程吗？

不是。Code 只是其中一个工作区；Kun 同时提供 Write、Design、PDF/图片研究、Schedule、Loop、MCP、Skills 和 Extension 能力。

必须使用 DeepSeek 吗？

不必。Kun 支持多种订阅、套餐、API、兼容协议和自托管模型；DeepSeek 是可选 Provider 之一。

“本地优先”是否表示数据绝不会离开电脑？

会话、偏好、日志和运行时数据默认本地保存；但当你选择云端 Provider 时，提示、附件和任务上下文会发送给所选模型服务。使用前请确认对应 Provider 的数据政策。

GUI 和 TUI 如何共享任务？

它们连接同一个本地 kun serve 运行时，因此可以同时打开，并共享线程、计划、审批、用量和后台任务。

什么时候使用 Direct，什么时候使用 Agent Graph？

单点修改、简单问答和短任务使用 Direct 更快；需要并行分工、依赖关系、持续监督和明确验收的复杂工作，适合使用实验性的 Agent Graph。

Kun 支持哪些系统？

桌面版支持 macOS、Windows x64 和 Linux x64；也提供独立 TUI 以适配服务器或无桌面环境。

从源码运行

环境要求：

依赖	版本
Node.js	22.19+
npm	随 Node.js 安装
模型连接	至少配置一个受支持的订阅、API 或自定义 Provider

git clone https://github.com/KunAgent/Kun.git
cd Kun
npm ci
npm run dev

单独启动开发版 TUI：

npm run dev:tui

中国大陆网络访问较慢时，可以使用 npm 镜像：

npm ci --registry=https://registry.npmmirror.com

常用开发命令

命令	说明
`npm run dev`	构建 Kun 运行时并启动 Electron 开发环境
`npm run dev:tui`	构建运行时并启动终端 TUI
`npm run typecheck`	TypeScript 类型检查
`npm run lint`	ESLint 检查
`npm run test`	运行测试
`npm run build`	生产构建
`npm run dist:mac`	构建 macOS 安装包
`npm run dist:win`	构建 Windows 安装包
`npm run dist:linux`	构建 Linux 安装包

文档

完整的用户文档位于 kun-agent.com/docs。仓库中的技术文档适合深入了解某一项能力：

文档	内容
docs/kun-tui.md	TUI 安装、启动、命令、快捷键、配置和运行时
docs/graph-mode.md	Agent Graph 的架构、调度、监督、权限和恢复
docs/DESIGN_MODE.md	Design 画布、原型、设计系统与 Design → Code
docs/workflow-loop.md	可视化 Loop 工作流与自动化思路
docs/project-mcp-skills.md	项目级配置、MCP 与 Skill 发现
docs/extensions/README.md	Kun Extension 开放平台
kun/README.zh-CN.md	Kun 运行时、CLI、环境变量和 HTTP API
docs/DEVELOPMENT.zh-CN.md	本地开发和发布流程
docs/CONTRIBUTING.zh-CN.md	贡献指南
SECURITY.zh-CN.md	安全漏洞披露

贡献

欢迎提交 bug 修复、UI/UX、运行时、Provider、扩展和文档改进。日常集成分支为 develop，PR 默认提交到 develop；开始前请阅读贡献指南，外部贡献需要接受 Contributor License Agreement。

累计有 5 个 PR 被正常 review 并合入 后，可以发送邮件到 zhongxingyuemail@gmail.com 申请成为 Kun Builder，并附上 GitHub 用户名和 PR 链接。

许可证

Kun 使用 PolyForm Noncommercial License 1.0.0，仅供学习、研究和非商业用途。商业使用、商业分发、SaaS / 托管服务、转售或集成到商业产品中，需要获得作者的单独书面授权。

企业仅用于内部员工提效时，可发送邮件到 zhongxingyuemail@gmail.com 免费申请书面内部使用授权。该授权不包含面向外部客户的 SaaS、托管、转售或商业分发。

致谢

感谢所有提交 issue、建议、代码和文档的贡献者。

Mininglamp-OSS/octo-web

Web & desktop (Electron) client for the OCTO open workplace — one React + TypeScript codebase shipping browser and PC surfaces, with first-class AI agent UX.

OCTO — the open workplace built for humans × AI agents.
_{Let Lobsters (OpenClaw-powered digital doubles) do the thinking and doing. You focus on taste.}

🏠 OCTO Home · 🚀 Quickstart · 📦 Ecosystem · 🤝 Contributing

🌐 Read in: English · 简体中文

OCTO Web

Web & PC (Electron) client for the OCTO messaging platform — one React codebase, two shipped surfaces.

octo-web is the TypeScript / React front-end that talks to octo-server over REST + WebSocket. The same codebase ships two ways: as a browser build (the canonical OCTO chat surface), and as an Electron-packaged desktop PC client.

🌟 Why OCTO Web

One codebase, two products. Browser + PC (Electron) are built from the same src/ — no parallel React trees, no diverging UX. Branch switches happen at platform-capability boundaries only.
Lobster-ready UI. First-class surfaces for AI agent conversations: streaming replies, typing indicator, inline tool-call previews, read receipts, agent-vs-human identity chips.
Full bilingual shell. English and Simplified Chinese ship together out of the box; i18n keys live in src/locales/ and are enforced in CI.

🚀 Quickstart

git clone https://github.com/Mininglamp-OSS/octo-web.git
cd octo-web
pnpm install
pnpm dev

By default the web build expects an octo-server instance reachable at http://localhost:8080. Point it at your own server by copying .env.example to .env.local and editing the VITE_API_* values.

📦 Modules / Architecture

Top-level layout:

Path	Purpose
`src/pages/`	Route-level React views (chat, channels, org, settings)
`src/components/`	Shared UI kit (message bubbles, inputs, agent chips, streaming renderers)
`src/store/`	Client state (auth, channels, draft, agent orchestration UI state)
`src/api/`	REST + WebSocket client talking to `octo-server`
`src/locales/`	i18n resources (English · 简体中文)
`electron/`	Electron main/renderer bootstrap for the PC build
`docs/`	Design docs, architecture notes, screenshots

Key build targets:

pnpm build        # build the browser bundle
pnpm pc:dev       # launch the Electron shell against the dev build
pnpm pc:package   # produce a distributable PC bundle (macOS / Windows / Linux)
pnpm test         # run unit + component tests

The PC Electron shell is intentionally thin — it hosts the same React app and forwards IPC for native capabilities (tray, notifications, file drop, auto- update). The browser build runs without any Electron dependency.

🔗 OCTO Ecosystem

graph TD
  subgraph Clients[Clients]
    Web[octo-web<br/>Web / PC]
    Android[octo-android<br/>Android]
    iOS[octo-ios<br/>iOS]
  end

  subgraph Core[Core Services]
    Server[octo-server<br/>Backend API]
    Matter[octo-matter<br/>Task / Todo]
    Summary[octo-smart-summary<br/>AI Summary]
    Admin[octo-admin<br/>Admin Console]
  end

  subgraph Shared[Shared Libraries & Integrations]
    Lib[octo-lib<br/>Core Go Library]
    Adapters[octo-adapters<br/>Third-party Adapters]
  end

  Web --> Server
  Android --> Server
  iOS --> Server
  Admin --> Server
  Server --> Matter
  Server --> Summary
  Server --> Adapters
  Server -.uses.-> Lib
  Matter -.uses.-> Lib
  Adapters -.uses.-> Lib

Repository	Language	Role
`octo-server`	Go	Backend API · business orchestration · Lobster agent scheduling
`octo-matter`	Go	Task / Todo / Matter micro-service
`octo-smart-summary`	Go	LLM-powered conversation summarisation
`octo-web`	TypeScript / React	Web & PC (Electron) client
`octo-android`	Kotlin / Java	Native Android client
`octo-ios`	Swift / Objective-C	Native iOS client
`octo-admin`	TypeScript / React	Admin console (tenant / org / user / channel management)
`octo-lib`	Go	Shared core library (protocol, crypto, storage, HTTP)
`octo-adapters`	TypeScript / Python	Third-party integrations (IM bridges, AI channels)

🧭 Philosophy

OCTO ships under three shared principles that apply to every repository in this matrix:

Local-first. Anything that can run on the user's own box — chats, embeddings, agents — should. Your data stays yours; cloud is a choice, not a requirement.
Humans judge, AI thinks and acts. Humans focus on taste (what matters, what's right, what to ship). Lobster agents — OpenClaw-powered digital doubles — carry the thinking and execution load.
Release-as-product. Every open-source cut is shipped as a self-contained product, not a code dump: one squash per release, Apache 2.0, no internal baggage, reproducible from this repo alone.

🤝 Contributing

We love pull requests! Before you open one, please read:

CONTRIBUTING.md — workflow, branch model, commit style
CODE_OF_CONDUCT.md — community expectations

For security issues please follow SECURITY.md instead of the public tracker.

📄 License

Apache License 2.0 — see LICENSE for the full text and NOTICE for third-party attributions.

🙏 Acknowledgments

octo-web owes its original scaffolding to:

TangSengDaoDaoWeb — our upstream, by the TangSengDaoDao team.
WuKongIM — the real-time messaging core that octo-server drives behind this client.

See NOTICE for the full attribution list and third-party component licenses.

_{Made with 🐙 by OCTO Contributors · Mininglamp-OSS}

garrytan/gstack

Use Garry Tan's exact Claude Code setup: 23 opinionated tools that serve as CEO, Designer, Eng Manager, Release Manager, Doc Engineer, and QA

gstack

"I don't think I've typed like a line of code probably since December, basically, which is an extremely large change." — Andrej Karpathy, No Priors podcast, March 2026

When I heard Karpathy say this, I wanted to find out how. How does one person ship like a team of twenty? Peter Steinberger built OpenClaw — 247K GitHub stars — essentially solo with AI agents. The revolution is here. A single builder with the right tooling can move faster than a traditional team.

I'm Garry Tan, President & CEO of Y Combinator. I've worked with thousands of startups — Coinbase, Instacart, Rippling — when they were one or two people in a garage. Before YC, I was one of the first eng/PM/designers at Palantir, cofounded Posterous (sold to Twitter), and built Bookface, YC's internal social network.

gstack is my answer. I've been building products for twenty years, and right now I'm shipping more products than I ever have. In the last 60 days: 3 production services, 40+ shipped features, part-time, while running YC full-time. On logical code change — not raw LOC, which AI inflates — my 2026 run rate is ~810× my 2013 pace (11,417 vs 14 logical lines/day). Year-to-date (through April 18), 2026 has already produced 240× the entire 2013 year. Measured across 40 public + private garrytan/* repos including Bookface, after excluding one demo repo. AI wrote most of it. The point isn't who typed it, it's what shipped.

The LOC critics aren't wrong that raw line counts inflate with AI. They are wrong that normalized-for-inflation, I'm less productive. I'm more productive, by a lot. Full methodology, caveats, and reproduction script: On the LOC Controversy.

2026 — 1,237 contributions and counting:

2013 — when I built Bookface at YC (772 contributions):

Same person. Different era. The difference is the tooling.

gstack is how I do it. It turns Claude Code into a virtual engineering team — a CEO who rethinks the product, an eng manager who locks architecture, a designer who catches AI slop, a reviewer who finds production bugs, a QA lead who opens a real browser, a security officer who runs OWASP + STRIDE audits, and a release engineer who ships the PR. Twenty-three specialists and eight power tools, all slash commands, all Markdown, all free, MIT license.

This is my open source software factory. I use it every day. I'm sharing it because these tools should be available to everyone.

Fork it. Improve it. Make it yours. And if you want to hate on free open source software — you're welcome to, but I'd rather you just try it first.

Who this is for:

Founders and CEOs — especially technical ones who still want to ship
First-time Claude Code users — structured roles instead of a blank prompt
Tech leads and staff engineers — rigorous review, QA, and release automation on every PR

Quick start

Install gstack (30 seconds — see below)
Run /office-hours — describe what you're building
Run /plan-ceo-review on any feature idea
Run /review on any branch with changes
Run /qa on your staging URL
Stop there. You'll know if this is for you.

Install — 30 seconds

Requirements: Claude Code, Git, Bun v1.0+, Node.js (Windows only)

Step 1: Install on your machine

Open Claude Code and paste this. Claude does the rest.

Install gstack: run git clone --single-branch --depth 1 https://github.com/garrytan/gstack.git ~/.claude/skills/gstack && cd ~/.claude/skills/gstack && ./setup then add a "gstack" section to CLAUDE.md that says to use the /browse skill from gstack for all web browsing, never use mcp__claude-in-chrome__* tools, and lists the available skills: /office-hours, /plan-ceo-review, /plan-eng-review, /plan-design-review, /design-consultation, /design-shotgun, /design-html, /review, /ship, /land-and-deploy, /canary, /benchmark, /browse, /connect-chrome, /qa, /qa-only, /design-review, /setup-browser-cookies, /setup-deploy, /setup-gbrain, /retro, /investigate, /document-release, /document-generate, /codex, /cso, /autoplan, /plan-devex-review, /devex-review, /careful, /freeze, /guard, /unfreeze, /gstack-upgrade, /learn. Then ask the user if they also want to add gstack to the current project so teammates get it.

Step 2: Team mode — auto-update for shared repos (recommended)

From inside your repo, paste this. Switches you to team mode, bootstraps the repo so teammates get gstack automatically, and commits the change:

(cd ~/.claude/skills/gstack && ./setup --team) && ~/.claude/skills/gstack/bin/gstack-team-init required && git add .claude/ CLAUDE.md && git commit -m "require gstack for AI-assisted work"

No vendored files in your repo, no version drift, no manual upgrades. Every Claude Code session starts with a fast auto-update check (throttled to once/hour, network-failure-safe, completely silent).

Swap required for optional if you'd rather nudge teammates than block them.

OpenClaw

OpenClaw spawns Claude Code sessions via ACP, so every gstack skill just works when Claude Code has gstack installed. Paste this to your OpenClaw agent:

Install gstack: run git clone --single-branch --depth 1 https://github.com/garrytan/gstack.git ~/.claude/skills/gstack && cd ~/.claude/skills/gstack && ./setup to install gstack for Claude Code. Then add a "Coding Tasks" section to AGENTS.md that says: when spawning Claude Code sessions for coding work, tell the session to use gstack skills. Include these examples — security audit: "Load gstack. Run /cso", code review: "Load gstack. Run /review", QA test a URL: "Load gstack. Run /qa https://...", build a feature end-to-end: "Load gstack. Run /autoplan, implement the plan, then run /ship", plan before building: "Load gstack. Run /office-hours then /autoplan. Save the plan, don't implement."

After setup, just talk to your OpenClaw agent naturally:

You say	What happens
"Fix the typo in README"	Simple — Claude Code session, no gstack needed
"Run a security audit on this repo"	Spawns Claude Code with `Run /cso`
"Build me a notifications feature"	Spawns Claude Code with /autoplan → implement → /ship
"Help me plan the v2 API redesign"	Spawns Claude Code with /office-hours → /autoplan, saves plan

See docs/OPENCLAW.md for advanced dispatch routing and the gstack-lite/gstack-full prompt templates.

Native OpenClaw Skills (via ClawHub)

Four methodology skills that work directly in your OpenClaw agent, no Claude Code session needed. Install from ClawHub:

clawhub install gstack-openclaw-office-hours gstack-openclaw-ceo-review gstack-openclaw-investigate gstack-openclaw-retro

Skill	What it does
`gstack-openclaw-office-hours`	Product interrogation with 6 forcing questions
`gstack-openclaw-ceo-review`	Strategic challenge with 4 scope modes
`gstack-openclaw-investigate`	Root cause debugging methodology
`gstack-openclaw-retro`	Weekly engineering retrospective

These are conversational skills. Your OpenClaw agent runs them directly via chat.

Other AI Agents

gstack works on 10 AI coding agents, not just Claude. Setup auto-detects which agents you have installed:

git clone --single-branch --depth 1 https://github.com/garrytan/gstack.git ~/gstack
cd ~/gstack && ./setup

Or target a specific agent with ./setup --host <name>:

Agent	Flag	Skills install to
OpenAI Codex CLI	`--host codex`	`~/.codex/skills/gstack-*/`
OpenCode	`--host opencode`	`~/.config/opencode/skills/gstack-*/`
Cursor	`--host cursor`	`~/.cursor/skills/gstack-*/`
Factory Droid	`--host factory`	`~/.factory/skills/gstack-*/`
Slate	`--host slate`	`~/.slate/skills/gstack-*/`
Kiro	`--host kiro`	`~/.kiro/skills/gstack-*/`
Hermes	`--host hermes`	`~/.hermes/skills/gstack-*/`
GBrain (mod)	`--host gbrain`	`~/.gbrain/skills/gstack-*/`

Want to add support for another agent? See docs/ADDING_A_HOST.md. It's one TypeScript config file, zero code changes.

See it work

You:    I want to build a daily briefing app for my calendar.
You:    /office-hours
Claude: [asks about the pain — specific examples, not hypotheticals]

You:    Multiple Google calendars, events with stale info, wrong locations.
        Prep takes forever and the results aren't good enough...

Claude: I'm going to push back on the framing. You said "daily briefing
        app." But what you actually described is a personal chief of
        staff AI.
        [extracts 5 capabilities you didn't realize you were describing]
        [challenges 4 premises — you agree, disagree, or adjust]
        [generates 3 implementation approaches with effort estimates]
        RECOMMENDATION: Ship the narrowest wedge tomorrow, learn from
        real usage. The full vision is a 3-month project — start with
        the daily briefing that actually works.
        [writes design doc → feeds into downstream skills automatically]

You:    /plan-ceo-review
        [reads the design doc, challenges scope, runs 10-section review]

You:    /plan-eng-review
        [ASCII diagrams for data flow, state machines, error paths]
        [test matrix, failure modes, security concerns]

You:    Approve plan. Exit plan mode.
        [writes 2,400 lines across 11 files. ~8 minutes.]

You:    /review
        [AUTO-FIXED] 2 issues. [ASK] Race condition → you approve fix.

You:    /qa https://staging.myapp.com
        [opens real browser, clicks through flows, finds and fixes a bug]

You:    /ship
        Tests: 42 → 51 (+9 new). PR: github.com/you/app/pull/42

You said "daily briefing app." The agent said "you're building a chief of staff AI" — because it listened to your pain, not your feature request. Eight commands, end to end. That is not a copilot. That is a team.

The sprint

gstack is a process, not a collection of tools. The skills run in the order a sprint runs:

Think → Plan → Build → Review → Test → Ship → Reflect

Each skill feeds into the next. /office-hours writes a design doc that /plan-ceo-review reads. /plan-eng-review writes a test plan that /qa picks up. /review catches bugs that /ship verifies are fixed. Nothing falls through the cracks because every step knows what came before it.

Skill	Your specialist	What they do
`/office-hours`	YC Office Hours	Start here. Six forcing questions that reframe your product before you write code. Pushes back on your framing, challenges premises, generates implementation alternatives. Design doc feeds into every downstream skill.
`/plan-ceo-review`	CEO / Founder	Rethink the problem. Find the 10-star product hiding inside the request. Four modes: Expansion, Selective Expansion, Hold Scope, Reduction.
`/plan-eng-review`	Eng Manager	Lock in architecture, data flow, diagrams, edge cases, and tests. Forces hidden assumptions into the open.
`/plan-design-review`	Senior Designer	Rates each design dimension 0-10, explains what a 10 looks like, then edits the plan to get there. AI Slop detection. Interactive — one AskUserQuestion per design choice.
`/plan-devex-review`	Developer Experience Lead	Interactive DX review: explores developer personas, benchmarks against competitors' TTHW, designs your magical moment, traces friction points step by step. Three modes: DX EXPANSION, DX POLISH, DX TRIAGE. 20-45 forcing questions.
`/design-consultation`	Design Partner	Build a complete design system from scratch. Researches the landscape, proposes creative risks, generates realistic product mockups.
`/review`	Staff Engineer	Find the bugs that pass CI but blow up in production. Auto-fixes the obvious ones. Flags completeness gaps.
`/investigate`	Debugger	Systematic root-cause debugging. Iron Law: no fixes without investigation. Traces data flow, tests hypotheses, stops after 3 failed fixes.
`/design-review`	Designer Who Codes	Same audit as /plan-design-review, then fixes what it finds. Atomic commits, before/after screenshots.
`/devex-review`	DX Tester	Live developer experience audit. Actually tests your onboarding: navigates docs, tries the getting started flow, times TTHW, screenshots errors. Compares against `/plan-devex-review` scores — the boomerang that shows if your plan matched reality.
`/design-shotgun`	Design Explorer	"Show me options." Generates 4-6 AI mockup variants, opens a comparison board in your browser, collects your feedback, and iterates. Taste memory learns what you like. Repeat until you love something, then hand it to `/design-html`.
`/design-html`	Design Engineer	Turn a mockup into production HTML that actually works. Pretext computed layout: text reflows, heights adjust, layouts are dynamic. 30KB, zero deps. Detects React/Svelte/Vue. Smart API routing per design type (landing page vs dashboard vs form). The output is shippable, not a demo.
`/qa`	QA Lead	Test your app, find bugs, fix them with atomic commits, re-verify. Auto-generates regression tests for every fix.
`/qa-only`	QA Reporter	Same methodology as /qa but report only. Pure bug report without code changes.
`/pair-agent`	Multi-Agent Coordinator	Share your browser with any AI agent. One command, one paste, connected. Works with OpenClaw, Hermes, Codex, Cursor, or anything that can curl. Each agent gets its own tab. Auto-launches headed mode so you watch everything. Auto-starts ngrok tunnel for remote agents. Scoped tokens, tab isolation, rate limiting, activity attribution.
`/cso`	Chief Security Officer	OWASP Top 10 + STRIDE threat model. Zero-noise: 17 false positive exclusions, 8/10+ confidence gate, independent finding verification. Each finding includes a concrete exploit scenario.
`/ship`	Release Engineer	Sync main, run tests, audit coverage, push, open PR. Bootstraps test frameworks if you don't have one.
`/land-and-deploy`	Release Engineer	Merge the PR, wait for CI and deploy, verify production health. One command from "approved" to "verified in production."
`/canary`	SRE	Post-deploy monitoring loop. Watches for console errors, performance regressions, and page failures.
`/benchmark`	Performance Engineer	Baseline page load times, Core Web Vitals, and resource sizes. Compare before/after on every PR.
`/document-release`	Technical Writer	Update all project docs to match what you just shipped. Catches stale READMEs automatically. Builds a Diataxis coverage map (reference / how-to / tutorial / explanation) so gaps are visible in the PR body.
`/document-generate`	Documentation Author	Generate missing docs from scratch using the Diataxis framework. Researches the codebase first, then writes reference / how-to / tutorial / explanation docs that actually match the code. Invokable standalone or chained from `/document-release` when the coverage map finds gaps. Learn more: tutorial • how-to • why Diataxis.
`/retro`	Eng Manager	Team-aware weekly retro. Per-person breakdowns, shipping streaks, test health trends, growth opportunities. `/retro global` runs across all your projects and AI tools (Claude Code, Codex, Gemini).
`/browse`	QA Engineer	Give the agent eyes. Real Chromium browser, real clicks, real screenshots. ~100ms per command. `/open-gstack-browser` launches GStack Browser with sidebar, anti-bot stealth, and auto model routing.
`/setup-browser-cookies`	Session Manager	Import cookies from your real browser (Chrome, Arc, Brave, Edge) into the headless session. Test authenticated pages.
`/autoplan`	Review Pipeline	One command, fully reviewed plan. Runs CEO → design → eng review automatically with encoded decision principles. Surfaces only taste decisions for your approval.
`/spec`	Spec Author	Turn vague intent into a precise, executable spec in five phases (why, scope, technical with mandatory code-reading, draft, file). Codex quality gate before file (blocks below 7/10), fail-closed secret redaction, dedupe against existing issues, archive to `$GSTACK_STATE_ROOT/projects/$SLUG/specs/` for team-corpus recall. `--execute` spawns `claude -p` in a fresh worktree; `/ship` auto-closes the source issue on merge. Plan-mode aware.
`/learn`	Memory	Manage what gstack learned across sessions. Review, search, prune, and export project-specific patterns, pitfalls, and preferences. Learnings compound across sessions so gstack gets smarter on your codebase over time.
`/make-pdf`	Publisher	Markdown in, publication-quality document out. Mermaid and excalidraw fences render as vector diagrams, fully offline. Images scale to the page and never truncate; wide diagrams get their own landscape page. `--to html` emits one self-contained file, `--to docx` a Word doc.
`/diagram`	Diagram Maker	English in, editable diagram out. Emits a triplet: mermaid source, `.excalidraw` you can open and edit on excalidraw.com (hand-drawn style), and rendered SVG/PNG. Zero network. Embed the source in markdown and `/make-pdf` renders it.

Which review should I use?

Building for...	Plan stage (before code)	Live audit (after shipping)
End users (UI, web app, mobile)	`/plan-design-review`	`/design-review`
Developers (API, CLI, SDK, docs)	`/plan-devex-review`	`/devex-review`
Architecture (data flow, perf, tests)	`/plan-eng-review`	`/review`
All of the above	`/autoplan` (runs CEO → design → eng → DX, auto-detects which apply)	—

Power tools

Skill	What it does
`/codex`	Second Opinion — independent code review from OpenAI Codex CLI. Three modes: review (pass/fail gate), adversarial challenge, and open consultation. Cross-model analysis when both `/review` and `/codex` have run.
`/careful`	Safety Guardrails — warns before destructive commands (rm -rf, DROP TABLE, force-push). Say "be careful" to activate. Override any warning.
`/freeze`	Edit Lock — restrict file edits to one directory. Prevents accidental changes outside scope while debugging.
`/guard`	Full Safety — `/careful` + `/freeze` in one command. Maximum safety for prod work.
`/unfreeze`	Unlock — remove the `/freeze` boundary.
`/open-gstack-browser`	GStack Browser — launch GStack Browser with sidebar, anti-bot stealth, auto model routing (Sonnet for actions, Opus for analysis), one-click cookie import, and Claude Code integration. Clean up pages, take smart screenshots, edit CSS, and pass info back to your terminal.
`/setup-deploy`	Deploy Configurator — one-time setup for `/land-and-deploy`. Detects your platform, production URL, and deploy commands.
`/setup-gbrain`	GBrain Onboarding — from zero to running gbrain in under 5 minutes. PGLite local, Supabase existing URL, or auto-provision a new Supabase project via Management API. MCP registration for Claude Code + per-repo trust triad (read-write/read-only/deny). Full guide.
`/sync-gbrain`	Keep Brain Current — re-index this repo's code into gbrain via `gbrain sources add` + `gbrain sync --strategy code`, refresh the `## GBrain Search Guidance` block in CLAUDE.md, and auto-remove guidance when the capability check fails. `--incremental` (default), `--full`, `--dry-run`. Idempotent; safe to re-run.
`/gstack-upgrade`	Self-Updater — upgrade gstack to latest. Detects global vs vendored install, syncs both, shows what changed.
`/ios-qa`	iOS Live-Device QA (v1.43.0.0+) — drive a real iPhone over USB CoreDevice via an embedded `StateServer` in the app. Read Swift source, codegen typed `@Observable` accessors, run the agent loop. Optional `--tailnet` flag exposes the device to OpenClaw or any HTTP-capable agent on your Tailscale tailnet so remote agents can run iOS QA without ever touching the hardware. Capability-tier allowlist (observe/interact/mutate/restore), per-device session lock, audit log.
`/ios-fix`, `/ios-design-review`, `/ios-clean`, `/ios-sync`	iOS bug-fix loop, designer's-eye HIG audit, debug-bridge cleanup, and accessor resync. See `docs/skills.md`. End-to-end walkthrough: docs/howto-ios-testing-with-gstack.md.

New binaries (v0.19)

Beyond the slash-command skills, gstack ships standalone CLIs for workflows that don't belong inside a session:

Command	What it does
`gstack-model-benchmark`	Cross-model benchmark — run the same prompt through Claude, GPT (via Codex CLI), and Gemini; compare latency, tokens, cost, and (optionally) LLM-judge quality score. Auth detected per provider, unavailable providers skip cleanly. Output as table, JSON, or markdown. `--dry-run` validates flags + auth without spending API calls.
`gstack-taste-update`	Design taste learning — writes approvals and rejections from `/design-shotgun` into a persistent per-project taste profile. Decays 5%/week. Feeds back into future variant generation so the system learns what you actually pick.
`gstack-ios-qa-daemon`	iOS QA daemon — Mac-side broker between an agent and a connected iPhone over USB CoreDevice. Loopback by default; `--tailnet` opens a Tailscale-facing listener with identity-gated capability tiers. Single-instance via flock on `~/.gstack/ios-qa-daemon.pid`. See docs/howto-ios-testing-with-gstack.md.
`gstack-ios-qa-mint`	iOS allowlist manager — owner-grant CLI for the tailnet allowlist. `grant`/`revoke`/`list` against `~/.gstack/ios-qa-allowlist.json` (mode 0600). Remote agents never auto-allowlist; this is the explicit-intent path.
`gstack-ios-qa-regen`	iOS bridge regenerator — deterministically installs the canonical DebugBridge package, generates typed state accessors, and records the installed gstack version. Safe to rerun after source changes or upgrades.

Continuous checkpoint mode (opt-in, local by default)

Set gstack-config set checkpoint_mode continuous and skills auto-commit your work as you go with a WIP: prefix plus a structured [gstack-context] body (decisions, remaining work, failed approaches). Survives crashes and context switches. /context-restore reads those commits to reconstruct session state. /ship filter-squashes WIP commits before the PR (preserving non-WIP commits) so bisect stays clean. Push is opt-in via checkpoint_push=true — default is local-only so you don't trigger CI on every WIP commit.

Domain skills + raw CDP escape hatch

Two new browser primitives compound the gstack agent over time:

$B domain-skill save — agent saves a per-site note (e.g., "LinkedIn's Apply button lives in an iframe") that fires automatically next time it visits that hostname. Quarantined → active after 3 successful uses → optional cross-project promotion via $B domain-skill promote-to-global. Storage lives alongside /learn's per-project learnings file. Full reference: docs/domain-skills.md.
$B cdp <Domain.method> — raw Chrome DevTools Protocol escape hatch for the rare case curated commands miss. Deny-default: methods must be explicitly added to browse/src/cdp-allowlist.ts with a one-line justification. Two-tier mutex serializes browser-scoped CDP calls against per-tab work. Output for data-exfil methods is wrapped in the UNTRUSTED envelope.

Want raw CDP with no rails, no allowlist, no daemon — just thin transport from agent to Chrome? browser-use/browser-harness-js is a different philosophy (agent-authored helpers vs gstack's curated commands) and a good fit if you don't want gstack's security stack. The two can coexist: gstack's $B cdp and harness can both attach to the same Chrome via Playwright's newCDPSession.

Deep dives with examples and philosophy for every skill →

Karpathy's four failure modes? Already covered.

Andrej Karpathy's AI coding rules (17K stars) nail four failure modes: wrong assumptions, overcomplexity, orthogonal edits, imperative over declarative. gstack's workflow skills enforce all four. /office-hours forces assumptions into the open before code is written. The Confusion Protocol stops Claude from guessing on architectural decisions. /review catches unnecessary complexity and drive-by edits. /ship transforms tasks into verifiable goals with test-first execution. If you already use Karpathy-style CLAUDE.md rules, gstack is the workflow enforcement layer that makes them stick across entire sprints, not just single prompts.

Parallel sprints

gstack works well with one sprint. It gets interesting with ten running at once.

Design is at the heart. /design-consultation builds your design system from scratch, researches what's out there, proposes creative risks, and writes DESIGN.md. But the real magic is the shotgun-to-HTML pipeline.

/design-shotgun is how you explore. You describe what you want. It generates 4-6 AI mockup variants using GPT Image. Then it opens a comparison board in your browser with all variants side by side. You pick favorites, leave feedback ("more whitespace", "bolder headline", "lose the gradient"), and it generates a new round. Repeat until you love something. Taste memory kicks in after a few rounds so it starts biasing toward what you actually like. No more describing your vision in words and hoping the AI gets it. You see options, pick the good ones, and iterate visually.

/design-html makes it real. Take that approved mockup (from /design-shotgun, a CEO plan, a design review, or just a description) and turn it into production-quality HTML/CSS. Not the kind of AI HTML that looks fine at one viewport width and breaks everywhere else. This uses Pretext for computed text layout: text actually reflows on resize, heights adjust to content, layouts are dynamic. 30KB overhead, zero dependencies. It detects your framework (React, Svelte, Vue) and outputs the right format. Smart API routing picks different Pretext patterns depending on whether it's a landing page, dashboard, form, or card layout. The output is something you'd actually ship, not a demo.

/qa was a massive unlock. It let me go from 6 to 12 parallel workers. Claude Code saying "I SEE THE ISSUE" and then actually fixing it, generating a regression test, and verifying the fix — that changed how I work. The agent has eyes now.

Smart review routing. Just like at a well-run startup: CEO doesn't have to look at infra bug fixes, design review isn't needed for backend changes. gstack tracks what reviews are run, figures out what's appropriate, and just does the smart thing. The Review Readiness Dashboard tells you where you stand before you ship.

Test everything. /ship bootstraps test frameworks from scratch if your project doesn't have one. Every /ship run produces a coverage audit. Every /qa bug fix generates a regression test. 100% test coverage is the goal — tests make vibe coding safe instead of yolo coding.

/document-release is the engineer you never had. It reads every doc file in your project, cross-references the diff, and updates everything that drifted. README, ARCHITECTURE, CONTRIBUTING, CLAUDE.md, TODOS — all kept current automatically. And now /ship auto-invokes it — docs stay current without an extra command.

Real browser mode. /open-gstack-browser launches GStack Browser, an AI-controlled Chromium with anti-bot stealth, custom branding, and the sidebar extension baked in. Sites like Google and NYTimes work without captchas. The menu bar says "GStack Browser" instead of "Chrome for Testing." Your regular Chrome stays untouched. All existing browse commands work unchanged. $B disconnect returns to headless. The browser stays alive as long as the window is open... no idle timeout killing it while you're working.

Sidebar agent — your AI browser assistant. Type natural language in the Chrome side panel and a child Claude instance executes it. "Navigate to the settings page and screenshot it." "Fill out this form with test data." "Go through every item in this list and extract the prices." The sidebar auto-routes to the right model: Sonnet for fast actions (click, navigate, screenshot) and Opus for reading and analysis. Each task gets up to 5 minutes. The sidebar agent runs in an isolated session, so it won't interfere with your main Claude Code window. One-click cookie import right from the sidebar footer.

Personal automation. The sidebar agent isn't just for dev workflows. Example: "Browse my kid's school parent portal and add all the other parents' names, phone numbers, and photos to my Google Contacts." Two ways to get authenticated: (1) log in once in the headed browser, your session persists, or (2) click the "cookies" button in the sidebar footer to import cookies from your real Chrome. Once authenticated, Claude navigates the directory, extracts the data, and creates the contacts.

Prompt injection defense. Hostile web pages try to hijack your sidebar agent. gstack ships a layered defense: a 22MB ML classifier bundled with the browser scans every page and tool output locally, a Claude Haiku transcript check votes on the full conversation shape, a random canary token in the system prompt catches session exfil attempts across text, tool args, URLs, and file writes, and a verdict combiner requires two classifiers to agree before blocking (prevents single-model false positives on Stack Overflow-style instruction pages). A shield icon in the sidebar header shows status (green/amber/red). Opt in to a 721MB DeBERTa-v3 ensemble via GSTACK_SECURITY_ENSEMBLE=deberta for 2-of-3 agreement. Emergency kill switch: GSTACK_SECURITY_OFF=1. See ARCHITECTURE.md for the full stack.

Browser handoff when the AI gets stuck. Hit a CAPTCHA, auth wall, or MFA prompt? $B handoff opens a visible Chrome at the exact same page with all your cookies and tabs intact. Solve the problem, tell Claude you're done, $B resume picks up right where it left off. The agent even suggests it automatically after 3 consecutive failures.

/pair-agent is cross-agent coordination. You're in Claude Code. You also have OpenClaw running. Or Hermes. Or Codex. You want them both looking at the same website. Type /pair-agent, pick your agent, and a GStack Browser window opens so you can watch. The skill prints a block of instructions. Paste that block into the other agent's chat. It exchanges a one-time setup key for a session token, creates its own tab, and starts browsing. You see both agents working in the same browser, each in their own tab, neither able to interfere with the other. If ngrok is installed, the tunnel starts automatically so the other agent can be on a completely different machine. Same-machine agents get a zero-friction shortcut that writes credentials directly. This is the first time AI agents from different vendors can coordinate through a shared browser with real security: scoped tokens, tab isolation, rate limiting, domain restrictions, and activity attribution.

Multi-AI second opinion. /codex gets an independent review from OpenAI's Codex CLI — a completely different AI looking at the same diff. Three modes: code review with a pass/fail gate, adversarial challenge that actively tries to break your code, and open consultation with session continuity. When both /review (Claude) and /codex (OpenAI) have reviewed the same branch, you get a cross-model analysis showing which findings overlap and which are unique to each.

Safety guardrails on demand. Say "be careful" and /careful warns before any destructive command — rm -rf, DROP TABLE, force-push, git reset --hard. /freeze locks edits to one directory while debugging so Claude can't accidentally "fix" unrelated code. /guard activates both. /investigate auto-freezes to the module being investigated.

Proactive skill suggestions. gstack notices what stage you're in — brainstorming, reviewing, debugging, testing — and suggests the right skill. Don't like it? Say "stop suggesting" and it remembers across sessions.

10-15 parallel sprints

gstack is powerful with one sprint. It is transformative with ten running at once.

Conductor runs multiple Claude Code sessions in parallel — each in its own isolated workspace. One session running /office-hours on a new idea, another doing /review on a PR, a third implementing a feature, a fourth running /qa on staging, and six more on other branches. All at the same time. I regularly run 10-15 parallel sprints — that's the practical max right now.

The sprint structure is what makes parallelism work. Without a process, ten agents is ten sources of chaos. With a process — think, plan, build, review, test, ship — each agent knows exactly what to do and when to stop. You manage them the way a CEO manages a team: check in on the decisions that matter, let the rest run.

Voice input (AquaVoice, Whisper, etc.)

gstack skills have voice-friendly trigger phrases. Say what you want naturally — "run a security check", "test the website", "do an engineering review" — and the right skill activates. You don't need to remember slash command names or acronyms.

Uninstall

Option 1: Run the uninstall script

If gstack is installed on your machine:

~/.claude/skills/gstack/bin/gstack-uninstall

This handles skills, symlinks, global state (~/.gstack/), project-local state, browse daemons, and temp files. Use --keep-state to preserve config and analytics. Use --force to skip confirmation.

Option 2: Manual removal (no local repo)

If you don't have the repo cloned (e.g. you installed via a Claude Code paste and later deleted the clone):

# 1. Stop browse daemons
pkill -f "gstack.*browse" 2>/dev/null || true

# 2. Remove per-skill directories whose SKILL.md points into gstack/
find ~/.claude/skills -mindepth 1 -maxdepth 1 -type d ! -name gstack 2>/dev/null |
while IFS= read -r dir; do
  link="$dir/SKILL.md"
  [ -L "$link" ] || continue
  target=$(readlink "$link" 2>/dev/null) || continue
  case "$target" in
    gstack/*|*/gstack/*)
      rm -f "$link"
      rmdir "$dir" 2>/dev/null || true
      ;;
  esac
done

# 3. Remove gstack
rm -rf ~/.claude/skills/gstack

# 4. Remove global state
rm -rf ~/.gstack

# 5. Remove integrations (skip any you never installed)
rm -rf ~/.codex/skills/gstack* 2>/dev/null
rm -rf ~/.factory/skills/gstack* 2>/dev/null
rm -rf ~/.kiro/skills/gstack* 2>/dev/null
rm -rf ~/.openclaw/skills/gstack* 2>/dev/null

# 6. Remove temp files
rm -f /tmp/gstack-* 2>/dev/null

# 7. Per-project cleanup (run from each project root)
rm -rf .gstack .gstack-worktrees .claude/skills/gstack 2>/dev/null
rm -rf .agents/skills/gstack* .factory/skills/gstack* 2>/dev/null

Clean up CLAUDE.md

The uninstall script does not edit CLAUDE.md. In each project where gstack was added, remove the ## gstack and ## Skill routing sections.

Playwright

~/Library/Caches/ms-playwright/ (macOS) is left in place because other tools may share it. Remove it if nothing else needs it.

Free, MIT licensed, open source. No premium tier, no waitlist.

I open sourced how I build software. You can fork it and make it your own.

We're hiring. Want to ship real products at AI-coding speed and help harden gstack? Come work at YC — ycombinator.com/software Extremely competitive salary and equity. San Francisco, Dogpatch District.

GBrain — persistent knowledge for your coding agent

GBrain is a persistent knowledge base for AI agents — think of it as the memory your agent actually keeps between sessions. GStack gives you a one-command path from zero to "it's running, my agent can call it."

/setup-gbrain

Four paths, pick one:

Supabase, existing URL — your cloud agent already provisioned a brain; paste the Session Pooler URL, now this laptop uses the same data.
Supabase, auto-provision — paste a Supabase Personal Access Token; the skill creates a new project, polls to healthy, fetches the pooler URL, hands it to gbrain init. ~90 seconds end-to-end.
PGLite local — zero accounts, zero network, ~30 seconds. Isolated brain on this Mac only. Great for try-first; migrate to Supabase later with /setup-gbrain --switch.
Remote gbrain MCP — your brain runs on another machine (Tailscale, ngrok, internal LAN) or a teammate's server; paste an MCP URL and bearer token. Optionally pair with a local PGLite for symbol-aware code search in split-engine mode. Best for cross-machine memory without standing up a local DB.

After init, the skill offers to register gbrain as an MCP server for Claude Code (claude mcp add gbrain -- gbrain serve) so gbrain search, gbrain put, etc. show up as first-class typed tools — not bash shell-outs.

Keeping the brain current. Run /sync-gbrain from any repo to re-index its code into gbrain (incremental by default, --full for a full reindex, --dry-run to preview). The skill registers the cwd as a federated source via gbrain sources add, runs gbrain sync --strategy code, and writes a ## GBrain Search Guidance block to your project's CLAUDE.md so the agent prefers gbrain search/code-def/code-refs over Grep. The block is removed automatically if the capability check fails — no stale guidance pointing at tools that aren't installed.

Per-remote trust policy. Each repo on your machine gets one of three tiers:

read-write — agent can search the brain AND write new pages back from this repo
read-only — agent can search but never writes (best for multi-client consultants: search the shared brain, don't contaminate it with Client A's work while in Client B's repo)
deny — no gbrain interaction at all

The skill asks once per repo. The decision is sticky across worktrees and branches of the same remote.

GStack memory sync (different feature, same private-repo infra). Optionally pushes your gstack state (learnings, CEO plans, design docs, retros, developer profile) to a private git repo so your memory follows you across machines, with a one-time privacy prompt (everything allowlisted / artifacts only / off) and a defense-in-depth secret scanner that blocks AWS keys, tokens, PEM blocks, and JWTs before they leave your machine.

gstack-brain-init

Running gstack in Conductor? Conductor explicitly strips ANTHROPIC_API_KEY and OPENAI_API_KEY from every workspace's process env, so paid evals and gbrain embeddings won't work out of the box. Set GSTACK_ANTHROPIC_API_KEY and GSTACK_OPENAI_API_KEY in Conductor's workspace env config instead — gstack's TS entry points promote them to canonical names at runtime. Full details and the contributor checklist for adding the import to new entry points: Conductor + GSTACK_* env vars.

Full monty — every scenario, every flag, every bin helper, every troubleshooting step: USING_GBRAIN_WITH_GSTACK.md

Other references: docs/gbrain-sync.md (sync-specific guide) • docs/gbrain-sync-errors.md (error index)

Docs

Doc	What it covers
Skill Deep Dives	Philosophy, examples, and workflow for every skill (includes Greptile integration)
Diagrams & Document Formats	Mermaid/excalidraw fences in PDFs, image sizing and safety defaults, `--to html\|docx`, `/diagram` triplets
Builder Ethos	Builder philosophy: Boil the Ocean, Search Before Building, three layers of knowledge
Using GBrain with GStack	Every path, flag, bin helper, and troubleshooting step for `/setup-gbrain`
GBrain Sync	Cross-machine memory setup, privacy modes, troubleshooting
Architecture	Design decisions and system internals
Browser Reference	Full command reference for `/browse`
Contributing	Dev setup, testing, contributor mode, and dev mode
Changelog	What's new in every version

Privacy & Telemetry

gstack includes opt-in usage telemetry to help improve the project. Here's exactly what happens:

Default is off. Nothing is sent anywhere unless you explicitly say yes.
On first run, gstack asks if you want to share anonymous usage data. You can say no.
What's sent (if you opt in): skill name, duration, success/fail, gstack version, OS. That's it.
What's never sent: code, file paths, repo names, branch names, prompts, or any user-generated content.
Change anytime: gstack-config set telemetry off disables everything instantly.

Data is stored in Supabase (open source Firebase alternative). The schema is in supabase/migrations/ — you can verify exactly what's collected. The Supabase publishable key in the repo is a public key (like a Firebase API key) — row-level security policies deny all direct access. Telemetry flows through validated edge functions that enforce schema checks, event type allowlists, and field length limits.

Local analytics are always available. Run gstack-analytics to see your personal usage dashboard from the local JSONL file — no remote data needed.

Troubleshooting

Skill not showing up? cd ~/.claude/skills/gstack && ./setup

/browse fails? cd ~/.claude/skills/gstack && bun install && bun run build

Stale install? Run /gstack-upgrade — or set auto_upgrade: true in ~/.gstack/config.yaml

Want shorter commands? cd ~/.claude/skills/gstack && ./setup --no-prefix — switches from /gstack-qa to /qa. Your choice is remembered for future upgrades.

Want namespaced commands? cd ~/.claude/skills/gstack && ./setup --prefix — switches from /qa to /gstack-qa. Useful if you run other skill packs alongside gstack.

Codex says "Skipped loading skill(s) due to invalid SKILL.md"? Your Codex skill descriptions are stale. Fix: cd ~/.codex/skills/gstack && git pull && ./setup --host codex — or for repo-local installs: cd "$(readlink -f .agents/skills/gstack)" && git pull && ./setup --host codex

Windows users: gstack works on Windows 11 via Git Bash or WSL. Node.js is required in addition to Bun — Bun has a known bug with Playwright's pipe transport on Windows (bun#4253). The browse server automatically falls back to Node.js. Make sure both bun and node are on your PATH.

On Windows without Developer Mode (MSYS2 / Git Bash), setup falls back to file copies instead of symlinks because ln -snf produces frozen copies that don't refresh on git pull. Re-run cd ~/.claude/skills/gstack && ./setup after every git pull so your skill files match the repo. setup prints a one-line note reminding you. Unix and WSL keep symlinks and don't need the re-run.

Claude says it can't see the skills? Make sure your project's CLAUDE.md has a gstack section. Add this:

## gstack
Use /browse from gstack for all web browsing. Never use mcp__claude-in-chrome__* tools.
Available skills: /office-hours, /plan-ceo-review, /plan-eng-review, /plan-design-review,
/design-consultation, /design-shotgun, /design-html, /review, /ship, /land-and-deploy,
/canary, /benchmark, /browse, /open-gstack-browser, /qa, /qa-only, /design-review,
/setup-browser-cookies, /setup-deploy, /setup-gbrain, /sync-gbrain, /retro, /investigate,
/document-release, /document-generate, /codex, /cso, /autoplan, /pair-agent, /careful, /freeze,
/guard, /unfreeze, /gstack-upgrade, /learn.

License

MIT. Free forever. Go build something.

CodebuffAI/freebuff

The free coding agent

Freebuff

English | 简体中文

Five free AI products for coding, building, and research. No subscription, credits, or API key required.

Freebuff brings specialized agents and a choice of leading models to your terminal, desktop, browser, and GitHub repositories. Text ads support access to the included models.

Choose your Freebuff

Product	What it does	Get started
Freebuff Desktop (Beta)	Run parallel agents locally	Download for macOS, Windows, or Linux
Freebuff CLI	Code from your terminal	Install the CLI
Freebuff Web	Build and ship full-stack apps	Build an app
Freebuff Cloud	Run agents on any GitHub repository	Connect a repository
Freebuff Chat	Research and think with AI	Start a chat

Quick start

Run Freebuff in any project from your terminal:

npm install -g freebuff
cd ~/my-project
freebuff

Then describe what you want. Freebuff finds the relevant files, makes changes, and runs the checks that matter for your project.

Models

Freebuff includes a curated model catalog. The regular picker currently offers:

Model	Access	Best for
DeepSeek V4 Flash 07/31	Full and limited access	The default for CLI and Desktop; fast coding and tool use
DeepSeek V4 Pro	Full access	Longer, deeper reasoning
GPT-5.6 Luna	Full access	The default for Web, Cloud, and Chat; deep reasoning with image support
MiniMax M3	Full access	Fast responses with image support
MiMo 2.5	Full and limited access	Balanced performance with image support

Beyond the regular picker:

GLM 5.2 is available through earned sessions rather than as an always-unlocked model.
Gemini 3.1 Flash Lite powers specialist tasks such as file finding and research rather than appearing in the main picker.

Availability and limits depend on your access tier, product, and current capacity. Freebuff Desktop can also run locally installed Claude Code and Codex agents using your existing provider account; those connected models are separate from Freebuff's included catalog.

How Freebuff works

Freebuff uses specialized agents instead of sending every task through one model and one prompt. Depending on the task, agents gather context, plan, edit or research, run tools, and review the result.

Codebase context — File-finding agents map the relevant parts of a project before editing.
Implementation and review — Agents can divide work, make changes, run commands, and inspect the result.
Research and browser use — Agents can investigate documentation and test applications in a real browser.
Parallel local work — Desktop isolates concurrent agents in separate workspaces.
Hosted environments — Web and Cloud provide sandboxes, previews, terminals, and deployment workflows.

Free access

Freebuff is available in every country. Supported regions receive full access; other regions and VPN users receive limited access, currently DeepSeek V4 Flash and MiMo 2.5 with six one-hour sessions per day.

Text ads support the included models. Freebuff shows the applicable session limits and any model-specific data-use notice before you start.

Is my data used to train AI? Only when a model or feature says data may be used for AI training. Freebuff or the provider may then keep submissions to develop, train, test, evaluate, fine-tune, and improve AI models or products.

How is my data used and stored? We use prompts, messages, code, files, and repository data to provide the service. We may analyze prompts and messages—including pasted content—to personalize ads, using Freebuff systems and service providers acting on our behalf. Separate uploads and connected repositories are not provided to advertising providers. Where required by law, we provide advertising choices and honor recognized opt-out signals; elsewhere, this processing may be required to use the free service. See the Privacy Policy for retention and details.

See the Privacy Policy for complete details.

Contributing

Freebuff is a TypeScript monorepo built with Bun. Contributions to the products, agents, tools, documentation, and underlying runtime are welcome.

Local development requires Docker and a configured .env.local; see the Contributing Guide before starting the services.

git clone https://github.com/CodebuffAI/freebuff.git
cd freebuff
bun install
bun up

Start the CLI separately with:

bun start-cli

See the Contributing Guide, development guide, and testing guide for environment setup and the checks to run before opening a pull request.

Built on Codebuff

Freebuff is built on Codebuff, the open multi-agent framework that powers its orchestration, tools, and SDK. To create custom agents or embed them in another application, see the Codebuff documentation and @codebuff/sdk.

lissy93/web-check

🕵️‍♂️ All-in-one OSINT tool for analysing any website

Web-Check

Comprehensive, on-demand open source intelligence for any website
🌐 web-check.xyz

Kindly supported by:

NinjaPear
_{API to get a full B2B profiles from any URL}

Terminal Trove
_{The $HOME of all things in the terminal}

Warp
_{Built for coding with multiple AI agents}

About
- Screenshot
- Live Demo
- Mirror
- Features
Usage
Community
License

About

Get an insight into the inner-workings of a given website: uncover potential attack vectors, analyse server architecture, view security configurations, and learn what technologies a site is using.

Currently the dashboard will show: IP info, SSL chain, DNS records, cookies, headers, domain info, search crawl rules, page map, server location, redirect ledger, open ports, traceroute, DNS security extensions, site performance, trackers, associated hostnames, carbon footprint. Stay tuned, as I'll add more soon!

The aim is to help you easily understand, optimize and secure your website.

Screenshot

Expand Screenshot

Live Demo

A hosted version can be accessed at: web-check.as93.net

Mirror

The source for this repo is mirrored to CodeBerg, available at: codeberg.org/alicia/web-check

Status

Build & Deploys:
Repo Management & Miscellaneous:

Features

Click to expand / collapse section

Useful Links

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Whois Lookup

Description

This task retrieves Whois records for the target domain. Whois records are a rich source of information, including the name and contact information of the domain registrant, the domain's creation and expiration dates, the domain's nameservers, and more. The information is usually obtained through a query to a Whois database server.

Use Cases

In an OSINT context, Whois records can provide valuable clues about the entity behind a website. They can show when the domain was first registered and when it's set to expire, which could provide insights into the operational timeline of the entity. The contact information, though often redacted or anonymized, can sometimes lead to additional avenues of investigation. The nameservers could also be used to link together multiple domains owned by the same entity.

Useful Links

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Domain Info

Description

Use Cases

Useful Links

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[undefined](function link() { [native code] })

DNS Security Extensions

Description

Without DNSSEC, it's possible for MITM attackers to spoof records and lead users to phishing sites. This is because the DNS system includes no built-in methods to verify that the response to the request was not forged, or that any other part of the process wasn’t interrupted by an attacker. The DNS Security Extensions (DNSSEC) secures DNS lookups by signing your DNS records using public keys, so browsers can detect if the response has been tampered with. Another solution to this issue is DoH (DNS over HTTPS) and DoT (DNS over TLD).

Use Cases

DNSSEC information provides insight into an organization's level of cybersecurity maturity and potential vulnerabilities, particularly around DNS spoofing and cache poisoning. If no DNS secururity (DNSSEC, DoH, DoT, etc) is implemented, this may provide an entry point for an attacker.

Useful Links

[undefined](function link() { [native code] })
[undefined](function link() { [native code] })
[undefined](function link() { [native code] })
[undefined](function link() { [native code] })
[undefined](function link() { [native code] })
[undefined](function link() { [native code] })

Site Features

Description

Checks which core features are present on a site. If a feature as marked as dead, that means it's not being actively used at load time

Use Cases

This is useful to understand what a site is capable of, and what technologies to look for

Useful Links

HTTP Strict Transport Security

For site owners, this is useful for diagnosing SEO issues, improving the site structure, understanding how content is inter-connected. External links can show partnerships, dependencies, and potential reputation risks. From a security standpoint, the outbound links can help identify any potential malicious or compromised sites the website is unknowingly linking to. Analyzing internal links can aid in understanding the site's structure and potentially uncover hidden or vulnerable pages which are not intended to be public. And for an OSINT investigator, it can aid in building a comprehensive understanding of the target, uncovering related entities, resources, or even potential hidden parts of the site.

Useful Links

W3C Link Checker

Wayback Machine

Global Ranking

Description

This check shows the global rank of the requested site. This is only accurate for websites which are in the top 1 million list. We're using data from the Tranco project (see below), which collates the top sites on the web from Umbrella, Majestic, Quantcast, the Chrome User Experience Report and Cloudflare Radar.

Use Cases

Knowing a websites overall global rank can be useful for understanding the scale of the site, and for comparing it to other sites. It can also be useful for understanding the relative popularity of a site, and for identifying potential trends.

Useful Links

Block Detection

Description

Checks access to the URL using 10+ of the most popular privacy, malware and parental control blocking DNS servers.

Useful Links

Screenshot

Description

This check takes a screenshot of webpage that the requested URL / IP resolves to, and displays it.

Use Cases

This may be useful to see what a given website looks like, free of the constraints of your browser, IP, or location.

Usage

Deployment

Deploying - Option #1: Netlify

Click the button below, to deploy to Netlify 👇

Deploying - Option #2: Vercel

Click the button below, to deploy to Vercel 👇

Deploying - Option #3: Docker

Run docker run -p 3000:3000 lissy93/web-check, then open localhost:3000

Docker Options

You can get the Docker image from:

DockerHub: lissy93/web-check
GHCR: ghcr.io/lissy93/web-check
Or build the image yourself by cloning the repo and running docker build -t web-check .

Deploying - Option #4: Render

Click the button below, to deploy to Render 👇

Uses the official lissy93/web-check image on a Standard web service. Optional enrichment API keys can be added after deploy.

Deploying - Option #5: From Source

Install the prerequisites listed in the Developing section, then run:

git clone https://github.com/Lissy93/web-check.git  # Download the code from GitHub
cd web-check                                        # Navigate into the project dir
yarn install                                        # Install the NPM dependencies
yarn build                                          # Build the app for production
yarn start                                          # Start the app (API and GUI)

Configuring

By default, no configuration is needed.

But there are some optional environmental variables that you can set to give you access to some additional checks, or to increase rate-limits for some checks that use external APIs.

API Keys & Credentials:

Key	Value
`GOOGLE_CLOUD_API_KEY`	A Google API key with the PageSpeed Insights API enabled (get here). This can be used to return quality metrics for a site
`REACT_APP_SHODAN_API_KEY`	A Shodan API key (get here). This will show associated host names for a given domain
`REACT_APP_WHO_API_KEY`	A WhoAPI key (get here). This will show more comprehensive WhoIs records than the default job

Full / Upcoming Vals

GOOGLE_CLOUD_API_KEY - A Google API key with the PageSpeed Insights API enabled (get here). This can be used to return quality metrics for a site
REACT_APP_SHODAN_API_KEY - A Shodan API key (get here). This will show associated host names for a given domain
REACT_APP_WHO_API_KEY - A WhoAPI key (get here). This will show more comprehensive WhoIs records than the default job
SECURITY_TRAILS_API_KEY - A Security Trails API key (get here). This will show org info associated with the IP
CLOUDMERSIVE_API_KEY - API key for Cloudmersive (get here). This will show known threats associated with the IP
TRANCO_USERNAME - A Tranco email (get here). This will show the rank of a site, based on traffic
TRANCO_API_KEY - A Tranco API key (get here). This will show the rank of a site, based on traffic
URL_SCAN_API_KEY - A URLScan API key (get here). This will fetch miscalanious info about a site
BUILT_WITH_API_KEY - A BuiltWith API key (get here). This will show the main features of a site
TORRENT_IP_API_KEY - A torrent API key (get here). This will show torrents downloaded by an IP

Configuration Settings:

Key	Value
`PORT`	Port to serve the API, when running server.js (e.g. `3000`)
`API_ENABLE_RATE_LIMIT`	Enable rate-limiting for the /api endpoints (e.g. `true`)
`PUBLIC_API_TIMEOUT_LIMIT`	The timeout limit for API requests, in milliseconds (e.g. `25000`)
`API_CORS_ORIGIN`	Enable CORS, by setting your allowed hostname(s) here (e.g. `example.com`)
`API_DISABLED_CHECKS`	Comma-separated list of checks to disable (e.g. `trace-route,ports`)
`API_ENABLED_CHECKS`	If set, only these checks will run (e.g. `get-ip,ssl,dns,headers`)
`API_BLOCKED_HOSTS`	Hosts that must never be scanned (e.g. `lan.example.com,192.168.0.0/16`)
`CHROME_PATH`	The path the Chromium executable (e.g. `/usr/bin/chromium`)
`DISABLE_GUI`	Disable the GUI, and only serve the API (e.g. `false`)
`REACT_APP_API_ENDPOINT`	The endpoint for the API, either local or remote (e.g. `/api`)

All values are optional.

You can add these as environmental variables. Either put them directly into an .env file in the projects root, or via the Netlify / Vercel UI, or by passing to the Docker container with the --env flag, or using your own environmental variable management system

Note that keys that are prefixed with REACT_APP_ are used client-side, and as such they must be scoped correctly with minimum privileges, since may be made visible when intercepting browser <-> server network requests

Developing

Clone the repo, git clone git@github.com:Lissy93/web-check.git
Cd into it, cd web-check
Install dependencies: yarn
Start the dev server, with yarn dev

You'll need Node.js (v22.12 or later) installed, plus yarn as well as git. Some checks also require chromium, traceroute and dns to be installed within your environment. These jobs will just be skipped if those packages aren't present.

Community

Contributing

Contributions of any kind are very welcome, and would be much appreciated. For Code of Conduct, see Contributor Convent.

To get started, fork the repo, make your changes, add, commit and push the code, then come back here to open a pull request. If you're new to GitHub or open source, this guide or the git docs may help you get started, but feel free to reach out if you need any support.

Reporting Bugs

If you've found something that doesn't work as it should, or would like to suggest a new feature, then go ahead and raise a ticket on GitHub. For bugs, please outline the steps needed to reproduce, and include relevant info like system info and resulting logs.

Supporting

The app will remain 100% free and open source. But due to the amount of traffic that the hosted instance gets, the lambda function usage is costing about $25/month. Any help with covering the costs via GitHub Sponsorship would be much appreciated. It's thanks to the support of the community that this project is able to be freely available for everyone 😃

Contributors

Credit to the following users for contributing to Web-Check

License

Lissy93/Web-Check is licensed under MIT © Alicia Sykes 2023 - 2026.
^{For information, see TLDR Legal > MIT}

Expand License

The MIT License (MIT)
Copyright (c) Alicia Sykes <alicia@omg.com>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sub-license, and/or sell
copies of the Software, and to permit persons to whom the Software is furnished
to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included install
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANT ABILITY, FITNESS FOR A
PARTICULAR PURPOSE AND NON INFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

DayuanJiang/next-ai-draw-io

A next.js web application that integrates AI capabilities with draw.io diagrams. This app allows you to create, modify, and enhance diagrams through natural language commands and AI-assisted visualization.

Next AI Draw.io

AI-Powered Diagram Creation Tool - Chat, Draw, Visualize

English | 中文 | 日本語

A Next.js web application that integrates AI capabilities with draw.io diagrams. Create, modify, and enhance diagrams through natural language commands and AI-assisted visualization.

Note: Thanks to ByteDance Doubao sponsorship, the demo site now uses the powerful glm-4.7 model!

https://github.com/user-attachments/assets/9d60a3e8-4a1c-4b5e-acbb-26af2d3eabd1

Next AI Draw.io

Examples

Here are some example prompts and their generated diagrams:

Animated transformer connectors Prompt: Give me a animated connector diagram of transformer's architecture.
RAG Technique Diagram Prompt: Generate a RAG architecture diagram for chat application. Use connected diagram for data ingestion	Authentication using React and AWS Prompt: Generate authentication process using React with AWS. Use Serverless architecture.
Open Innovation Prompt: Create visualization of Henry Chesbrough's Open Innovation model.	Cat sketch Prompt: Draw a cute cat for me.

Features

LLM-Powered Diagram Creation: Leverage Large Language Models to create and manipulate draw.io diagrams directly through natural language commands
Image-Based Diagram Replication: Upload existing diagrams or images and have the AI replicate and enhance them automatically
PDF & Text File Upload: Upload PDF documents and text files to extract content and generate diagrams from existing documents
AI Reasoning Display: View the AI's thinking process for supported models (OpenAI o1/o3, Gemini, Claude, etc.)
Diagram History: Comprehensive version control that tracks all changes, allowing you to view and restore previous versions of your diagrams before the AI editing.
Interactive Chat Interface: Communicate with AI to refine your diagrams in real-time
Cloud Architecture Diagram Support: Specialized support for generating cloud architecture diagrams (AWS, GCP, Azure)
Animated Connectors: Create dynamic and animated connectors between diagram elements for better visualization

MCP Server

Use Next AI Draw.io with AI agents like Claude Desktop, Cursor, and VS Code via MCP (Model Context Protocol).

{
  "mcpServers": {
    "drawio": {
      "command": "npx",
      "args": ["@next-ai-drawio/mcp-server@latest"]
    }
  }
}

Claude Code CLI

claude mcp add drawio -- npx @next-ai-drawio/mcp-server@latest

Then ask Claude to create diagrams:

"Create a flowchart showing user authentication with login, MFA, and session management"

The diagram appears in your browser in real-time!

See the MCP Server README for VS Code, Cursor, and other client configurations.

Getting Started

Try it Online

No installation needed! Try the app directly on our demo site:

Bring Your Own API Key: You can use your own API key to bypass usage limits on the demo site. Click the Settings icon in the chat panel to configure your provider and API key. Your key is stored locally in your browser and is never stored on the server.

Desktop Application

Download the native desktop app for your platform from the Releases page:

Supported platforms: Windows, macOS, Linux.

Run with Docker

Go to Docker Guide

Installation

Clone the repository:

git clone https://github.com/DayuanJiang/next-ai-draw-io
cd next-ai-draw-io
npm install
cp env.example .env.local

See the Provider Configuration Guide for detailed setup instructions for each provider.

Run the development server:

npm run dev

Open http://localhost:6002 in your browser to see the application.

Deployment

Deploy to EdgeOne Pages

You can deploy with one click using Tencent EdgeOne Pages.

Deploy by this button:

Check out the Tencent EdgeOne Pages documentation for more details.

Additionally, deploying through Tencent EdgeOne Pages will also grant you a daily free quota for DeepSeek models.

Deploy on Vercel

The easiest way to deploy is using Vercel, the creators of Next.js. Be sure to set the environment variables in the Vercel dashboard as you did in your local .env.local file.

See the Next.js deployment documentation for more details.

Deploy on Cloudflare Workers

Go to Cloudflare Deploy Guide

Multi-Provider Support

ByteDance Doubao
AWS Bedrock (default)
OpenAI
Anthropic
Google AI
Google Vertex AI
Azure OpenAI
Ollama
OpenRouter
AIHubMix
DeepSeek
SiliconFlow
ModelScope
SGLang
Vercel AI Gateway

All providers except AWS Bedrock and OpenRouter support custom endpoints.

📖 Detailed Provider Configuration Guide - See setup instructions for each provider.

Server-Side Multi-Model Configuration

Administrators can configure multiple server-side models that are available to all users without requiring personal API keys. Configure via AI_MODELS_CONFIG environment variable (JSON string) or ai-models.json file. For a single-provider quick setup, list comma-separated model IDs in AI_MODEL.

Admin Panel

Set the ADMIN_PASSWORD environment variable and visit /admin to manage server settings (models, access codes, features, observability, quota) from a web panel instead of hand-editing .env.

📖 Admin Panel Guide — setup, precedence rules, and notes.

Model Requirements: This task requires strong model capabilities for generating long-form text with strict formatting constraints (draw.io XML). Recommended models include Claude Sonnet 4.5, GPT-5.1, Gemini 3 Pro, and DeepSeek V3.2/R1.

Note that the claude series has been trained on draw.io diagrams with cloud architecture logos like AWS, Azure, GCP. So if you want to create cloud architecture diagrams, this is the best choice.

How It Works

The application uses the following technologies:

Next.js: For the frontend framework and routing
Vercel AI SDK (ai + @ai-sdk/*): For streaming AI responses and multi-provider support
react-drawio: For diagram representation and manipulation

Diagrams are represented as XML that can be rendered in draw.io. The AI processes your commands and generates or modifies this XML accordingly.

Support & Contact

Special thanks to ByteDance Doubao for sponsoring the API token usage of the demo site! Register on the ARK platform to get 500K free tokens for all models!

If you find this project useful, please consider sponsoring to help me host the live demo site!

For support or inquiries, please open an issue on the GitHub repository or contact the maintainer at:

Email: me[at]jiang.jp

FAQ

See FAQ for common issues and solutions.

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Built for Long-Running Work

Documentation

Acknowledgements

License

pingdotgg/t3code

T3 Code

"Wait, what are you selling me?"

Installation

Try it out (install-free)

Desktop app

Windows (winget)

macOS (Homebrew)

Arch Linux (AUR)

Some notes

Documentation

If you REALLY want to contribute still.... read this first

Install vp

macOS / Linux

Windows

Install dependencies

diegosouzapw/OmniRoute

🚀 OmniRoute — The Free AI Gateway

💰 ~1.53B Free Tokens / Month

⭐ Star the repo if OMNIROUTE helped you save money and make your work easier.

💬 Join the community

🧩 Available

🆓 Works the second you install it — no keys, no config

💥 The Promise

🤔 Why OmniRoute?

🤝 Supported by our Open Source Friends

🎯 Combos — The Flagship

⚡ Zero-config — just use auto

🔀 Or build your own — 19 routing strategies

🧱 Resilience is built in (3 independent layers)

🏆 What Sets OmniRoute Apart

💚 Support OmniRoute

✨ What's New

🤖 Compatible CLIs & Coding Agents

🌐 291 AI Providers — 90+ Free

🏢 Every major lab — through one endpoint

🆓 Free Forever — $0, no card

🖥️ Where OmniRoute Runs — Anywhere

🔒 Private & Local-First

🔌 Full CLI + A2A & MCP

⌨️ A real CLI (not just start)

🛰️ Remote mode — run the CLI here, OmniRoute on a VPS

🤝 Connect an agent — and it controls OmniRoute itself

🗜️ Save 15–95% Tokens — Automatically

📖 How it works — pipeline, architecture & savings math

🧱 The 12-engine stack

🎚️ Beyond the engines — output styles, the adaptive dial & per-request control

⚡ Quick Start

📦 More install methods — Docker, source, pnpm, Arch

🎬 OmniRoute in Action

📧 Community & Help

🛠️ Tech Stack

📖 Documentation

📘 Getting Started

🔧 Operations & Deployment

🧠 Features & Architecture

🤖 Protocols & APIs

📋 Project & Quality

⭐ Top Contributors

💖 Sponsors

👥 500+ Contributors

How to Contribute

Releasing a New Version

📊 Stars

🌍 StarMapper

🙏 Acknowledgments

🧬 Lineage & gateway

🗜️ Context & token compression — engines

🧩 Compact formats, token research & code-aware tooling

🧠 Memory & RAG

🛰️ Traffic inspection, MITM & transparent proxy

📚 Model data, observability & UI

Windows (`winget`)

Install `vp`

⚡ Zero-config — just use `auto`

⌨️ A real CLI (not just `start`)