Open-source, desktop-grade AI agent that gets real work done — data analysis, slides, docs, video & web research. Built on OpenClaw; runs tools on your real desktop and takes commands from your phone via WeChat, Feishu, DingTalk & Telegram.
# Add to your Claude Code skills
git clone https://github.com/netease-youdao/LobsterAILast scanned: 6/18/2026
{
"issues": [
{
"file": "AGENTS.md",
"line": 47,
"type": "secret-exfiltration",
"message": "Instruction appears to send credentials/secrets to an external endpoint",
"severity": "medium"
}
],
"status": "PASSED",
"scannedAt": "2026-06-18T08:48:19.949Z",
"npmAuditRan": false,
"pipAuditRan": true,
"promptInjectionRan": true
}See how LobsterAI compares with popular alternatives.
LobsterAI is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by netease-youdao. Open-source, desktop-grade AI agent that gets real work done — data analysis, slides, docs, video & web research. Built on OpenClaw; runs tools on your real desktop and takes commands from your phone via WeChat, Feishu, DingTalk & Telegram. It has 6,107 GitHub stars.
Yes. LobsterAI passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/netease-youdao/LobsterAI" and add it to your Claude Code skills directory (see the Installation section above).
LobsterAI is primarily written in TypeScript. It is open-source under netease-youdao on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh LobsterAI against similar tools.
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This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
LobsterAI is a desktop Agent that can operate in your real working environment: local files, terminal commands, browser workflows, documents, spreadsheets, slides, IM channels, scheduled jobs, and project workspaces.
Cowork is the LobsterAI product/session layer. OpenClaw is the runtime and gateway underneath it. That split lets LobsterAI keep local persistence, permissions, UI state, artifacts, agents, memory, and IM bindings in the desktop app while using OpenClaw for agent execution.
Run long-form Agent tasks against local projects and files. LobsterAI streams progress, keeps session history, renders tool output, and asks for approval before sensitive actions such as file operations, terminal commands, or network access.
Create custom Agents with their own identity, model choice, skills, working directory, enabled state, and IM bindings. Keep the Main Agent for general work and use specialized Agents for repeatable roles.
Install scenario-oriented Expert Kits that package capability selections and references for common workflows. Kits are selected independently from direct skills, so a workflow can combine curated kits with individual tools.
LobsterAI ships with 28 built-in skills configured in SKILLs/skills.config.json, including web search, Word documents, spreadsheets, PowerPoint, PDF processing, Remotion video generation, browser automation, image/video generation, stock research, content writing, email, weather, and skill creation.
Connect external tools and data sources through Model Context Protocol servers. LobsterAI stores user-configured servers locally and syncs enabled servers into OpenClaw.
Create recurring work either by conversation or through the scheduled task UI. Use it for daily news digests, inbox summaries, website monitoring, weekly reports, and other repeatable work.
Reach your desktop Agent from WeChat, WeCom, DingTalk, Feishu/Lark, QQ, Telegram, Discord, NetEase IM, NetEase Bee, POPO, and email. Multi-instance platforms can bind different accounts or channels to different Agents.
Preview and manage generated HTML, SVG, images, video, Mermaid diagrams, code, Markdown, text, documents, and local service artifacts inside the desktop app.
Sessions and app data live locally in SQLite. OpenClaw workspace memory uses files such as MEMORY.md, USER.md, SOUL.md, and daily notes, so durable preferences and project context can carry across sessions.
| Scenario | Example prompt |
|---|---|
| Build a local system | "I still track inventory and sales in Excel. Build a local inventory system that records purchases and sales, calculates stock and profit, and opens in my browser." |
| Analyze local data | "Use product-growth.xlsx to build a visual dashboard and summarize the main growth drivers." |
| Generate a deck | "Research the AI Agent market and turn the findings into a presentation." |
| Automate browser checks | "Open the ads dashboard every morning, check spend and conversion anomalies, and summarize likely causes." |
| Screen documents | "Turn the resumes in this folder into a screening sheet and shortlist the strongest candidates against the JD." |
| Run scheduled work | "Every weekday at 9 AM, collect yesterday's AI news and send me a concise digest." |
openclawEngineManager, openclawConfigSync, openclawRuntimeAdapter, and coworkEngineRouter translate LobsterAI state into OpenClaw runtime behavior.Download the latest macOS and Windows installers from Official Website or GitHub Releases.
Requirements:
>=24.15.0 <25>=11.17.0 <12 (older versions: npm install -g npm@11.17.0)../openclaw checkoutbetter-sqlite3@13.0.3 includes prebuilt N-API binaries for Windows, macOS,
and Linux on x64/arm64. The allowScripts entry in package.json skips npm's
unnecessary implicit rebuild for this version, so installing it does not require
Visual Studio C++ Build Tools on Windows. Other dependencies' install scripts
still run. Recheck this entry when upgrading better-sqlite3.
git clone https://github.com/netease-youdao/LobsterAI.git
cd LobsterAI
npm install
First development run:
npm run electron:dev:openclaw
Daily development after the pinned OpenClaw runtime exists:
npm run electron:dev
The renderer dev server runs at http://localhost:5175.
# Production renderer bundle
npm run build
# Electron main/preload TypeScript build
npm run compile:electron
# Official Vitest entry used by CI
npm test
# Full ESLint across src; may expose existing legacy debt
npm run lint
# CI-style lint for touched TypeScript files
npx eslint --ext ts,tsx --report-unused-disable-directives --max-warnings 0 <files>
The pinned OpenClaw version and third-party plugin list live in package.json under openclaw.
# Build the current-platform runtime manually
npm run openclaw:runtime:host
# Use a custom OpenClaw source checkout
OPENCLAW_SRC=/path/to/openclaw npm run electron:dev:openclaw
# Force runtime rebuild
OPENCLAW_FORCE_BUILD=1 npm run electron:dev:openclaw
# Keep a local OpenClaw checkout on its current branch/tag
OPENCLAW_SKIP_ENSURE=1 npm run electron:dev:openclaw
The pinned dsh version and one archive descriptor per platform live in package.json under dsh. Development reads vendor/dsh-runtime/current; shipped apps download the archive on first use and verify it against the digest they carry.
# Build and activate the current-platform runtime
npm run dsh:runtime:host
# Boot it once and assert the web UI answers
npm run dsh:runtime:verify
# Full gate: build, pack, install over HTTP, boot, assert provider/model over RPC
npm run dsh:e2e
Each target must be built on a matching machine: native dependencies install for the host, so a cross-architecture build produces an archive that packs cleanly and only fails on users' machines. The build refuses to run on a mismatched host.
| Target | Build on |
|---|---|
mac-arm64 |
Apple Silicon mac |
mac-x64 |
Intel mac |
win-x64 |
Windows 10 1803+ (ships tar.exe) |
Run these on that machine, substituting the target:
# 1. Build (applies the pinned patches, prunes to ~160 MB)
npm run dsh:runtime:mac-arm64
# 2. Pack; prints the sha256 and size
npm run dsh:runtime:pack mac-arm64
# 3. Upload vendor/dsh-dist/dsh-runtime-<version>-mac-arm64.tar.gz to the CDN,
# then record where it landed. Digest and size come from the local manifest,
# so they cannot drift from the bytes that were packed.
npm run dsh:runtime:url mac-arm64 "https://cdn.example.com/<uploaded>"
# 4. Confirm the URL serves exactly those bytes
npm run dsh:runtime:verify-urls mac-arm64
Step 3 writes dsh.runtimes[target] into package.json; commit that hunk so every platform's descriptor ships in one build. Each target holds one absolute URL and nothing is appended to it, so a CDN that mints an unrelated URL per file needs no shared directory.
dsh.version in package.json.