netease-youdao/LobsterAI

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...

View on GitHub ↗Jump to charts ↓Open shareable report →

netease-youdao/LobsterAI has 6k+ stars and 950+ forks on GitHub, is written in TypeScript, and was last pushed on Oct 17, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 11 minutes ago
Added to GitGenius on August 31st, 2026
Created on February 12th, 2026
Open Issues & Pull Requests: 197 (+0)
GitHub issues: Enabled
Number of forks: 979
Total Stargazers: 6,124 (+1)
Total Subscribers: 35 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.6 days
Mean response time: 68.9 days
90th percentile: 213.2 days
Tracked items: 419

Maintainer activity

2 people did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

How this project is maintained

Roughly one issue in two opened in the past year never receives a reply. 99% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 62% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. 80% of issues opened in the past year have been closed, leaving a working backlog. Three people close 86% of everything that gets resolved.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 147
New in 7 days: 2
Closed in 7 days: 5
Avg open age: 145 days
Stale 30+ days: 106
Stale 90+ days: 90

Recent activity

Opened in 7 days: 2
Closed in 7 days: 5
Comments in 7 days: 1
Events in 7 days: 3

Top labels

  • stale (292)
  • obsolete (118)
  • wontfix (11)
  • invalid (7)
  • bug (4)
  • duplicate (1)
  • question (1)

Most active issues this week

Sign in to see which issues are moving.
Sign in

Detailed Description

LobsterAI is a desktop-grade AI agent that executes real work tasks including data analysis, slide creation, document editing, video research, and web research.

The tool addresses the gap between conversational AI assistants and agents that can actually perform work on a user's computer. It operates by running on a desktop environment built with Electron and TypeScript, where it can execute tools directly on the user's system. The agent accepts commands through multiple messaging platforms—WeChat, Feishu, DingTalk, and Telegram—allowing users to delegate tasks from their phone while the agent performs the work on the desktop. The architecture is built on OpenClaw, which provides the underlying framework for tool execution and agent coordination.

LobsterAI suits developers and teams who need an AI agent capable of autonomous task execution across productivity applications rather than just conversation. It works well for workflows involving data processing, document generation, and research tasks where the agent needs direct access to desktop applications and files. The project is particularly valuable for users in environments where WeChat, Feishu, or DingTalk are primary communication tools, as it integrates natively with these platforms. Organizations seeking to automate office work without relying on cloud-only solutions will find the desktop-first approach relevant, since the agent runs locally and can access real applications on the user's machine.

The project shows active development with regular commits across its TypeScript codebase. The repository maintains a clear focus on practical automation capabilities rather than experimental features. Documentation and examples are present in the repository, indicating ongoing effort to support adoption. The project appears to prioritize cross-platform compatibility and integration with widely-used communication platforms in specific regions, suggesting responsiveness to user needs in those markets.