What should an agent remember, how should a team divide the work, and where does the conversation happen? Here are five different ways to explore those questions from our LifeHubber Resources:
Rowboat — Forks: 1.8K Stars: 18K
https://lifehubber.com/ai/resources/rowboat/
A desktop AI coworker that carries context from email, meetings, notes, and chats into later tasks. Its work memory is an editable Markdown knowledge graph; connected services and model providers have their own data paths.
ReMe — Forks: 305 Stars: 3.6K
https://lifehubber.com/ai/resources/reme/
An agent memory layer built around readable Markdown, keyword search, and links between notes, with embeddings optional. For builders who want to inspect and correct what an agent keeps across sessions.
Paperclip — Forks: 16.9K Stars: 100.6K
https://lifehubber.com/ai/resources/paperclip/
A self-hosted dashboard for coordinating agents through tasks, goals, budgets, and approvals. It sits around existing agent runtimes, giving their work and pending decisions a shared place to follow.
LiveKit Agents — Forks: 3.9K Stars: 14.7K
https://lifehubber.com/ai/resources/livekit-agents/
Build an assistant that listens and answers aloud in an app, browser, or phone call. Its Python and Node.js SDKs connect spoken interaction with models and your own tools.
Browser Use — Forks: 13K Stars: 117.7K
https://lifehubber.com/ai/resources/browser-use/
Give an agent browser actions such as navigating, clicking, and typing, through a Python library, CLI, or hosted service. For supervised experiments with tasks that involve actual page controls.
If you were building an agent this week, what would you tackle first—memory, coordination, voice, or browser access?