WUPHF by Nex.ai
FreeOpen-source local AI employee office with a shared autonomous knowledge base.
About WUPHF by Nex.ai
WUPHF is an open-source, collaborative office of AI employees that run locally on your machine. It features a team of specialized agents—including a CEO, Engineer, Designer, CMO, and PM—who share a 'brain' or knowledge base to maintain context across tasks. Unlike simple prompt chains, these agents interact autonomously in shared channels, manage dependencies, and use local tools (like GitHub CLI and filesystem ops) to execute complex goals such as shipping code, creating assets, and writing documentation without constant human intervention.
How to Use
To start, run 'npx wuphf@latest' in your terminal to open the web interface at localhost:7891. Simply type a goal into the #general channel, such as 'Ship the onboarding flow by Friday.' The CEO agent will decompose the goal and assign tasks to other agents (ENG, DSG, CMO), who will then coordinate and execute the work while you walk away.
Key Features
- Collaborative AI agents with specialized roles (CEO, ENG, DSG, CMO, PM)
- Shared knowledge base and local wiki for persistent context
- Runs locally with no cloud dependencies or account requirements
- Supports multiple LLMs including Claude Code, Codex, and local models via OpenCode
- Autonomous coordination with emergent behavior and real tool calls
- MIT Licensed and fully open-source
Use Cases
- Automating software development workflows from spec to Pull Request
- Managing marketing launches and documentation updates
- Running a private, local AI team to handle sensitive project context
- Building custom AI departments by forking JSON agent configurations
FAQ
What happens if an agent gets stuck in a loop? Verdent Build Your Product With Plain Words In Minutes
Key Features
Pros & Cons
- Free and open-source with no subscription fees or hidden costs
- Runs locally on the user's machine, offering data privacy and offline capability
- Autonomous agents reduce the need for constant human oversight on multi-step tasks
- Shared knowledge base enables agents to maintain context and consistency across long projects
- Easy to install and set up with minimal configuration compared to traditional AI orchestration tools
- Agent roles and behaviors are highly customizable via simple JSON configuration
- Requires local machine resources (CPU, memory) to run agents; performance may vary on low-spec hardware
- Initial setup may require basic command-line familiarity (npx, Go build) for non-technical users
- Output quality and reliability depend on the underlying AI models and agent configurations; results may need human review
- Limited to tasks within the defined agent toolset; advanced integrations may require additional setup
- The tool is still relatively new; documentation and community support may be limited
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