Hey Hacker News, I’m excited to share LangManus, an open-source project that’s all about community-driven AI automation. Built on the shoulders of amazing open-source work, LangManus combines language models with specialized tools to tackle tasks like web search, crawling, and Python code execution—while giving back to the community that inspires it. At its core, LangManus uses a hierarchical multi-agent system, where a supervisor coordinates a team of specialized agents to handle complex workflows. It integrates with LLMs like Qwen and powers up with tools like Tavily for search and Jina for crawling. Plus, it comes with Docker support and a web UI to make interaction a breeze. Want to see it in action? Check out the demo video: https://youtu.be/sZCHqrQBUGk. In it, LangManus calculates the influence index of DeepSeek R1 on HuggingFace using a fully automated plan—think web searches, data retrieval, and Python code crunching, all seamlessly tied together. Here’s what makes LangManus stand out: Multi-Agent Magic: A supervisor delegates tasks to agents like Researcher, Coder, and Browser for efficient execution.

Tool Integration: Web search with Tavily, neural search with Jina, and Python REPL for coding on the fly.

Ease of Use: Docker-ready and a web UI for quick setup and control.

If you’re into AI automation, multi-agent systems, or just love contributing to open-source projects, swing by the GitHub repo: https://github.com/langmanus/langmanus. Dive into the code, play with the demo, or drop some feedback—we’d love to hear from you. Join the community and let’s build something awesome together!

Would be great if you put together a start-to-finish tutorial showing the step-by-step process from initial Git clone to completed successful run.

cool