Choosing Our Project

Our group ultimately chose Nanobot, an ultra-lightweight personal AI assistant inspired by OpenClaw. It packs core agent functionality into roughly 4,000 lines of Python, supporting multiple LLM providers, MCP tool integration, and a wide range of chat platforms like Telegram, Discord, and Slack.

We all share a strong interest in AI and agent-based systems, so the project space was an easy decision. The harder part was narrowing down which project to contribute to. We initially evaluated LangChain, Hummingbot, and OpenClaw. LangChain and OpenClaw are massive codebases with huge contributor bases, and we were concerned that our contributions might get lost ignored or take a long time to be reviewed and merged. Nanobot stood out because it’s a young project with a lot of attention and active development. Since it’s essentially a lightweight reimplementation of OpenClaw, there are plenty of pending features to build and bugs to fix. It felt like the sweet spot: meaningful work with a realistic chance of our contributions actually landing.

On the practical side, Nanobot is written entirely in Python. Python is the preferred language for most of us. That made it the clear choice from a technical fit perspective as well.

Progress So Far

Everyone on the team has successfully set up the development environment, and so far we haven’t hit any major obstacles. Right now we’re in the exploration phase: actively using Nanobot, testing its features, and comparing its behavior against the more mature OpenClaw to understand where the gaps are.

I’ve already identified a few potential issues worth investigating. One is that reasoning content isn’t being passed back to the agent during the agent loop, which results in an incomplete chain of thought. I’ve also noticed that there are several missing LLM provider configurations that would be worth adding. These feel like solid starting points for contributions.

What’s Next

Our plan is for each team member to propose small issues or contributions to work on individually first. Once we’ve each gotten comfortable with the contribution workflow and the codebase, we’ll look into tackling a larger feature together as a group. The specifics are still being finalized, and we’re hoping to lock down a more detailed plan in the upcoming week.

I’m optimistic about this project. The codebase is clean and readable, the maintainers seem active, and there’s no shortage of work to be done. It feels like the kind of project where we can make a real impact within the semester.