Ever wondered what kind of AI agent a YC president would build for himself? Turns out, he actually wrote one from scratch and then put the core on GitHub — the part that decides what to do next, the “brain” of the agent. In just over a week, it racked up over 10,000 stars.
I’ve been poking around the repo after hearing about it from a few friends in the YC batch, and it’s genuinely refreshing. Most open-source agent frameworks these days are either too heavy (think full WebUI, vector databases, plugin marketplaces) or too academic (tons of papers but no runnable code). This one sits right in the sweet spot: a minimal, modular decision loop that you can drop into your own project without rewriting everything.
The structure is clean. There are three main components: a task planner that breaks goals into sub-steps, a context manager that keeps track of what’s been done, and a tool dispatcher that calls external APIs or local functions. The whole thing is written in Python, under 2,000 lines of code, with zero dependencies beyond openai and httpx. That’s it. You can literally import the planner, give it a goal, and watch it reason step by step.
Why does this matter? For anyone building their own agent — whether it’s a customer support bot, a code review assistant, or a personal research tool — the hardest part is always the decision logic. How do you handle ambiguity? When do you retry? How do you pass context between steps? Most people end up hardcoding a bunch of if-else statements and praying it works. This project shows you a battle-tested pattern that its author actually uses in production (he runs his own agent for email triage, meeting scheduling, and even drafting YC applications).
I also appreciate the debug mode built in. You can set a flag and see every thought the agent had, including “why it chose tool X instead of tool Y”. That’s the kind of transparency that makes you trust the system. And if you don’t like the default planner, you can swap it out — the interface is just a few abstract methods.
The only minor downside is the documentation is a bit light for beginners. The README gives you a quick example, but if you’re new to agent architectures, you might need to read the source code to understand the flow. Then again, the code is well-commented, so it’s not a deal breaker.
One thing I found interesting: the repo explicitly avoids any “agent memory” system. No vector store, no long-term memory module. The author’s reasoning (outlined in a GitHub issue) is that most use cases only need short-term context within a session, and adding persistent memory introduces complexity that’s rarely worth it. That’s a bold take, but it makes sense for the 80% of agent use cases — and it keeps the code lean.
If you’re experimenting with LLM agents and want a reference implementation that’s actually production-friendly, this is a good place to start. It’s not another AutoGPT clone. It’s a focused, pragmatic design from someone who ships code every day.
Check it out, star it if you find it useful, and maybe fork it to build your own assistant. I know I’m planning to adapt the planner for a small internal tool.