It demystifies agent architecture by providing a fully transparent, minimal codebase that demonstrates memory pillars, retrieval gates, and dual eval methods (deterministic and LLM-as-judge), all with a local dashboard.
Your own AI assistant. On your laptop. In code you can read in an afternoon.
Meet Waku — a local-first personal assistant that shows the four pillars behind every serious agent: Harness · Loop · Memory · Eval/LLM-Ops. No frameworks hiding the good parts. Built for Sean's AI Stories.
The system-design whiteboard from the series. Every box maps to a file — see the whiteboard maps to the code.
▶ Watch the 20-min code walkthrough — the loop, the memory pillars, the evals, the Telegram gateway and the "Waku Waku" wake word, live.
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git clone https://github.com/ShenSeanChen/waku-agent && cd waku-agent
uv venv && uv pip install -e . # create the env + install the `waku` command
cp .env.example .env # pick a provider, paste ONE key
uv run waku # talk to your Waku in the terminal
uv run waku dashboard # …or the browser cockpit → localhost:7777
uv run waku … needs no venv activation. Three ways to run it:
| Command | When |
|---|---|
uv run waku dashboard | quick s |
Waku Waku! Waku agent is your personal AI agent, on your own laptop, in code you can read in an afternoon — harness + loop + memory + eval
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