waku-agent
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 by seanchen.io.
- Local-first. Your memory is one SQLite file. Open it. Read it. It's yours.
- Memory is the hero. Semantic + episodic + procedural — with a gate that decides whether
to remember, and a pass that decides what to keep.
- The loop is ~95 lines of plain Python. Step through it.
- Watch it think. A local dashboard lights up every message as it flows through the harness.
- Eval built in. Deterministic tests and LLM-as-judge, side by side, with a release gate.
The system-design whiteboard from the series.
Every box maps to a file — see the architecture.
▶ Watch the 20-min code walkthrough — the loop, the memory pillars, the evals, the Telegram gateway and the "Waku Waku" wake word, live.
Waku Memory — the same memory in Claude Code, Codex, Grok Bot and this agent: waku.one · docs
YouTube · X · LinkedIn · Instagram · TikTok · Discord ·
哔哩哔哩 · 小红书 · 抖音
Buy me a coffee — it keeps this repo (and the videos) coming
Quickstart
Just want to run it:
pip install waku-agent
waku # talk to your Waku in the terminal
waku dashboard # …or the browser cockpit → localhost:7777
It will tell you which key to set the first time. Want to read the code (the
point of this repo) or contribute — clone it instead:
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
Now try it. "Remember that Alex prefers morning meetings." Quit. Restart.
"Book a catch-up with Alex on Friday." → it remembers, and books 9am. Your memory is one
file: ~/.waku/state.db, the same from every folder.
Use the model you already pay for. Anthropic (default), OpenAI, Gemini, DeepSeek, MiniMax,
Kimi, GLM, OpenRouter (one key, hundreds of hosted models), OpenCode Zen, or OpenCode Go —
set WAKU_PROVIDER=, paste the key, done. One dialect in the loop;
a ~60-line adapter handles the rest.
New to it? Getting started walks the whole setup, with a check
at the end of every step.
Connect Waku Memory
Waku's own memory is local. Waku Memory is the hosted memory you
share across agents: save something in Claude Code, recall it here.
waku plugin install waku@waku-memory # or /plugin install waku@waku-memory in the dashboard chat
waku mcp login waku_memory # sign in, once, in your browser
waku skill export --to claude,codex # carry Waku's skills to Claude Code and Codex too
That installs the Waku plugin, the one Claude Code and Codex use. To connect Claude Code, Codex, Hermes or Grok Bot to the
same memory, see integrations.
For live data when it researches, waku connect treg (or /connect treg) signs in to your own
treg account the same way; see integrations.
What's inside
| Pillar | In one line | Read more |
|---|
| Harness | gateways (terminal, dashboard, voice, Telegram, Discord, WhatsApp) and tools around one loop | architecture |
| Loop | ~95 lines of plain Python: reason, act, repeat, with two ways to stop | the tour |
| Memory | semantic, episodic and procedural (skills); a gate decides whether to remember, consolidation decides what to keep | the tour |
| Eval / LLM-Ops | deterministic tests and LLM-as-judge side by side, a release gate, a trace for every turn | evals |
How is this different from ChatGPT or Claude Desktop? Those are products you use. This is a
codebase you own: the loop, the memory schema, the gate and the eval harness are all yours to
read and change. Versus the big open-source assistants (OpenClaw, Hermes)? Same architecture,
1/100th the code.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
