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# 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](https://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](docs/architecture.md).
**▶ [Watch the 20-min code walkthrough](https://www.youtube.com/watch?v=rvRyBhILrls&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=42)** — the loop, the memory pillars, the evals, the Telegram gateway and the "Waku Waku" wake word, live.
**[Waku Memory](https://www.waku.one)** — the same memory in Claude Code, Codex, Grok Bot and this agent: [waku.one](https://www.waku.one) · [docs](https://www.waku.one/docs)
[YouTube](https://www.youtube.com/@SeanAIStories) · [X](https://x.com/ShenSeanChen) · [LinkedIn](https://linkedin.com/in/shen-sean-chen) · [Instagram](https://www.instagram.com/sean_ai_stories) · [TikTok](https://www.tiktok.com/@sean_ai_stories) · [Discord](https://discord.gg/ebbdvSCXqu) ·
[哔哩哔哩](https://space.bilibili.com/479332937) · [小红书](https://www.xiaohongshu.com/user/profile/5cf02cfb0000000005014371) · [抖音](https://www.douyin.com/user/MS4wLjABAAAAWCkd62_e8q4n-S34LIL04HsYN3m03l8MFdVYZToojP8)
### [Buy me a coffee](https://buy.stripe.com/5kA176bA895ggog4gh) — it keeps this repo (and the videos) coming
## Quickstart
Just want to run it:
```bash
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:
```bash
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](waku/loop/models.py) handles the rest.
New to it? **[Getting started](docs/getting-started.md)** walks the whole setup, with a check
at the end of every step.
## Connect Waku Memory
Waku's own memory is local. **[Waku Memory](https://www.waku.one)** is the hosted memory you
share across agents: save something in Claude Code, recall it here.
```bash
pip install 'waku-agent[mcp]' # in a checkout: uv pip install -e '.[mcp]'
waku connect waku-memory # or /connect waku-memory in the dashboard chat
waku skill export --to claude,codex # carry Waku's skills to Claude Code and Codex too
```
Your browser opens once to sign in. To connect Claude Code, Codex, Hermes or Grok Bot to the
same memory, see [integrations](docs/integrations.md#share-one-memory-with-your-other-agents-waku-memory).
For live data when it researches, `waku connect treg` (or `/connect treg`) signs in to your own
[treg](https://treg.to) account the same way; see [integrations](docs/integrations.md#live-data-for-research-treg).
## What's inside
| Pillar | In one line | Read more |
|---|---|---|
| **Harness** | gateways (terminal, dashboard, voice, Telegram, Discord, WhatsApp) and tools around one loop | [architecture](docs/architecture.md) |
| **Loop** | ~95 lines of plain Python: reason, act, repeat, with two ways to stop | [the tour](docs/tour.md#the-loop) |
| **Memory** | semantic, episodic and procedural (skills); a gate decides *whether* to remember, consolidation decides *what* to keep | [the tour](docs/tour.md#the-retrieval-gate) |
| **Eval / LLM-Ops** | deterministic tests and LLM-as-judge side by side, a release gate, a trace for every turn | [evals](docs/evals.md) |
**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.
## Docs
| Read | For |
|---|---|
| [Getting started](docs/getting-started.md) | installing, the first run, connecting Waku Memory |
| [The tour](docs/tour.md) | the dashboard, things to try, the loop, graph workflows, skills |
| [Architecture](docs/architecture.md) | every box on the whiteboard, and the file behind it |
| [Integrations](docs/integrations.md) | voice, Telegram, calendars, MCP servers, Waku Memory |
| [Commands](docs/commands.md) | every `waku` and `make` command |
| [Evals & tracing](docs/evals.md) | the two kinds of eval, the Docker tier, the release gate, traces and spend |
| [Roadmap](docs/roadmap.md) | what is live, what is still a skeleton, upgrade paths |
| [Whiteboards](docs/README.md#whiteboards) | the editable system-design charts from the videos |
| [lab/](lab/README.md) | Waku meets other agents and models: the video experiments |
| [AGENTS.md](AGENTS.md) · [CONTRIBUTING.md](CONTRIBUTING.md) | the rules, and how to send a PR |
## Community
Star the repo, join the [Discord](https://discord.gg/ebbdvSCXqu), and grab a
[good first issue](https://github.com/ShenSeanChen/waku-agent/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
— that link is the live list, so it's always current. Gateways, memory backends and
community skills are all shaped to be first PRs; the easiest needs no Python at all
(see [contributing a skill](CONTRIBUTING.md)).
**Comment on an issue before you start** and it gets assigned to you, so two people
never build the same thing.
## Also from me
- **[launch-mvp-stripe-nextjs-supabase](https://github.com/ShenSeanChen/launch-mvp-stripe-nextjs-supabase)** — NextJS + Supabase + Stripe, everything you need to ship a SaaS.
- **[AutoManus.io](https://automanus.io)** — my AI startup: a sales lead manager for made-to-order products. It embeds where conversations already happen (WhatsApp, email, web chat) to capture inbound, automate follow-ups and kill CRM busywork. Pre-seed backed by Character VC. ([AutoManus Discord](https://discord.gg/SxXATg9rSK))
Code is MIT — see [LICENSE](LICENSE) — **except `hosted/`, which is Elastic
License 2.0**: run it yourself, including commercially, but not as a service for
others. See [hosted/LICENSE](hosted/LICENSE). `waku/`, the harness itself and
everything PyPI ships, is MIT. The Waku name, mark and design system belong to
AutoManus Technologies, Inc. and are not MIT — see [LICENSE-BRAND](LICENSE-BRAND). Built by [@ShenSeanChen](https://github.com/ShenSeanChen)
([YouTube](https://www.youtube.com/@SeanAIStories) · [X](https://x.com/ShenSeanChen)).
Lire le README source