Maintainer of this skill?
Automated security scan — not a certification.
by ShenSeanChen
Waku Waku! Waku Agent is a local-first AI agent harness you actually own, including loop, memory, eval, all in code built to stay legible as it grows.
# Add to your Claude Code skills
git clone https://github.com/ShenSeanChen/waku-agentLast scanned: 7/27/2026
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"issues": [],
"status": "PASSED",
"scannedAt": "2026-07-27T07:29:58.098Z",
"npmAuditRan": true,
"pipAuditRan": true,
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}See how waku-agent compares with popular alternatives.
waku-agent is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by ShenSeanChen. Waku Waku! Waku Agent is a local-first AI agent harness you actually own, including loop, memory, eval, all in code built to stay legible as it grows. It has 2,093 GitHub stars.
Yes. waku-agent passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/ShenSeanChen/waku-agent" and add it to your Claude Code skills directory (see the Installation section above).
waku-agent is primarily written in Python. It is open-source under ShenSeanChen on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh waku-agent against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
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.
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
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.
Waku's own memory is local. Waku Memory is the hosted memory you share across agents: save something in Claude Code, recall it here.
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.
For live data when it researches, waku connect treg (or /connect treg) signs in to your own
treg account the same way; see integrations.
| 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.
| Read | For |
|---|---|
| Getting started | installing, the first run, connecting Waku Memory |
| The tour | the dashboard, things to try, the loop, graph workflows, skills |
| Architecture | every box on the whiteboard, and the file behind it |
| Integrations | voice, Telegram, calendars, MCP servers, Waku Memory |
| Commands | every waku and make command |
| Evals & tracing | the two kinds of eval, the Docker tier, the release gate, traces and spend |
| Roadmap | what is live, what is still a skeleton, upgrade paths |
| Whiteboards | the editable system-design charts from the videos |
| lab/ | Waku meets other agents and models: the video experiments |
| AGENTS.md · CONTRIBUTING.md | the rules, and how to send a PR |
Star the repo, join the Discord, and grab a good first issue — 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).
Comment on an issue before you start and it gets assigned to you, so two people never build the same thing.
Code is MIT — see LICENSE — except hosted/, which is Elastic
License 2.0: run it yourself, including commercially, but not as a service for
others. See 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. Built by @ShenSeanChen
(YouTube · X).