ShenSeanChen writes about waku-agent, a local-first LLM agent harness built as an open-source Python package that lets users own the entire loop, memory, and eval pipeline in readable code. The project pairs a ~95-line reasoning loop with a SQLite-backed memory schema, a retrieval gate, and a release gate backed by deterministic tests and LLM-as-judge, and it ships a local dashboard for watching every message flow. Core `waku/` code is MIT-licensed and installable from PyPI, while the hosted platform lives in `hosted/` under Elastic License 2.0 so nobody can resell it as a managed service.
- The author is also building the commercial startup AutoManus.io, a sales-lead manager for made-to-order products, and AutoManus Technologies holds the Waku brand and design system under a separate brand license.
- Waku Memory is a hosted memory service at waku.one that the agent can share with Claude Code, Codex, Grok Bot, and Hermes through MCP.
- The repo includes a `treg` connector for pulling live research data from a personal treg account during agent turns.
- The `lab/` directory holds the video experiments comparing Waku against other agent harnesses and models, including Grok Bot and Meta's Muse agent.
- Recent commits add a trim step that shortens long already-read tool results to keep token spend down, and a final-answer pass that answers from what a turn already gathered when it hits the iteration limit.
- The author is also building the commercial startup AutoManus.io, a sales-lead manager for made-to-order products, and AutoManus Technologies holds the Waku brand and design system under a separate brand license.
- Waku Memory is a hosted memory service at waku.one that the agent can share with Claude Code, Codex, Grok Bot, and Hermes through MCP.
- The repo includes a `treg` connector for pulling live research data from a personal treg account during agent turns.
- The `lab/` directory holds the video experiments comparing Waku against other agent harnesses and models, including Grok Bot and Meta's Muse agent.
- Recent commits add a trim step that shortens long already-read tool results to keep token spend down, and a final-answer pass that answers from what a turn already gathered when it hits the iteration limit.
