praxis

<div align="center">

The practice of doing — a filesystem-based methodology for agentic development.

From the Greek *πράσσω (prássō)* — "to do, to act, to practice."

[](https://github.com/luisfaxas/praxis) [](https://www.npmjs.com/package/praxis-mcp) [](LICENSE) [](#provider-integration) [](https://faxas.net/methodology)

*Zero dependencies. Just folders, markdown, and native AI tools.*

</div>

---

In philosophy, Aristotle coined the modern usage of *praxis* to mean the process by which theory becomes practice. It is the bridge between knowing and doing — you have theory (*theōría* / θεωρία) on one side, and *praxis* on the other, where knowledge is enacted through deliberate action.

That is exactly what this methodology does. It bridges the gap between what AI agents *know* (their training, their context window, their capabilities) and what they *do* (writing code, researching, auditing, reporting) — through structured context, persistent memory, and traceable work.

---

AI agents are powerful but forgetful. Every new session starts from zero. The context window is a blank slate — yesterday's decisions, last week's architecture choices, the reason you picked PostgreSQL over MySQL — all gone unless someone writes it down.

Most people solve this by writing longer prompts. They paste project context, repeat instructions, hope the AI remembers what matters. This works for small tasks. It collapses for anything real.

The problems with prompt-driven development:

• Ephemeral — prompts disappear when