[ arisa.sh ] v1.0.0online00:00:00
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personal Telegram assistant·powered by Pi Agent

// the agent builds itself around the user, not the other way around

$npm install -g arisa
1arisa.about
readme ▸ overview

Arisa keeps exactly two things and nothing more: Telegram as the human interface and Pi Agent as the reasoning engine. No pre-loaded opinions about what the agent should do or which tools it should have.

If a capability does not exist yet, the system adds a new tool for it. It grows from real use — not from assumptions.

  • Telegram is the human interface
  • Pi Agent is the reasoning engine
  • everything is an artifact
  • capabilities live in isolated CLI tools
  • tools can be chained through pipes
2system.load
footprint
arisa76.7 kB
openclaw185 MB
openclaw is ≈ 2,400× heavier — same engine, none of the weight
telegram ▸ inboundmsgs/s
pi-agent ▸ reasoningtok/s
version 1.0.0footprint 76.7 kBdeps minimaliface Telegram
3install.sh
npm i -g arisa

Install globally, then run arisa. On first launch it walks you through a guided bootstrap; or skip the questions entirely with CLI overrides.

commandaction
arisarun in foreground, blocking
arisa startstart in background
arisa stopstop background service
arisa statusshow background service status
arisa flushremove ~/.arisa
4architecture
two pipes ▸ one engine

Arisa separates pre-reasoning normalization from reasoned action. Some transformations belong to the input layer; others to the agent's runtime decisions.

telegram
text · voice · media
pi agent
reasoning engine
tools + artifacts
isolated CLI · chained
5pi.packages
experimental

Arisa can install Pi Agent packages from the public registry into your user runtime, using the same package manager as Pi Agent.

⚠ experimental The registry, package formats, and install behavior follow Pi Agent and may change.
6philosophy
manifesto

The agent should not come preloaded with vices or assumptions. It starts minimal and grows through real use — shaped by the user, not by the framework.

6.1when a capability is missing
  1. check whether an existing tool can solve the task
  2. if not, build the missing tool
  3. keep the solution inside the tool architecture