Openloomi Feature Guide

Note: If OpenLoomi readiness is unknown, use openloomi-setup first. If OpenLoomi Desktop is not installed, follow Getting Started.

OpenLoomi Product Features

Use this skill when users ask about openloomi features, usage, or capabilities. Provide accurate and easy-to-understand feature introductions and operation guides. The terms used here (Loop, Signal, Decision, Card, ActionKind, Attention Agent, Connector, Memory, Audit Log, Plugin, Agent Runtime, Composio, etc.) are defined in the Glossary below — read it first if a user asks anything conceptual.

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What is OpenLoomi

OpenLoomi is an open-source AI coworker, driven by an attention agent. It connects your authorized work tools and screen content, builds a holistic context of your people, projects, and decisions, and tells you what happened, why it matters, what to do next — and surfaces daily summaries — saving your attention for what matters.

Use it standalone, or plug any Agent framework into the same resident desktop: Claude Code, Codex, OpenCode, Hermes, and OpenClaw all work. The desktop attention agent (a small fox named Loomi) lives on top of your screen and surfaces the day's decisions as gentle bubbles you can approve in one tap.

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Glossary

OpenLoomi is best read as a single chain from input to action, not as one big chat window. Connectors bring events in, Memory holds context, Loop decides what matters and turns each signal into a Decision card, the Attention Agent (Loomi) shows the card on your desktop, you tap Approve, and an Action Runner executes through a Connector — then the outcome is written back to Memory and recorded in the Audit Log.

This page exists to make that chain legible. It first walks the chain end-to-end, then defines every concept you're likely to meet in other pages, and finishes with a short table of distinctions that get confused in practice. Every cross-reference links to an existing page; nothing here duplicates their full setup or configuration.

How the concepts fit together

OpenLoomi is one pipeline. The same pieces appear on every page, just framed differently.

Two parallel input paths feed the same context layer. The main one is real-time and runs through Loop; the side one is screen-based and lands in Memory without going through Signals.

Image: OpenLoomi concept map — Inputs (Connectors and Screen Capture) feed Signals into the Judgement & Store pillar (Loop and Memory), which produces Decisions; the Surface & Action pillar renders Cards in the Attention Agent, waits for user Approve, and runs Actions through Connectors; everything writes back to Memory and the Audit Log.

A short reading guide for the figure:

• Connectors are the real-time input path: they pull raw events from external platforms and turn them into Signals, which Loop polls on its tick. Everything that becomes a Decision card comes in this way.
• Screen Capture (macOS only) is the side input path: pressing the global capture shortcut summarises the frontmost window and the result is stored directly as a Memory record. Screen memories show up alongside messages, summaries, and insights inside Memory; they do not flow through Signals and Loop won't tick on them.
• Memory is the long-lived context layer: people, projects, prior decisions, summaries, insights, screen memories, and Knowledge Base chunks. Loop reads the relevant slice before it judges; Chat reads Memory through retriever skills; the result of every approved action is written back into Memory.
• Loop is the only thing that produces Decisions. A Decision is the typed judgement ("this email needs a reply", "this PR needs a review"), and a Card is how that judgement looks in the UI.
• The Attention Agent (also called Loomi, the pet, or the fox) is the messenger. It surfaces cards on the desktop. It does no judging of its own.
• You always tap Approve before anything runs. After approval, an Action Runner calls a Connector to actually send mail / post the comment / update the ticket.
• The result lands back in Memory (so the next Loop tick has sharper context) and in the Audit Log (so you can see who did what and when).

Worked example — "An email needs a reply"

9:12 AM. Sarah writes: "Hi — I tweaked tomorrow's Q2 review agenda, can you take a look? Also, I'd like to move our Wednesday 1:1 to Thursday same time — works for you?"

Step by step through the chain:

Core concepts

These are the terms that anchor every other page.

Connector

A Connector is the integration boundary between OpenLoomi and an external platform — Gmail, Outlook, Telegram, WhatsApp, iMessage, Slack, GitHub, Linear, Calendar, Notion, and so on. Connectors handle both directions: pulling raw events out of the platform so the rest of the system can see them, and pushing actions back in once you approve them. See Connectors.

Signal

A Signal is one raw event produced by a Connector — "email arrived", "calendar invite received", "@mention on Telegram", "PR review requested". Signals are unopinionated: they carry the payload from the source platform and a shape so the rest of OpenLoomi can reason about them. They are the only thing Loop consumes. (Screen Capture is a separate input — see Memory.)

Memory

Memory is OpenLoomi's long-lived context layer — people, projects, prior decisions, summaries, insights, uploaded documents, and Screen Capture memories — held locally on your machine. Both Chat and Loop read from Memory to ground their answers in your history, and approved Actions write back into Memory so the next judgement has sharper context. See Memory.

Loop

Loop is OpenLoomi's proactive judgement engine. On a regular tick it pulls Signals from connected sources, reads the matching slice of Memory, and turns the ones that need you into typed Decisions. Loop never executes anything itself — it only "sees, thinks, and queues a card". Default cadence is every 10 minutes; tune it via PUT /api/loop/preferences. See Loop.

Decision

A Decision is the smallest unit Loop produces — one signal becomes one structured judgement. A Decision carries a typed action (for example email_reply, rsvp, review_pr, im_reply, todo), a confidence score, a short preview of the proposed action, and any sender / subject / memory slice that's relevant.

Card

A Card is what a Decision looks like in the UI. A Card has the typed action, the confidence score, a preview body, the four-button tray (Approve / Edit Draft / Later / Skip), and — for any outbound action — a red "⚠ OUTBOUND" warning. Cards surface in three places at once: the main OpenLoomi window, the Attention Agent bubble, and the pet card. See Loop — Approvals and dry-run.

Action / ActionKind

An Action is the actual side effect that runs after you Approve a Card. The shape of an Action is fixed by an ActionKind literal — email_reply, im_reply, calendar_rsvp, github_review, linear_review, todo, deadline_notify, and a small set of others. Custom decision types register a label + icon, but always route to one of the built-in ActionKind runners.

Attention Agent / Loomi / Pet

The Attention Agent is the desktop companion — a 168×168, always-on-top, transparent little fox that lives on your desktop and shows Card bubbles when Loop queues something for you. Loomi is the fox's name; Pet is a generic nickname. The Attention Agent is the messenger, not the judge. See Attention Agent.

Audit Log

The Audit Log records the consequential moments of OpenLoomi's day: every Memory read/write, every Loop judgement, every Approve / Edit / Skip, every outbound Action invocation and its result, and every Connector authorization change. See Privacy & Security — Audit Logs.

Supporting concepts

These are the surfaces and side capabilities that show up across the product.

Chat

Chat is the conversational entry point in the main OpenLoomi window. It solves "I have a question, a request, or a draft to write". It does not watch your day or queue cards — that's Loop's job. Chat reads Memory to ground answers and, when you tap Edit Draft on a Decision Card, you land here with the draft already loaded so you can refine it with the AI before Approving. See Chat.

Automation

An Automation (also called a Task or Scheduled Job) is a prompt and schedule that you have defined in advance — "every weekday at 9 AM, summarise my unread inbox". Loop, by contrast, discovers Signals on its own and judges whether to surface them; Automation is the executor that runs whatever you told it to run. Both share the same scheduled-jobs runtime; Loop's own brief (9 AM) and wrap (6 PM) are themselves scheduled jobs. See Automation.

Library / Knowledge Base

The Library is the user-facing surface; the Knowledge Base is what's behind it — the set of uploaded documents (PDF, DOCX, TXT, Markdown, spreadsheets, slides, images) that you want OpenLoomi to reason over. Library is explicit, user-uploaded context and is not the whole of Memory. See Library.

Skills

Skills are reusable capabilities the agent can call when it needs to do something specific — code generation, PDF creation, data analysis, browser automation, search, image generation, and many more. Inside a Skill, OpenLoomi runs through its Agent Runtime; Skills are how those runtimes are reached from Chat, Loop, or Automation. See Skills.

Agent Runtime

An Agent Runtime is the underlying execution environment OpenLoomi uses when it needs a model that can act — Claude Agent SDK (the default), Codex CLI, OpenCode, Hermes, or OpenClaw. Runtime selection is deployment configuration. Agent Runtime powers Skills; Plugins (below) are the inverse bridge that lets those runtimes call OpenLoomi. See Agent Runtimes.

Plugin

A Plugin is a thin bridge in the opposite direction: it lets an external agent shell (Claude Code, Codex CLI) call into the local OpenLoomi runtime. Once installed, Memory, Connectors, scheduled jobs, and the Pet become reachable as slash commands, skills, or @OpenLoomi prompts inside your existing shell. A Plugin does not run OpenLoomi's models — it just exposes OpenLoomi to the shell you already live in. See Plugins.