# Introducing Colony: personal agents with memory and tools · Good AI Labs

> Give an agent an ongoing job, a place to work and memory it can use next time. Colony is opening for technical previews.

URL: https://www.goodailabs.com/blog/colony-runtime-and-model-budget/
Date: 2026-10-03

Colony is a personal agent system for work that continues across conversations.
Give an agent a project, access to its tools
and a result to work toward. Its files, memory and responsibilities stay
available across conversations.

We built Colony because useful work rarely fits inside one exchange.
An experiment needs checking later. A project has decisions worth
remembering. A procedure that worked once should be available the next
time it is needed.

## From a conversation to saved work

A working app, saved for the next task.

Based on the recorded calculator session and reopened application. This example does not measure general coding reliability.

1. Work in a real project.
An agent uses the project's files and permitted tools to produce an app. In this recorded session, Colony built a scientific calculator and committed six files with 450 inserted lines.
Figure: Recorded commit e790c96. The saved project contains the interface, logic, app data and definition.

2. Inspect what was actually saved.
We reopened the application and checked sqrt(9), which returned 3. The image is the saved app itself, so the result can be inspected alongside the session record.
Figure: Actual app capture. One arithmetic check demonstrates this saved artifact, not exhaustive correctness.

3. Give the next request somewhere to begin.
The app files, data and change history stay in the workspace. A later conversation can inspect or change the same project. Agent memory and reusable skills support that continuing work.
Figure: The same files, app data and version history remain available for later work.

The example above comes from an app-building session. Colony wrote the
calculator's interface and logic, then committed the files. We reopened the
saved app and checked that `sqrt(9)` returns `3`. Expand the figure above
to inspect the original session excerpt and the app capture.

The important object is the app on disk. A later request can change those
files, inspect the history or continue the same project. A conversation has
produced something the next conversation can use.

## An agent with somewhere to work

Colony agents can work in repositories, run commands, research the web and
use connected tools. On a Mac, they can also read application windows
and operate their controls with the permissions you grant. You can talk
by text or voice, assign a background goal, set a reminder or schedule a
recurring task. Reports return to the conversation, with an interruption
when you asked for one.

An agent can also build a small app: a tracker for experiments, a reading
list or a dashboard with scheduled data updates. Several agents can
share a project, exchange messages and carry out dependent steps.

## Keep what the work teaches you

Colony stores inspectable memories and full tool results outside the
model's immediate context. Large outputs are retrieved in relevant
pieces. Completed background work can become a proposed skill for you
to review and install; repeated operations can become reusable tools.

Keeping an agent running has a compute cost. Colony records model usage
and provider-reported cache use, and lets you inspect the context sent
to the model. Memory consolidation and skill drafting also use inference.
Our next question is how much this accumulated work helps on later tasks
under the same model and compute budget.

Colony runs on a computer you control, with a local model server or a
hosted endpoint you choose. Background work requires that computer to
stay available. The current build is private, and we are opening
technical previews for people with an ongoing job to put it to work on.

Contact: research@goodailabs.com