Tool

Deploy multiplayer AI agents across Slack and web workspaces

Multiplayer agent harness for startups, giving every employee and room its own scoped workspace in Slack and web.

Works with slackpostgresgithubfastifybolt

91
Spark score
out of 100
Updated 3 days ago
Source checked Sep 17, 2026
Version 0.1.11
Models
claude

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Why it matters

Organizations hire this to run collaborative AI agents that give each employee isolated workspaces with personal memory, files, credentials, and sandboxes while enabling team collaboration in Slack channels and web projects, all with admin-controlled security and model flexibility.

Outcomes

What it gets done

01

Provision per-user sandboxes with durable files, tools, and authenticated services that persist across sessions

02

Manage scoped memory, keychains, and permissions so employees work independently without affecting each other

03

Run background crons and watches that execute scheduled tasks and monitor repositories or systems

04

Deploy custom internal web apps with controlled access and share skills across teams with admin-gated promotion

Source

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Overview

Qm

QM is a multiplayer agent harness for startups that gives each employee a personal, scoped workspace (memory, files, permissions, sandbox) in Slack and on the web, while supporting shared team collaboration through one model- and harness-agnostic core. Use it when a startup wants an agent deployment employees can personalize individually while still collaborating with it together in shared channels and projects.

What it does

QM is a multiplayer agent harness for work, available in Slack and on the web, designed for startups rather than as a single personal assistant. Employees each get their own isolated workspace and work independently without affecting each other, while also collaborating with the agent in channels, group messages, and projects. Each person and each room has its own scoped memory, files, keychain view, permissions, crons, web apps, and durable sandbox. It is harness- and model-agnostic: Pi, OpenCode, Codex, and Claude Code all drive the same core, so a deployment is not tied to any single vendor. With it, teams can search internal notes, email, documents, databases, and the web together; build internal apps and publish them to the right people; learn a person's writing voice from past sends and triage their inbox on a schedule with labels and reply drafts; work in an existing repository running tests, opening PRs, and monitoring CI; and track a project in a shared channel with automatic updates.

When to use - and when NOT to

Use QM when a startup wants one agent deployment that individual employees can each make their own, while still collaborating with it in shared channels and projects, with admin control over org-level configuration, security posture, and which harnesses and models are available. It is not a fit if you want a single, centrally-managed assistant with no personal scoping, or if you need GitHub's native fork feature for customization - the project explicitly recommends a plain private clone instead, since a GitHub fork of a public repo cannot be made private and shares one object network with the source.

Inputs and outputs

Every turn runs through a central headless core (API, identity, policy, scheduler) that can use a variety of models and harnesses to generate a response, backed by Postgres for session history and durable state. The agent has a small, fixed tool surface; one tool, execute, runs commands in the scope's own isolated sandbox, its durable computer where installed tools stay installed. The web UI, admin panel, and public portal are optional plugins over the core's HTTP API; Slack is an optional in-process plugin the core starts and supervises. Deploy for an org with:

npm exec --yes --package=@yc-software/qm@latest -- \
  qm init . --org <slug> --target <fly-or-aws>
npm install

Initialization walks through infrastructure, web sign-in, connector credentials, optional Slack access, deployment, and live verification, with each deployment running in the operator's own cloud account.

Integrations

The core runs TypeScript on Node with Fastify for HTTP; the Slack plugin uses Bolt; the web UI builds with Vite and renders with Lit. Everything specific to one company - org config, custom tools and skills, sandbox image, infrastructure - lives in a deployment directory the qm CLI validates and deploys, with every substrate (harness, session store, sandbox, memory) behind an interface so production implementations swap in via one wiring file. Security follows local coding-agent conventions: the agent acts as the person it works for, with their credentials, under one of three org-wide postures - Strict (every tool call pauses for approval), Auto (a classifier screens provenance-labelled external data before it reaches the model), or Dangerous (no screening or pauses) - with a predeclared command policy of hard denials applying in every posture. Licensed under MIT.

Who it's for

Startups that want employees to have their own personalized agent workspace while still collaborating with it as a team in Slack and shared projects, without being locked into one model vendor or harness.

Source README

qm

A multiplayer agent harness for work. In Slack and on the web.

Setup

Tell your coding agent of choice Let's deploy https://github.com/yc-software/qm. From here, it should follow the deployment guide in this repo.

You can also try out a 3rd-party hosted version of QM here.

If you're an infra provider interested in offering a hosted version of QM, feel free to reach out.

What is QM?

Most agents are designed like personal assistants. You can make one work for a whole
company, but it quickly gets complex. QM is designed for startups. Employees each get
their own isolated workspace and work independently without affecting each other, and
they can also collaborate with the agent in channels, group messages, and projects.

Each person and each room has its own scoped memory, files, keychain view, permissions,
crons, web apps, and durable sandbox.

It's built with open source in mind. Pick your own harness and model and switch between
them - Pi, OpenCode, Codex, and Claude Code all drive the same core, so a deployment
isn't tied to any single vendor.

Features

  • Personal and shared scopes. People customize the agent to be theirs, and still
    work with it collaboratively in Slack channels and projects.
  • Slack and web. The same identity and configuration carries between Slack and the
    web app.
  • Admin control. Set org-level configuration, security and sharing postures, and which
    harnesses and models are available.
  • Web apps. Spin up custom internal apps and publish them to the right people.
  • Shared skills. Skills are scope-owned and shareable by grant, with admin-gated
    promotion to the whole org and skill packs imported from git repositories.
  • Background work. Crons, watches, and inbound webhooks run work while nobody's
    watching.

What you can do with it

  • Search internal notes, email, documents, databases, and the web together
  • Build internal apps, publish them to the right people, and keep their data current
  • Learn your writing voice from past sends, then triage your inbox on a schedule -
    labels and reply drafts included
  • Work in an existing repository: run tests, open PRs, monitor CI, check system logs
  • Track a project in a shared channel and post updates and follow-ups

Architecture

flowchart LR
  DB[("Postgres<br/>sessions · memory · queue")]

  subgraph CORE["Headless core"]
    API["API · identity · policy · scheduler"]
    LOOP["Agent loop<br/>(Pi, OpenCode, Claude Code)"]
    API <--> LOOP
  end

  SBX["Per-scope sandbox<br/>files · tools · logged-in services"]

  DB <--> API
  LOOP <--> SBX

For durability, set DATABASE_URL and SESSION_STORE=postgres - without it, sessions
live in process memory and vanish on restart. To exercise a branch against a real model and real Postgres, run
npm run dev-instance:web for web/admin or npm run dev-instance:slack for Slack.
Use npm run dev-instance:both when testing both surfaces together. Bare
npm run dev-instance defaults to web for new instances and preserves the surface
on reload. Switch an existing instance with an explicit surface command.

Architecture

Every turn runs through a central core, which can use a variety of models and harnesses
to generate the response. A Postgres persistence layer holds user data, session history,
and other durable state. The agent has a small, fixed tool surface; one of those tools is
execute, which runs commands in the scope's own isolated sandbox - its durable computer,
where installed tools stay installed. The web UI and admin panel share one service; the portal and optional built-in
auth broker share another. These modules communicate with core over its HTTP API.
See combined services for configuration and migration;
Slack is an optional in-process plugin that core starts
and supervises through a direct service client.

The core runs TypeScript directly on Node and uses Fastify for HTTP. The Slack plugin
uses Bolt; the web UI builds with Vite and renders with Lit.

The core itself is generic. Everything specific to one company - org config, custom tools
and skills, sandbox image, infrastructure - lives in a deployment directory that the
qm CLI validates and deploys. Every substrate (harness, session
store, sandbox, memory) sits behind an interface. Memory can also be routed by scope to
external providers while retaining the built-in notebook.

Security and secrets

QM's approach follows local coding agents like OpenCode, Codex, and Claude Code: the
agent acts as the person it's working for, with their credentials and permissions, and
everything it does is audited. An org picks one security posture, which narrower scopes
can only tighten:

  • Strict - every harness tool call pauses for human approval, except the two
    no-effect turn enders.
  • Auto (default) - blocks private-network access and uses a content screener when
    the deployment configures one. Model screening is off by default; deployments can
    use an external proxy or explicitly opt into the built-in model classifier.
  • Dangerous - no content screening, no pauses between tool calls.

The predeclared command policy - approval rules and hard denials for things like
recursive deletes or destructive SQL - applies in every posture, Dangerous included.

Sharing posture is independent:

  • Isolated (default) - resources stay in their scope unless explicitly shared.
  • Open - on a live authenticated internal human turn, the speaker's opted-in personal
    files, artifacts, skills, and memory may be read in an opted-in shared room. In the
    speaker's DM, files and skills from up to 25 recent shared contexts where they are still
    a member are available. Included memories are loaded into the prompt in full with source-scope
    labels and are also searchable; relevance ranking is not applied.
    The candidate window is limited to 100 recent sessions and file discovery to 200 files.
    Binary files still require explicit sharing before entering another conversation's computer.
    Cross-context memory search is available only through the active turn's memory tool;
    reusable sandbox API tokens retain their original memory scope.

The organization value is a ceiling, and personal and room scopes can opt out; Isolated
wins. “Follow organization” removes a personal or room override. Disabled memory recall
and writable-only recall still apply. Open does not mount a personal workspace into a room, carry credentials or message
history, widen writes, run in automation or ambient turns, cross organizations, add a
teammate's entitlement, or weaken screening, command approvals, or egress. It can still
reveal private information in a shared reply, so cross-context reads are provenance-labelled
and audited.

SECURITY.md has the threat model, the operator assumptions, and the
known limitations.

Deploy it for your org

Create an organization-owned deployment repository that depends on @yc-software/qm:

npm exec --yes --package=@yc-software/qm@latest -- \
  qm init . --org <slug> --target <fly-or-aws>
npm install

Initialization materializes a deployment skill for an agent and walks through
infrastructure, web sign-in, connector credentials, optional Slack access, deployment,
and live verification - no source checkout required. Each deployment runs in the
operator's own cloud account; initialization does not generate or enable deployment CI,
and this repository has no production deployment workflow. See
deployment.md for the details.

Customize your instance

Choose how you want to customize QM:

  • Config, tools, skills, and services: use the deployment repository above. It
    pins @yc-software/qm and uses that release's runtime images; no source copy is needed.
  • Changes to QM itself: keep your own source fork, public or private. You may
    modify any part of core, including the runtime, plugins, CLI, docs, and CI.
    Contributing those changes upstream is optional.

Create a source fork

For a private source fork, create a standalone private repository, outside GitHub's
fork network. Seed only main and explicitly set it as the default branch:

gh repo create <org>/qm-private --private
git clone --single-branch --branch main --no-tags git@github.com:yc-software/qm qm-private
git -C qm-private remote rename origin upstream
git -C qm-private remote add origin git@github.com:<org>/qm-private
git -C qm-private push -u origin main
gh repo edit <org>/qm-private --default-branch main

Do not seed with git push --mirror: it copies unrelated upstream branches and tags,
leaves default-branch selection implicit, and can delete destination-only refs on later
pushes. The upstream remote supplies source updates without copying those refs to your
repository. For a public source fork, GitHub's Fork button is also an option. A GitHub
fork of a public repository cannot be private; keep private work outside that network.

Review inherited workflows before enabling Actions or adding credentials. Choose the CI
checks you want, and disable or adapt upstream release and publishing workflows for your
own package and image registries. Copying the source does not configure production
deployment CI.

Customize and run your source

Keep deployment configuration, tools, skills, plugin images, and infrastructure in
deploy/layers/<org>/ in a private source fork, or in a separate private deployment
repository when your source is public. Never commit secrets. See
deploy/layers/README.md for initialization and layout.
Keep deployment data separate from core code, but change core wherever your desired
behavior requires it.

From the source checkout, install dependencies with npm ci and use the in-tree CLI.
After completing the provider setup in deployment.md, build and
deploy your modified services explicitly:

node cli/bin/qm.ts check --config <deployment-dir>/qm.config.jsonc
node cli/bin/qm.ts plan --config <deployment-dir>/qm.config.jsonc --build-from .
node cli/bin/qm.ts up --config <deployment-dir>/qm.config.jsonc --build-from .
node cli/bin/qm.ts check --config <deployment-dir>/qm.config.jsonc --live

Use this checkout's CLI when changing the CLI itself. Without --build-from, the
normal deployment path selects published images, so editing source alone does not
change the deployed runtime. If you publish custom images instead, configure their
immutable references through imageOverrides. Follow the provider guide for sandbox
image builds; service builds do not replace that step.

Keep it current

For a source fork, update-qm merges upstream changes while preserving intentional
local behavior. Land sync PRs with their merge ancestry intact, never squash or rebase
them. Conflicts are expected maintenance work, not a requirement to discard
customizations. Use upstream-pr only when you want to contribute a generic change;
it prepares a clean upstream branch without private deployment data or history.

For a package deployment, upgrade the exact @yc-software/qm dependency and lockfile,
review contract changes and generated assets, then validate and deploy. There is no
upstream source history to merge.

Going deeper

FAQ

Common questions

Discussion

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