DevSpace Ultra

DevSpace Ultra is an MIT-licensed distribution of DevSpace with an elastic ChatGPT Classic multi-agent runtime layer.

It keeps the original DevSpace local MCP workspace capabilities — local files, code search, editing, terminal execution, artifacts, skills, and secure self-hosting — and adds a production-oriented Chat Swarm control plane for running multiple independent ChatGPT Classic worker conversations on one computer.

Upstream project: Waishnav/devspace. DevSpace Ultra preserves the upstream MIT license and attribution and adds the Ultra runtime/orchestration layer.

What Ultra adds

• Elastic worker pool — the main agent can scale workers up or down according to the current workload instead of using a fixed worker count.

• Live Swarm resize — backend capacity can grow or shrink without replacing the orchestrator or losing completed work. Shrink is safety-first and refuses to evict busy/tail workers.

• Independent ChatGPT Classic runtimes — on Windows, worker packages use isolated package identities, profiles, sessions, and conversations.

• Same-worker context continuity — a worker can be reopened at its exact saved ChatGPT conversation and continue with the worker token held by that conversation.

• Zero-copy bootstrap — workers can be launched, minimized, sent into a configured sub-agents ChatGPT Project, joined to a Swarm, and parked without manual invite-code copy/paste.

• Backend-first routing — normal work is always dispatched through the DevSpace Chat Swarm backend. UI/CDP automation is lifecycle/bootstrap/recovery only.

• Recovery — detects missing runtimes, interrupted connections, stale worker loops, and blocking UI notices; can reopen the exact worker conversation and resume it.

• Update compatibility manager — detects ChatGPT Classic version drift, supports a canary runtime, profile backup, rolling worker update, exact-conversation restore, verification, and rollback.

• Configurable runtime reservation — operators can reserve any runtime numbers for standalone/private use; no runtime number is reserved by default in the public package.

Related MCP server: ATMcp

One-click install

Windows (PowerShell)

irm https://raw.githubusercontent.com/enwong93-sketch/devspace-ultra/main/install.ps1 | iex

macOS / Linux

curl -fsSL https://raw.githubusercontent.com/enwong93-sketch/devspace-ultra/main/install.sh | bash

Or install directly from GitHub with npm:

npm install -g github:enwong93-sketch/devspace-ultra#main

Then initialize and run:

devspace-ultra init
devspace-ultra serve

devspace remains available as a compatibility alias.

Minimum requirements and compatibility

DevSpace core

• Windows 10/11 x64, macOS, or a mainstream Linux distribution supported by Node/native dependencies

• Node.js >=22.19 <27 (Node 22 LTS recommended)

• npm (included with Node.js)

• Git for installation directly from this GitHub repository

• Network access for the initial install and for the ChatGPT/MCP connection path you configure

• Tailscale is optional; DevSpace Ultra does not require it

ChatGPT Classic elastic desktop workers

• Windows 10/11 x64 only for automatic isolated desktop runtime cloning/recovery

• ChatGPT Classic Windows Desktop app installed and signed in

• A ChatGPT account able to use the worker conversations

• RAM sized to the worker count: 16 GB is a practical starting point for 2–4 workers; 32 GB+ is recommended for larger pools. These are operational recommendations, not hard limits.

• No GPU is required by DevSpace Ultra or the ChatGPT Classic worker runtimes themselves

macOS/Linux users still receive the DevSpace coding/MCP core and Chat Swarm backend, but do not currently receive the Windows package-identity worker-cloning path.

Platform support

DevSpace Ultra installs and runs the base DevSpace/Chat Swarm layer on supported Node platforms. The Windows-only rows depend on Windows AppX package identity and the current ChatGPT Classic desktop distribution model. Ultra feature-detects those capabilities rather than pretending they exist on platforms where the same desktop package mechanism is unavailable.

Production flow

A normal main-agent session can operate at this level:

assess workload
  -> choose desiredWorkers
  -> elastic scale runtime + Swarm capacity
  -> dispatch independent or targeted tasks
  -> collect / synthesize
  -> shrink idle tail workers when no longer needed

The main agent does not have to keep all workers open. Existing worker conversations are reused whenever possible.

Runtime lifecycle tools

The Ultra server registers runtime tools such as:

• chat_swarm_runtime_status

• chat_swarm_runtime_ensure

• chat_swarm_runtime_scale

• chat_swarm_runtime_recover

• chat_swarm_runtime_autojoin

• chat_swarm_runtime_setup

• chat_swarm_runtime_stop

• chat_swarm_elastic_scale

• chat_swarm_update_status

• chat_swarm_update_rollout

The Chat Swarm backend includes:

• create / join / status

• dispatch / collect / cancel

• long parked worker waits and submit/repark

• targeted or first-available routing

• idempotent taskKey retries

• persistence across DevSpace restart

• worker recycle fallback

• safe live capacity resize

Elastic scaling policy

Ultra deliberately separates runtime capacity from task routing.

• The main agent may choose a small worker count for simple work and expand for parallelizable work.

• reservedWorkers can exclude any operator-chosen runtime numbers from elastic production scaling; the public default is an empty reservation list.

• Scaling down only removes safe idle tail capacity; it does not interrupt a busy worker merely to reach a number immediately.

• Existing worker conversations and saved context are preferred over creating throwaway conversations.

• Normal tasks are never typed into worker UI by the controller. They travel through the shared Chat Swarm backend.

sub-agents Project routing

When configured with a ChatGPT Project URL, new worker conversations are created inside the sub-agents Project instead of cluttering the general chat list. Project-scoped conversation URLs are persisted and accepted by the recovery path.

Update safety

chat-swarm-classic-update-manager.ps1 is designed around a canary-first rollout:

1. detect primary ChatGPT Classic version and worker drift;

2. prepare a free canary runtime from the new primary package;

3. restore a known authenticated seed profile;

4. verify the canary renderer/login/composer and run a real worker task at the orchestration layer;

5. update production workers one at a time;

6. back up profile/session state before each worker update;

7. reopen the exact saved conversation and verify the worker after update;

8. rollback the affected worker if verification fails.

If there is no version drift, no rollout is needed.

Security model

DevSpace Ultra inherits DevSpace's self-hosted MCP model. Keep the server bound and exposed only through a transport you control, use authentication, and avoid exposing the local MCP endpoint directly to the public Internet.

Worker tokens and orchestrator tokens are not intentionally written to normal controller logs. Runtime state stores package/profile/conversation mappings, not raw Swarm tokens.

See SECURITY.md for reporting and deployment guidance.

Verification

Distribution-level verification:

npm run verify:ultra

The Chat Swarm regression covers multi-worker fan-out, targeted routing, submit/repark, sparse wake-up, retry idempotency, persistence, close wake-up, recycle safety, and resize invariants.

Windows lifecycle testing additionally covers isolated runtime startup, minimized CDP control, worker recovery, long lease soak, same-conversation continuity, and elastic provisioning.

Documentation

• Classic operator guide

• Productization and verification record

• Contributing

License

MIT. See LICENSE and NOTICE.

DevSpace Ultra is an independent community fork/distribution and is not an official OpenAI product. ChatGPT and OpenAI product names are trademarks of their respective owners.