Cloud-native platform for managed agents in team workspaces
CubePlex is a cloud-native platform for long-lived, team-owned Agents. A Workspace gives a team one Agent with a stable role, shared knowledge, approved tools, and a persistent work environment—available from the web and the channels where the team already works.
Why CubePlex
- One long-lived Workspace Agent — The team returns to the same Agent across tasks, with its role, Skills, memory, MCP connections, and working state intact.
- Team-owned state, not personal state — Agent configuration, shared knowledge, approved tools, deliverables, and working state belong to the Workspace and remain with the team.
- One Agent across every entry point — Use the same Workspace Agent from the web, Slack, Discord, Teams, Feishu, DingTalk, WeCom, and more, while each conversation keeps its own participants and execution context.
- Durable, governed execution — Isolated persistent sandboxes and organization controls make ongoing Agent work inspectable, repeatable, and manageable.
CubePlex's Agent runtime is built on CubeLoop, an async-native agent framework for multi-provider model access, tool execution, streaming, middleware, and durable checkpoints. Workspace sandboxes are isolated execution environments with persistent working state; external model providers, MCP servers, and IM platforms remain outside CubePlex's trust boundary.
Demos
Build Interactive Website — a full product website generated from a single prompt.
More demos
Skills Workflow — find a skill, install it, and use it to build an agentic frontend end to end.
Data Analysis — transform raw tabular data into a formatted spreadsheet.
One-Page PDF — turn a one-page PDF into a polished, navigable page.
Browser Control — an Agent drives the browser to complete a task autonomously.
Platform capabilities
| Area | What you get |
|---|---|
| Long-lived Workspace Agents | Give each team a stable Agent role, shared configuration, approved capabilities, and a work environment that continues across tasks. |
| Team conversations | Use private chats, group chats, and Topics to keep participants, conversation context, and execution separate while the Workspace Agent keeps its shared role and knowledge. |
| Layered memory | Recall personal preferences, shared workspace facts and procedures, and organization policies at the appropriate scope. |
| Skills | Package reusable Agent workflows from built-in capabilities, organization-provided skills, or remote registries such as skills.sh, then make them available in the right workspace. |
| Governed MCP tools | Org admins curate a connector catalog; workspaces enable the tools they need. Credentials can be organization-, workspace-, or user-scoped, using OAuth or static credentials. |
| Workspace sandboxes | Per-workspace isolated runtimes with persistent storage — files, packages, and the working tree survive restarts so Agents resume the same work site. |
| Secrets and credential controls | Credentials are encrypted at rest. Kubernetes deployments can resolve approved sandbox secrets at request time rather than exposing them as plain environment variables. |
| Multi-model chat | Use hosted and custom providers including Anthropic, OpenAI, and more. Attach files, stream replies, and switch models mid-conversation. |
| Automation | Run scheduled tasks (cron, interval, or one-shot) and webhook event triggers. |
| Artifacts | Deliver versioned files, previews, code, images, and other outputs directly in the conversation. |
| IM bridges | Bring the same Workspace Agent to Slack, Discord, Teams, Feishu, DingTalk, WeCom, and more; configure shared or per-member conversations in group chats. |
| Organization and workspace governance | Manage organizations, workspaces, roles, model access policies, connector catalogs, credentials, and cost tracking. |
| Deploy anywhere | Use Docker Compose for a single host or Helm for Kubernetes. |
How CubePlex compares
- CubePlex vs DeerFlow — team-owned Workspace Agents versus personal Agent environments.
- CubePlex vs Dify — long-lived Agents versus scenario-specific Apps.
- CubePlex vs QM — a long-lived team Agent with entry-point-specific execution contexts versus Agent-computer scopes.
Get started
- Docker Compose (single host): installation guide
- Kubernetes with Helm: installation guide
Both modes use the same backend and frontend images. Guides cover image builds, configuration, secrets, and verification.
Develop locally
Prerequisites: Python 3.12+, Node.js 20+, pnpm 10+, and Docker (recommended for local services).
git clone https://github.com/cubeplexai/cubeplex.git
cd cubeplex
make install
# Terminal 1 — API
cd backend && python main.py
# Terminal 2 — web UI
cd frontend && pnpm dev
Backend: http://localhost:8000 · Frontend: http://localhost:3000.
Local setup also needs backend env/config files described in the contribution guide.
Repository layout
backend/ FastAPI API and Cubeloop-based agent runtime
frontend/ Next.js web app and shared TypeScript packages
deploy/ Docker Compose and Kubernetes/Helm assets
docs/ Product docs site and engineering reference
scripts/ Worktree provisioning and dev helpers