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CubePlex vs DeerFlow: Personal Agent Environments and Team Agent Workspaces
· 17 min read

CubePlex vs DeerFlow: Personal Agent Environments and Team Agent Workspaces

xfgong
CubePlex

DeerFlow 2.0 and CubePlex are both general-purpose agent environments. Both support long-term memory, Skills, MCP, Sandboxes, Sub-agents, and messaging integrations. Both let users research, write, code, and work with files from one conversation instead of building a separate App for every use case. Their open-source repositories are available at bytedance/deer-flow and cubeplexai/cubeplex, with usage and deployment guidance in the DeerFlow Documentation and CubePlex Documentation.

The difference starts with who owns the Agent. DeerFlow gives each user a personal Agent environment. After signing in, a user sees their own Agents, Projects, conversations, memories, Skills, and files. CubePlex places the Agent inside a Workspace. An organization creates the Workspace, adds members, configures the Agent, and decides which Skills, MCP servers, and credentials the team can use.

If OpenClaw represents a long-running desktop Agent for one person, a CubePlex Workspace is the team version of that product shape. The Agent has a stable name and way of working, knows the project context the team has confirmed, and uses team-approved Skills and MCP connections. It can work with organization accounts, project accounts, or each member's own account. Members can reach it from the web, Slack, Feishu, or other channels. Private chats, group conversations, and Topics can keep separate conversation and execution contexts, while the Agent's identity, team knowledge, and tool configuration remain with the Workspace. When a member leaves, the Agent and the team's accumulated working state remain with the team.

Personal Agent environment and team Agent Workspace

CubePlex vs Dify: Long-Lived Agents vs Scenario-Specific Apps
· 7 min read

CubePlex vs Dify: Long-Lived Agents vs Scenario-Specific Apps

xfgong
CubePlex

Agent products are taking two different approaches to how people get work done.

Dify starts with a defined scenario. A team identifies a problem such as customer support, knowledge Q&A, contract review, or report generation, then configures the model, Workflow, Knowledge, and tools for that problem. The result is published as a web app, API, embed, or MCP service. Users choose an App and work within the capabilities it was built to provide. Dify's source code and usage guides are available on GitHub and in its documentation.

CubePlex starts with a long-lived Agent. A person or team keeps using the same Agent in a Workspace and gives it different kinds of work over time. The Agent retains its role, memory, Skills, and MCP connections, and can operate a durable computer environment when a task requires one. Instead of switching among Apps for different scenarios, users return to an Agent that becomes familiar with how the team works. CubePlex also provides its source code and product documentation.

That difference shapes how the two products organize capabilities, state, and collaboration.

How long-lived Agents and scenario-specific Apps start work