Your Work Should Outlive Any AI Agent

An ownership dispute around Manus gave some users a deadline to back up and restore their data. It exposed a broader risk: work stored in an AI workspace may be difficult to carry elsewhere when the agent, model, vendor, or infrastructure changes.

Meta acquired Manus. Regulators forced the deal apart.

Meta acquired Manus for more than $2 billion in December 2025. Four months later, Chinese regulators ordered the parties to withdraw from the acquisition. On August 11, Manus told users it would return to independent operation.

The corporate separation affected users directly. In a note to its users, Manus said data generated by certain users on or after December 29, 2025 would be deleted during August 23–24, 2026, Singapore time. Affected users were instructed to back up their data before the window and restore it afterward.

The Financial Times reported that former Chinese backers were assembling a route back to independence, with Tencent potentially becoming the largest minority shareholder and Manus continuing to operate from Singapore.

Users did not choose the acquisition or the forced unwind, but some still had to back up, restore, and verify work stored in the product.

Providers, models, and agents change. Your work should remain usable when they do.

The circumstances around Manus are unusual. The underlying dependency is not. Companies are acquired, models are retired, prices change, and products shift direction. A durable workspace should make those changes manageable.

Protect the work you already have

Before comparing alternatives, make sure you have usable copies of the work that matters.

Do not assume the official backup is the same thing as a vendor-neutral export. It is designed to restore data into Manus. Your own standard-format copies are what give you an independent record of the work.

Move the workspace, not only the agent

The agent is only the execution layer. The surrounding workspace contains the context, permissions, reusable behavior, and operational setup that make it useful.

Include these in the migration inventory:

• Outputs and history. Documents, code, datasets, original requests, corrections, decisions, and source trails.
• Project context and memory. Brand rules, terminology, examples, preferences, and facts saved across sessions.
• Skills and workflows. Prompts, templates, scripts, procedures, and quality checks.
• Connections and permissions. Files, databases, SaaS applications, email, calendars, APIs, and authorization scopes.
• Automations and schedules. Recurring work, triggers, webhooks, and notifications.
• Operational dependencies. Hosting, runtimes, environment variables, secrets, domains, and external services.

A downloaded report preserves an output. It may not preserve the process and access needed to produce it again.

The AI workspace migration checklist

This checklist is useful whether you move from Manus to MindsHub Cowork, to another commercial product, or to a stack you operate yourself.

1. Prioritize by business impact. List active projects, recurring workflows, connected systems, hosted assets, and important historical work. Move critical items first.
2. Export in standard formats. Keep important outputs in formats that open without the original product: Markdown or plain text for instructions, CSV or XLSX for structured data, DOCX or PDF for documents, PPTX for presentations, and source code plus configuration files for applications.
3. Capture how the result was made. For every recurring workflow, document the inputs, instructions, data sources, tools, review steps, expected output, and common corrections.
4. Map every connection and permission. Record what each workflow can read or change. Revoke stale authorizations, rotate product-specific credentials when appropriate, and give the replacement workspace only the access it needs.
5. Package reusable behavior. Export skills, templates, prompts, scripts, and procedures in a form people can inspect and edit.
6. Recover operational dependencies. For hosted websites, apps, and scheduled work, record domains, databases, environment variables, webhooks, runtime requirements, and ownership of every external service.
7. Rebuild one representative workflow. Choose an important, repeatable workflow and recreate it with the same inputs and acceptance criteria.
8. Run both systems in parallel. Compare quality, time, cost, reliability, source traceability, editability, and review effort across several real runs.
9. Test the next exit. Export an artifact, a skill, a workflow definition, and the relevant context before committing more work to the replacement.

What makes an AI workspace durable?

Once the inventory is clear, compare how much of the working system remains usable when the agent, model, or vendor changes.

1. Replaceable models and agents. Changing the model or harness should not require rebuilding the surrounding workspace.
2. Visible context. Memory, skills, project instructions, and workflow logic should be inspectable, editable, and transferable.
3. Deployment choice. Local, VPC, on-premises, air-gapped, or hosted options reduce dependence on one runtime.
4. Scoped credentials. The workspace should show what each agent can access without exposing raw secrets.
5. Portable artifacts and workflows. Files, code, applications, and workflow definitions should remain useful outside the original product.

Manus vs. MindsHub Cowork: compare the workspace, not just the agent

Both products are designed to do more than chat. Both can take on multi-step work, connect to tools, create artifacts, preserve reusable instructions, and run recurring tasks. The meaningful differences are in how the workspace is structured around those capabilities.

Workspace question	Manus	MindsHub Cowork
Core experience	A managed general-purpose agent intended to take a goal and produce a finished result.	A workspace for delegating projects to open-source agent harnesses while retaining the surrounding data, memory, skills, artifacts, and operations.
Model choice	Users choose among Manus-managed agent and model profiles; underlying routing and execution remain part of the managed Manus service.	A Model Router spans proprietary and open models, making the model an explicit, replaceable layer.
Agent layer	The Manus agent is the central execution experience.	Open-source agent harnesses are part of the product architecture: Anton and Hermes are interchangeable, swappable from a dropdown.
Deployment	Primarily a vendor-managed cloud experience, supplemented by desktop features that can work with approved local folders, tools, and applications.	Desktop app for macOS and Windows today, with the repository documenting cloud, VPC, on-premises, air-gapped, and hybrid deployment, under an MIT license.
Data and tools	Connectors, browser operation, and local-computer access bring external systems into the managed agent experience.	A credentials vault provides scoped connections to SaaS tools, files, databases, warehouses, email, calendars, CRM systems, and APIs.
Memory and skills	Projects, persistent context, reusable skills, and scheduled tasks. Manus skills can be shared as .skill files, ZIP archives, or GitHub repositories.	Cross-session memory, a reusable skill library, projects, scheduled work, and workspace components designed to remain separate from a single model or harness.
Artifacts	Creates slides, spreadsheets, websites, applications, design assets, reports, and other finished deliverables.	Creates documents, dashboards, applications, code, and spreadsheets, publishable to a live URL.
Continuity approach	Official backup and restore tools, exportable deliverables, portable skills, and local-computer capabilities, within a predominantly managed product environment.	Open-source components, model and harness choice, and deployment flexibility aim to reduce how much of the workspace must be rebuilt when one layer changes.

Portability is not binary

Manus skills can be downloaded and shared, its output formats include editable files, and its desktop experience can work with approved local resources. MindsHub Cowork is still a hosted service and cannot eliminate every dependency. Compare how much of each workspace you can inspect, replace, self-host, or carry forward.

Which approach fits your work?

Manus may still be the better fit when you prioritize:

• A highly managed experience with fewer architectural decisions.
• A single integrated agent for research, browser work, slides, spreadsheets, websites, applications, and creative deliverables.
• Existing Manus workflows that already perform well and can be restored successfully.
• Convenience over control of the underlying model, harness, or deployment environment.

MindsHub Cowork becomes more relevant when you prioritize:

• The ability to change between open and proprietary models without treating the whole workspace as disposable.
• Open-source agent harnesses and a more replaceable agent layer.
• Scoped access to operational data across SaaS tools, files, databases, and warehouses.
• Visible, reusable memory, skills, artifacts, and scheduled work around the agent.
• A workspace architecture intended to reduce dependence on any single model or agent provider.

MindsHub Cowork is designed around one principle: the durable asset should be the workspace, not the current agent running inside it.

Test one workflow before moving more

Back up first. Then evaluate a replacement with one workflow that matters.

1. Choose a recurring project with clear inputs and an objective definition of “done.”
2. Bring the instructions, examples, data sources, and review criteria, not only the last finished file.
3. Recreate it in MindsHub Cowork with the minimum necessary permissions.
4. Run it several times and compare quality, speed, cost, reliability, traceability, editability, and human review effort.
5. Test what you can retain: the artifact, the skill, the context, the schedule, and the ability to change the model or agent layer.
6. Move additional work only when the test produces a clear advantage.

The details of the Manus dispute are unusual. The dependency is common: work that exists only inside one managed product is exposed to decisions made by that provider.

Restore your Manus work if continuing there is the right choice. When evaluating any replacement, ask:

Can I change the agent without losing the work around it?

Keep the work. Test the workspace.

Bring one repeatable workflow to MindsHub Cowork. Compare the result and what you can retain, inspect, replace, deploy, and control afterward.

Try MindsHub Cowork → · Use the AI workspace migration checklist