(Manus) Alternative
Your Work Should Outlive Any AI Agent
An AI workspace can hold months of useful work: reports, instructions, connected tools, and the corrections that make a recurring job run properly. Moving it takes more than downloading the last report.
The Manus ownership transition made that dependency concrete. Some users had to back up and restore their data while the company separated from Meta. Manus has since resumed independent operations, but the question applies to any workspace: what can you keep using when the provider changes?
What changed at Manus
Meta acquired Manus in December 2025. In its August 11 notice, Manus said its return to independence required deleting data generated by certain users on or after December 29, 2025. Affected accounts had a backup window, a temporary interruption, and a restoration process. Unaffected accounts did not need to act.
On September 1, Manus confirmed it had resumed independent operations. That update says users who backed up their data can still restore it, with no restoration deadline. The August backup deadline has passed.
A product’s restoration archive and your own working copies serve different purposes. Keep standard-format copies of important outputs that open outside the original app. Test the files rather than assuming an export contains everything.
Inventory the work around the agent
A report may be easy to move. Reproducing it next month can require the original sources, saved instructions, permissions, and schedule.
| What to preserve | Details to record |
|---|---|
| Outputs and history | Documents, code, datasets, original requests, corrections, decisions, and sources |
| Project context | Brand rules, terminology, examples, preferences, and saved facts |
| Skills and workflows | Prompts, templates, scripts, procedures, and quality checks |
| Connections | Data sources, authorized accounts, access scopes, and the person who owns each connection |
| Recurring work | Schedules, triggers, webhooks, notifications, and expected outputs |
| Hosted services | Sites, applications, domains, databases, runtimes, and configuration; keep secrets in an appropriate vault |
This inventory helps whether you stay with Manus, try MindsHub Agents, or operate your own tools.
The AI workspace migration checklist
- Prioritize by business impact. Identify the workflows people rely on and the historical work you cannot afford to lose. Start there.
- Export readable files. Use Markdown or plain text for instructions, CSV or XLSX for data, DOCX or PDF for documents, PPTX for presentations, and source code plus configuration for apps.
- Write down how recurring work runs. Capture the inputs, sources, tools, review steps, expected output, and common corrections. Include an example your team considers good.
- Map permissions. Record what each connection can read or change. Reauthorize the replacement with the access it needs, and revoke obsolete access when the move is complete.
- Save reusable behavior. Export skills, prompts, templates, and scripts in a form someone can inspect and edit. Check which parts depend on the original agent’s tools.
- Account for hosting. A downloaded website may still depend on a database, a domain, a secret, or a vendor’s runtime. Record each dependency and who controls it.
- Rebuild one representative workflow. Use the same inputs and acceptance criteria in the replacement. Reconnect the necessary services and verify the output.
- Compare several runs. Record quality, time, cost, reliability, source traceability, and review effort. Use cost per accepted result so retries count.
- Test another export. Before moving more work, take an artifact, a skill, and its instructions back out of the replacement. Confirm they remain useful elsewhere.
Compare Manus and MindsHub Agents on a real workflow
Both products can connect tools, carry out multi-step work, and create files. The useful comparison is what your workflow requires and how much of its setup you can retain when something changes.
| Workspace question | Manus | MindsHub Agents |
|---|---|---|
| How is the agent chosen? | Manus manages the agent experience and its underlying execution. | MindsHub Agents runs on an open-source agent harness, and you choose the model. |
| Can I change the model? | Model and agent profiles operate within the managed Manus service. | You can choose among models in the MindsHub catalog and switch at any time. |
| Where can I use it? | A managed cloud service with desktop features for approved local work. | In the browser or through desktop apps for macOS, Windows, and Linux. |
| How does it reach my data? | Connectors, browser tools, and local-computer features provide access to external systems. | Connections to databases, files, and SaaS tools use credentials scoped to the connection. |
| What can I reuse? | Projects, saved context, skills, and scheduled work. Skills can be shared as .skill files, ZIP archives, or GitHub repositories. | Projects, memory, skills, and artifacts remain in the workspace when the model changes. |
| What can I take away? | Exported files and supported skill formats; restoration archives serve Manus’s own recovery process. | Files and code produced by the agent, plus supported artifacts that can be published to a URL. Test export and hosting dependencies for each workflow. |
Use Manus’s product documentation to check the features your account needs. For MindsHub Agents, see the download options, model catalog, and pricing. A model mentioned in our broader LLM comparison is not necessarily available through MindsHub.
An open-source agent harness makes the execution component inspectable. That alone does not prove that an entire hosted workspace, its credentials, or its schedules can move automatically. Verify each part of the inventory.
Decide what is worth moving
Staying with Manus can be the sensible choice when restored workflows work well and you prefer its managed experience. Test your exports and keep independent copies of important work even if you stay.
MindsHub Agents is worth testing when explicit model choice and an open-source agent harness matter to you. Bring a recurring task, connect only the data it needs, and compare the output with the result you already trust. Our model selection guide helps separate model quality from problems in the prompt, context, or tools.
Move more work after that test shows a practical benefit. Keep the instructions and evaluation criteria alongside the exported files so the next model or workspace can be judged against the same standard.