Key facts
| Manus | General AI agent by Butterfly Effect that plans and executes tasks |
| Execution | Runs in a cloud virtual computer with browser and filesystem |
| Mode | Asynchronous: tasks continue while the app is closed |
| Typical uses | Research, data analysis, spreadsheets, slide decks, small apps |
| Plugsky | OpenAI-compatible API with live function calling and 30+ models |
| Control | Your tools, state, review gates and deployment environment |
| Deployment | Cloud, VPC, on-prem and air-gapped with region choice |
| Honest limit | No hosted workspace, browser or artifact apps included |
TL;DR
- Hosted general agents trade control for zero setup.
- Building your own keeps data, tools and approvals inside your systems.
- Asynchronous execution matters for long tasks; design for it explicitly.
- Flat platform plans are easier to forecast than per-task consumption.
- Start with the free tier and a 14-day full-access trial.
How it works, step by step
- List the tasks you would delegate and their tolerance for autonomy.
- Decide where the agent must run: a vendor workspace or your own product.
- If building, define tools with least privilege and a state store you control.
- Design for async: task queue, status polling or webhooks, and cancellation.
- Add approval gates for irreversible actions and a per-run budget.
- Route planning to a frontier model and routine steps to cheaper models.
- Measure completion rate and cost per finished task, then expand scope.
Try it yourself
Open the Manus alternative selector →
What hosted general agents are good at
Manus popularised the pattern of a general agent with its own computer: the user describes an outcome, the agent decomposes it, runs tools in a cloud sandbox, and returns a finished artifact. Its asynchronous model means work continues while you are away, and typical outputs include research briefs, spreadsheets, slide decks and small applications.
The strengths are real: no infrastructure, no orchestration code, and a usable result in minutes. The trade-offs are equally real. The interface, execution environment and tool set belong to the vendor, cost is consumption-based, and integrating the agent into your own systems, permissions and audit trail is limited by design.
When building your own is the better trade
Build your own agent when the work touches systems a hosted sandbox cannot reach, or when data cannot leave your environment. The architecture is well understood: a task queue, a worker that runs the agent loop, typed tools that call your services, durable state for resumption, and approval gates for anything irreversible.
- Tools: your internal APIs, database queries and workflows.
- State: threads and checkpoints under your retention policy.
- Approvals: gates where your risk actually sits.
- Async: queue plus status endpoint, with cancellation and timeouts.
The model layer for a self-built agent
Once you own the loop, the remaining decision is where models come from. A single OpenAI-compatible endpoint keeps the integration small and the exit cost low, and lets you route steps to different model tiers without another vendor.
Plugsky serves that layer: 30+ models on one key with live function calling, streaming, JSON mode, embeddings, RAG and agents, plus scoped keys, RBAC, SSO/SCIM and audit logs, and deployment from shared cloud to VPC, on-prem and air-gapped. It does not include a hosted workspace, cloud computer or document apps, and batch, files and fine-tuning endpoints are coming soon. Self-serve plans are flat monthly and the free tier covers plugsky-micro and plugsky-lite; current plans are on the live pricing page, and a 14-day full-access trial is available.
Honest comparison
| Dimension | Hosted general agent | Plugsky API build | Traditional RPA |
|---|---|---|---|
| Setup effort | None | Moderate | High, brittle scripts |
| Autonomy | High, vendor-bounded | You define the limits | Deterministic paths only |
| Data path | Vendor sandbox | Cloud, VPC, on-prem, air-gapped | Your servers |
| Tool reach | Curated integrations | Your systems and APIs | Fixed UI targets |
| Forecasting | Consumption-based | Flat monthly plans | Licence and maintenance |
Frequently asked questions
Is Plugsky a Manus replacement?
No. Manus is a hosted agent product with a workspace; Plugsky is an API platform. You use Plugsky to build your own agent inside your software, not as a consumer app.
Can I get the same results by building my own?
For research, summarisation, data processing and drafting, yes, with your own tooling. The bundled file, deck and app-generation experiences are not part of the API.
How do I handle long-running tasks?
Use a queue with a worker per task, persist state for resumption, expose status and cancellation, and set a wall-clock timeout plus a per-run budget.
What stops an autonomous agent doing damage?
Least-privilege credentials, narrow tools, approval gates on irreversible actions, rate limits, sandboxed execution and complete action logging.
How should I model cost?
Measure cost per completed task, not per call. Flat platform plans plus capped runs are easier to forecast than per-task vendor metering.
What is available on the free plan?
plugsky-micro and plugsky-lite with no card required, plus a 14-day full-access trial for evaluating the wider catalogue.
Can I self-host?
Yes. Plugsky supports deployment in your VPC, on-prem and even air-gapped, which suits regulated teams that cannot use a public agent workspace.