OpenManus is the closest broad, self-hosted analogue to Manus, while OpenHands Agent Canvas is the better choice for an always-on coding workspace. Browser Use and Skyvern focus on browser automation, and Stagehand is the most code-led SDK. None is a drop-in replacement for Manus’s managed experience: self-hosting means you provide model access, compute, browser sessions, security controls, updates and monitoring.
The right option depends on whether you need a general-purpose agent, a coding environment, or reliable browser workflows. The comparison below separates those use cases, explains the infrastructure burden, and identifies where published benchmark figures are not directly comparable.
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What Manus provides as the reference point
Manus is a hosted general agent designed to turn high-level requests into multi-step work. Its official feature description covers web and mobile app building, game development, Figma-to-code, video and slide creation, broad web research, a browser operator, a cloud computer, recurring automations, email actions, agent skills and projects. Its API documentation describes a REST interface for creating and managing agent tasks, automating workflows and integrating those tasks into applications.
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Quick comparison
| Project | Best fit | Self-hosting profile | Model and key ownership | Browser reach | License stated in the available documentation |
|---|---|---|---|---|---|
| OpenManus | Broad general-purpose agent experimentation | Run and administer the framework locally; configure the runtime and model provider yourself | Configurable LLM API keys and models | Local Chrome/Chromium attachment; optional Browser Use MCP | Not stated |
| OpenHands Agent Canvas | Always-on coding agents and developer automation | Self-hosted control center on a server or Droplet, with a browser UI and persistent backend | Supports optional OpenAI-compatible inference; operator manages credentials | Chromium included for browser tooling | Open-source; specific license not stated here |
| Browser Use | Browser-first task automation | MIT Python library runs on your machine; managed cloud is a separate service | Your own model key for the open-source library | Managed or local browser workflows, depending on deployment | MIT for the open-source library |
| Skyvern | Self-hosted browser workflows with an optional cloud service | Deploy the AGPL-3.0 framework from its source repository or use the hosted service | Depends on deployment and configured providers | Browser workflow automation | AGPL-3.0 |
| Stagehand | Teams that want readable, code-led browser control | Run the SDK locally or use Browserbase for execution | Use AI for selected steps while keeping orchestration in code | Browser actions, extraction and observation | MIT |
“Self-hosted” has different depth across this table. OpenManus and the MIT Browser Use library can run on your own machine, but you still need to operate models, browsers and credentials. OpenHands provides a more complete server workspace. Skyvern offers a self-hostable framework as well as a cloud option. Stagehand is an SDK rather than a complete autonomous desktop.
OpenManus: the closest broad self-hosted analogue
Choose OpenManus when you want a general agent that you can inspect, modify and run under your own infrastructure. Its README presents it as an open-source framework for building general AI agents and documents several capabilities that make it the nearest conceptual match in this group:
- Attachment to a local Chrome or Chromium instance for browser tasks.
- An optional Browser Use MCP integration.
- Configurable LLM API keys and model selections.
- A terminal-oriented quick start for local operation.
- An optional DataAnalysis Agent for analysis and visualization.
- An experimental multi-agent flow described as unstable.
The practical trade-off is administration. OpenManus is a framework, not a managed service that supplies a model, browser fleet, isolation boundary or uptime. You must choose a compatible model provider, store its credentials, configure the runtime, keep the browser available, and decide how agent actions are isolated from your personal files and accounts.
When OpenManus is the right choice
- You want one project that can be extended beyond a single browser or coding workflow.
- You are comfortable editing configuration and operating a local or server runtime.
- You need control over which model API receives prompts and tool results.
- You want to experiment with data analysis or multi-agent orchestration.
Where it is weaker than a hosted agent
The README does not turn OpenManus into a turnkey replacement for every Manus feature. The multi-agent flow is explicitly unstable, and the framework requires your own model and runtime setup. There is no stated single-machine hardware baseline in the available documentation, so size the host according to the selected model, browser concurrency, context length and workload rather than assuming a fixed requirement.
Deployment checklist
- Read the current OpenManus README and select a supported Python/runtime environment.
- Create a dedicated configuration for the model provider and keep API keys outside source control.
- Start with one browser session attached to a separate Chrome/Chromium profile.
- Run a low-risk task, inspect tool calls and file changes, and verify that the agent can reach only the resources you intended.
- Add the DataAnalysis Agent or Browser Use MCP only after the basic loop works.
- Treat the multi-agent flow as experimental and test it on disposable data before relying on it.
OpenHands Agent Canvas: the practical self-hosted coding workspace
OpenHands Agent Canvas is aimed at developers who want an always-on control center rather than a general consumer assistant. DigitalOcean describes it as an open platform for cloud coding agents and a self-hosted developer control center. It provides a browser UI, keeps agents and settings on the deployed server, supports an always-on backend, and includes Chromium for browser tooling. Optional OpenAI-compatible inference lets an operator connect a compatible model endpoint instead of using only a fixed provider.
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Documented server requirements
| Deployment level | Memory | CPU | Storage |
|---|---|---|---|
| Single-user minimum | 4 GB RAM | 2 vCPU | 50 GB |
| Recommended | 8 GB RAM | 4 vCPU | 100 GB |
These figures are the documented minimum and recommendation for a single-user deployment; they are not a promise of a particular model’s speed. Larger models, several concurrent agents, browser-heavy tasks, retained logs or large repositories can require more memory, CPU and storage.
Why developers choose it
- Persistent server operation is suitable for agents that should remain available without a laptop running.
- A browser interface gives you a central place to launch and inspect coding tasks.
- Chromium is available for browser-assisted development workflows.
- The deployment model is clearer than assembling a general framework from individual components.
Security implications
The deployment documentation warns that the agent can read and write the service user’s filesystem and execute commands. Put the service in a dedicated account or isolated host, limit mounted directories, and do not expose the management interface directly to the public internet. Use network egress controls, strong authentication, regular updates and backups. Keep production credentials out of the agent’s working directory unless the task genuinely requires them.
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Browser Use: browser-first automation with two operating models
Browser Use separates an MIT-licensed open-source Python library from Browser Use Cloud. The library runs on your machine with a model key that you supply. The cloud service accepts tasks through an API and supplies managed browsers, proxies and CAPTCHA solving. Those are materially different products: local execution gives you control over the machine and sessions, while the cloud option reduces browser operations but reintroduces a hosted dependency.
Published benchmark figures and how to read them
The comparison page reports 98% on all 300 Online-Mind2Web tasks for Browser Use in 2026, using its own judge and described as self-reported. It also reports 30% on Online-Mind2Web with GPT-4o from an OSU NLP human evaluation in March 2025 for the open-source library. The systems, judges and dates differ, so these numbers are not a head-to-head independent trial and should not be used to rank the projects directly.
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Best use cases
- Automating websites where the main challenge is clicking, typing, navigation and extraction.
- Running browser tasks locally while retaining your own model credentials.
- Using a managed browser fleet when proxies, CAPTCHA handling or browser operations would otherwise dominate your engineering time.
Browser Use is not positioned as a general desktop-and-mobile agent or a complete coding workspace. Pair it with your own orchestration, data store and approval steps when a workflow needs durable state or business rules.
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Skyvern’s comparison entry describes an AGPL-3.0 browser automation framework that can be self-hosted from its source repository or used through Skyvern Cloud. That makes it suitable when your requirement is a repeatable browser workflow rather than an unconstrained general agent.
The same page reports a 90.5% Odyssey benchmark result, labeled as Skyvern’s October 2026 self-reported result. Treat that as vendor evidence: check the task definitions, model configuration, browser environment and judging method before making a performance commitment. It is not directly comparable with Browser Use’s figures because the benchmark and evaluation procedure differ.
Advantages
- AGPL-3.0 licensing is explicit, which helps teams evaluate obligations before modifying or embedding the framework.
- Self-hosting keeps the workflow service under your operational control.
- A cloud option is available if browser infrastructure is more burden than you want to operate.
Limitations
Skyvern’s center of gravity is browser workflows. It should not be selected solely because a benchmark percentage resembles a general-agent score. Confirm that the pages, authentication flows, data handling and approval controls in your process are supported before production deployment.
Stagehand: deterministic code with AI at selected steps
Stagehand is an MIT-licensed SDK for TypeScript, Python and Go. Its design is different from a fully autonomous general agent: you write the workflow and use AI for selected browser operations. The documented primitives are act, extract and observe. They can run locally or on Browserbase.
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Why a code-led approach can be preferable
- Business logic, retries and branching remain visible in source code.
- AI can handle variable page layouts without surrendering the entire workflow to an autonomous planner.
- TypeScript, Python and Go support lets teams use an existing service stack.
- Tests and code review are easier when the agent’s scope is narrow and explicit.
Stagehand is a good answer to “Can I automate this browser process in my application?” It is less suitable if you want to type a vague objective and receive a finished research report, slide deck or app without writing orchestration code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Comparison by the decisions that affect deployment
Task breadth
| Need | Most suitable candidate | Reason |
|---|---|---|
| General agent experimentation across research, tools and analysis | OpenManus | Broadest scope in this shortlist, with browser attachment, terminal operation and an optional data-analysis agent |
| Persistent coding and developer automation | OpenHands Agent Canvas | Server-based control center, browser UI, always-on backend and Chromium |
| Browser tasks with minimal application code | Browser Use or Skyvern | Both focus on browser workflow execution; choose based on local versus hosted operations and licensing |
| Browser tasks embedded in an application | Stagehand | SDK primitives keep orchestration in your code |
Infrastructure burden
The lightest local starting point is usually a browser library or SDK, but “light” does not mean maintenance-free. You still manage the model endpoint, browser version, secrets, logs and failure recovery. OpenHands adds a persistent service, storage and access control. OpenManus adds framework configuration and optional components. Cloud versions reduce browser operations but charge for, and place trust in, an external service.
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Model flexibility and key ownership
OpenManus exposes configurable model keys and models. The local Browser Use library expects your own model key. OpenHands can use an OpenAI-compatible inference endpoint. Stagehand’s code-led design lets you decide where AI is used. In every case, the operator is responsible for quotas, spend limits, rotation and revocation of credentials. A self-hosted application does not make model inference local unless you actually run a local model.
Browser and desktop reach
OpenManus can attach to local Chrome or Chromium. OpenHands includes Chromium for its coding-agent environment. Browser Use and Skyvern are browser specialists. Stagehand exposes browser actions, observation and extraction. None of the self-hosted projects in this shortlist is documented here as a complete replacement for Manus’s cloud computer, mobile-app tooling and media-generation surface.
Licensing
Stagehand and Browser Use’s open-source library are MIT licensed. Skyvern is AGPL-3.0. OpenManus and OpenHands are described as open-source in the available material, but a specific license is not stated here; verify the repository license before redistributing, modifying or offering them as part of a service. License obligations can matter more than a benchmark score when an agent becomes part of a commercial product.
How to choose in five steps
- Define the output. If the output is code and test results, begin with OpenHands. If it is a browser transaction or extraction, evaluate Browser Use, Skyvern or Stagehand. If you need a broad experimental agent, evaluate OpenManus.
- Decide where inference runs. Local application code and self-managed endpoints maximize control; hosted inference or browser services reduce operations but add recurring service dependence.
- Set the autonomy boundary. Require approval before sending email, making purchases, changing production files or submitting forms. Browser automation should begin with read-only tasks.
- Estimate concurrency. Count simultaneous agents, browser sessions, repository builds and retained artifacts. Use the OpenHands 4 GB/2 vCPU/50 GB minimum only as its documented single-user floor, not as a universal hardware recommendation.
- Run a representative pilot. Measure completion rate, recovery from changed pages, latency, model spend, human interventions and the quality of saved artifacts on your own tasks.
Self-hosting architecture and security checklist
Moving from a hosted agent to a self-hosted one transfers responsibility rather than removing it. Before granting an agent access to real accounts or repositories, establish the following controls:
- Isolation: use a dedicated operating-system account, container or virtual machine; mount only the directories required for the task.
- Credential scope: create task-specific API keys with the lowest available permissions, keep them in a secret store or environment configuration, and rotate them regularly.
- Network policy: restrict outbound destinations where practical and block access to internal administration panels that the agent does not need.
- Browser profiles: separate automation cookies from personal browsing and use disposable profiles for testing.
- Human approval: gate irreversible actions such as email sends, account changes, financial transactions, code merges and production deployments.
- Filesystem protection: make important repositories read-only until the workflow is trusted; retain version control and backup coverage.
- Observability: record task prompts, tool calls, command output, browser events, model usage and failures without logging secrets.
- Updates: patch the framework, model client, browser and operating system; browser automation often breaks when page or browser behavior changes.
- Recovery: define how to stop a runaway task, revoke credentials, restore files and replay a failed workflow.
OpenHands deserves particular caution because its documented agent can read and write the service user’s filesystem and execute commands. The same principle applies to any framework with terminal or browser access: treat the agent as an untrusted process until its permissions and behavior are proven.
Cost: budget the whole system, not just the software
None of these projects makes self-hosting free. Your total cost has at least four parts:
- Model inference: API usage or the hardware and electricity needed for a model you run yourself.
- Compute and storage: a local workstation, server or hosted virtual machine, plus space for repositories, browser profiles, logs and artifacts.
- Browser operations: proxy, CAPTCHA, managed-browser or remote-execution charges when you use a cloud browser service.
- Operations: backups, monitoring, patching, incident response and engineering time.
The available documentation does not establish recurring prices for the model providers, cloud services or hosting options, so a credible total cannot be reduced to one monthly number. Compare a realistic task sample: count model tokens, browser minutes, concurrent sessions, storage growth and human review time over a week, then project that usage before committing.
Benchmark and feature claims: avoid false equivalence
Published percentages can be useful signals but not universal rankings. Browser Use’s 98% result is self-reported and uses its own judge on 300 Online-Mind2Web tasks in 2026; the 30% GPT-4o figure is an OSU NLP human evaluation from March 2025. Skyvern’s 90.5% Odyssey result is labeled self-reported for October 2026. Different benchmarks, models, prompts, browsers and judges can produce very different outcomes.
For a purchase or deployment decision, reproduce a small evaluation on your own pages and tasks. Include logged-in flows, changed layouts, validation errors, rate limits, file permissions and the handoff points where a person must approve an action. Completion percentage without recovery behavior and safety measurements is not enough.
Bottom line
Pick OpenManus for the broadest direct self-hosted experiment, accepting that you must configure models and operate the runtime. Pick OpenHands Agent Canvas for a persistent, self-hosted coding-agent workspace, starting with its documented 4 GB RAM, 2 vCPU and 50 GB single-user minimum and scaling upward for real concurrency. Pick Browser Use or Skyvern when browser automation is the core problem, and compare local control, hosted operations and license obligations. Pick Stagehand when your team wants deterministic application code with AI-assisted browser steps. In all cases, self-hosting is a control and responsibility trade: you own the keys, sessions, permissions, updates, monitoring and failures that a hosted Manus workflow hides behind its service.
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