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Master Manus with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign up at manus.im with the free tier to test task decomposition on a real workflow Describe the outcome, not the steps — Manus performs better when you specify goals not instructions Watch the live desktop view on your first few runs to build intuition for how the agent thinks Save a working prompt as a 'Skill' so you can rerun the same workflow with different inputs Connect MCP servers (Notion, Linear, internal databases) for enterprise
relevant integrations Upgrade to Starter or Pro once you have identified 2–3 repeatable workflows worth the credits
💡 Quick Start: Follow these 2 steps in order to get up and running with Manus quickly.
Explore the key features that make Manus powerful for ai agents workflows.
Manus keeps a persistent virtual computer alive for the whole task — browser sessions, files, running processes — and orchestrates specialized sub-agents behind the scenes. That is meaningfully more autonomous than a chatbot that fires a tool and clears state between turns.
You get a live desktop view and can intervene mid-task, but the planner is not exposed as an editable graph or config. If you need programmatic control over the plan, use a framework like CrewAI, LangGraph, or the OpenAI Agents SDK.
Each task consumes credits based on LLM tokens, browser minutes, and compute — heavier or longer tasks cost more. Free tier is enough to evaluate; expect $19–$39/mo to be the right zone for a serious user, more for teams.
Yes — Manus is an MCP client, so any MCP server (Notion, Linear, your internal API) plugs in through the standard protocol rather than a bespoke plugin system.
Not on consumer tiers. For regulated workloads you need the enterprise plan with SSO, audit logs, and negotiated data-handling terms — evaluate carefully and probably keep sensitive data out of consumer credits.
Now that you know how to use Manus, it's time to put this knowledge into practice.
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Tutorial updated March 2026