Manus AI: autonomous multi-agent system for complex research
Manus AI is a Chinese multi-agent system that made waves by scoring 86.5% on the GAIA Level 1 benchmark, beating OpenAI Deep Research. The company was acquired by Meta in late 2025. Manus architecture relies on multiple specialized agents coordinating to carry out complex research and execution tasks: planning, web navigation, file handling, writing. The user gives a high-level goal and Manus breaks it down and executes. Its credit-based pricing is attractive for occasional but intensive use: market research, due diligence, competitive intelligence, deal prep. The system shines on multi-step tasks that would cost a human half a day.
For who
Analysts, consultants, research teams, investors and founders who need deep multi-step investigations in minutes.
Input
Objectif de recherche ou d'execution, contexte, URLs de reference, contraintes specifiques.
steps (5)
Create a Manus account
infoSign up on manus.im and buy an initial credit pack. Each task consumes credits based on its complexity.
Frame a clear goal
infoWrite a high-level goal (1-3 sentences) describing the expected deliverable, depth, and constraints.
Provide initial context
infoAdd reference URLs, files or specific criteria to scope the investigation.
Start the session
infoLaunch the task. Manus breaks the goal into sub-tasks handled by its specialized agents and shows the plan live.
Iterate on the deliverable
infoFollow up with targeted deep-dives, reformatting, or exports (PDF, Docx, Sheets).
Output
Livrable complet : rapport, tableau comparatif, document structure, avec plan d'execution trace et sources citees.
Example
Input
Goal: "Compare the 5 main CRMs for a 50-employee French B2B SaaS SMB, with 2026 pricing, integrations across HubSpot vs Pipedrive vs Salesforce vs monday vs Attio, and a final recommendation."
Output
Manus launches 4 agents: research (pricing and feature collection), navigation (vendor sites), analysis (comparison matrix), writing (memo). Delivers an 8-page report in 20 minutes with a table, qualitative analysis, and a recommendation.
Customization
| Parameter | Description | Default |
|---|---|---|
| Profondeur | Niveau de recherche attendu (survol, standard, approfondi). | Standard |
| Langue de sortie | Langue du livrable final. | Francais |
| Format de sortie | PDF, Docx, Google Sheets ou rapport markdown directement dans l'interface. |
Technical Notes
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