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Manus vs Devin vs Lindy

ManusDevinLindy
ManusManus

A general-purpose AI agent that plans and executes multi-step tasks on its own.

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DevinDevin

Cognition's autonomous AI software engineer that takes on tasks end to end.

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LindyLindy

Lindy is a no-code AI agent platform that automates email management, meeting scheduling, call notes, and workflow tasks across sales, marketing, support, and recruiting

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Votes000
PricingFreemiumPaidFreemium
CategoryAi AgentsAi AgentsAi Agents
Tags
automate-workflowsanswer-questions
write-codebuild-apps
automate-workflowsmanage-schedule
Best for——
  • Founders and executives who want an AI assistant for admin work
  • Small and mid-sized teams automating cross-department workflows
  • Sales and recruiting teams handling high email and scheduling volume
Pros——
  • Genuinely no-code agent building with a template library and 100+ integrations lets non-technical users automate email, calendar, and meeting workflows quickly.
  • Bridges the gap between simple connectors and full agent frameworks — embedded Python and JavaScript sandboxes plus HTTP requests give power users custom logic when they need it.
  • Strong executive-assistant use case out of the box: inbox triage, meeting scheduling, meeting recording and notes, and automated follow-up work together as a coherent system.
  • Conversational control via iMessage/SMS means you can direct your assistant without opening a dashboard, which lowers day-to-day friction.
  • Higher tiers add meaningful control, including model selection and computer-use capabilities, so teams can tune behavior and cost as needs grow.
Cons——
  • Cost is tied to a usage-credit model, and credits can be consumed faster than expected under heavy volume — making spend hard to predict for high-throughput teams.
  • Entry pricing around $49.99/month is steep for a solo tool compared with traditional automation platforms, and value depends on how much admin work you actually offload.
  • Its broad, do-everything positioning means a purpose-built tool may outperform it for a single specialized job like high-volume support ticketing.
  • Building reliable multi-step agents still involves a learning curve, and complex workflows require thoughtful configuration to behave consistently.

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