Operations, sales, and support teams reach for Relevance AI to automate repeatable, judgment-heavy workflows without coding every model call and handoff.
Lindy
No-code AI agents for business workflows
Overview
What is Lindy?
Repetitive inbox, scheduling, and customer-facing work can be delegated to Lindy, which builds agents that reason across connected apps and complete multistep tasks.
From request to finished task
Lindy combines a visual workflow builder with language-model reasoning. An agent can read an incoming message, extract the relevant details, check another system, draft a response, and continue based on conditions you set. It suits processes that need judgment rather than a fixed sequence of Zapier-style rules.
- Start agents from email, schedules, webhooks, or app events.
- Pass work between specialized agents.
- Pause sensitive actions for manual approval.
Where setup gets real
The initial builder is approachable, especially when starting from a template. Reliable production workflows still require careful prompts, test cases, permissions, and fallback paths. Integrations cover common tools such as Gmail, Slack, Google Calendar, HubSpot, and Airtable. Voice agents can also place or answer calls, though call scripts and edge cases need more tuning than basic back-office automations.
Plans and usage
| Tier | Best fit |
|---|---|
| Free | Testing small agents with limited credits |
| Pro | Regular individual or small-team automation |
| Business | Higher usage and team controls |
| Enterprise | Custom volume, security, and support |
The honest read
Highlights & limitations
What stands out
- The visual builder makes sophisticated agent workflows accessible without writing orchestration code.
- Agents can interpret unstructured emails and documents before deciding which action to take.
- Human approval steps provide practical control over messages, updates, and other consequential actions.
- A broad integration catalog supports useful workflows across sales, support, and internal operations.
- Templates provide working starting points for common jobs instead of leaving every canvas blank.
Worth knowing
- Credit consumption becomes difficult to predict when agents branch, retry steps, or use several model calls.
- Debugging a failed multistep run can take time because the problem may be a prompt, integration, or model decision.
- Autonomous workflows need extensive testing since plausible-looking AI output can still trigger the wrong action.
- Higher-volume agents, especially voice and complex workflows, can become expensive compared with fixed-rule automation.
See it in action
Lindy in pictures
Video overview
Considering a switch?
Lindy’s alternatives
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n8n connects apps, APIs, and data sources through node-based workflows. It is especially good at technical automations that need branching logic, custom code, or control over where data runs.
Sales, recruiting, and marketing teams reach for Bardeen to move information between websites and business apps without building or maintaining conventional scripts.
Developers and AI teams reach for CrewAI when a process needs specialized model workers, delegated tasks and more control than a single prompt can provide.
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