Lindy and Relevance AI are both no-code platforms for building AI agents, and both let a non-technical team stand up something working in an afternoon. I keep getting asked which one to use, and the honest answer is that they solve different problems. Lindy builds an assistant that runs your day. Relevance AI builds agents that run a process.
How they differ
Lindy's center of gravity is the personal and business assistant. It is built around email, scheduling, meeting notes, and the multi-step admin that clusters around those, connecting across thousands of apps through a natural-language interface. You describe what you want handled and a Lindy agent does it: it reads an inbound message, classifies it against categories you set, applies a Gmail label, and leaves a drafted reply in your drafts for one click. A scheduling agent reads a "can we meet next week" email, checks your calendar, and answers with open slots. For a small team buried in inbox and calendar back-and-forth, that is the direct hit, and it is why Lindy feels less like software and more like a hire.
Relevance AI points at the structured business workflow, mostly sales and operations. Instead of one assistant, you assemble agents, and whole multi-agent teams, where each agent owns a step and hands its output to the next the way a real team passes a record down a pipeline. The work it shines on is the rules-plus-judgment stuff that quietly eats a team's week: lead enrichment, scoring and routing, ticket triage, email classification. You give it the criteria, it applies them across many records, and the people who used to do the sorting move to cases that need a human. That makes it the stronger pick when the goal is automating an internal procedure at volume rather than clearing one person's desk.
The tell is how you would describe the task out loud. "Handle my email and my calendar" is Lindy. "For every lead, run these numbered steps with these decision rules" is Relevance AI.
Pricing compared
Both are freemium with credit-based paid plans, so the meter is how much work the agents actually do, not how many seats you buy. Lindy's free tier is 400 credits a month, enough to wire up one agent and watch it run for a few days, not enough to keep it on. Paid plans climb toward the $49.99/month tier and up, and the thing to understand is that credits are spent per action, and actions are not equal. A plain email send is cheap; parsing a long thread or running a multi-step lead workflow costs many times more, and voice work over a connected phone number burns credits by the minute on top of a $10/month per-number add-on.
Relevance AI also has a free tier and starts around $19/month, which keeps the cost of proving out one workflow low. Same credit mechanic applies: a high-volume agent firing constantly runs well past $19, because every run draws down credits. With either tool, map run frequency against credit cost before you scale. Relevance AI starts lower on sticker price, but I would not let that decide it, since real cost tracks usage on both.
Where each wins
Lindy wins when the pain is admin where each step needs judgment: deciding what an email is about, drafting a reply in your voice, booking a meeting from a loose request. Its pre-built templates ship the trigger, prompt, and action wiring already connected, so you paste in your context and tone instead of designing the flow.
Relevance AI wins when you have a defined, repeatable procedure and want to build it yourself. The visual builder lets the person who knows the process cold be the one who builds the agent, and multi-agent teams let you model handoffs explicitly instead of overloading one prompt.
Which to choose by use case
If you are a solo operator or small team and the work is email, scheduling, and follow-ups, go Lindy and start with its Email Triage template. If you are a sales, ops, or support team with a workflow you could write on a whiteboard as numbered steps with decision rules, go Relevance AI and brief it that way. If the job is open-ended "go research this and figure it out" work, a goal-and-walk-away agent like Manus fits better than either. Both are genuinely no-code and both act rather than just chat, so most teams know within one built workflow which problem they actually have.