Lindy AI

Lindy is a no-code platform for building agents that read context, make a call, and take an action across email, calendar, meetings, CRM, support, and internal workflows. You configure an agent, connect its tools and triggers, test a workflow, add approval steps where impact matters, then watch credits and outcomes.

The mental model that keeps you out of trouble is autonomy as a dial. Low-risk, read-and-summarize work can run on its own. Anything that sends an external message, moves money, or changes a record should sit behind an approval until you trust it. Lindy supports that, you have to design it.

The sweet spot is a repeatable internal job with a clear queue and a human fallback: meeting and email workflows, lead operations, support triage, the tasks that eat an hour a day and follow the same rules every time.

Start with one low-risk workflow that has a measurable queue and an approval step. Prove it clears work reliably and cheaply before you point it at anything customer-facing. Setup is genuinely fast, so the risk is building ten agents before you have validated one. It scores a striking 4.9 out of 5 on G2 across 171 reviews.

Cost bites first. Credits get consumed by agent loops, model choice, and volume, so a flat subscription can turn expensive at scale in ways the plan name does not warn you about. Measure cost per completed workflow at your expected volume, and watch what happens when an agent retries or loops.

Action risk is the second bite. An agent with broad connector permissions and no approval gate can send the wrong message or edit the wrong record fast. Review least-privilege access, shared workspace scope, logging, and offboarding.

One practical note: pricing has shifted across the vendor's own surfaces, with older figures still floating around, so confirm the live docs price at purchase.

Current pricing docs show Plus at $30 per user with 3,000 credits, Pro at $100 with 15,000, and Max at $200 with 35,000, plus custom Enterprise. Top-ups run $10 per 1,000 credits, and workspace credits are pooled across the team.

The subscription is the floor. Credit burn from loops and heavier models drives the rest of the bill, so budget against real workflow volume before you commit.