Sistava

Lindy AI

No-code AI agent builder with 1,000+ integrations, MCP support and phone agents

About Lindy AI

Lindy AI is a no-code platform for building AI agents that automate business tasks like email management, scheduling, and customer support. They offer "Societies" for multi-agent coordination and phone agents via Gaia. Sistava differs by providing pre-built teams with real org structure, deeper memory, and an employment-model experience rather than a workflow-automation tool.

Platform details

Official website

What does Lindy AI actually do?

Lindy AI builds task-specific AI agents you describe in plain English. Agent Builder takes a sentence like watch my inbox, draft replies to investor intros, and generates an agent that listens for the trigger, reads context, drafts the reply and waits for approval. No flowcharts, no JSON rules. It is strongest on email automation, calendar management, meeting prep, research and lightweight CRM hygiene. The product targets the gap between Zapier (rigid if-this-then-that) and ChatGPT (powerful but stateless). Lindy lives in between: agents that react to events, reason about context and choose an action. Compared to packaged AI workforce platforms, Lindy is closer to a personal-automation toolkit than a roster of coworkers. You build the helper you need, point it at a trigger, and let it run. Sistava packages roles instead: an AI marketer, SDR, support agent, each with duties and schedules already defined, so a founder does not have to build the helper before getting value. Mental model: Lindy is the AI version of building your own Zap with judgment. Sistava is hiring the person who would have built it.

How much does Lindy AI cost and what do you get for the price?

Lindy dropped its always-free plan in early 2026 and simplified the lineup to three paid tiers: Plus at $49.99/month for standard usage, Pro at $99.99/month with roughly three times the usage plus model selection and computer use, and Max at $199.99/month with about seven times the Plus volume. A 7-day trial replaces the old free tier, billing is monthly only with no annual discount, and custom Enterprise pricing covers security and compliance needs above Max. Credits are the cost meter. Each agent run consumes credits based on task complexity and model choice, with overage billed at double the standard rate once you hit the plan cap. Light email triage burns few credits, multi-step research with a strong model burns many. Cost predictability is the complaint that comes up most often in reviews, and it is a structural consequence of metering by task complexity rather than a flaw anyone can patch. That credit volatility matters for founders watching cash. A workflow that worked at $50/month can creep to $150 once you upgrade the model or add a research step. Sistava prices AI Employees on a flat plan plus a credit pool with role-default models, so the meter rarely surprises you. Same problem, different default. Net: Lindy Plus at $50 works for light personal use as long as you stay inside the standard usage cap. Pro at $100 is where most small teams land once model selection and computer use matter. With the free plan gone and billing complaints, including charges after cancellation, now a recurring theme in reviews, budget for a real month of usage before you commit to a tier.

When does Lindy AI beat the alternatives?

Lindy wins when you need AI judgment inside an event-driven workflow. The job is something like: when X happens, figure out the right response, draft it, and ping me. That is exactly the territory where Zapier feels brittle and ChatGPT feels disconnected. Agent Builder describing the workflow in English compresses a half-day Zapier build into ten minutes. It is the right pick for solo operators who already know what they want automated. Inbox triage, meeting prep, follow-up drafting, calendar negotiation, lead research handoff. If you can describe the workflow in three sentences, Lindy will probably build it. It loses when you need a worker who owns a role end to end. A marketer who plans the quarter, writes the posts, reviews performance and adjusts is a job, not a workflow. Sistava is built for that: AI Employees with duties and memory who run a function, not just react to triggers. Decision rule: Lindy if you have a list of automations to ship. Sistava if you have a list of roles to fill.

Where does Lindy AI fall short for founders who want hands-off execution?

The cost-predictability gap is the first issue. Credit consumption scales with model and task complexity, and reviewers report that real costs are hard to forecast until you have run the agent for a few weeks. For a founder budgeting tightly, that volatility breaks the planning loop. The second gap is role coherence. Lindy thinks in agents, each one wired to a trigger and a task. A real role is many tasks, with memory, with judgment that improves over time. You can stitch multiple Lindy agents together but the seams show: shared context is limited, work journals are not first-class, and there is no team layer where agents collaborate as colleagues. Sistava addresses both gaps. AI Employees come with a defined role, persistent memory, shared team context and duties that run on schedules. The cost meter sits behind role defaults so you stop tuning models per task. Different philosophy: shape the worker, not the workflow. Lindy is the better tool when you are an automation thinker. A packaged workforce is the better tool when you are a hiring thinker.

How does Lindy AI handle memory and multi-step workflows?

Lindy agents can carry context across steps inside a single run and recall a defined memory store across runs. That makes it usable for tasks like ongoing email threads, follow-up loops and recurring meeting prep where prior context matters. Memory is configured per agent, not shared automatically across agents. Multi-step workflows are handled inside Agent Builder. You can chain steps, branch on conditions, call external tools and route between agents using triggers. The reasoning model decides path within the bounds you described in English. For 80% of automation jobs this is enough; for the other 20% you wish for explicit step controls. Where it gets thin is shared memory across many agents acting as a team. Lindy is agent-centric, not team-centric. Sistava is team-centric: every AI Employee on a team shares organizational memory plus its own role memory, and journals make handoffs explicit. That matters when an SDR books a meeting that an AE then needs to prep for. Verdict on memory: solid for single-agent recurring tasks, limited for teams of agents that need a shared brain. If you need the second, you want a workforce product, not an agent builder.

Is tool coverage the real difference between Lindy and Sistava?

No, and it is worth saying plainly because most comparison pages get this wrong. Lindy connects to 1,000+ apps natively and supports MCP, so it can reach any tool exposed by an MCP server. Anyone telling you Lindy cannot reach your stack has not looked at it recently. On raw reach the two products are close enough that it should not decide your purchase. The difference is what sits on top of the tools. In Lindy a tool belongs to an agent you wired to a trigger. In Sistava a tool belongs to a role, switched on per employee, bounded by plain-English rules that hold on every run, and gated for approval on anything consequential. The question is not whether the software can reach Gmail. It is what happens on run four hundred when nobody is watching. The second real difference is where the work happens. Lindy runs computer use in its own cloud environment. Sistava drives your actual desktop and browser through a companion app, which is what reaches the vendor portal that only exists behind your VPN, the accounting package installed on one machine, and the internal tool nobody will ever build an API for. So the honest framing: pick on operating model and on where the work has to run, not on integration counts. Both lists are long enough.

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