The 8 Best Lindy AI Alternatives in 2026

Lindy is an AI teammate that plugs into your tools, learns how you work, and takes real work off your plate. It lives natively in Slack threads and mentions, runs scheduled routines (daily briefs, Friday reports, meeting follow-ups), manages inboxes, records and files meetings into a shared library, and comes with 40+ skills covering research, data analysis, decks, and dashboards. It connects to Gmail, Slack, Notion, HubSpot, and 1,500+ other apps, supports MCP, and is model-agnostic so you can pick the model per task. Pricing is per user with a shared workspace credit pool: everyday asks cost 2–250 credits, deep work 250–1,000, and big builds 1,000–2,500. Anything with outside impact — sending email, updating a ticket, posting to a channel — waits for human approval. Lindy is SOC 2 Type II and GDPR compliant, with SSO, SCIM, and HIPAA (signed BAA) on Enterprise. In hands-on testing it completed a lead-research agent task in under ten minutes with nothing to build.

Agents by AIQ is the agent-focused arm of AIQ Labs, and it takes a deliberately different approach from every DIY toolkit on this list: instead of handing you a canvas, credits, and a tutorial, its team designs, builds, connects, and runs done-for-you AI agents for small and mid-size businesses. The offer covers AI receptionists and phone answering on a real phone number, voice agents, sales follow-up and SDR agents, customer support agents, email agents, marketing agents, appointment setters, and workflow automation — integrated with the tools the business already uses. The positioning is explicit: not another AI wrapper or DIY toolkit, but an agent build operated by AIQ Labs, the team behind the AI Business Sites platform. The site itself is styled as an engineer's notebook, sketching the agents being designed for the visitor's business. Engagements are month-to-month, the client owns everything built, and the primary next step is booking a call to scope the agent. Target buyers are owner-operators and small teams in trades, legal, healthcare, insurance, real estate, ecommerce, and professional services losing missed calls, slow lead follow-up, and manual busywork.

Make is a no-code visual automation platform where you design workflows on a canvas by chaining modules together, each module being one action an app performs. Every module execution costs one credit; filters, routers, and error-handler modules are free. In 2025 Make added Make AI Agents, which read context and decide what to do next instead of running a fixed sequence, and Maia, which maps a plain-English request into a scenario. Pricing runs Free ($0, 1,000 credits, two active scenarios), Core ($16/month, 10,000 credits), Pro ($28/month, priority execution), and Teams ($51/month), with Enterprise custom. AI costs depend on setup: using Make's built-in AI provider bills tokens plus operations, while bringing your own OpenAI or Anthropic API key costs one credit per operation plus your provider's token charges — the more predictable route. Make notifies you at 75% and 90% credit usage and lets paid plans auto-purchase extra bundles. It suits teams that need complex, structured automation and want to see exactly how every step works.

n8n is a workflow-automation platform aimed at technical teams, distinguishable by two things: you can self-host it, and it supports JavaScript code nodes for custom logic. The cloud offering starts at $24/month, while the Community edition is free with no execution cap if you run it yourself — a meaningful cost advantage at high volumes compared with per-credit tools like Make and Lindy. In hands-on comparison testing, n8n was one of only two tools (alongside Lindy) where a human-approval step — parking an agent before it sent anything — was successfully configured within the test window. Its shape is developer-first: you get primitives and control rather than hand-holding, which is exactly why engineering teams like it and why non-technical operators bounce off it. It suits teams running high-volume automation who want to avoid per-execution billing and are comfortable hosting and maintaining their own instance, or who want code-level control inside a visual workflow tool.

Zapier Agents is the agentic layer on top of Zapier's automation platform, and its core advantage is contextual: it sits on the Zaps your team already runs. For a small business with a messy SaaS stack — 20 or more Zaps connecting a CRM, forms, email, and billing — Agents lets you add AI decision-making to existing automations rather than rebuilding them in a new tool. Zapier's integration catalog is the deepest in the category at 9,000+ apps, and its billing is task-only, which is simpler to reason about than credit systems where a single complex action can consume hundreds of units. The entry price is $50/month, the highest starting price among the mainstream options in this comparison, which reflects its positioning as an add-on for teams already invested in the Zapier ecosystem. In comparative testing it was grouped with the tools that give you enough free runs for a proper evaluation. It's best treated as an evolution of an existing Zapier investment, not a first automation platform.

Gumloop is a visual agent-building platform positioned for marketing and ops teams that build flows together. Its distinguishing feature in comparison testing was the shared visual canvas: unlike solo-builder tools, Gumloop is designed for multiple teammates to collaborate on the same automation and agent workflows, which matters for a small marketing or operations team where one person builds and others review or extend. It starts at $37/month billed monthly, placing it in the middle of the pack on price — above Make and MindStudio, below Zapier Agents. In the nine-tool agentic AI comparison it was ranked among the lightest-to-moderate build-effort tools: more structure than Lindy's nothing-to-build approach, but far more accessible than developer frameworks like CrewAI or LangChain. It was also among the six of nine tools that publish a free tier with enough runs to evaluate properly. Choose it when the building itself is a team activity and marketing/ops use cases dominate.