AgentOS vs Lindy: what's the difference, and do you need both?

Updated October 5, 2026 · by Ashtin Dowler · 7 min read

Lindy is an AI agent you hand tasks to. AgentOS is the layer underneath all of your AI tools. Lindy reads your inbox, joins your meetings and updates your CRM, and it remembers how you like things done. AgentOS keeps your company's knowledge in one place, shares it with every AI tool your team uses, and shows you what those tools are doing and what they cost. If Lindy is the only AI your team uses, you may not need anything else. If it's one of five, that's the problem AgentOS is built for.

AgentOS vs Lindy at a glance

Lindy details come from Lindy's own pricing, security and product pages, checked October 5, 2026. Lindy changed its plans more than once this year, so check the sources below before you buy.

What is Lindy good at?

Lindy is one of the easiest ways to get an AI agent doing real work. You describe what you want in plain language, connect your apps, and it starts handling things like inbox triage, meeting notes, follow-up emails and CRM updates. It lives in Slack, so people can ask it for things where they already work.

A few things stand out. The setup is genuinely no-code, which is why reviewers keep mentioning it. Its memory is a set of files you can open and change, so you can see what it has learned about you. Saved skills can be shared with your whole team. And when credits run out, Lindy pauses instead of sending a surprise bill.

It also takes security seriously. Lindy lists SOC 2 Type II, HIPAA and GDPR on its security page, and its Enterprise plan adds single sign-on, SCIM and audit logs.

Where does Lindy stop?

Lindy is built to be the agent. Everything it knows about your business lives inside Lindy. That works well until your team uses more than one AI tool, which most teams now do.

Say sales runs Lindy for follow-ups, the founder writes in Claude, and finance lives in ChatGPT. Each of those tools builds its own picture of the company, and none of them can see the others. When a client changes their renewal date, someone has to tell three different AIs. Nobody has one place to see what all of this is costing, either. Lindy can show you Lindy's credits. It can't show you the ChatGPT and Claude bills sitting next to it.

Credits are the other thing people ask about. Lindy prices work in credits, and a complex task can use hundreds or thousands of them. That's fair for what it does, but it makes AI spend harder to forecast once several tools are each billing their own way.

How is AgentOS different?

AgentOS can do the work too. You connect your tools once and describe a workflow in plain language, and Otto, the agent built into AgentOS, builds it, runs it on schedule and checks in when a decision needs you. The bigger difference is where the knowledge lives. AgentOS keeps it in one shared place, your documents, conversations, customer records and the way your team does things, with rules about who can see what, and every AI tool you use can read from it.

Claude, ChatGPT, Cursor and your own agents connect to AgentOS and read from the same place. You decide what each one can see and change, and anything important it wants to change waits for a person to approve it. Answers link back to the email, call or document they came from, so you can check them.

Because every tool runs through one place, you also get one view of AI use across the company: which models each team uses, what that costs, and where an expensive model is doing work a cheaper one could handle. AgentOS works with any model, so you can mix Claude, ChatGPT, Gemini and lower-cost models, or bring your own API key.

Should you choose AgentOS or Lindy?

Choose Lindy if you want one capable assistant to take repeat work off a few people's plates, and Lindy will be the main AI tool your team uses.

Choose AgentOS if your team already uses several AI tools, if you want company knowledge in one place instead of inside each vendor's app, or if someone is starting to ask what all this AI is costing.

You can also run both. Lindy lists MCP support, which is how AI tools connect to AgentOS, so a Lindy agent could work from the same company context as everything else. Check with us for your setup before you plan around it.

What do Lindy alternatives usually get wrong?

Most "Lindy alternatives" lists swap one agent for another: Zapier, Relevance AI, n8n, Gumloop. If you're unhappy with Lindy itself, those are worth a look. But if the real issue is that your company now has too many AI tools that don't share anything, another agent adds to the pile. That's the gap AgentOS fills. It's the reason we built it.

New to AgentOS? Start with what AgentOS is, or read what a context layer is and how to manage AI costs.

Questions people ask

Sources

• Lindy homepage
• Lindy pricing
• Lindy docs: pricing and credits
• Lindy security
• Lindy changelog
• AgentOS: what is a context layer?

AgentOS is built by Devcore. Found something out of date on this page? Tell us at tryagentos.net and we'll fix it.