AgentOS vs Relevance AI: build an AI workforce, or share one company memory?

Updated October 7, 2026 · by Ashtin Dowler · 8 min read

Relevance AI is a platform for building your own team of AI agents. AgentOS is the shared memory those agents, and every other AI tool you use, can work from. With Relevance AI you design agents, give them tools and knowledge, and link them into a "workforce" that hands work from one to the next. With AgentOS you connect your company's tools once, describe the workflows you want, and let Claude, ChatGPT and your own agents all draw on the same knowledge. If you want to design a multi-agent system and have someone to run it, Relevance AI is built for that. If you want your existing AI tools to know the business, AgentOS is the simpler fit.

AgentOS vs Relevance AI at a glance

	Relevance AI	AgentOS
What it is	A low-code platform for building AI agents and multi-agent teams, which it calls an AI workforce	A workspace that holds your company's knowledge and tools, runs workflows on them, and shares them with every AI tool you use
Getting work done	Create agents from a description, a template or from scratch. Give each one tools, then link agents into a workforce on a canvas	Describe a workflow in plain language. Otto, the built-in agent, builds it from your connected tools and runs it on a schedule
Who it's for	Sales, support, marketing and HR teams with someone to design and maintain the agents	Operations, sales and finance teams, with nothing to design
Company knowledge	Knowledge tables built from uploads, websites, Google Drive, SharePoint or Notion. You connect a table to each agent that needs it	Already there. Every workflow and every connected AI tool works from the same shared knowledge
Approval before changes	Per task: require approval, let the agent decide, or run automatically. Escalations go to Slack or email	Important changes to your business systems wait for a person to approve them
Models	Major model providers, with the option to bring your own key on every plan	Model-agnostic. Use any model side by side, or bring your own key
Seeing what AI costs	Usage by agent and a cost breakdown per run, for work done inside Relevance AI	One view of AI use and spend across the tools and teams you connect
Pricing	The pricing page lists an Enterprise plan with custom pricing. Usage is metered in Actions and Vendor Credits (as of Oct 7, 2026)	Free to start. See the pricing page for current plans

Relevance AI details come from relevanceai.com and its documentation, checked October 7, 2026. Its public pricing page changed recently and now shows only an Enterprise plan, so confirm current plans with Relevance AI before you buy.

What does Relevance AI do well?

Relevance AI is one of the more complete tools for building agents without writing much code. You can describe an agent and have one generated, start from a marketplace template, or build from scratch. Each agent gets tools, which are small automations it can run, and you can link several agents into a workforce where one hands work to the next.

Its controls are thoughtful. For each task you can require approval, let the agent decide, or let it run automatically, and you can write escalation rules in plain English. It works with MCP in both directions, lets you bring your own model keys on every plan, and is SOC 2 Type II compliant with a choice of data regions.

If your goal is a designed system of agents, for example one that researches a lead, another that writes the outreach and a third that updates the CRM, this is the kind of product made for it.

Where does Relevance AI get harder?

Building a workforce is a design job. Simple agents are quick to set up. Multi-agent systems are where reviewers say the work gets technical: you decide what each agent does, which tools it has and how the handoffs go. Someone needs to own that and keep it working.

Knowledge is connected agent by agent. Relevance AI stores knowledge in tables you build from files, websites or a synced drive, and one table can serve many agents. You still decide which agents get which tables, and the knowledge serves the agents inside Relevance AI.

Cost has two meters. An Action is one run of a tool, and failed runs count. Vendor Credits cover what the models cost. The reporting is good for work inside Relevance AI, and it stops there.

Pricing is harder to plan than it used to be. As of October 7, 2026, the public pricing page shows an Enterprise plan with custom pricing. Other sites still list cheaper self-serve plans, but we couldn't confirm those on Relevance AI's own site.

How is AgentOS different?

AgentOS starts from your company's knowledge, not from an agent design. You connect the places the knowledge already lives, such as Gmail, Slack, Google Drive and your CRM, and AgentOS keeps one shared, current picture of the business.

Every AI tool your team already uses can work from it. Claude, ChatGPT, Cursor and your own agents connect over MCP, so each one starts out knowing the same clients, projects and history. You decide what each tool can see and change, and answers link back to the email, call or document they came from.

When you want something done on a schedule, you describe it. "Every Monday, list the deals that haven't moved in two weeks and draft a nudge for each owner." Otto, the agent built into AgentOS, builds the workflow and runs it, and important changes wait for a person to approve them.

And because everything runs through one place, you see AI use and spend across all the tools you connect, not only one platform's share.

Should you choose Relevance AI or AgentOS?

Choose Relevance AI if you want to design a team of specialised agents, you have someone to build and maintain it, and most of the work will happen inside that system.

Choose AgentOS if your team already uses AI tools and the problem is that none of them know the business, if you want workflows a non-technical person can set up, or if you need one view of what AI is costing.

They aren't mutually exclusive. Agents built in Relevance AI can connect to outside systems over MCP, which is how they could draw on the company knowledge AgentOS holds.

What about other Relevance AI alternatives?

Roundups usually suggest other agent builders, such as Lindy, n8n, Make, Zapier, Gumloop and Dust. We've compared several of them directly: AgentOS vs Lindy, AgentOS vs n8n, AgentOS vs Make and AgentOS vs Zapier.

If you keep hitting the same wall with every builder, where each agent has to be taught the business from scratch, read what a context layer is. For the cost side, see how to manage AI costs.

Questions people ask

Is AgentOS a Relevance AI alternative?

Yes, for teams whose main need is AI that knows the business. Relevance AI is for designing and running your own team of agents. AgentOS gives the AI tools you already use one shared company memory and runs workflows you describe in plain language.

What is an AI workforce?

It's Relevance AI's name for a team of AI agents that work together. Its documentation describes workforces as multi-agent teams where specialised agents collaborate on complex tasks, handing work from one to the next.

How much does Relevance AI cost?

As of October 7, 2026, Relevance AI's pricing page lists an Enterprise plan with custom pricing. Usage is measured in Actions, which are tool runs, and Vendor Credits, which cover model costs. Its documentation lists top-ups at $80 per 1,000 Actions.

Is Relevance AI free?

We couldn't confirm a free plan on Relevance AI's own pricing page on October 7, 2026. Third-party sites still describe a free tier, so check with Relevance AI directly.

Is Relevance AI hard to learn?

Simple agents are quick to build, especially from a description or a template. Reviewers say multi-agent workforces take more technical effort to design and maintain.

Can I use my own API keys with Relevance AI?

Yes. Relevance AI's documentation says bringing your own key is optional on every plan, and it lets you pay the model provider directly.

AgentOS is free to try. Connect two or three of the apps your team uses every day, and see what your AI tools can do once they know the business.

Try AgentOS free

Sources

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