The best Relevance AI alternatives are hosted agent platforms your team can set up without engineers, open-source builders you can host yourself, and code-first frameworks for developers building multi-agent systems. The deciding question isn't features: it's who will maintain the agents once they're live and something breaks on a busy afternoon.
Decide who looks after it first
Agent platforms built for enterprise teams sell an AI workforce: specialist agents for sales, customer success, marketing and HR, plugged into a long list of apps and running daily tasks at volume. The copy is written for people reporting return on investment to a board. If you're a founder or a small marketing team who wants a few agents doing useful work, that can feel like more platform than you need.
Before you compare features, answer one question honestly. When an agent breaks on a Tuesday afternoon, who fixes it? If the answer is a marketer or an ops lead, you want a hosted, no-code tool. If it's an engineer, open-source or code-first options give you far more control. Get this wrong and you'll either drown a non-technical team in set-up or frustrate developers with a tool they can't extend.
- No-code team, results this week: a hosted agent platform.
- Support or customer experience team: a hosted platform built around chat and voice agents.
- Ops lead who likes workflows: an automation tool with AI steps, ideally one you can self-host.
- Developers building multi-agent systems: a code-first framework with a visual editor for the rest of the team.
The seven builders below are listed in no particular order, alphabetically. Each description comes from the vendor's own website, so the claims are theirs.
Seven agent builders side by side
| Tool | Type | Good for |
|---|---|---|
| Activepieces | Open source builder | Chat, agents, flows and tables in one workspace |
| CrewAI | Framework and platform | Role-based multi-agent systems in code or visually |
| Flowise | Open source builder | Designing agents and LLM orchestration visually |
| Gumloop | Hosted agent | Company-wide agent building with IT controls |
| Lindy | Hosted agent | An AI assistant working across your everyday tools |
| n8n | Workflow automation | AI steps inside wider automation, self-hosted if needed |
| Voiceflow | Hosted agent | Chat and voice agents for customer support |
The seven, one line each
Activepieces
Best for: Ops people who want an open-source workspace for chat, agents, flows and tables, with 770+ app connections claimed and a free plan named.
CrewAI
Best for: Developers building teams of role-based agents, who like that the no-code visual editor exports to Python.
Flowise
Best for: Technical builders who want to design agents visually, keep the option of running it themselves and start on a named free tier.
Gumloop
Best for: Companies where lots of people build agents with their choice of AI model, while IT keeps control of access and usage.
Lindy
Best for: Business users who want an assistant working across their tools, including Gmail through a Chrome extension, with time-limited free credits.
n8n
Best for: Automation fans who want AI inside wider workflows, with self-hosting on-premises or with Docker and access to the source code.
Voiceflow
Best for: Customer support leads building chat and voice agents with deep customisation.
Hosted or self-hosted: the trade-off
Hosted tools get you running fastest. Somebody else handles updates, uptime and security, and you exchange some control for that convenience. The downside is that your agents, prompts and data live on their platform, so moving later takes work.
Self-hosted and open-source tools flip that. You can run them on your own servers, see the code and keep data in-house, which matters if you handle anything sensitive. The trade is time: someone has to install, update and watch them. Code-first frameworks sit in the middle, often with an open-source core and a managed platform for teams that want one.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
If you're leaning towards the automation route, my guide to building AI agents in a workflow tool walks through how that works in practice.
Mistakes to avoid with AI agents
- Starting with ten agents. Build one that saves a real hour a week, prove it, then add the next.
- No human check on outgoing messages. Let agents draft emails and replies at first, and only let them send once you trust the output.
- Ignoring logs. If you can't see what an agent did and why, you can't fix it when it goes wrong.
- Connecting everything at once. Give each agent access only to the apps it needs.
Questions to ask on any demo call
- Which apps does it connect to natively? Check your top five tools by name, not the headline integration count.
- Can I choose the AI model? Being tied to one model limits you as quality and terms change.
- How do I see what an agent did? Logs and usage tracking matter the first time an agent sends something odd.
- Can I export my agents? Ask how you'd leave before you arrive.
- Who controls access? If several teams will build agents, admin controls save headaches later.
For agents that work inside a web browser rather than your apps, see my list of AI browser agents, and for the wider toolkit, my roundup of AI tools for marketers.
My advice before you build your first agent
Choose the tool that matches the person who'll maintain it, not the longest feature list. A no-code team should stay hosted and start small. A technical team should favour open source or code-first options so it owns the setup. Whichever route you take, build one useful agent first and live with it for a fortnight before you add more.
Do you make AI agent or automation software? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
What is an AI agent platform?
It's software for building AI assistants that carry out tasks, such as answering emails, updating records or researching leads, by connecting a language model to your apps.
Do I need to code to build AI agents?
Not with hosted no-code tools. Code helps with open-source and framework options, especially when you want custom behaviour or self-hosting.
Is self-hosting an AI agent tool worth it?
It is if you handle sensitive data or want full control and have someone to maintain it. If not, a hosted tool usually saves time overall.
Can I use my own AI model with these tools?
Some builders let you choose any model, while others are tied to a few. Check the supported model list before you commit.
How do I avoid lock-in with an agent platform?
Keep your prompts and process documented outside the tool, prefer platforms with exports or open-source cores, and test moving one agent early.
