I tested Lindy, n8n, and Relevance AI by building real automations across all three platforms. After weeks of hands-on work, Lindy is the clear winner if you want AI-first automation that handles agents, workflows, and integrations in one place. Here's what I learned so you can pick the right tool without wasting time.
Lindy vs n8N vs Relevance AI: At a Glance
Choose Lindy if…
You want one place to build and run your AI agents and workflows. It reduces setup time, avoids extra tools, and stays reliable as you scale. Best choice if you prefer something simple that just works.
Choose n8n if…
You want a visual builder with lots of integrations and don’t mind adding AI manually. It fits teams with technical skills who want full control over how every step runs.
Choose Relevance AI if…
Your work depends on LLM tasks like research, analysis, or retrieval. It’s good for quick agent experiments and knowledge-heavy jobs. You’ll need another tool for longer workflows, and you should track usage.
What’s the Difference Between Lindy, n8n, and Relevance?
After testing all three for real projects, the biggest learning was that each tool solves a slightly different problem. Once you understand that, choosing between them becomes much easier.
Lindy: The AI-First Automation Platform
Lindy is designed for teams that want AI agents and automation in one place. When I built workflows here, I didn’t have to jump between multiple tools or write code. Agents, memory, tools, and workflows all sit under one system, and it handles the reasoning and the execution together.
The interface is simple enough that you can set up working automations quickly, but it still gives you the structure you need to run them reliably. If you’ve tried other Lindy AI alternatives that feel more like prototypes, Lindy feels steadier and more predictable.
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n8n: Open, Visual Workflow Automation
n8n is the strongest when you need classic workflow automation. The visual builder is simple, and the integration catalogue includes 1200+ native apps. When I had to connect APIs or move data between SaaS tools, n8n handled those connections smoothly and gave me full control over each step.
The limitation is that AI isn’t built into the platform. If you want AI agents, memory, or vector search, you have to wire them in manually using features like n8n AI integration or n8n Qdrant integration. This works, but it takes more time, and you end up managing more moving parts.
Relevance AI: Low-Code LLM Agent Builder
Relevance AI is best when your main goal is to build LLM agents or RAG-style assistants. The vector tools are built in, which saves setup time, and the editor makes it easy to test ideas quickly.
But when I used it for longer workflows that needed branching, multi-step coordination, and updates across several apps, it reached its limits. It handled the thinking parts well, but the execution layer needed a separate tool to keep everything in order.
You often need a second system to handle that side of the work. It’s worth knowing that relevance AI pricing depends heavily on usage, so costs can shift based on how much the LLM is doing for you.
In simple terms:
- Lindy is the only one built for AI-driven automation end-to-end.
- n8n is for traditional workflows with lots of integrations.
- Relevance AI is for fast LLM agent work.
Lindy vs n8n vs Relevance AI: Feature Breakdown
Lindy gives you end-to-end AI agents that can read, decide, and act across your tools; n8n offers a visual workflow builder with fine-grained control over every step; and Relevance AI specializes in agents that analyze, search, and summarize information with minimal setup.
AI Agents and Automation Depth
Lindy
In many teams, one task spans several steps: reading an email, checking the CRM, opening a document, updating a system, and sending a reply. In Lindy, a single agent handles that entire flow.
You describe the job, connect your tools, and the agent reads, decides, and acts inside one system.
The same pattern applies to lead qualification, document processing, or phone calls. The agent has access to memory, tools, knowledge, and workflows in one place, so you are not handling a separate workflow builder or prompt router.
Once you shape the instructions and guardrails, the agent behaves like a repeatable process that you can trust. This is helpful for teams that want to turn a recurring task into a dependable “AI teammate” without building a custom stack around it.
n8n
With n8n, you design the full life cycle of an AI-driven process yourself. You can build a flow where a form submission triggers an enrichment step, an LLM drafts a personalized reply, another step logs data in your CRM, and a final branch alerts a human if certain conditions are met.
Every part of that chain is visible as a node. You decide how context is stored, which model to use, when to branch, and when to fall back to deterministic logic.
If you want the agent to access a vector store, you can wire that as well. This level of control is powerful, especially if your team is comfortable thinking in workflow graphs.
But nothing is handed to you ready-made. Even simple agent behaviour takes a series of nodes, tests, and refinements. It suits teams that prefer control over convenience.
Relevance AI
Relevance AI works well when the main job is thinking through information. For example, you can create a research agent that reads articles, extracts key points, compares sources, and hands you a concise brief. Or a sales prep agent that scans a prospect’s site, recent news, and call notes before a meeting.
You give each agent its own context, tools, and triggers, so it behaves like a focused specialist. It can search, analyze, and summarize with very little setup.
It feels less natural when the work continues into long, multi-step execution, such as updating many systems, coordinating handoffs, or managing complex approvals.
In those cases, you often need a second tool to orchestrate the wider process around the agent’s output.
In my tests, Lindy consistently handles the entire workflow inside one system. It understands the task, executes each step, and finishes the job without relying on extra tools. n8n offers full control, and Relevance AI is strong for analysis, but Lindy provides the most complete end-to-end setup.
Integrations and Ecosystem
Lindy
Lindy is designed so agents can work directly inside the tools you already use. A sales agent can pull leads from your CRM, send emails, log outcomes, and update fields without leaving the platform. A support agent can read tickets, consult your knowledge base, and respond through your helpdesk.
In practice, you think in terms of “jobs” rather than individual app steps. You attach the required apps to the agent, define what it is allowed to do, and the workflow engine handles the calls behind the scenes.
You do not need a separate orchestration layer just to keep apps together.
Lindy already integrates with the core apps most teams rely on, including common CRM, support, and communication tools, and webhooks or custom connectors cover anything niche.
n8n
With n8n, integrations work as building blocks. You connect each app or service as a node and decide how information should move between them. I set up a flow that collected form submissions and checked the data against an internal database.
Another step sent part of that data to an LLM to create a summary, and the workflow then posted the final result into Slack for the team.
Everything's visible in n8n. You can actually see what each step is doing, which is helpful when troubleshooting.
You can add retries, routes for errors, or conditions without guessing how the system behaves. The library covers a wide range of tools, and anything missing can be added through an HTTP call or custom code.
This approach works well for teams that prefer full control and want the workflow to follow precise logic. The only problem is the time needed to configure each part, especially when the process grows larger.
Relevance AI
Relevance AI treats integrations as a way to give agents more context and actions. You can connect Gmail, HubSpot, Slack, or a database, then let an agent read from or write to those systems when certain triggers fire.
When I tested it, a new ticket in my helpdesk triggered an agent that pulled the customer’s details from the CRM and checked the account history. It then drafted a suggested reply and logged the outcome back into the support system without extra setup.
Because there is Zapier and webhook support, you can extend this further into thousands of tools. The integrations work well for event-based scenarios where an agent reacts to something new and performs a focused task.
But it is less suited for long-running automation across many systems. For that, teams often pair Relevance AI with a dedicated workflow tool.
My Verdict:
Lindy wins because agents and integrations run in the same environment. It updates records, reads data, and completes actions without an external orchestrator. n8n has the widest connector library, and Relevance AI works well for event-based tasks, but Lindy offers the most practical balance of coverage and simplicity.
Ease of Use and Learning Curve
Lindy
When I worked with Lindy, the learning curve felt short because most of the setup happens in plain language. You describe the agent, connect the apps, and Lindy handles the structure behind it.
Lindy Academy provides documentation, tutorials, and workflow templates for building and deploying agents. The resource library covers setup fundamentals, integration guides, and use case examples across sales, support, and marketing.
The workflow builder is simple, and the agent instructions behave predictably once you add the right context. I did not have to keep adjusting prompts or building extra logic around it.
This helps teams that want to get working automations running quickly. You do not need deep technical knowledge to create something useful. If someone wants to build more advanced automations later, Lindy gives you enough structure to grow without becoming complicated.
n8n
n8n takes more time to learn, especially if you want to use it for AI work. The visual builder is familiar, but you often need to understand how data flows through each node and how to configure conditions, loops, and branching. While building more complex flows, I had to think through every step carefully.
This is not a problem if you prefer full control or if your team has technical experience. It actually becomes an advantage because you know exactly how the workflow behaves.
But for teams that want something simple or fast to set up, n8n requires more effort than the other two platforms.
Relevance AI
Relevance AI is easy to start with if you are building focused agents. Giving an agent its skills, knowledge, and triggers feels simple, and the interface is clear enough that you can understand what the agent will do next.
In my tests, most of the effort went into shaping the agent’s behaviour rather than learning the platform.
The learning curve becomes noticeable when you try to build larger workflows. It is possible, but you need to think more carefully about how each agent hands over work to the next step.
Relevance AI stays accessible, but it asks for more planning when the workflow gets longer.
My Verdict:
Lindy is the easiest platform to get productive with. Most setup happens in plain language, and the system handles the structure behind the scenes. n8n works best for technical teams, and Relevance AI is simple for focused agents, but Lindy gives the fastest route from idea to working automation.
Pricing and Scalability
Lindy
With Lindy, costs follow a clear pattern. There is a free plan, and you pay based on agents and work, starting from $49.99/month. When you roll out a support agent across more inboxes or expand a sales agent to new regions, you can predict the effect on your bill without guessing at token counts.
From a scaling point of view, this means you can start with one or two agents and grow to a larger “AI workforce” without redesigning your setup.
It handles small experiments as well as high-volume use. For teams that need to explain budgets to finance or leadership, the predictability is useful, especially compared to open-ended usage models.
n8n
n8n’s pricing structure is built around workflow executions. On the cloud side, you can start with the Starter plan at €24/month for 2,500 workflow executions. The Business tier is €800/month for 40,000 executions in a self-hosted environment.
When you self-host via the community edition, the software license is free, but you carry all the infrastructure, maintenance, scaling, and reliability overhead.
For teams with in-house DevOps, this option can reduce software spend, but it shifts cost into operations, monitoring, and support. Cloud hosting simplifies setup and relieves you of infrastructure tasks, but you are limited by the number of executions included in your plan.
If your workflows expand, you may move into higher tiers. On the self-hosted path, managing performance, backups, high availability, and security becomes your responsibility.
Relevance AI
Relevance AI charges based on how many actions your agents perform, so the monthly cost changes with usage. The free plan includes 200 actions, which is enough to test a few agents or run small tasks. The Pro plan is $29/month for 2,500 actions.
If you run a small set of agents that handle light workloads, the spend stays manageable. Heavy vector search, frequent tool calls, and long LLM conversations can shift costs significantly month to month.
The model works if you track agent usage closely. But Relevance requires active cost management, especially for research and analysis-heavy workloads.
My Verdict
Lindy offers the most predictable pricing. Costs stay steady even as you increase tasks or agents, which makes budgeting easier for growing teams. n8n adds operational overhead when self-hosted; Relevance AI’s usage model can rise quickly. For most teams, Lindy provides the clearest long-term cost path.
What Users Like (and Don’t Like) About Lindy, n8n, and Relevance AI
Lindy vs n8n vs Relevance: Which Tool Should You Choose?
Choosing between these three tools is easier once you match them to the type of work you want to automate. Each platform fits a different way of working, and the right choice depends on how much control you want, how fast you need to move, and how much of the workflow you want the agent to handle on its own.
Choose Lindy if you want automation that is ready to use with minimal setup
Lindy is the best fit when you want an agent to understand a task and complete it from start to finish. It reduces the number of tools you need, since agents can read documents, take actions in your apps, run workflows, and manage follow-ups inside one system.
If your priority is to automate real work without spending time stitching together separate components, Lindy is the simplest choice. It is also a good fit for teams that want predictable performance and pricing.
Choose n8n if you want full control and do not mind configuring everything yourself
n8n fits teams that like to design workflows step by step. It works well when you need detailed logic, many integrations, or the ability to mix AI with complex conditions and routing. The limitation is setup time, since you wire most of the AI and automation yourself.
Choose Relevance AI if your work depends on interpretation or analysis
Relevance AI works well for tasks that involve research, summarization, information extraction, or knowledge retrieval. It gives each agent its own tools and context, which helps you build focused assistants quickly. It is less suited for long, complex workflows, but very good for teams that rely on content, insights, or data-heavy tasks.
Summary
- If your priority is to automate end-to-end work with the least amount of effort, choose Lindy.
- If you want complete control over large workflows, choose n8n.
- If you want fast agent creation for interpretation or research tasks, choose Relevance AI.
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Try Lindy: An AI assistant that handles support, outreach, and automation
Lindy uses conversational AI that handles chat, but also lead gen, meeting notes, and customer support. It handles requests instantly and adapts to user intent with accurate replies.
Here's how Lindy goes the extra mile:
- Fast replies in your support inbox: Lindy answers customer queries in seconds, reducing wait times and missed messages.
- 24/7 agent availability for async teams: You can set Lindy agents to run 24/7 for round-the-clock support, perfect for async workflows or round-the-clock coverage.
- Support in 30+ languages: Lindy’s phone agents support over 30 languages, letting your team handle calls in new regions.
- Add Lindy to your site: Add Lindy to your site with a simple code snippet, instantly helping visitors get answers without leaving your site.
- Integrates with your tools: Lindy integrates with tools like Stripe and Intercom, helping you connect your workflows without extra setup.
- Handles high-volume requests without slowdown: Lindy handles any volume of requests and even teams up with other instances to tackle the most demanding scenarios.
- Lindy does more than chat: There’s a huge variety of Lindy automations, from content creation to coding. Check out the full Lindy templates list.
Try Lindy free and automate your first 40 tasks today.
FAQs
1. Is Relevance AI better than n8n for automation?
Relevance AI is better than n8n if your automation depends on LLM reasoning, research tasks, or knowledge-driven work. n8n is better for large, structured workflows with many app connections. If you want a flexible visual builder, choose n8n. If you want agent behaviour and vector search, choose Relevance AI.
2. What is the main difference between Relevance AI and n8n?
The main difference between Relevance AI and n8n is that Relevance focuses on building LLM agents, while n8n focuses on detailed workflow automation. Relevance AI handles interpretation and analysis well. n8n handles routing, logic, and multi-step sequences. If you need agent behaviour, choose Relevance. If you need complex workflows, choose n8n.
3. How does Lindy compare to n8n for automation workflows?
Lindy compares well to n8n for automation workflows because Lindy combines reasoning and execution inside one system. n8n gives more control, but asks you to configure each step. Lindy is better for teams that want fast, reliable agents. n8n fits teams that prefer full customization and are comfortable setting up each layer manually.
4. Does Relevance AI integrate with n8n?
Yes. Relevance AI can integrate with n8n through webhooks, API calls, and triggers. This lets you use relevance AI n8n integration to combine agent behaviour from Relevance with workflow logic in n8n. It is helpful when you want agents to process information, but you rely on n8n to manage longer, multi-step workflows.
5. Is Lindy an alternative to n8n?
Yes. Lindy is a strong alternative to n8n because it handles both agent reasoning and workflow execution in one platform. n8n gives more control for technical teams, but requires more setup. If you want simpler automation with fewer moving parts, Lindy AI alternatives like n8n require more configuration than most teams want.
6. Which platform is best for AI-powered automation in 2025?
Lindy is the best platform for AI-powered automation in 2025 because it handles reasoning, actions, workflows, memory, and integrations in one place. Relevance AI is best for agent behaviour and research tasks. n8n is strongest for complex logic. For reliable, end-to-end automation, Lindy offers the most complete setup.
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