Relevance AI vs Lindy AI (2026): Which AI Agent Platform Wins?

Feature comparison at a glance

How both platforms connect to your stack

Both platforms sit in the middle layer of your automation stack. They receive a trigger, reason through it, and fire an output action. Make.com typically handles the routing on either side — catching the incoming event and receiving the agent’s output payload.

The choice between Relevance AI and Lindy comes down to what happens inside the middle box — how many reasoning steps are needed, how much data is being processed, and how fast the output needs to appear.

Relevance AI — the data pipeline

Relevance AI is built around the idea that one agent is rarely enough. For anything involving real data volume — pulling company records, enriching lead profiles, scoring intent signals — you need agents handing work off to other agents. Relevance calls this a sub-agent architecture, and the Relevance AI documentation covers how to chain them in detail.

A typical pipeline might look like this: a Research Agent pulls 500 company profiles from Apollo. It passes the raw JSON to an Analysis Agent, which extracts the fields that matter and scores each record by buying intent. That agent hands the scored list to a Formatting Agent, which shapes the data into your CRM’s required field structure and fires the output via webhook. Each agent handles one step cleanly — no single agent is asked to do everything.

The cost of this depth is setup time. Relevance requires careful prompt engineering at each agent layer, and the batch processing model means you are not getting real-time responses. For high-volume data work, that is a reasonable trade.

Lindy AI — the action engine

Lindy is built for speed, not volume. It runs as a single agent connected directly to your tools — calendar, email, Slack, CRM — and reacts the moment something happens. There is no batch queue. When a trigger fires, Lindy processes and acts within seconds.

The practical difference becomes obvious in a scenario like pre-call prep. A calendar booking triggers Lindy. It reads the prospect’s company URL, pulls the last few news headlines, drafts a two-paragraph briefing, and posts it to a Slack channel five minutes before the call starts. The whole chain runs on its own, every time, with no manual steps. You can see the full list of what Lindy connects to in the Lindy integration documentation.

Where Lindy falls short is volume and complexity. It does not have the multi-agent handoff architecture that Relevance offers, so tasks requiring more than a few reasoning steps start to strain a single Lindy agent. If you need to process a thousand records with branching logic at each step, Lindy is not the right choice.

Routing agent output via Make.com webhook

Regardless of which platform processes the data, the output needs to go somewhere — a CRM, a spreadsheet, a Slack message. Both Relevance AI and Lindy support outgoing HTTP requests, so you configure a custom tool or action inside the agent that fires a POST request to a Make.com webhook URL when the task finishes.

Below is a reference JSON structure for an agent pushing an enriched prospect record to a Make.com webhook catcher:

{
  "agent_id": "agt_789xyz",
  "execution_status": "success",
  "target_record": {
    "email": "operator@example.com",
    "crm_id": "ghl_4455"
  },
  "ai_output": {
    "pain_point_analysis": "Struggling with outbound API rate limits.",
    "suggested_action": "Route to infrastructure consulting pipeline.",
    "confidence_score": 0.94
  },
  "timestamp": "2026-05-20T14:30:00Z"
}

Make.com receives this payload, maps the fields, and routes the record into GoHighLevel — creating or updating the contact, applying the correct tag, and triggering the matching campaign based on the suggested_action field.

The most common failure mode

This applies equally to both platforms. Relevance AI’s sub-agent model makes it easier to enforce step boundaries — each agent is scoped to one task by design. With Lindy, you need to write the boundaries into the agent’s instructions manually. Neither platform enforces limits automatically.

Verdict — which platform to use when

For most agencies running both prospecting workflows and day-to-day operations, using Relevance AI for data pipelines and Lindy for real-time task execution is a natural split. They do not compete — they handle different layers of the same operation.

Frequently asked questions