Relevance AI vs Lindy AI (2026): Workflow Comparison

Feature comparison at a glance

How both platforms connect to your stack

These platforms can occupy different positions in an automation architecture. Make.com can provide the routing and integration layer, while Relevance AI or Lindy can provide the agentic processing layer. The exact flow depends on which system owns the trigger, reasoning, transformation, and final action.

From there, Make can route the result onward to a CRM such as GoHighLevel, a Slack channel, a spreadsheet, or another connected system. The choice between Relevance AI and Lindy comes down to what happens inside the “AI Workflow” box — how many reasoning steps are involved, how many specialized agents the task benefits from, and which tools the workflow needs to reach.

Relevance AI — agents, tools & Workforces

Relevance AI provides a low-code environment for building AI agents, tools, and multi-agent Workforces. Its Workforce model lets you connect specialized agents so different parts of a process can be handled by different agents rather than placing every responsibility inside one agent.

That makes Relevance AI particularly interesting for structured workflows where the work naturally breaks into stages — for example, research, enrichment, analysis, classification, review, and output formatting. A workflow might use one agent to gather information, another to analyze it, and another to prepare the result for a downstream system.

A practical example: a Research Agent pulls 500 company profiles from a data source. It passes the raw data to an Analysis Agent, which extracts the fields that matter and scores each record. That agent hands the scored list to a Formatting Agent, which shapes the data into your CRM’s required field structure and sends the output onward. Each agent handles one step — no single agent is asked to do everything.

The important distinction is not simply “batch versus real time.” Relevance AI can also be triggered programmatically through its API, so the better question is whether its agent, tool, and Workforce model fits the process you’re designing.

Lindy AI — teammates & integrations

Lindy is designed around AI teammates that connect to the tools where operational work already happens. Its current integration ecosystem spans areas such as email, calendars, CRM, communication, productivity, and data tools, allowing workflows to react to events and carry out actions across connected systems.

A practical example is pre-call preparation. A calendar event can become the starting point for a workflow that gathers information, prepares a briefing, and delivers the result through another connected tool. The exact trigger, integrations, and workflow steps depend on the current Lindy configuration and available integrations.

The useful distinction is therefore not that Lindy cannot handle complex workflows. Instead, consider Lindy when the workflow is centered around connected business applications and operational actions, while considering Relevance AI when the workflow benefits from a more explicit multi-agent Workforce structure.

Routing agent output via Make.com webhook

Both platforms can participate in HTTP-based automation, although the exact implementation differs. Relevance AI documents API triggers for programmatically starting agents, while Lindy supports webhook-based triggers and connected actions. Make.com can provide the routing layer by receiving webhook data, transforming it, and sending it onward to other systems.

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": "2026-09-19T14:30:00Z"
}

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

These controls are useful regardless of which platform you choose. Relevance AI provides explicit agent, tool, and Workforce configuration, while Lindy provides agents, integrations, triggers, actions, and workflow controls. The right level of decomposition depends on the task rather than a fixed number of API calls.

Which architecture fits your workflow?

These products can also be combined. For example, one platform can handle a specialized AI workflow while Make.com routes the resulting data into another system or automation. Whether that combination is useful depends on the workflow, integrations, governance requirements, and operating model.

Frequently asked questions