Migrate from Relevance AI to Lindy Without Rebuilding GTM Agents

The trigger is almost always the same: a Relevance AI bill renewal hits, an agent breaks silently after an API update, or a new RevOps hire looks at the agent stack and says “none of this is documented.” I’ve been in that room three times in the last year. The instinct is to rebuild everything from scratch in something cleaner. The smarter move is a structured migration that preserves your agent logic and rewires the connectors. This guide walks through that migration to Lindy, specifically for GTM agents: inbound qualifiers, outbound researchers, meeting prep bots, and CRM update agents.

Before you migrate

Do this audit before you touch Lindy. Skipping it is how you end up rebuilding instead of migrating.

Pull up every agent in Relevance AI and document the following for each one:

• Trigger type: Is it webhook-based (form fill, CRM field change), scheduled (daily enrichment run), or manual (SDR clicks a button)?
• Tool chain: List every tool step in order. Note which ones call external APIs vs. Relevance AI’s native LLM steps.
• Prompt logic: Copy the full system prompt and any variable injection patterns. These do not export cleanly and are easy to lose.
• Knowledge stores: If the agent queries a vector store, note what documents live there and what retrieval strategy it uses.
• Output destinations: Where does the agent write its results? HubSpot contact property, Slack message, Google Sheet, email draft?
• Failure handling: Does it have retry logic or error branches? Most Relevance AI agents I’ve audited have none, which is a migration opportunity, not a loss.

Also pause any agents that run on a schedule before you start. Running parallel versions during migration creates duplicate CRM writes and duplicate outbound touches. I’ve watched this burn a prospect list on a client’s most important segment. It is not a theoretical risk.

Migration steps

What breaks, and what does not transfer

The integrations are the easy part. The hard part is the invisible logic.

What transfers cleanly: Single-agent flows, LLM instruction blocks, HTTP request steps (rewired as webhook actions), CRM read/write steps to HubSpot or Salesforce, scheduled triggers, and email-based triggers all map well to Lindy primitives.

What does not transfer: Relevance AI’s Teams feature (multi-agent orchestration where agents hand off tasks to other agents) has no direct Lindy equivalent. If you use Teams, you will need to either flatten the logic into a single Lindy agent or use n8n as the orchestration layer and call individual Lindy agents as webhook endpoints. Relevance AI’s vector knowledge stores also do not transfer. The closest Lindy equivalent is its per-contact memory, which stores facts about individual contacts across interactions but is not a queryable document store. For RAG-style agents that retrieve from a large document corpus, my recommendation is Gumloop for that retrieval step, or keep it in n8n and call it from Lindy. Don’t try to squeeze a 50,000-token knowledge base into a Lindy system prompt.

RELEVANCE AI (broken after paste):
"Research [contact.company] and 
summarize their recent funding."

LINDY (correct variable syntax):
"Research [Company Name from trigger] 
and summarize their recent funding.
Use the contact's LinkedIn URL 
[LinkedIn URL from trigger] as a 
starting point."

Check: After building the step, use 
Lindy's "Test with sample data" button 
and verify the resolved prompt shows 
real values, not placeholder text.

Verify it worked

Before you call a migration done, run these four checks:

1. Trigger fires on a real event - Create a test contact or send a test email and confirm the Lindy agent activates. Check the run log in Lindy to confirm the trigger data populated correctly.
2. LLM output matches intent - Compare the AI instruction block output against five real Relevance AI outputs from your export doc. Look for missing context, hallucinated company names, or dropped instructions.
3. CRM writes land correctly - Open HubSpot or Salesforce and confirm the properties the agent updates are receiving the right values, not blank fields or stringified JSON.
4. No duplicate actions - If you ran parallel for 48 hours, confirm the Relevance AI agent is fully paused and no contact is receiving two sets of outputs.

Which tool fits your migration destination?

Choose Lindy if

• Your agents are single-agent flows: one trigger, a few LLM steps, one or two CRM outputs
• You want native Gmail, HubSpot, Slack, and Calendly integrations without webhook plumbing
• Your team is RevOps-led and needs agents non-engineers can maintain

From $49/mo Try Lindy →

Choose n8n if

• Your agents use multi-step branching logic or multi-agent hand-offs that Lindy cannot flatten
• You need to self-host for data residency or compliance reasons
• You have an engineer on the RevOps team who can maintain workflow JSON

From $20/mo (cloud) or self-host Try n8n →

Choose Gumloop if

• Your Relevance AI agents do heavy document retrieval or RAG over large knowledge bases
• You want a visual canvas closer to Relevance AI's builder feel
• You are migrating data pipeline agents, not just GTM interaction agents

From $97/mo Try Gumloop →

If your agents are doing something genuinely complex, like multi-hop research chains or dynamic knowledge retrieval, think carefully before leaving Relevance AI. The migration pays off for standard GTM patterns: qualifier bots, meeting prep agents, CRM enrichment loops. For teams building closer to autonomous research workflows, the flexibility gap is real and worth pricing in. Similarly, if the GTM stack already involves n8n for orchestration, you may find it easier to keep n8n as the backbone and bolt Lindy on as the AI layer for specific interaction steps rather than migrating wholesale. I’ve written about using n8n as a routing layer in the context of automating demo scheduling with Cal.com, HubSpot, and n8n, and the same pattern applies here.

The GTM agent category is moving fast. G2’s AI agent category has grown from under 20 listed tools in early 2024 to over 80 by mid-2026. Picking a platform with a stable trigger model and native CRM integrations is more durable than picking the most powerful builder. That is the case for Lindy right now for most RevOps teams.