Relevance AI vs Lindy vs Gumloop: AI Agent Builder Comparison

By Navneet Arya · Updated July 3, 2026🕒 11 min read

Lindy quietly dropped its free tier at some point this year. Its own pricing page now offers only a 7-day trial, though plenty of "best AI agent" roundups still list a free plan that no longer exists. If you want to test-drive an AI agent builder without paying, start with Relevance AI or Gumloop instead.
— Navneet Arya, AI Nexus

Relevance AI vs Lindy vs Gumloop: Why These Three Get Compared

Relevance AI, Lindy, and Gumloop all get called "AI agent builders," and all three show up on the same shortlists. But they were built to solve different problems, and treating them as interchangeable is the fastest way to pick the wrong one. Relevance AI leans toward data-heavy workflows, Lindy toward non-technical setup, and Gumloop toward developer-controlled logic.

Navneet Arya has tracked all three since covering the broader agent-versus-automation split in an earlier AI Agents vs AI Automation post. The gap between these platforms has only widened as each one has specialized further in 2026.

Lindy is assistant-first. Describe a job in plain English — manage my inbox, prep me for meetings, qualify inbound leads — and Lindy hands you a working "AI employee" fast.

Relevance AI is workforce-first — a structured "Tools + Agents" setup where several agents share context and hand off work toward one goal. It's built for teams that need auditable, multi-step reasoning.

Gumloop is canvas-first — a visual, node-based builder where AI is just one step in a data pipeline (scrape, transform, enrich, publish). It's closer to an AI-native successor to Zapier or Make than to a chat-based assistant.

How to Pick the Right One in 5 Minutes

1. Describe the job in one sentence. If it sounds like "manage my inbox," Lindy fits. If it sounds like "research, then draft, then schedule," Relevance AI fits. If it sounds like "scrape, transform, publish," Gumloop fits.
2. Check whether you want to test before paying. Relevance AI and Gumloop both have real ongoing free tiers. Lindy only gives a 7-day trial.
3. Decide if auditability matters. Sales, compliance, or ops workflows that need to show why an agent did something favor Relevance AI's Tools + Agents structure.
4. Estimate your monthly volume. High-volume, AI-heavy workflows burn through Gumloop's credit-based free tier fastest; Lindy's flat fee is easier to predict at scale.
5. Start free wherever you can. Relevance AI's or Gumloop's free tier will tell you more in an afternoon than another comparison post will.

Pricing across all three changed meaningfully in the past year, and this is where most existing comparisons are already stale. Relevance AI restructured its whole model in September 2025. Lindy removed its free tier a few months back.

Gumloop's old plan names ("Solo," "Team") that still circulate online were retired for a simpler Free/Pro/Enterprise setup. Here's what each platform's own pricing page shows today.

Relevance AI Pricing: Actions + Vendor Credits

Relevance AI splits cost two ways: Actions (what your agent does) and Vendor Credits (the model cost, passed through with no markup). The Free plan gives 200 Actions/month plus a one-time 1,000 Vendor Credits.

Pro starts at $19/month on annual billing, including 2,500 Actions and $20 in Vendor Credits monthly. Team runs $234/month annual (or $349/month billed monthly) for 7,000 Actions plus $70 in Vendor Credits. Enterprise is custom-priced. Paid plans let you bring your own API key to skip Vendor Credits entirely — handy if you already manage OpenAI or Anthropic spend directly.

Lindy Pricing: No Free Tier, Flat Monthly Fees

Lindy's current pricing page states plainly: there is no free plan, only a 7-day free trial with full Plus-tier access. Plus costs $49.99/month for standard usage and up to 2 connected inboxes.

Pro is $99.99/month for roughly 3x the usage and up to 3 inboxes, adding computer-use (browser automation). Max is $199.99/month for 7x the usage and up to 5 inboxes. Enterprise is custom, adding SSO, SCIM, HIPAA compliance, and audit logs.

Unlike Relevance AI and Gumloop, Lindy bundles AI model cost into the flat fee rather than metering it separately. That's simpler to predict, but you pay the same rate whether your agent does light or heavy reasoning.

Gumloop Pricing: Free Tier Plus Credit-Based Pro

Gumloop's Free plan includes 5,000 credits/month, 1 seat, 1 active trigger, 2 concurrent workflow runs, and 5 concurrent agent interactions. That's enough to build and test real workflows, not just click around a demo.

Pro is $37/month for 20,000+ credits, unlimited seats and teams, 5 concurrent runs, 25 agent interactions, and one hosted MCP server. Enterprise is custom, adding role-based access control, SCIM/SAML, audit logs, and a virtual private cloud option.

A standard AI call costs 2 credits, while an advanced call using a frontier model like GPT-4.1 or Claude costs roughly 20 credits. Heavy AI-reasoning workflows burn through the free tier faster than simple data-moving ones.

Architecture Comparison: What Each Platform Is Actually Built For

Price alone doesn't answer which tool fits. The underlying architecture determines what kind of work each platform is good at, and where it starts to strain.

Relevance AI's Tools + Agents Framework

Relevance AI splits things into two layers: "Tools" (search a CRM, classify intent, draft outreach) and "Agents," which chain Tools together toward a goal. Multiple agents can share outputs and run in sequence. One agent researches a prospect. It hands enriched data to a second agent, which drafts outreach. A third agent then handles scheduling.

This two-layer structure makes agent decision-making more auditable than instruction-only platforms. That's a real advantage for sales and compliance-sensitive workflows where someone needs to see why an agent did what it did. The tradeoff is setup time. Building a multi-agent workflow with conditional logic takes real work, not just a one-line prompt.

Lindy's AI Employee Model

Lindy works from natural-language goal descriptions rather than a visual builder. A Lindy agent given the goal "manage my inbox" reads incoming email on its own, classifies intent, and drafts a reply in your voice. It escalates anything matching urgency rules you set, all from one instruction, with approvals built in so nothing sends without review.

Setup is the fastest of the three: most users have a working email or meeting-prep agent live within a couple of hours. The ceiling is lower for complex, multi-step business logic, though. Lindy is built for one assistant handling recurring tasks, not for running a chain of specialized agents the way Relevance AI does.

Gumloop's Node-Based AI Pipeline Canvas

Gumloop looks and feels closer to Zapier or Make than to a chatbot. It's a drag-and-drop canvas where each node is a step. AI calls (GPT, Claude, Gemini) are first-class node types alongside scraping, API calls, and data transforms.

That makes it the strongest of the three for structured, multi-stage data work — scrape a source, summarize with AI, pull structured fields, write to a database. Each stage needs to feed cleanly into the next.

It's a weaker fit for conversational or inbox-centric agent use cases than Lindy. AI-heavy or enrichment-heavy nodes (roughly 60 credits per contact) can also burn through the free tier faster than the price tag suggests.

Integration Ecosystem: App Count Isn't the Whole Story

Raw integration counts vary widely across the three, and the number alone can mislead. Relevance AI claims the broadest reach: over 9,000 integration tools, spanning email, calendar, CRM, and spreadsheet connections, per its own G2 listing.

Lindy connects to 3,000–4,000+ apps depending on the source, with deep, purpose-built links to Gmail, Outlook, Calendar, Slack, Salesforce, and Notion. That's the exact stack a solo operator or small ops team already runs.

Gumloop has the smallest native integration count of the three, at roughly 125 apps. That sounds like a real gap until you account for its MCP (Model Context Protocol) server hosting on the Pro plan.

MCP is a newer, standard way for AI systems to reach external tools without a dedicated connector built for each one. For most day-to-day business apps, more native connectors still means less setup friction. For teams already comfortable with MCP-based tooling, though, Gumloop's smaller list matters less than it first appears.

User Reviews and Reliability: What G2 Ratings Actually Show

Third-party review volume differs a lot across these three, which matters for how much weight to put on any single rating. Lindy carries the largest, most established review base on G2: 4.9/5 across 168+ reviews. That reflects its longer time on the market and bigger non-technical user base.

Relevance AI sits at 4.3/5 from a smaller pool of roughly 20 reviews. Reviewers praise its tool breadth (9,000+ integrations, per its own listing) but flag the learning curve for complex multi-agent setups.

Gumloop is the newest and least-reviewed of the three: 4.8/5 from just 6 verified G2 reviews as of mid-2026. That's a genuinely good early sign, but too small a sample to treat as solid the way Lindy's number is.

Reddit sentiment across r/AI_Agents and r/automation is more mixed for all three than G2 alone suggests. Common complaints: unpredictable credit use at scale (Relevance AI, Gumloop), plus Lindy's roughly 20-second startup delay per task and limited debugging visibility when an agent loop misbehaves.

A Worked Cost Example: The Same Workflow on All Three Platforms

Consider a common small-business case. An agent researches 200 inbound leads a month, drafts a personal outreach email for each, and logs the result to a CRM. That's three AI-touching steps per lead, 600 AI actions total a month.

On Relevance AI's Pro plan, each step uses one Action. 600 Actions/month sits comfortably within the 2,500-Action allowance at $19/month, with Vendor Credits covering the underlying model cost.

On Gumloop, the same workflow built as three nodes per lead uses roughly 2–20 credits per AI node, depending on model tier. That lands somewhere between 1,200 and 12,000 credits a month, and likely needs the $37/month Pro tier once research and drafting both use higher-tier models.

On Lindy, the same workflow runs as credit-metered tasks bundled into the flat $49.99/month Plus fee. The real constraint isn't the price — it's whether 200 leads/month with research and drafting stays under Plus-tier usage limits.

Lindy's own docs warn that complex actions, like research or multi-step lead workflows, can use 5–10x more credits than a simple message send.

Relevance AI vs Lindy vs Gumloop: Feature Comparison Table

Which Should You Choose? A Decision Framework

Choose Lindy if: you want a working AI assistant for inbox, meeting prep, or scheduling — live within a couple of hours. Predictable flat monthly billing should also matter more to you than a free tier. Skip it if you need multi-agent orchestration or want to test the category without paying.

Choose Relevance AI if the job needs a team of agents handing off work to each other, not just one assistant. It's the strongest of the three for sales and revenue-operations use cases: research, enrichment, outreach, qualification. Auditability of agent decisions matters here for compliance or quality control.

Choose Gumloop if: the work looks like a data pipeline, not a conversation — scraping, transforming, enriching, and publishing data at each stage. It's the most technical of the three to build in. But it's the most flexible for multi-stage automation once you're past the learning curve.

These platforms are not mutually exclusive. A common 2026 stack: Gumloop handles the data layer, and Relevance AI runs multi-step agent reasoning on top of it. Lindy sits at the front as the assistant a human actually interacts with day to day.

For a broader look at agent platforms built for SMB operations specifically, see Best AI Agents for Small Business 2026. For the wider automation category these tools sit alongside, see Best No-Code AI Automation Tools 2026. If you need a straightforward workflow-automation platform rather than an AI-agent builder, our n8n vs Make vs Zapier comparison covers that simpler tier.

Final Verdict: Relevance AI vs Lindy vs Gumloop

There's no single winner here because these three platforms aren't really competing for the same job.

If forced to a single recommendation for most small teams starting from zero: begin with Relevance AI's free tier. It's the closest match to what serious multi-agent work looks like, and $19/month is the lowest committed cost of the three.

Add Lindy once a specific recurring task (inbox, scheduling) justifies its higher flat fee. Bring in Gumloop when a workflow starts looking more like a data pipeline than an assistant. None of the three is a mistake. The mistake is picking based on marketing language ("AI agent," "AI employee," "AI workforce") instead of the actual shape of the work.

Relevance AI — Free Tier, Multi-Agent Orchestration. Start free with 200 Actions/month, no card required, before deciding whether Pro's $19/month is worth it.

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