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. Navneet Arya has tracked all three since covering the broader agent-versus-automation split in AI Agents vs AI Automation: What's the Real Difference?, and 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" with minimal setup. Relevance AI is workforce-first: a structured "Tools + Agents" framework where multiple specialized agents share context and hand off work toward one goal, built for teams that need auditable, multi-step reasoning. Gumloop is canvas-first: a visual, node-based builder where AI is one first-class step among many in a data pipeline — scrape, transform, enrich, publish — closer to an AI-native successor to Zapier or Make than to a chat-based assistant.

Relevance AI vs Lindy vs Gumloop Pricing — Verified July 2026

Pricing across all three changed meaningfully in the past year, and this is where most existing comparisons are already stale. Relevance AI restructured its entire model in September 2025; Lindy removed its free tier at some point in the past several months; Gumloop's plan names ("Solo," "Team") that still circulate online were retired in favor of a simpler Free/Pro/Enterprise structure. Here's what each platform's own pricing page shows today.

Relevance AI Pricing: Actions + Vendor Credits

Relevance AI splits cost into two meters — Actions (what your agent does) and Vendor Credits (the underlying model cost, passed through at zero 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 support bringing your own API key to bypass Vendor Credits entirely — useful 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) capability. 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 — 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 — enough to build and test real workflows, not just click around a demo. Pro is $37/month for 20,000+ credits/month, unlimited seats and teams, 5 concurrent runs, 25 concurrent 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; an advanced call using a frontier model like GPT-4.1 or Claude costs roughly 20 credits, so 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 separates individual capabilities ("Tools" — search a CRM, classify intent, draft outreach) from the orchestration layer ("Agents") that chains Tools together toward a goal. Multiple agents can share outputs and run in sequence: one agent researches a prospect, hands enriched data to a second that drafts personalized outreach, which passes to a third that handles scheduling. This two-layer structure makes agent decision-making more auditable than instruction-only platforms — 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 configuration, 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" independently reads incoming email, classifies intent, drafts a reply in the user's voice, and escalates anything matching defined urgency criteria — 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 — Lindy is optimized for one assistant handling recurring personal or team-level tasks, not for orchestrating 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: a drag-and-drop canvas where each node represents a step, and AI calls (GPT, Claude, Gemini) are first-class node types alongside scraping, API calls, and data transforms. This makes it the strongest of the three for structured, multi-stage data work — scrape a source, summarize with AI, extract structured fields, write to a database — where each stage needs to feed cleanly into the next. It's a weaker fit for conversational or inbox-centric agent use cases than Lindy, and credit consumption on AI-heavy or enrichment-heavy nodes (roughly 60 credits per contact enrichment) can burn through the free tier faster than the sticker price 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 advertises the broadest reach — over 9,000 tools for integration, spanning email, calendar, CRM, and spreadsheet connections, according to its own G2 listing. Lindy connects to 3,000–4,000+ apps depending on the source, with especially deep, purpose-built connections to Gmail, Outlook, Google Calendar, Slack, Salesforce, and Notion — the exact stack a solo operator or small ops team is already running.

Gumloop has the smallest native integration count of the three at roughly 125 apps, which sounds like a real gap until you account for its MCP (Model Context Protocol) server hosting on the Pro plan — a newer, standardized way for AI systems to reach external tools without a dedicated connector being 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, Gumloop's smaller native list matters less than it first appears.

User Reviews and Reliability: What G2 Ratings Actually Show

Third-party review volume differs enormously across these three platforms, 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 — reflecting its longer time on the market and larger non-technical user base. Relevance AI sits at 4.3/5 from a smaller pool of roughly 20 reviews, with reviewers consistently praising its tool breadth (9,000+ integrations, per its own listing) and flagging 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 — a genuinely positive early signal, but too small a sample to treat as statistically meaningful the way Lindy's is. Reddit sentiment across r/AI_Agents and r/automation is more mixed for all three than G2 alone suggests: common complaints include unpredictable credit consumption at scale (Relevance AI, Gumloop) and Lindy's roughly 20-second task initialization delay plus limited debugging visibility when an agent loop misbehaves.

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

Consider a common small-business use case: an agent that researches 200 inbound leads per month, drafts a personalized outreach email for each, and logs the result to a CRM — three AI-touching steps per lead, 600 AI actions total per month. On Relevance AI's Pro plan, each step consumes 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 at roughly 2–20 credits per AI node (depending on model tier) lands somewhere between 1,200 and 12,000 credits/month — likely requiring 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 monthly price but whether 200 leads/month with research and drafting stays under Plus-tier usage limits, since Lindy's own documentation warns that complex actions like research or multi-step lead workflows can consume 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 management, meeting prep, or scheduling live within a couple of hours, and predictable flat monthly billing matters 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 is better described as "I need a team of agents that hand off work to each other" than "I need one assistant." It's the strongest of the three for sales and revenue-operations use cases — research, enrichment, outreach, qualification — where auditability of agent decisions matters for compliance or quality control.

Choose Gumloop if: the work looks more like a data pipeline than a conversation — scraping, transforming, enriching, and publishing content or structured data at each stage. It's the most technical of the three to build in, but the most flexible for multi-stage, AI-native automation once you're past the learning curve.

These platforms are not mutually exclusive. A common stack in 2026: Gumloop handles the data layer, Relevance AI orchestrates multi-step agent reasoning on that data, and 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, and for the wider automation category these tools sit alongside, see Best No-Code AI Automation Tools 2026.

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 most representative of what serious multi-agent work looks like, and the $19/month entry price is the lowest committed cost of the three. Add Lindy once a specific recurring task (inbox, scheduling) justifies its higher flat fee, and 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.

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