Gumloop
Visual automation platform for building AI-powered pipelines and agents without code.
Gumloop is a no-code platform for building AI-powered automations and agents on a visual canvas. It sits in the gap between two familiar kinds of tool: those that generate content, like a chat assistant, and those that connect apps with simple triggers, like traditional workflow automators. Gumloop combines them, so a workflow does not just move data from one app to another but can reason about it, generate from it, and make decisions at each step. Builders drag, drop, and connect modular nodes to assemble processes that previously needed either custom code or a lot of manual effort.
What sets it apart from a basic if-this-then-that tool is that any step in a flow can be an AI model, OpenAI, Anthropic, or Google among them, doing real work: categorizing a support ticket, drafting personalized outreach, summarizing a document, or scoring a lead. Native web scraping pulls structured data straight from websites, and the platform handles batch and high-volume runs well, which makes it a fit for repeatable, defined work like enriching thousands of contacts, processing large document sets, or updating CRM records at scale. Automations can run manually, on a schedule, or fire from webhooks and other conditions.
Gumloop connects to a wide range of business applications across CRM, sales, support, and productivity, and supports the Model Context Protocol for plugging into AI tools and data sources. Its center of gravity tends to be operations, marketing, sales, and research teams that have clearly mappable, repetitive processes. The tradeoff of its node-based model is that it rewards people who can lay out a process as explicit steps rather than expecting the AI to infer everything.
For organizations, Gumloop layers on governance through a control plane it calls Gumstack: a single logging and analytics layer that traces every tool call, scoped access controls for shared credentials and secrets, integration with a team's identity provider, and policies that restrict which models can be used and cap spend. It can run on Gumloop's own secure infrastructure or inside a customer's private cloud, does not use customer data to train models, and is compliant with SOC 2 Type II and GDPR.
Vendor details
Canonical URL
https://www.gumloop.com
Category
Agent builder
Company status
independent
Use cases & customers
Target customers
Deployment options
In practice
Your weekly competitor check is manual: gather titles, find content gaps, draft ideas, drop them in a sheet. Gumloop chains those steps on a canvas where an AI node does the reasoning and the rest runs on schedule.
Zapier moves your data but can't reason about it. Gumloop lets any step in a workflow be an AI model that categorizes, summarizes, or scores, so you can automate sales-ops work like enriching and qualifying leads at volume.
Your org needs to give teams AI automation without losing control. Gumloop's Gumstack adds a single audit and analytics layer, scoped credentials, model and spend policies, and the option to run inside your own private cloud.
Capability coverage
11.5 / 14 capabilities · 82%
| Integrations & Tool CallingAgent Features research report + JSON Feature Rubric | Full |
|---|---|
| Workflow OrchestrationAgent Features research report + JSON Feature Rubric | Full |
| Knowledge Grounding & RAGAgent Features research report + JSON Feature Rubric | Partial |
| Human Oversight & GuardrailsGumloop changelog 9.11.0 + App Policies blog | Full |
| Security, Identity & GovernanceAgent Features research report + JSON Feature Rubric | Full |
| Observability & AuditabilityAgent Features research report + JSON Feature Rubric | Full |
| Memory & State PersistenceAgent Features research report + JSON Feature Rubric | Partial |
| Deployment & Data ResidencyAgent Features research report + JSON Feature Rubric | Full |
| Prebuilt Agents, Templates & PacksAgent Features research report + JSON Feature Rubric | Full |
| Triggers & Channel CoverageAgent Features research report + JSON Feature Rubric | Full |
| Model Flexibility & RoutingAgent Features research report + JSON Feature Rubric | Full |
| APIs, SDKs & MCP ExtensibilityAgent Features research report + JSON Feature Rubric | Full |
| Testing, Debugging & OptimizationAgent Features research report + JSON Feature Rubric | Partial |
| Browser & Computer UseAgent Features research report + JSON Feature Rubric | Unable to verify |
Recent platform changes
Gumloop released version 10.13.0, introducing the ability to sync organizational agent skills directly from GitHub repositories. The update also expands the Company Brain feature to ingest knowledge from any connector, adds a Reddit Ads MCP integration, and supports secure RSA key-pair authentication for Snowflake.
View sourceAgent Chat Evaluations let teams define criteria and automatically grade agent chats.
View sourceHuman-in-the-loop for agents lets agents pause mid-task to request approval before running a tool, or ask a question with options, then resume where they left off in agent chats and Slack.
View sourcePricing
From $37/mo · free tier (5,000 credits)
credits
Included quota
Free ($0): 5,000 credits/mo, 1 seat, 1-2 active triggers, ~2 concurrent runs, Gummie Agent, unlimited nodes/flows, forum support. Pro ($37/mo, ~$29.60 annual): 20,000+ credits/mo, UNLIMITED seats, unlimited triggers, ~4 concurrent runs, webhooks, BYO API key, custom nodes, email support. Enterprise (custom): everything in Pro + RBAC, audit logs, data exports, SCIM/SAML SSO, virtual private cloud/private infrastructure, custom rules, incognito mode, on-call/dedicated support, custom credit allocations.
What is public
Gumloop (gumloop.com - no-code AI workflow-automation platform, visual node-based canvas; YC W24, $70M+ raised incl. a $50M Series B led by Benchmark, March 2026; used by Shopify, Instacart, Webflow, Ramp) has a streamlined three-tier, credit-based structure: Free ($0 - 5,000 credits, 1 seat, ~2 concurrent runs, forum support), Pro ($37/mo - 20,000+ credits, UNLIMITED seats, more concurrency, webhooks, BYO API key; ~$29.60/mo with 20% annual discount), and Enterprise (custom - RBAC, audit logs, SSO/SCIM, VPC/private infra). Note: the old Solo/Team tiers were retired into this Free/Pro/Enterprise lineup.
Billing mechanics
Credit-metered: every workflow execution costs a base 1 credit PLUS per-node costs that scale with complexity - simple/connector nodes 1-3 credits, standard AI calls ~2 credits, advanced AI (GPT-4/Claude Opus) ~20 credits, and ENRICHMENT ~60 credits per contact/row. Loop mode multiplies (a 60-credit node over a 10-item list = 600 credits). BYO API key drops AI-node cost to 1 credit. nodes are free. Failed mid-run workflows only charge for executed nodes. Plans renew monthly; paid tiers get 20% off annual.
Cost watchouts
Credit math is the trap - AI-heavy flows burn fast: advanced-model calls (GPT-4/Claude Opus) cost ~20 credits each and ENRICHMENT is ~60 credits PER CONTACT (enriching 100 contacts = ~6,001 credits, nearly a third of the Pro plan in one run), and this is buried in docs, not the pricing page; loop mode multiplies node costs; a 50-page PDF summarized with Claude Opus can eat 3,000-5,000 credits per run; the Pro-to-Enterprise jump leaves NO mid-tier for mid-market; live chat is Enterprise-only
Variable cost rationale
Usage-driven and genuinely hard to predict - spend scales with which NODES a flow uses (1 credit for simple, 20 for advanced AI, 60 for enrichment) x run frequency x loop size; BYO API key (AI node to 1 credit) is the biggest lever to control cost; without published token-to-credit rates, budgeting requires baselining
Additional watchouts
Credit-based pricing is unpredictable - enrichment (60 credits/contact) + advanced-AI nodes (20 credits) burn Pro credits shockingly fast (offload bulk enrichment to a dedicated tool; feed clean data in for AI steps); steep-ish learning curve (node/batch-data paradigm favors technical users); 130 integrations vs Zapier's thousands; no mid-tier between Pro ($37) and custom Enterprise; cloud-only (VPC is Enterprise); use BYO API keys to cut AI costs
Overage / add-ons
No automatic shutoff at the credit limit - workflows keep running and accrue OVERAGE at $0.005/credit (e.g., 15,000 credits over = $75), so monitor usage to avoid surprise bills. Higher needs move to Enterprise for custom credit allocations. Annual billing (20% off) is the main discount; no published prepay tiers beyond that.
Sales call required
Mixed (some tiers require a call)
Free / trial
Free
Lowest paid plan
$37/mo
Commercial notes
YC W24 (founded 2023 as AgentHub, Max Brodeur-Urbas & Rahul Behal); $70M+ raised - $3.1M seed (First Round, July 2024), $17M Series A (Nexus, Jan 2025), $50M Series B led by Benchmark (March 2026, Shopify Ventures/YC/First Round participating); customers include Shopify, Instacart (1,000+ users), Webflow, Ramp, Gusto, Samsara; positioned as an AI-NATIVE Zapier alternative (AI is the core, not an add-on) - best for AI-heavy batch workflows (scraping, analysis, generation); competes with Lindy, Make, n8n, Zapier, Relay, Relevance AI, Vellum
Key ambiguities
Lots of STALE pricing online - many breakdowns cite deprecated 'Solo'/'Team' tiers or wrong numbers (free credits as 2,000 vs current 5,000; phantom $97/$197/$497 or $244 Team plans); current official structure is Free (5,000 credits) / Pro $37 (20,000+, unlimited seats) / Enterprise custom; exact token-to-credit conversion rates for AI models aren't published, so precise cost forecasting needs 1-2 months of real runs
Cancellation / refund
Free plan (no card, 5,000 credits/mo); Pro self-serve monthly or annual (20% off annual, ~$29.60/mo; no cancellation/early-termination fees on standard tiers); plan changes allowed without penalty; overage billed at $0.005/credit; Enterprise custom-contracted (multi-year discounts via sales)
Support SLA / resale
Forum/community support (Free); email support (Pro); Team/Enterprise add real-time/Slack + on-call/dedicated support; Enterprise adds RBAC, audit logs, SCIM/SAML SSO, VPC/private infrastructure, custom data retention, incognito mode; 130+ native integrations + MCP nodes; model-agnostic per-node (GPT/Claude/Gemini/DeepSeek); Gumstack observability; Chrome extension; cloud-only
Missing data
Exact token-to-credit conversion rates for AI models aren't published (forecast needs 1-2 months of real runs); free-credit count is cited as 5,000 (current) vs 2,000 (older); Enterprise is custom (budget ~15-25% of subscription for implementation per industry norms). Seed 'Free (5k credits, 1 seat); Pro from $37/mo (20k+ credits, unlimited seats); Enterprise custom; 20% off annual' is accurate and current - note the deprecated Solo/Team tiers some sources still cite, and that enrichment costs ~60 credits/contact.
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