Compare
Gumloop vs Lindy
A plain-English comparison to help you choose between them.
Both are no-code agent platforms a step beyond classic automation, aimed at different shapes of work: Gumloop at pipelines you draw, Lindy at roles you delegate. Pick Gumloop when the job is batch AI processing, folders of documents scraped, extracted and transformed on a visual canvas where model calls are first-class nodes. Pick Lindy when the job is a recurring administrative loop, inbox triage, scheduling and CRM upkeep described in plain language and run on triggers, with judgement allowed inside your rules. The tell is the input: a dataset points to the canvas, a job description points to the agent.
Both tools chosen. Compare is enabled.
Side by side
- Summary
Gumloop is a visual canvas for AI-heavy automation: drag nodes together and batch-process documents, scrape and transform data, and chain AI steps into workflows without code. Its shape suits data-processing jobs that are too AI-centric for classic automation platforms.
The canvas is the interface and the argument: workflows read visually, AI operations are first-class nodes rather than bolted-on steps, and batches run over whole datasets.
MoreLess
Credits meter the AI work, so heavy batch runs cost accordingly: the arithmetic belongs in the plan before the pipeline scales.
- Best for
- Batch document and data processing with AI steps
- Visual workflow building without code
- Scraping and transforming data in one canvas
- AI operations as first-class workflow nodes
- Data jobs too AI-centric for classic automation
- Cost
- Paid only
- Ease
- Openness
- Hosted service
- Data
- Workflows touch whatever systems you connect; credential scoping deserves the same care as any automation platform.
- Summary
Lindy is a platform for building AI agents that do real work across your existing tools. Each agent, a Lindy, is set up in plain language rather than code: describe the job, connect the apps it needs, such as email, calendar, a CRM or Slack, and set the rules it must follow.
Agents run on triggers rather than waiting to be asked. Typical deployments triage and draft email, schedule and reschedule meetings, prepare briefs before calls, take notes and send follow-ups, keep HubSpot or Salesforce records current and chase leads. A library of prebuilt templates covers the common jobs, so a first agent is usually assembled rather than designed.
MoreLess
Lindy has moved from a free-plan model to subscription tiers with a short trial, and now presents itself as an AI assistant for the whole work loop rather than a single-purpose bot. It suits people who want delegation, not another dashboard.
- Best for
- Delegating whole jobs such as inbox triage or meeting scheduling to an agent
- Building agents in plain language without code or flowcharts
- Keeping CRM records current from email and meeting activity
- Meeting preparation, notes and follow-ups handled end to end
- Starting from prebuilt agent templates rather than a blank canvas
- Cost
- Paid only
- Ease
- Openness
- Hosted service
- Data
- Agents hold live access to email, calendar and connected tools; grant scopes per agent, per job.
Pricing
- Gumloop
from $37·Custom
Prices as of August 2026.
- Pro
- from $37per monthbilled monthly
- Enterprise
- Price on applicationno list price published
- Lindy
$49.99·$99.99·$199.99·Custom
Prices as of August 2026.
- Plus
- $49.99per month
- Pro
- $99.99per month
- Max
- $199.99per month
- Enterprise
- Price on applicationno list price published
By area
Where each one pulls ahead, area by area.
| Area | Gumloop | Lindy |
|---|---|---|
| By job | ||
| AI agents & automation | every step is drawn before it runs, so the same batch goes through the same sequence each time and the whole thing can be inspected while it is still being built | there is no batch and no end to it: customer records are kept current out of email and meeting activity as that activity happens, and the lead that went quiet gets chased without anybody noticing it had |
| By task | ||
| Automation & agents | Gumloop's strength is the AI-processing canvas rather than the connector directory, and app-to-app integration breadth still favours the incumbents | Lindy's agents exercise judgement, which is a feature where variation is acceptable and a liability where it is not, so anything that must behave identically every run belongs elsewhere |
Common questions
How do the AI economics compare?
Both meter the intelligence. Gumloop's credits price the AI steps, so heavy batch runs cost accordingly and large recurring pipelines need the arithmetic done before they scale. Lindy's costs scale with how much the agents actually do, so always-on delegation deserves a look at plan allowances first. In both cases, model the unattended workload before trusting it to run unattended.
Which needs closer supervision?
Lindy, structurally: its agents act on live email, calendars and customer records in your name, so new agents deserve a probation period, narrow permissions and reviewed output while trust builds. A Gumloop pipeline is inspectable on the canvas as it is built, and its failures tend to land in a dataset rather than in a customer's inbox, which is a gentler place to find them.
When is neither the right platform?
When the work is pure plumbing or pure software. Classic app-to-app integration breadth favours the incumbent automation platforms on both tools' own admission. At the other end, some Gumloop pipelines eventually justify being written as code, and genuinely complex agent systems belong in developer frameworks. These two hold the judgement-shaped middle between those poles.
Related comparisons
Read the full guides
Where to start
Not sure what to adopt first?
Five quick questions about your job, task and constraints. We'll suggest your top three tools, plus the one to try first.
Tool facts last checked August 2026