Best AI agent builders for startups (2026): What ChatGPT, Claude & Gemini Recommend

Lindy is the AI agent builder the models name most often. It appears in 33 of 50 recorded answers and comes first in 9 of 50. n8n and Zapier follow, each named in 29 of 50. The list below ranks the builders by that count. The category is platforms a startup can use to build working AI agents without a big engineering team. The count shows which names ChatGPT, Claude, Gemini and Perplexity models give. It does not rate the products.

TL;DR

• Lindy leads on presence, but the answers put it first in only 9 of 50. Treat it as the default shortlist entry, not a settled pick.
• Zapier is the only product every model named. That spread is the steadiest signal in the set.
• n8n suits a startup with one technical owner. The answers that recommend it say so themselves.
• The leader changes with the model family: Zapier for OpenAI, Lindy for Anthropic and Perplexity, n8n for Google.
• For customer-facing support agents, the answers point to Voiceflow and Botpress, and several leave the tracked set for Intercom Fin.
• Shortlist two builders that different families lead with, then run the same workflow in both.

1. Lindy

Pick Lindy if the people building the agents are founders or operators rather than engineers, and the work lives in email, calendars and the CRM.

Measured: named 33 of 50 (Lindy 66%), first 9 of 50 (Lindy 18%), average position 3.03.

The answers treat Lindy as an agent-first builder. Describe the role, connect the business apps, add approvals, deploy. GPT-5.6 Sol put it plainly: “Lindy is my top recommendation for a small team that wants useful business agents without hiring engineers.” Its reach is uneven, though. Claude Opus 5 and Sonar Reasoning Pro named it in 5 of 5 answers. ChatGPT named it in 0 of 5. Most answers list Lindy as one option among several rather than the pick, which is why its first count sits so far below its named count. The weakness the answers repeat is integration breadth, where GPT-5.6 Sol places it behind Zapier.

Pros

• Named by nine of the ten models
• Leads six of the ten models, more than any other product
• Plain-English setup, in GPT-5.6 Sol’s account: describe the role, connect apps, add approvals
• GPT-5.6 Luna rates the engineering effort very low

Cons

• ChatGPT named it in 0 of 5 answers
• The named-versus-first gap is 48 points, the widest in the set
• Fewer integrations than Zapier, in GPT-5.6 Sol’s comparison

Pricing: no pricing record is held for this edition. Best for: non-technical teams automating email, calendar, CRM and scheduling work, as GPT-5.6 Luna frames it.

2. n8n

Pick n8n if one person on the team is comfortable with APIs and the startup wants self-hosting or control over running costs later.

Measured: named 29 of 50 (n8n 58%), first 8 of 50 (n8n 16%), average position 3.72.

n8n is the builder the answers qualify most. GPT-5.6 Sol and GPT-5.6 Luna both open answers by recommending it, and Claude Opus 5 named it in 5 of 5. The same answers attach a condition. GPT-5.6 Sol wrote: “Calling it fully no-code is misleading.” It expects JSON, authentication and occasional scripting. Claude Opus 5 gives cheap self-hosting and execution-based pricing as the reasons startups choose it. Read together, the answers describe a tool for a team with one technical owner, not a team with none. Coverage has a hole at the Perplexity end. Sonar Reasoning Pro never named it, and ChatGPT named it once.

Pros

• Top name for GPT-5.6 Sol (n8n 60%)
• Claude Opus 5 named it in 5 of 5 answers
• Leads the Google family (n8n 70%)
• Self-hosting and execution-based pricing, as Claude Opus 5 describes it

Cons

• Sonar Reasoning Pro named it in 0 of 5 answers
• Usually needs a technical owner, says GPT-5.6 Sol
• Self-hosting adds security, upgrade and monitoring work, per GPT-5.6 Sol

Pricing: no pricing record is held. Claude Opus 5 describes execution-based rather than per-seat pricing. Best for: startups with one technically confident automation owner, in GPT-5.6 Sol’s words.

3. Zapier

Pick Zapier if the agents mainly need to act across SaaS tools the team already runs, and a first working agent this month matters more than depth.

Measured: named 29 of 50 (Zapier 58%), first 6 of 50 (Zapier 12%), average position 3.17.

Zapier is the only product all ten models named. No other entry has that spread. ChatGPT named it in 4 of 5 answers, and its reason was reach: “Zapier’s large integration catalog is its decisive advantage for a small team.” The answers draw two limits. ChatGPT notes that Zapier Agents are personal automations that cannot be embedded as a customer-facing experience. GPT-5.6 Sol warns that costs can climb and complex logic hits limits. So the models place Zapier as the first agent for internal operations, not the agent inside a product.

Pros

• Every model in the panel names it at least once
• Zapier (60% of OpenAI answers), the family’s leader
• ChatGPT and Gemini 3.5 Flash both lead with it (Zapier 80% each)
• Extends apps a small team already uses, in Claude’s framing

Cons

• Perplexity named it once in 5 answers
• Cannot be embedded as a customer-facing experience, per ChatGPT
• Costs can climb and complex logic hits limits, per GPT-5.6 Sol

Pricing: no pricing record is held. GPT-5.6 Sol describes task- and usage-based costs. Best for: internal operations agents across common SaaS apps.

4. Gumloop

Pick Gumloop if the agent’s job is processing documents, research or batches of data rather than general business automation.

Measured: named 24 of 50 (Gumloop 48%), first 2 of 50 (Gumloop 4%), average position 3.5.

Gumloop is a regular mid-list name that the answers rarely choose. GPT-5.6 Sol frames it for AI-heavy document, research and data pipelines. It also marks Gumloop as less suited to general business automation. Support is lopsided by family. The Anthropic and Perplexity models carry most of the count, while ChatGPT and GPT-5.6 Luna never named it. Its own site matters here. gumloop.com was cited 27 times in the recorded answers, and Claude Opus 5 pointed out that Gumloop’s own list starts with Gumloop. A buyer should read the count with that in mind.

Pros

• Gumloop (73.3% of Anthropic answers)
• Fits document, research and data pipelines, per GPT-5.6 Sol
• Claude Opus 5, Claude Fable 5 and Sonar Reasoning Pro each named it in 4 of 5

Cons

• No mention at all from ChatGPT or GPT-5.6 Luna
• First in only 2 of 50 answers
• gumloop.com supplied 27 citations to the recorded answers
• Less suited to general business automation, in GPT-5.6 Sol’s table

Pricing: no pricing record is held. Best for: AI-heavy document, research and data work, as GPT-5.6 Sol frames it.

5. Relevance AI

Pick Relevance AI if the plan is several specialist agents working together and someone can own testing and usage costs.

Measured: named 23 of 50 (Relevance AI 46%), first 1 of 50 (Relevance AI 2%), average position 4.

Relevance AI has broad reach and almost no first placements. Nine of the ten models named it, yet only one answer put it first. GPT-5.6 Sol describes it as built for specialist agent teams, such as a researcher, analyst, writer and reviewer sharing work. The same answer flags a steeper learning curve and credit use that is harder to predict. Claude Fable 5 aims it at fast-growing startups running agents across departments. The Google models name it most. The pattern fits a second-stage tool: named often, rarely the starting point.

Pros

• Nine of the ten models name it
• Relevance AI (70% of Google answers)
• Purpose-built for teams of specialist agents, per GPT-5.6 Sol

Cons

• A single first placement across 50 answers
• Absent from every ChatGPT answer
• Steeper learning curve and less predictable usage costs, per GPT-5.6 Sol

Pricing: no pricing record is held. GPT-5.6 Sol flags action-based overages at scale. Best for: multi-agent designs with an owner for architecture and cost control.

6. Voiceflow

Pick Voiceflow if the agent’s main interface is a customer conversation on chat or voice.

Measured: named 18 of 50 (Voiceflow 36%), first 3 of 50 (Voiceflow 6%), average position 3.67.

Voiceflow is the specialist the models reach for when the question turns to support. It is GPT-5.6 Luna’s most-named product (Voiceflow 80%). That model describes Voiceflow for support and customer-experience agents across web chat, apps, WhatsApp, SMS and voice. GPT-5.6 Sol sends customer-support conversation work to Voiceflow or Botpress. The weakness in the answers is the mirror image. GPT-5.6 Luna rates it less suited to complex back-office automation, and the Claude and ChatGPT models never named it. A startup whose agents run internal workflows can leave it off the shortlist.

Pros

• GPT-5.6 Luna’s leader (Voiceflow 80%)
• Runs on web chat, apps, WhatsApp, SMS and voice, per GPT-5.6 Luna
• Claude Fable 5 named it in 4 of 5

Cons

• Missing from all answers by the Claude and ChatGPT models
• Less ideal for complex back-office automation, per GPT-5.6 Luna

Pricing: no pricing record is held. Best for: customer-facing chat and voice agents.

7. Make

Pick Make if an operations or RevOps person will design the flows and needs to see exactly how data moves.

Measured: named 18 of 50 (Make 36%), first 0 of 50 (Make 0%), average position 3.94.

Make ties Voiceflow on mentions and was never named first. GPT-5.6 Sol calls it a middle ground between Zapier’s simplicity and n8n’s technical depth, with more control over branching and error paths than prompt-only builders. Claude groups it with Zapier as an option for non-developers that trades some simplicity for more conditional logic. The cost the answers name is upkeep. Larger scenarios take real workflow-design skill. Neither ChatGPT nor GPT-5.6 Luna named it. Make reads as the tool the models add to a list, not the one they open with.

Pros

• Sits between Zapier’s simplicity and n8n’s depth, per GPT-5.6 Sol
• Picked up by Claude, Claude Fable 5 and Gemini 3.5 Flash in 3 of 5 answers each
• More branching control than Zapier, in Claude’s description

Cons

• Zero first placements in 50 answers
• Larger scenarios take real workflow-design skill, per GPT-5.6 Sol
• Never named by ChatGPT or GPT-5.6 Luna

Pricing: no pricing record is held. Best for: operations teams comfortable with visual logic.

8. Botpress

Pick Botpress if the first build is a support bot and a developer may extend it later.

Measured: named 16 of 50 (Botpress 32%), first 1 of 50 (Botpress 2%), average position 4.56.

Botpress sits in the conversational lane with Voiceflow. Claude describes it as open-core: no-code to start, without locking the team out of code as it grows. GPT-5.6 Luna named it in 4 of 5 answers and rates it more oriented to conversational bots than to general agent work. The spread is the weakness. Neither Perplexity model named it, and ChatGPT never did either. Claude Opus 5 also noted Botpress ranking Botpress among its sources. botpress.com appears among the vendor-owned hosts cited in the run, with 15 citations.

Pros

• Open-core route from no-code to code, in Claude’s description
• 4 of 5 answers from GPT-5.6 Luna name it

Cons

• Neither Perplexity model named it
• botpress.com drew 15 citations in the run
• Built more for conversational bots, says GPT-5.6 Luna

Pricing: no pricing record is held. Best for: support bots with a path to developer extension.

9. Pickaxe

Pick Pickaxe if the startup plans to sell or white-label agents to clients rather than run them internally.

Measured: named 11 of 50 (Pickaxe 22%), first 2 of 50 (Pickaxe 4%), average position 5.18.

The answers present Pickaxe as an agency tool. Claude says it is built for selling agents to clients, citing built-in Stripe billing, branded portals, access control and white-labelling. No OpenAI model named it, so its count comes mostly from the Anthropic models. It also has the largest vendor-owned citation footprint in the run. pickaxe.co was cited 47 times, behind only rasa.com and braintrust.dev across all hosts. Claude Opus 5 gave Pickaxe ranking Pickaxe as an example of vendor marketing in its sources. Weigh the count against that.

Pros

• Stripe billing, branded portals and white-labelling, per Claude
• Claude and Claude Fable 5 named it in 3 of 5 each

Cons

• Not named by any OpenAI model
• pickaxe.co drew 47 citations, the most of any vendor-owned host
• Placed later in answers than any other product on this shortlist

Pricing: no pricing record is held. Best for: agency-style startups selling agents to clients, as Claude frames it.

10. Dify

Pick Dify if the agent is part of the product and the team needs a deployable app or API, not an internal automation.

Measured: named 10 of 50 (Dify 20%), first 3 of 50 (Dify 6%), average position 3.3.

Dify has the narrowest spread on this shortlist, but it sits high when it appears. Four of the ten models named it. GPT-5.6 Sol made it the default for customer-facing agents. It notes that Dify produces deployable web apps and APIs, supports several model providers and allows a move from managed cloud to self-hosting. Claude Opus 5 files Dify with the code frameworks, for RAG-heavy prototyping. That places it closer to a developer tool than the builders above it. The two Google models carry most of its count.

Pros

• Dify (60% of Google answers)
• Ships deployable web apps and APIs, per GPT-5.6 Sol
• More first placements than Gumloop, Relevance AI or Botpress

Cons

• Named by only four of the ten models
• Grouped with code frameworks by Claude Opus 5

Pricing: no pricing record is held. Best for: agents built into a product rather than run as internal automations.

How do the tools compare?

Lindy leads, and n8n and Zapier tie on 29 below it. Gumloop and Relevance AI sit close behind. After Relevance AI the counts drop to 18 and below. Every product under Dify was named in 8 of 50 answers or fewer. The full edition data sits on the AI agent builders index.

The pattern below the leader is breadth without selection. Gumloop and Relevance AI are each named in roughly half the answers, yet first in only 2 and 1 of 50. MindStudio is the outlier in the other direction: 8 mentions, 4 of them first. Vapi was tracked and never named, so it carries no row. No pricing record is held in this edition, so the table has no pricing column.

Where do the models disagree?

The models do not agree on a leader. Four different products lead at least one model.

The OpenAI models split three ways. GPT-5.6 Sol leads with n8n (n8n 60%), ChatGPT with Zapier (Zapier 80%) and GPT-5.6 Luna with Voiceflow (Voiceflow 80%). ChatGPT is also the one model that never names Lindy.

The Anthropic models agree on Lindy. Claude Opus 5 named it in every answer (Lindy 100%), and Claude and Claude Fable 5 lead with it too. Claude Opus 5 also named n8n in every answer.

The two Google models part ways. Gemini leads with Lindy (Lindy 80%). Gemini 3.5 Flash leads with Zapier (Zapier 80%). Gemini is also where Flowise shows up, in 3 of its 5 answers.

The Perplexity models also lead with Lindy but draw on a different set below it. Sonar Reasoning Pro named Lindy in every answer and never named n8n. Both Perplexity models name MindStudio (MindStudio 50% of Perplexity answers) and Taskade (Taskade 50% of Perplexity answers), two products the other families barely mention.

One disagreement is about meaning rather than rank. Claude Opus 5 named Sierra in 3 of 5 answers. In one of them it told the reader to skip Sierra, grouping it with enterprise platforms a startup does not need. Being named is not the same as being recommended.

Why does the leader change with the model family?

The family decides the leader, and the answers themselves suggest why. Zapier leads the OpenAI answers (Zapier 60%), Lindy leads the Anthropic answers (Lindy 86.7%) and the Perplexity answers (Lindy 90%), and n8n leads the Google answers (n8n 70%). The overall total hides that split.

The answers show what each family reads. ChatGPT’s Zapier answers cite Zapier’s own help pages, and GPT-5.6 Sol’s answers cite the n8n, Dify, Lindy and Relevance AI pricing pages. The Claude answers read roundup articles and warn about them. Claude Opus 5 put it most directly: “nearly every source here is a vendor blog ranking its own product first.” It named Gumloop’s list starting with Gumloop and Lindy’s with Lindy.

The citation counts point the same way. Five vendor-owned hosts sit among the sources the answers cited: pickaxe.co (47), gumloop.com (27), lindy.ai (23), stackai.com (16) and botpress.com (15). The inference, not a measurement, is that a builder which publishes its own comparison pages gets into the answers of models that read comparison pages. This edition does not break citations down by model, so the link between a family and a host is not measured.

The practical reading for a buyer: a name every family gives is harder to publish into existence than a name one family gives. Zapier is named by all ten models. Lindy’s lead is real, but it is concentrated outside the OpenAI models.

What should a buyer do with this?

Start from the agent’s job, then use the shortlist that matches it.

Then test before committing. Claude Opus 5, GPT-5.6 Sol and Claude Fable 5 all advise a small pilot on a real workflow before choosing. GPT-5.6 Sol goes further and suggests running the same workflows on two finalists. Pick the pair from different family leads, for example Lindy and Zapier, so the test covers two different readings of the category.

How was the sample built?

The sample is 10 models x 5 fixed prompts = 50 recorded answers, edition 2026-09. Each model answered each prompt once. The five prompts, verbatim:

1. What is the best platform to build AI agents without a big engineering team for a startup? Name specific products.
2. Which platform to build AI agents without a big engineering team would you recommend to a startup in 2026?
3. Compare the top platform to build AI agents without a big engineering team options right now.
4. I’m a startup and I need a platform to build AI agents without a big engineering team. What should I use and why?
5. Best platform to build AI agents without a big engineering team for customer support agents?

The models come from four families. OpenAI gave 15 answers (GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna). Anthropic gave 15 (Claude Opus 5, Claude, Claude Fable 5). Google gave 10 (Gemini, Gemini 3.5 Flash). Perplexity gave 10 (Perplexity, Sonar Reasoning Pro).

The panel tracked 21 products and 20 were named. Named means the product appeared in the answer. Named first means it appeared before any other tracked product. Average position is where it sat, on average, among the tracked products an answer named. The full method is on the method page.

How does this list sit against the AI agent guides?

The pages ranking for this question answer a different question.

Anfloy’s guide ranks ten AI agent development agencies, not self-serve platforms. Anfloy lists itself first and says so in its introduction. Its pricing is compiled from published rate cards, Clutch profiles and third-party review coverage. Its closing advice is to get an itemised number, confirm who owns the system after the engagement and ask for a named client outcome.

Knowlee’s guide compares AI coding agents such as Lovable, Cursor and Devin for engineering teams. It carries a conflict-of-interest disclosure noting that Knowlee publishes the comparison. For non-engineers building MVPs, it points to Lovable.

Avahi’s guide sorts agentic AI platforms by type, including workflow-integrated platforms that offer agent capabilities through low-code or no-code interfaces. It says those suit business teams that need minimal engineering effort. The page closes by promoting Avahi’s own services as an AWS Cloud Consulting Partner.

Port’s article is about agentic engineering inside software teams, contrasting fixed agent pipelines with agents that are handed a goal. It presents Port as the control plane for that work.

Those guides rank agencies, coding tools or architectures, mostly written by a vendor in the space. This page ranks the no-code and low-code builders a startup would actually shortlist, by how often AI answers name them, and shows which models name which products.

What can these counts not tell you?

The counts measure presence in AI answers. They say nothing about product quality, uptime, support or fit with a particular stack. Being named also differs from being recommended, as the Sierra case shows.

Each model answered each prompt once, so a single answer can move a vendor’s count. This is one dated snapshot, edition 2026-09. Names are matched as strings, so “Zapier Agents” counts as Zapier and “Make.com” as Make. Answers came through model APIs, which can differ from the consumer chat apps. The prompts were in English.

Some products the answers recommend are not tracked. The customer-support prompt led each OpenAI model to Intercom Fin, and other answers named Retool Agents and Microsoft Copilot Studio. None of those carries a count here. Citation counts reflect the citations returned in the recorded API responses, and coverage varies by model.

Frequently asked questions

Which platform is best for building AI agents?

No single platform leads every model. Across 50 recorded answers, Lindy is named most often (33 of 50), with n8n and Zapier next (29 each). Zapier is the only product all ten models named. The count measures how often AI answers name a product, not which product is better.

What are the big 4 AI agents?

This panel does not define a “big four”. It does show four different builders leading at least one model: Lindy, n8n, Zapier and Voiceflow. It also queries four model families: OpenAI, Anthropic, Google and Perplexity.

What is the easiest way to build AI agents?

The answers point to no-code builders, starting with one narrow workflow. Lindy and Zapier carry the lowest engineering effort in GPT-5.6 Luna’s comparison. Avahi’s guide describes the same category as workflow-integrated platforms with low-code or no-code interfaces.

Which AI agent is the most useful to build?

The recorded answers favour narrow internal agents first. ChatGPT lists lead qualification, customer-support triage, meeting prep, research, CRM updates and document processing. Claude Opus 5 advises picking one narrow, painful workflow and building it before choosing a platform.

Is n8n really no-code?

Not in the answers’ view. GPT-5.6 Sol calls n8n low-code and expects someone to handle APIs, JSON and authentication. Claude Opus 5 and GPT-5.6 Luna still recommend it for startups with one technical person.

Can a Zapier agent talk to customers?

ChatGPT’s answer says no for Zapier Agents. It describes them as personal automations that cannot be embedded as a customer-facing experience, and points to Zapier’s chatbot product or a custom app for that job.

Do vendors pay to appear on this list?

No. Positions are not sold, sponsored or influenced. Vendors cannot pay to appear, be reordered or be removed. The order comes from the count of recorded answers.