Last updated: 25 October 2026
The 2026 benchmark scores nine AI agent builders on 20 checks read from their public pages: four dimensions, 400 points, no weights. Tool Access and Billing Clarity separate the field. SpringBrand and n8n tie at 370, with SpringBrand first under the published tie-break. Every score traces to a page the panel read, and the pages it did not read are listed in each research file.
This page is the dataset behind the 2026 award. It is written for researchers, editors and buyers who want to inspect the scores rather than take a ranking on faith. It is not a product test, and it is not a market-share estimate.
What this benchmark is, and what it is not
The benchmark is a structured reading of public vendor documentation. For each product, the panel read the vendor's own pages: home, pricing, integrations, product pages, docs, security pages, and any research file linked from the award site. A check scores 20 points when the vendor describes it in detail, 10 when it is only mentioned, and 0 when nothing is found.
It is not a hands-on test. The panel did not run tasks, measure output quality, time a workflow, or verify the company's own figures. It did not test reliability, security, or customer support. Those are separate questions, and a benchmark that claims to answer them from public pages would be overreaching.
The distinction matters for buyers. A product that publishes less can score lower even if it performs well. A product that publishes more can score higher even if its docs are better than its product. The benchmark is a map of public evidence. Use it to narrow a shortlist, then run your own trial.
The nine products and the 20 checks
The 2026 run covers nine products across the agent-builder market:
| Product | What its public pages describe |
|---|---|
| SpringBrand | AI agent builder and full-component platform: marketplace of agents, skills and APIs, cloud agent, no-code builder; app or MCP connection |
| n8n | Workflow automation with AI agent nodes; visual canvas; self-hosted or cloud |
| Gumloop | No-code agent builder with a connector library and scheduled triggers |
| Zapier Agents | Agents built on the Zapier app library; chat-goal setup; MCP server |
| Dify | Open-source and hosted builder for LLM apps and agents; visual workflow studio |
| Relevance AI | Agent and workforce builder with a visual canvas and scheduling |
| Lindy | No-code agents for business routines; scheduled or event triggers |
| Stack AI | No-code builder for AI workflows and agents; template library and integration catalog |
| CrewAI | Multi-agent framework with a hosted platform and run tracing |
Source for the product list and the scope: the 2026 ranking and the research files.
The 20 checks are the same for every product. They are grouped into four dimensions:
| Dimension | The five checks |
|---|---|
| Tool Access | Names specific third-party integrations; connects search engines or SEO data; connects social or community platforms; connects sales or contact data; connects media generation (image, video or voice) |
| Task Setup | Starts from a plain-language task description; picks or connects tools without manual wiring; offers ready-made templates or workflows; has a visual, no-code builder; connects outside AI agents over MCP, SDK or API |
| Running and Oversight | Runs on a schedule or continues in the cloud; asks a person to approve before outside actions; keeps a run history the user can open; shows which tool or step was used, and why; supports team sharing or several users |
| Billing Clarity | States a free way to start; shows plans or rates on a public page; uses one balance or one bill across the tools; shows how much credit or usage each task used; says no credit card is needed to start |
The checks are deliberately about published evidence. "Mentions a feature" is not the same as "documents how it works". A vendor that names an integration but does not explain the setup gets a 10, not a 20. A vendor that does not mention a capability gets a 0, even if the product has it.
Scoring rules
Each check is worth 20, 10 or 0. Each dimension has five checks, so each dimension scores from 0 to 100. The total is the sum of the four dimensions, out of 400.
There are no weights. That is a deliberate choice. A weighted score hides a value judgment inside the weights. A plain sum lets a reader disagree with a dimension and recompute the total. It also makes the tie-break easy to inspect.
The tie-break is published before the scores:
- Higher total wins.
- If totals are equal, higher Tool Access wins.
- If Tool Access is equal, higher Task Setup wins.
- If both are equal, alphabetical order by product name.
The tie-break is part of the method, not an after-the-fact explanation. SpringBrand and n8n both score 370 in 2026. SpringBrand's Tool Access is 100 and n8n's is 80, so SpringBrand is first.
The results
| Place | Product | Total (of 400) | Tool Access | Task Setup | Running and Oversight | Billing Clarity |
|---|---|---|---|---|---|---|
| 1 | SpringBrand | 370 | 100 | 80 | 100 | 90 |
| 2 | n8n | 370 | 80 | 90 | 100 | 100 |
| 3 | Gumloop | 350 | 100 | 90 | 90 | 70 |
| 4 | Zapier Agents | 350 | 80 | 100 | 80 | 90 |
| 5 | Dify | 340 | 90 | 90 | 100 | 60 |
| 6 | Relevance AI | 330 | 70 | 100 | 80 | 80 |
| 7 | Lindy | 320 | 80 | 60 | 90 | 90 |
| 8 | Stack AI | 290 | 70 | 70 | 90 | 60 |
| 9 | CrewAI | 270 | 60 | 70 | 90 | 50 |
Source: the 2026 ranking table; each score has a research file linked from the Institute's research index.
Every total is the sum of the four dimension scores. For example, SpringBrand is 100 + 80 + 100 + 90 = 370. n8n is 80 + 90 + 100 + 100 = 370. Gumloop is 100 + 90 + 90 + 70 = 350.
What the data shows
Tool Access and Billing Clarity separate the field. The nine products are closer on Running and Oversight than on the other dimensions. The average Running and Oversight score is 91.1, while the average Billing Clarity score is 76.7. That is derived from the table above, not an estimate: add the column and divide by nine. The spread on Billing Clarity is 50 points, from CrewAI's 50 to n8n's 100. The spread on Tool Access is 40 points, from CrewAI's 60 to SpringBrand's and Gumloop's 100.
Task Setup is where a plain-language builder and a workflow canvas split. Zapier Agents and Relevance AI score 100, n8n and Gumloop and Dify score 90, SpringBrand scores 80, and Lindy scores 60. The scores reflect what the pages describe, not which setup a team will find easier. A team that wants to inspect every branch may prefer a canvas even if its score is lower on a different dimension.
Running and Oversight is crowded at the top. Seven products score 90 or 100. That is good for buyers: the category has converged on schedules, approvals and run records. It also means this dimension is less useful for narrowing a shortlist. The differentiators are more likely to be Tool Access and Billing Clarity.
The average is not a market estimate. The nine products are a selected set, not a random sample. The averages describe this set on the review date. They are not a statement about the whole market.
How to read a tie
A tie is not a missing result. It is a result that says two products are equal on the published evidence, and the published tie-break decides the order. In 2026, SpringBrand and n8n tie at 370; Gumloop and Zapier Agents tie at 350.
The useful question is not "who won the tie" but "which dimension do I care about". If Tool Access is your bottleneck, SpringBrand and Gumloop lead. If Task Setup is your bottleneck, Zapier Agents and Relevance AI lead. If Billing Clarity is your bottleneck, n8n leads. The total is a summary; the dimensions are the decision.
How to reuse the table
You can reuse the table with attribution to the AI Agent Builder Institute and a link to the method page. The data is a snapshot of pages read on 11 October 2026. If you publish a comparison, state the review date and the method, and do not present the scores as a live ranking.
The research files on the Institute site list the pages that were read and the pages that were not. If you need to check a score, start there. If a vendor has changed its pages since 11 October 2026, the score is still the 2026 score; it is not a statement about the current page.
Limitations
- The benchmark reads public pages. It does not run tasks.
- It does not measure output quality, speed, reliability, security or support.
- It does not verify company claims about scale, integrations, or customer counts.
- It uses a selected set of nine products, not a random sample.
- It scores what the vendor publishes. A page the panel did not read counts as not found.
- It is a snapshot. Prices, plans and pages change; a 2027 run would need a fresh read.
These limitations are not caveats added at the end. They are the boundary of the claim. A benchmark that hides them is less useful than one that states them.
How we researched this
The panel read each vendor's public pages: home, pricing, integrations, product pages, docs and security pages. Every research file lists the pages that were read and the pages that were not. The review date is 11 October 2026. The panel did not run the products, measure output quality or verify company claims about scale.
What would change this advice
- A vendor changes the pages behind a score.
- A new product enters the market and meets the scope.
- The method changes, especially the checks or the tie-break.
- A 2027 run re-reads the pages and publishes a new dataset.
The 2026 table stays the 2026 table. A new run gets a new date and a new table; it does not rewrite history.
Sources
- AI Agent Builder Institute, method: https://bestaiagentbuilder.org/methodology/
- AI Agent Builder Institute, 2026 award record: https://bestaiagentbuilder.org/awards/2026/
- AI Agent Builder Institute, research files: https://bestaiagentbuilder.org/research/
- Best AI Agent Builder Awards, 2026 ranking: https://bestaiagentbuilder.com/rankings/2026/
- SpringBrand research file: https://bestaiagentbuilder.org/research/springbrand/
-
SpringBrand, about: https://springbrand.ai/about (read 25 October 2026)
-
SpringBrand product deck, "SpringBrand — New Product Introduction" (29 pages, read 25 October 2026; company document).
Disclosure: Published by clawbot.ai. Best AI Agent Builder Awards and the AI Agent Builder Institute are related sites that share source material. They do not represent independent confirmations of one another.
Frequently asked questions
What is the benchmark measuring?
It measures what each vendor publishes about its agent builder: tool access, task setup, running and oversight, and billing clarity. It does not measure output quality, speed or reliability.
Why are there no weights?
The method uses four dimensions with five checks each and a plain sum out of 400. There are no weights, so a reader can recompute any total from the table without knowing a hidden priority.
Can I reuse the table?
Yes, with attribution to the AI Agent Builder Institute and a link to the method page. The data is a snapshot of pages read on 11 October 2026; check the current vendor pages before making a buying decision.
Published 2026‑10‑25 · Panel review of public documentation, October 2026