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n8n AI Automation Index.

How AI agents build automations in n8n. Eight questions answered every week from n8n-mcp telemetry and a corpus of AI-built workflows.

Last updated
Sep 10, 2026
Data window
Aug 25–31, 2026
Author
Romuald Członkowski
98,122
n8n workflows created by AI agents in the week
1.87M
n8n-mcp tool calls in the week
74%
of new workflows contain a Code node
69%
of new installs return the following week

Which LLM powers n8n AI agents: OpenAI, Anthropic, Gemini or Ollama?

In n8n workflows built last week, OpenAI holds 37% of model nodes, Anthropic 21%, Google Gemini 18% and OpenRouter 11%. In Mar 2026 OpenAI had 48% and Anthropic 12%.

Model nodes in workflows built last week32,797 workflows
  1. OpenAI37%
  2. Anthropic21%
  3. Google Gemini18%
  4. OpenRouter11%
  5. Groq3%
  6. Ollama3%
  7. DeepSeek2%
  8. Azure OpenAI2%

Shares are measured among model nodes in workflows that entered the corpus in the period, so they reflect current choices rather than the installed base. Anthropic holds 21% of model nodes in Aug 2026 versus 12% in Mar 2026; Google Gemini 15% versus 16%.

Full answer and history

Which n8n nodes do AI agents use most, and which trigger nodes start their workflows?

The most used node in AI-built n8n workflows is Code, present in 74% of last week's workflows, followed by HTTP Request (60%) and Webhook (55%). The median workflow has 7 nodes.

Share of workflows containing the nodelast week of the corpus
  1. Code74%
  2. HTTP Request60%
  3. Webhook55%
  4. IF34%
  5. Schedule Trigger24%
  6. Respond to Webhook19%
  7. Edit Fields (Set)15%
  8. Manual Trigger13%
  9. Postgres13%
  10. Execute Workflow12%
  11. Execute Workflow Trigger11%
  12. Google Sheets11%
  13. Data Table10%
  14. Telegram6%
  15. Loop Over Items6%

The top three are general-purpose tools: code, API calls and webhook entry. Application integrations come lower, led by Google Sheets, Postgres and Telegram. 12% of workflows call another workflow, so composition from sub-workflows is the norm.

Full answer and history

Which n8n node is growing fastest?

The fastest-growing n8n node is Data Table: its share of AI-built workflows rose from 4.3% in May 2026 to 8.6% in Aug 2026, a 2.0× increase. Convert to File is second (1.8×). Last week Data Table was the most searched node among AI agents.

Share growth over the last quarterMay 2026 → Aug 2026
  1. Data Table1.99× · 8.6%
  2. Convert to File1.85× · 1.3%
  3. Limit1.50× · 1.3%
  4. Read/Write Files from Disk1.39× · 1.4%
  5. Filter1.35× · 2.9%
  6. Webhook1.33× · 54.3%
  7. Structured Output Parser1.32× · 1.6%
  8. Execute Workflow1.29× · 10.5%
  9. Extract from File1.17× · 1.9%
  10. Send Email1.16× · 2.9%

The ranking compares the last complete month with the month a quarter earlier and only counts nodes with at least a 1% share in the last month, so a jump from 0.01% to 0.1% cannot beat real adoption. Searches are a leading signal: an agent searches for a node when it is new or undocumented in its context.

Full answer and history

Do AI-built n8n workflows handle errors?

Rarely. 1.8% of AI-built n8n workflows from last week contain an Error Trigger node, and 2.0% of those built in Aug 2026. Since March the share has never exceeded 2.5%.

Share of workflows with an Error Trigger node, by month
  1. Mar 20261.9%
  2. Apr 20262.5%
  3. May 20262.4%
  4. Jun 20262.2%
  5. Jul 20262.0%
  6. Aug 20262.0%
  7. Sep 20261.9%

An agent builds the happy path because that is what the user asked for. Retries, failure alerts and a separate error path appear only when someone asks for them explicitly or after the workflow has already failed.

Full answer and history

Do AI agents build n8n workflows from nodes or from code?

From code. The Code node appears in 74% of AI-built n8n workflows from last week and HTTP Request in 60%. In Aug 2026 the Code share was 73%. Agents would rather write JavaScript and call an API than look for a ready-made integration.

Share of workflows with a Code node, by month
  1. Mar 202653%
  2. Apr 202670%
  3. May 202674%
  4. Jun 202671%
  5. Jul 202674%
  6. Aug 202673%
  7. Sep 202673%

For the team that inherits these workflows, it means more logic in code and less in configuration. A Code node is harder to review in the editor and does not benefit from the built-in authentication and pagination that native nodes provide.

Full answer and history

How large are AI-built n8n workflows, and which fail validation?

55% of workflows created last week have at least 11 nodes and 21% have 31 or more. The larger the workflow, the less often it fails validation: 2.2% for 2–3 nodes versus 0.18% for 31–100.

Validation failure rate by workflow size98,122 workflows in the week
  1. 2-3 nodes2.20%
  2. 4-10 nodes0.77%
  3. 11-30 nodes0.39%
  4. 31-100 nodes0.18%
  5. 101-300 nodes0.04%

The picture of AI producing only simple three-node automations does not hold. The validation result looks counter-intuitive at first: large workflows are built incrementally through many small updates, each of them checked (9 updates per create). Small workflows are often one-off drafts sent without a check.

Full answer and history

Last week the most searched n8n node was data table (286 installs), ahead of webhook (172) and http request (95). The ranking counts unique installs, not queries.

Searched nodes by number of installsmin. 3 installs per query
  1. data table286
  2. webhook172
  3. http request95
  4. schedule trigger86
  5. google sheets80
  6. ai agent74
  7. gmail66
  8. telegram55
  9. form trigger53
  10. whatsapp53
  11. slack49
  12. openai43

Searches reveal what agents do not know by heart: new nodes and application integrations. General-purpose nodes such as Code or IF are almost never searched although they appear in most workflows; messengers and spreadsheets are searched often while ending up in a small share of workflows. The ranking counts unique installs per week with a floor of three installs per query.

Full answer and history

How do AI agents (Claude Code, Cursor) build n8n workflows through MCP: build or maintain?

Maintain. Of 1,870,624 tool calls last week, 57% read workflows, executions and lists, and 15% write. Fetching executions alone is 28% of calls. In the same week 98,122 workflows were created, 14,017 a day.

Workflows created per day
  1. Tue 2521,253
  2. Wed 2619,521
  3. Thu 2720,651
  4. Fri 2815,474
  5. Sat 297,999
  6. Sun 308,033
  7. Mon 315,191

Agents check what happened after a run before fixing the workflow. Each create is followed by 9 updates. Only 10% of active installs created a workflow during the week, and 448 installs (3.7% of creators) built 31 or more each.

Full answer and history
//Weekly

What changed this week

A short record of changes between issues. Full answers and charts live on the question pages.

//n8n pitfalls

Mistakes AI agents make in n8n

Known n8n pitfalls that n8n-mcp hands to AI agents as context before they build. The list is maintained by hand from mistakes seen in real deployments. One pitfall goes into the weekly issue each week.

Methodology

n8n-mcp telemetry is anonymous and opt-out; it contains no workflow contents or personal data, and install identifiers are hashed. Events are aggregated daily in UTC with roughly an eight-day lag.

The workflow corpus consists of workflows built or modified by AI agents, deduplicated by structure and screened for secrets and personal data. The month is the date a workflow entered the corpus, not its first creation date, which is why we report shares rather than absolute counts.

Searches are counted by unique installs, not by number of queries, so a single looping install cannot dominate the ranking.

Pitfalls are a hand-maintained list of known n8n behaviours that n8n-mcp hands to AI agents as context before building a workflow. They are not derived from user data.

Every figure is generated by a script from the same queries each week; the prose changes together with the numbers.

How to cite

Członkowski, R. (2026). n8n AI Automation Index, week 35/2026 (Aug 25–31, 2026). AiAdvisors. https://aiadvisors.pl/en/n8n-ai-index

Data and charts may be reused with attribution (CC BY 4.0). Source data (JSON)

//Implementation

Need someone to build it?

I build and maintain n8n automations for clients with the same method this index measures: agents build, I own the result, and the team learns to build on its own. First stage from €1,000.

n8n consulting and implementation
//Contact

Let's talk about AI in your business

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or write: romuald@aiadvisors.pl · +48 695 263 884