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[ARTICLE · art-152981] src=jeffyclassify.com ↗ pub= topic=ai-tools verified=true sentiment=↑ positive

Show HN: Free text classification for your n8n workflows

Nico Brenner released Jeffy, a free community node for n8n that classifies text with logistic regression on frozen embeddings instead of an LLM call, shipping 70 pretrained classifiers including a banking77 model with 77 intent classes. Brenner cites eight separate n8n community forum threads reporting JSON parsing failures in n8n's built-in Text Classifier, and claims Jeffy runs at roughly 15ms locally or 300ms remotely versus 500ms to 3s per item for the LLM-based node, with no API key required on the free playground.jeffyclassify.com instance. The node is installable via npm install n8n-nodes-jeffy and can be self-hosted with pip install jeffy-classify and jeffy serve --port 8400 so data stays on the local network.

read2 min views1 publishedOct 19, 2026
Show HN: Free text classification for your n8n workflows
Image: source

Oct 2026 · Community Node · Nico Brenner

n8n's built-in Text Classifier wraps an LLM call. It works until it doesn't: the model wraps output in markdown fences, returns an array instead of an object, or just drifts between runs. Eight separate forum threads report the same parsing failures

Jeffy takes a different approach. Logistic regression on frozen embeddings. No LLM, no prompt, no parsing. Same input always gives the same output. The Jeffy node brings 70 pretrained classifiers into n8n as a dropdown

Customer message comes in. Jeffy classifies it against 77 banking intents, then IF nodes route it. Account closure goes to the retention team, lost/stolen cards go to urgent support, everything else hits the general queue. Each path carries the original label and confidence score

Waiting for input...

This calls the real Jeffy API. Type a message, it classifies against banking77 (77 intent classes), then routes it the same way the n8n workflow does

The built-in node sends your text to an LLM and parses the response as JSON. Here's what people run into

``` markdown fences. Reported across GPT-4o, Claude, Llama, and Mistral. The workaround is a Code node that strips backticks before parsing

n8n Text Classifier Jeffy
Reliability JSON parse failures in production Deterministic, always valid JSON
Latency 500ms – 3s per item ~15ms local, ~300ms remote
Cost LLM API credits per call Free (self-hosted or playground)
API key Required (OpenAI, Anthropic, etc.) Optional, public playground works
Setup Configure LLM + write category descriptions Pick a task from the dropdown
Privacy Data sent to LLM provider Self-hostable, data stays local

npm install n8n-nodes-jeffy playground.jeffyclassify.com (free, no key needed) Import this into n8n to get the customer support router from the demo above. Uses Manual Trigger + random sample messages so you can run it right away

The community node defaults to playground.jeffyclassify.com, a free public instance with all 70 classifiers loaded. No API key, no signup

For production, self-host Jeffy on any machine with a CPU:

$ pip install jeffy-classify
$ jeffy serve --port 8400

Then point the n8n credential to http://localhost:8400. Your data never leaves your network

The node supports four operations: classify single text, batch classify, list available tasks, and health check. Batch sends an array in one request, useful for spreadsheet imports or email batches

If you're hitting reliability issues with the built-in Text Classifier, this is a drop-in replacement that won't surprise you at 3am

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