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N8n Code Python

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czlonkowski
n8n-code-python

Write Python in n8n Code nodes (native Python, `language` pythonNative, n8n 2.x). Use when the user explicitly wants Python in a Code node, when migrating old Pyodide/"Python (Beta)" code that used _input/_json/_node/_now, or when a Python Code node fails with NameError, "Security violations detected", "Import of standard library module … is disallowed", "__build_class__ not found", "A 'json' property isn't a dictionary", or "Python runner unavailable". Covers the only two variables (_items/_item), dict-only access, imports blocked by default, the sandbox's denied builtins, accepted return shapes, and how errors interact with onError. JavaScript is the default for Code nodes — native Python has no n8n helpers and, by default, no imports. EXCEPTION — for Python in the AI-agent-callable Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode), use the n8n-code-tool skill instead (input is _query, return must be a string).

Overview

Publisherczlonkowski
Repositoryn8n-skills
Skill namen8n-code-python
Stars
6.2K
Forks
1K
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by czlonkowski on GitHub. Read the source before you install it.

Installation

Install the N8n Code Python AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/czlonkowski/n8n-skills.git /tmp/n8n-skills
mkdir -p .claude/skills
cp -r /tmp/n8n-skills/skills/n8n-code-python .claude/skills/n8n-code-python
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable N8n Code Python in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads N8n Code Python on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so N8n Code Python is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Python Code Node (native)

Since n8n 2.0 the Code node's Python runs as native Python in a task runner (language: "pythonNative"). The old Pyodide "Python (Beta)" is gone, and with it every n8n helper it had: _input, _json, _node, _now, _today and _jmespath all raise NameError now. Code copied from old templates, forum posts or older docs usually breaks on this.


JavaScript first — stronger than before

Native Python gives you two variables and plain Python, and by default no imports: no json, datetime or re. Everything n8n-specific ($('Node'), $jmespath, Luxon, this.helpers.httpRequest, static data) exists only in JavaScript. Use Python only when the user explicitly asks for it. Even then, first check whether an expression, Edit Fields, or a native node (Crypto, Date & Time, HTML, XML) does the job. See n8n-code-javascript and the transform gatekeeper in n8n-expression-syntax.


Quick start

python
# Run Once for All Items (default mode)
return [
    {"json": {"name": it["json"]["name"], "revenue": it["json"]["revenue"]}}
    for it in _items
    if it["json"].get("active")
]
python
# Run Once for Each Item
row = _item["json"]
return {"json": {**row, "name_upper": (row.get("name") or "").upper()}}

Node config: {"language": "pythonNative", "mode": "runOnceForAllItems" | "runOnceForEachItem", "pythonCode": "..."} on n8n-nodes-base.code typeVersion 2.


The only inputs: _items and _item

ModeVariableShape
Run Once for All Items_itemslist of plain dicts {"json": {...}, "pairedItem": {...}}
Run Once for Each Item_itemone plain dict {"json": {...}, "pairedItem": {...}}
  • Each variable exists only in its own mode. _items in each-item mode (or _item in all-items mode) raises NameError.
  • Dict access only. it["json"]["name"] or it["json"].get("name"). it.json.name raises AttributeError: 'dict' object has no attribute 'json'.
  • No other nodes. There is no _node / $('Node') equivalent. If you need data from another branch, bring it in with Merge first, or read it in JavaScript.
  • Webhook payloads are under ["body"]: _items[0]["json"].get("body", {}).get("email").
  • Binary data isn't covered here. Read and write binary in JavaScript (see n8n-binary-and-data).
  • pairedItem: returning _items / _item keeps it. When you build new dicts and downstream uses $('Node').item, add "pairedItem": {"item": i} to each returned item. That isn't verified on native Python yet, so test-run before relying on it.
  • Missing keys: prefer .get(key, default). row["missing"] raises KeyError.

Migration table for legacy code:

Legacy (Pyodide)Native
_input.all()_items
_input.first()["json"]_items[0]["json"] (guard if _items)
_input.item / _json_item / _item["json"]
_node["X"]not available: Merge upstream, or use JS
_now, _todaynot available: pass {{ $now.toISO() }} in via Edit Fields, or use JS
_jmespath(data, q)not available: $jmespath in an expression, or a comprehension
item.json.fielditem["json"]["field"]

Imports: blocked by default

Every import (standard library and third-party) is checked against an allowlist before the code runs. The default allowlist is empty, so even import json rejects the whole node:

Security violations detected
Line 1: Import of standard library module 'json' is disallowed. Allowed stdlib modules: none
  • n8n Cloud: no imports at all.
  • Self-hosted: the admin can allowlist modules in the task-runner config (see n8n-self-hostingTASK_RUNNERS.md). Some instances therefore allow json, datetime and re, and most don't.
  • Default to import-free code. If an import would really help, confirm it first: a one-line test node import json + return [{"json": {"ok": True}}], or ask the user. Never assume.
  • Without imports: parse JSON strings upstream ({{ JSON.parse($json.payload) }} in Edit Fields). Do date math in expressions (Luxon) or JS. ISO-8601 strings still compare and sort correctly as plain strings. Use the Crypto node for hashing.
  • requests, pandas and numpy are never available unless the admin built a custom runner image. Use the HTTP Request node for HTTP.

Sandbox limits (these fail even without imports)

You writeWhat happensUse instead
eval, exec, compile, open, input, type, getattr, setattr, hasattr, vars, dir, globals, locals, object, memoryview, breakpointNameError: name 'type' is not defined (runtime)isinstance(x, dict); key in d / d.get(key)
class Foo: ...__build_class__ not found (runtime)dicts + functions
x.__class__, "{0.__class__}".format(x), __import__("json")Security violations detected (whole node rejected before running)
global counter inside a functionNameError: name 'counter' is not defined, because your code runs inside a wrapper functionnonlocal counter

Everything else in plain Python works (verified): comprehensions, generators, lambdas, closures, recursion, try/except, f-strings / .format() / %, sorted/min/max/sum/any/all/ enumerate/zip/round, sets, isinstance, print() (output goes to the browser console).


Return shapes (verified)

Observed on n8n 2.38.5. The auto-wrapping and passthrough behaviours below are undocumented and could change in a later release. Re-check with a test run after upgrading n8n.

Run Once for All Items

ReturnResult
[{"json": {...}}, ...]canonical, N items
[{...}, ...] (plain dicts)auto-wrapped under json, N items
{"json": {...}} or a single plain dict1 item
_items (mutated in place)passthrough with your changes
None / no returnerror Cannot read properties of null (reading 'json')

Run Once for Each Item

ReturnResult
{"json": {...}}, a plain dict, or _item1 item
Nonethe item is dropped (a built-in filter)
a listerror A 'json' property isn't a dictionary [item 0]

Value conversion on output: tuple becomes a list, set becomes the string "{1, 2}", and a datetime becomes str(dt) ("2026-09-16 10:18:00.025792", not ISO). Convert explicitly (sorted(s), dt.isoformat()).

Prefer the explicit [{"json": ...}] in all-items mode and {"json": ...} in each-item mode. The auto-wrapping works, but the explicit shape makes the intent obvious to the next reader.


Errors and onError

A plain raise ValueError("bad row") fails the node with that message. When the node has an onError continue mode, the three failure kinds behave differently (verified):

FailurecontinueErrorOutputcontinueRegularOutput
Runtime exception (raise, KeyError, NameError, denied builtin){"error": "<message>"} on the error output (main[1]) ✅{"error": "<message>"} on the main output
Static rejection (Security violations detected: import, dunder)node marked failed, but the input items, unchanged, go out the success output; main[1] stays emptyinput items, unchanged, on the main output
Bad return shape (list in each-item mode, None in all-items mode)same: unchanged input on the success outputsame

The last two are silent-data traps: downstream nodes receive unprocessed input as if the code had run, and the execution still shows success. No error branch catches them. Prevent them (no imports unless confirmed, correct return shape) and confirm with a real test run, checking the Code node's status and output (see n8n-error-handling).

Other messages:

  • Python runner unavailable: Python 3 is missing from this system: the self-hosted instance has no Python task runner (the stock image ships none). It's an infrastructure problem, not a code problem. See n8n-self-hostingTASK_RUNNERS.md.
  • validate_node / validate_workflow catch a few Python mistakes (import requests, missing return, return None). They do not catch _input/_json, dot access, blocked stdlib imports, dunder access or classes. A test execution is the only reliable check.

Performance

Each Python Code node costs roughly 0.4 s (about 1 s when the runner is cold), noticeably more than a JS Code node or an expression. Process lists in Run Once for All Items mode rather than per item, and don't chain several small Python nodes where one would do.


Checklist

  • The user actually asked for Python. Otherwise JS, an expression, or a native node.
  • Mode matches the variable: _items (all items) / _item (each item).
  • Only dict access. No _input, _json, _node, _now or _jmespath.
  • No import unless confirmed allowlisted on this instance.
  • No classes, type(), getattr/hasattr or dunders. nonlocal instead of global.
  • Return shape fits the mode. Sets and datetimes converted explicitly.
  • Ran a real test execution and inspected the output items (validation alone won't catch the traps above).

Reference

  • COMMON_PATTERNS.md: 12 import-free patterns, each verified on a live n8n instance (filter, aggregate, group, dedupe, top N, flatten, validate, drop items, text report, safe nested access, running totals, ISO timestamps).

Related skills

  • n8n-code-javascript: the default for Code nodes, with all n8n helpers.
  • n8n-expression-syntax: $jmespath, Luxon and the transform gatekeeper, often a better fit than any Code node.
  • n8n-code-tool: Python in the AI-agent Custom Code Tool (_query, returns a string).
  • n8n-error-handling: wiring error outputs; the passthrough trap above.
  • n8n-self-hosting: enabling the Python task runner and allowlisting modules.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the N8n Code Python AI skill do?

Write Python in n8n Code nodes (native Python, `language` pythonNative, n8n 2.x). Use when the user explicitly wants Python in a Code node, when migrating old Pyodide/"Python (Beta)" code that used _input/_json/_node/_now, or when a Python Code node fails with NameError, "Security violations detected", "Import of standard library module … is disallowed", "__build_class__ not found", "A 'json' property isn't a dictionary", or "Python runner unavailable". Covers the only two variables (_items/_item), dict-only access, imports blocked by default, the sandbox's denied builtins, accepted return...

Why use N8n Code Python on TypingMind?

Because you install it once and use it with any model. N8n Code Python is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install N8n Code Python in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/czlonkowski/n8n-skills/tree/main/skills/n8n-code-python. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use N8n Code Python?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with N8n Code Python?

As many as you like. As long as a model supports skills, you can use N8n Code Python with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the N8n Code Python AI skill free?

Yes. It is published on GitHub by czlonkowski under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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