---
type: source
title: "Should I Build My AI Agents with n8n or Python?"
description: "Use n8n (no-code) for integration-heavy internal tools, proofs of concept, and mixed-skill teams; move to Python (code) when you need production scale, speed, control, and flexibility, or combine both in a hybrid architecture."
youtube_id: V-0ExE6imTg
url: https://www.youtube.com/watch?v=V-0ExE6imTg
slug: should-i-build-my-ai-agents-with-n8n-or-python
published: 2025-10-19
duration: "0:16:01"
recency_rank: 61
raw: "../raw/should-i-build-my-ai-agents-with-n8n-or-python.md"
tags: [no-code, python, agent-frameworks, architecture]
created: 2026-07-21
updated: 2026-07-21
---

Cole answers his most-asked question, n8n or Python for building AI agents, by reframing it as no-code versus code and giving a use-case-by-use-case decision framework rather than crowning a single winner. His high-level recommendation: reach for n8n when the work is integration-heavy, an internal automation, a fast proof of concept, or a project shared across skill levels, because the learning curve is small and it is a beautifully visual canvas with hundreds of built-in integrations and thousands of templates. Move to Python when you want production-scale applications where speed, control, and flexibility matter, designing the solution around exactly what you need.

He walks a detailed pros and cons list. n8n's strengths are easy onboarding, visual node building, huge integration and template libraries, surprisingly good scaling via queue mode, and open-source self-hostability so agents can run fully local for sensitive data. Its weaknesses are commercial licensing costs, the no-code performance and integration "box" that limits you to supplied integrations and forces brittle HTTP-node workarounds (for example, watching Google Drive for deleted files), inefficient code nodes with restricted libraries, ugly JSON diffs that make git version control painful, and choking on large files like 20MB-plus PDFs that break RAG pipelines. Python's costs are a higher learning curve, more security responsibility, and harder visualization (mitigated by tools like LangGraph Studio and Langfuse). Its payoffs are full control and customizability: true parallel execution of agents, token streaming, better performance, access to top open-source libraries (Graphiti, Docling, Crawl4AI), leverage from powerful AI coding assistants that erode n8n's ease-of-use edge, better scaling, local options with no license concerns, and real git version control.

He closes by rejecting the false binary and recommending a hybrid approach: use n8n for orchestration, entry points, and integrations, then offload heavy processing (agents, RAG chunking, large-file handling) to external Python microservices, and deploy both together on the same cloud instance to exploit the strengths of each.

## Concepts covered

- [No-Code vs. Code](../concepts/no-code-vs-code.md)
- [Rapid Prototyping](../concepts/rapid-prototyping.md)
- [Agentic Workflow Engineering](../concepts/agentic-workflow-engineering.md)
- [Parallel Agent Execution](../concepts/parallel-agent-execution.md)
- [No-Code Automation](../concepts/no-code-automation.md)
- [Web Crawling & Ingestion](../concepts/web-crawling.md)
- [Git as Long-Term Memory](../concepts/git-as-long-term-memory.md)
- [Local & Self-Hosted AI](../concepts/local-ai.md)
- [The Evolution of AI Coding](../concepts/the-evolution-of-ai-coding.md)

## Entities

- [n8n](../entities/tools/n8n.md)
- [Python](../entities/tools/python.md)
- [LangGraph](../entities/tools/langgraph.md)
- [Langfuse](../entities/tools/langfuse.md)
- [Graphiti](../entities/tools/graphiti.md)
- [Docling](../entities/tools/docling.md)
- [Crawl4AI](../entities/tools/crawl4ai.md)
- [Claude Code](../entities/tools/claude-code.md)
- [Codex](../entities/tools/codex.md)
- [Dynamous](../entities/organizations/dynamous.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:00:00]** Frames the video: should you build AI agents with n8n or Python (no-code vs code)?
- **[0:01:27]** High-level recommendation: n8n for integration-heavy internal tools, PoCs, mixed-skill teams; Python for production scale
- **[0:03:03]** n8n pros: easy to start, visual canvas builder, hundreds of integrations, thousands of templates
- **[0:04:48]** n8n scales well via queue mode, is open-source and self-hostable for local/secure agents
- **[0:05:22]** n8n cons begin: commercial license fee, no-code performance and integration "box"
- **[0:06:49]** Integration-box example: Google Drive can't watch deleted files, forcing an HTTP-node API workaround
- **[0:08:05]** n8n cons: hard to version-control (ugly JSON diffs), chokes on large files (20MB+ PDFs) in RAG
- **[0:09:31]** Python cons: higher learning curve, more security responsibility, harder to visualize
- **[0:11:25]** Python pros: full control, true parallel execution of research agents, token streaming
- **[0:13:13]** Python pros continue: open-source libraries (Graphiti, Docling, Crawl4AI), AI coding assistants, git version control
- **[0:14:37]** Hybrid approach: n8n for orchestration/integrations, Python microservices for heavy processing, deployed together

## Transcript

[Raw transcript](../raw/should-i-build-my-ai-agents-with-n8n-or-python.md)
