n8n has set out itself as one of the best low-code AI development platforms. The characteristic drag-and-drop interface of n8n has won the hearts of many coders and non-coders alike. The low entry barrier and high skill ceiling makes it the perfect tool for executing ideas on the go.
But there is something missing… It’s hard to come by challenging projects on n8n! This makes it difficult to test proficiency on the platform.
The following projects are here to solve that. Curated to test the depth of your understanding of the n8n platform, the projects range from automation to generation to help widen your horizon.
Table of contents
Use the workflow images of the projects as reference for building them.
1. AI Chat Agent with n8n
This project evaluates whether you can design a conversational system that behaves responsibly. You build a chat agent using n8n, an LLM, SerpAPI (for internet functionality) and memory. This would emulate the functionality of some of the best AI agents on offer.
You’d be challenged on:
- Designing conversation flow and fallback logic
- Managing context and short-term memory
- Deciding when the agent should respond vs defer
- Preventing confident but incorrect answers
Tech involved: ChatGPT (or equivalent chat model), SerpAPI
Project source: n8n complete guide to automation
2. Content Creator Agent with n8n
This extension turns content generation into a system. Topics and audiences live in Google Sheets, research is pulled dynamically via search, and outputs are generated across LinkedIn, X and blog draft, before being written back into storage.
You’d be challenged on:
- Orchestrating multi-step workflows
- Integrating live research APIs
- Generating platform-specific outputs
- Maintaining clean data flow
Tech involved: Google Sheets, Tavily Search, ChatGPT, LinkedIn API, X API, Google Docs API
Project source: Content Creator Agent using n8n
3. Invoice Reader Bot using n8n
This project rewards precision over flash. You build a bot that extracts structured data from messy invoices and routes it downstream with validation.
You’d be challenged on:
- Parsing semi-structured documents
- Handling missing or inconsistent fields
- Designing validation logic
- Managing edge cases cleanly
Tech involved: OCR or document parsing tools, LLMs, Google Sheets API
Project source: Invoice Reader using n8n
4. End-to-End Marketing Automation using n8n + Nano Banana
This project tests whether you can automate creativity without losing control. You build a pipeline that turns raw product input into usable marketing visuals, end to end.
You’d be challenged on:
- Structuring inputs for reliability
- Crafting prompts that guide creativity
- Orchestrating agent and image-generation steps
- Producing usable assets without cleanup
Tech involved: Google Gemini 2.5 flash (Nano Banana), Travily search
Project source: Marketing Automation using n8n
5. Automating WhatsApp Business Queries using n8n
This project drops you into real-time automation. You build a WhatsApp agent trained on your own product data, answering customer queries through retrieval rather than guesswork.
You’d be challenged on:
- Creating and reusing a vector store
- Routing supported vs unsupported messages
- Grounding responses with RAG
- Preventing hallucinations
Tech involved: WhatsApp Business Cloud API, LLM, RAG
Project source: Automating Whatsapp using n8n
Approach
These aren’t run-of-the-mill automations. Each project forces a different kind of discipline: reasoning in flows, designing for failure, and knowing where automation should stop.
If you can build these cleanly, you’re not just familiar with n8n, you’re fluent in it. At that point, the platform stops feeling like a tool you operate and starts behaving like an environment you think in. That’s the shift that matters.
If you’re struggling to get through these projects, you might consider taking the free course on n8n automation.
Frequently Asked Questions
Q1. Do I need prior experience with n8n to attempt these projects?
A. You don’t need expert-level experience, but you should be comfortable with basic nodes, triggers, and data flow to get real value from them.
Q2. Are these projects suitable for non-developers?
A. Yes. They’re no-code or low-code, but they test system thinking rather than programming syntax.
Q3. What’s the best way to approach these projects?
A. Treat them as system design exercises. Focus on control, edge cases, and failure handling instead of rushing to a working demo.
I specialize in reviewing and refining AI-driven research, technical documentation, and content related to emerging AI technologies. My experience spans AI model training, data analysis, and information retrieval, allowing me to craft content that is both technically accurate and accessible.
