Has anybody here build an AI automated workflow in N8N and scrapes websites for content and builds its own knowledge based to train an AI model? I know Firecrawl offers this option but I am more interested to know if you have implemented this using Crawl4AI. Cheers!!

As the title says, I am running into issues when trying to integrate/install GSC MCP to my Claude Desktop app or even with N8N. The issue arises when trying to create a key on my Google Cloud Service Account (Check attached image). I ask on a Slack Community build around Tech SEO and someone mentioned this: "most companies try to avoid using keys because they're a security risk, and prefer to use other auth mechanisms. You might try setting up application default credentials. Most Google Cloud SDKs support it by default https://cloud.google.com/docs/authentication/set-up-adc-local-dev-environment" But I don´t know how to do any of that so, wondering, if any of you guys have run into a similar issue and care to give me a hand with this? Thanks in advance!

Nate Herkelman Can you build a workflow that triggers using a webhook to another app, potentially deployed on Streamlit? - Maybe I have an app deployed on Streamlit based on python that generates an in-depth analysis or an in-depth report and I would like to link that to a workflow as an input?

I think the question/request/title is self-explanatory but I am wondering if anybody here have been able to build a workflow that generates a comprehensive and detailed Ideal Customer Persona to be used as part of the content marketing plan/strategy? Looking for the workflow to actively take into account the business UVP as well as the ICP´s pain point to craft engaging and clickworthy title that will effectively resonate with that ICP, as well as use that information to develop copies/drafts. Thoughts?

Hi guys! I’ve spent a lot of time building AI agents for real clients and found that agents made with the basic n8n node are very bad at tool selection, remembering context, and following prompts. I use only OpenAI LLMs because they provide the widest range of features, and I interact with them directly using OpenAI nodes or HTTP requests. After working on 3–5 cases, I noticed that simply replacing the AI Agent node with the OpenAI Assistant node (without changing prompts or tools) significantly increases agent performance. I decided to research how the AI Agent node works internally, and I want to share what I found.. n8n is open-source, so anyone can check how each node works internally. I looked at the code for the AI Agent node and explored how it operates. It uses the LangChain framework to make the code LLM-agnostic and add features like memory, tools, and structured output to any LLM model. Key Points: ⏺️ Universal Convenience, Performance Cost The AI Agent node lets you swap between AI models easily, but it uses LangChain’s “universal” format. This means your messages get converted back and forth, which can cause detail loss and miss optimizations specific to OpenAI. ⏺️ Main Bottlenecks 1. Message Format Translation OpenAI is optimized for its own format, ChatML (roles like system, user, assistant). LangChain uses its own message objects (HumanMessage, AIMessage), which must be converted to ChatML before reaching OpenAI’s API. This leads to subtle translation losses and missed improvements. 2. Context Management: OpenAI’s API handles conversation history and context much more effectively than LangChain’s generic method, especially for long chats. OpenAI uses smart summarization for older messages, while the AI Agent node only takes the last X messages from the memory node. I’m not saying you should strictly avoid the AI Agent node, but you need to understand the pros and cons of each approach to use them in the right cases.