# n8n, Flowise, and Langflow: Build AI Workflows without Sending Data Outside Europe Low-code AI orchestration platforms like n8n, Flowise, and Langflow have made it incredibly easy to build complex AI agents. However, for European companies dealing with proprietary data, plugging these workflows into default US-based LLM providers creates immediate compliance friction. By leveraging an [OpenAI-compatible endpoint](https://docs.regolo.ai/) located within the EU, you can deploy advanced AI automation while maintaining strict data sovereignty. https://www.youtube.com/watch?v=FUYQQzHFVKE&t=51s **In a nutshell:** You don't need a custom provider integration to use Regolo.ai in your low-code tools. By selecting the standard OpenAI nodes in n8n, Flowise, or Langflow and replacing the Base URL with `https://api.regolo.ai/v1`, you gain instant access to [leading open-weight models](https://regolo.ai/models/). This ensures [EU data residency](https://regolo.ai/privacy-and-compliance/) and guarantees [zero data retention](https://regolo.ai/privacy-and-compliance/). ## Why visual AI builders need European inference When you build an automated workflow that reads customer support tickets, summarizes legal contracts, or parses internal HR documents, the payload sent to the LLM contains sensitive information. Using default cloud inference means accepting that your data leaves your jurisdictional control. Integrating Regolo.ai directly into your visual builders ensures that the compute layer resides entirely within European data centers, bypassing the legal overhead of complex Data Processing Agreements (DPAs) for US data transfers. ## 1. Configuring n8n with Regolo.ai n8n is a powerful workflow automation tool that includes advanced AI nodes. To use Regolo.ai in n8n, you configure a standard OpenAI API credential. - **Step 1:** Add an **OpenAI Chat Model** node to your canvas. - **Step 2:** In the node settings, click **Create New Credential**. - **Step 3:** Enter your Regolo.ai API Key. - **Step 4:** Toggle **Use Custom API Base URL** to true. - **Step 5:** Set the Base URL to `https://api.regolo.ai/v1`. - **Step 6:** Manually type the model name you want to use (e.g., `meta-llama/Llama-3-70b-chat-hf`). Your n8n workflows will now execute using secure European infrastructure without modifying the logic of your automation. ## 2. Configuring Flowise with Regolo.ai Flowise makes it easy to visually build LangChain-based applications. Because it inherits LangChain's architecture, overriding the OpenAI endpoint is natively supported. - **Step 1:** Drag and drop the **ChatOpenAI** component onto your canvas. - **Step 2:** Under Connect Credential, create a new OpenAI API credential using your Regolo.ai API Key. - **Step 3:** In the **Additional Parameters** section of the node, locate **BasePath**. - **Step 4:** Enter `https://api.regolo.ai/v1` in the BasePath field. - **Step 5:** Type the specific model identifier (e.g., `mistralai/Mixtral-8x7B-Instruct-v0.1`) in the Model Name field. ## 3. Configuring Langflow with Regolo.ai Langflow offers a sleek interface for chaining LLM components. Similar to Flowise, you just configure the standard OpenAI LLM wrapper. - **Step 1:** Add the **OpenAI** LLM component to your flow. - **Step 2:** Paste your Regolo.ai API Key in the `openai_api_key` field. - **Step 3:** Expand the Advanced settings. - **Step 4:** Find the `openai_api_base` field and enter `https://api.regolo.ai/v1`. - **Step 5:** Set your target open-weight model in the `model_name` parameter. --- ## FAQs **Does this still work if my company bans OpenAI?** Yes. Corporate bans on OpenAI usually stem from data residency and training concerns. By changing the Base URL to Regolo.ai, no data is sent to OpenAI. Your traffic is routed entirely to Regolo's EU-based infrastructure, where there is zero data retention and no training on customer data. **What is an inference provider?** An inference provider runs pre-trained AI models on cloud infrastructure and exposes them via an API, so you can integrate AI without managing GPUs yourself. **How is inference different from training?** Training creates the model by feeding it massive datasets. Inference is the process of using that already-trained model to generate responses or predictions in real time. **Can I use an OpenAI-compatible endpoint and keep data in Europe?** Yes. By pointing your existing OpenAI integration (or nodes in n8n/Flowise) to a European inference provider like Regolo.ai, your data processing remains strictly within the EU. **Is zero data retention enough for GDPR compliance?** Zero data retention is a massive advantage because it means user prompts are not stored after processing. However, full GDPR compliance also depends on where the processing happens and how you handle data on your end. --- Sta**rt your free 30-day trial at [regolo.ai](https://regolo.ai/) and deploy LLMs with complete privacy by design.** 👉 [Talk with our Engineers](https://regolo.ai/contacts/) or [Start your 30 days free →](https://regolo.ai/pricing) --- - [Discord](https://discord.gg/ZzZvuR2y) - Share your thoughts - [GitHub Repo](https://github.com/regolo-ai/) - Code of blog articles ready to start - Follow Us on X [@regolo\_ai](https://x.com/regolo_ai) - Open discussion on our [Subreddit Community](https://www.reddit.com/r/regolo_ai/) - Youtube tutorial: [How to connect Flowise to the Regolo API to create a chatbot](https://www.youtube.com/watch?v=FUYQQzHFVKE&t=56s) --- *Built with ❤️ by the Regolo team. Questions? [regolo.ai/contact](https://regolo.ai/contact)* or chat with us on [Discord](https://discord.gg/ZzZvuR2y)