Creating An AI Chat Assistant with Auth0 Authentication

A submission for the Auth0 for AI Agents Challenge featuring a secure AI chat application powered by Gemini and Auth0.

Building Grok - An AI Chat Assistant with Auth0 Authentication

This is a submission for the Auth0 for AI Agents Challenge

What I Built

I built Grok, an AI-powered chat assistant application that leverages Google's Gemini 2.5 Flash-Lite model to provide conversational responses and analyze text documents, including PDFs and plain text files. This application solves the problem of creating a secure, user-friendly interface for interacting with AI agents, allowing users to ask questions, upload files for analysis, and maintain chat histories—all while ensuring robust authentication and data privacy. The app is designed for both personal use and potential enterprise integration, offering a scalable solution for AI-driven communication.

Demo

You can try the application directly at: https://auth0-developement-challenge.onrender.com

How I Used Auth0 for AI Agents

I integrated Auth0 for AI Agents to secure the application and enable seamless user authentication. Auth0 provided a robust identity management system, allowing users to sign in or sign up with ease. I implemented the following:

• Authentication Flow: Used Auth0's OAuth 2.0 authorization code flow with PKCE to handle login and signup, redirecting users to /api/auth/callback after authentication.
• User Management: Leveraged Auth0's user info endpoint (/api/auth/user) to retrieve user details (e.g., sub, name, email) and associate them with chat sessions in a demo database.
• Security: Applied @requires_auth decorators to protect API endpoints, ensuring only authenticated users can create chats, send messages, or upload files.
• Logout: Integrated Auth0's logout endpoint to clear sessions and redirect users securely.

This setup ensured that the AI agent's interactions are tied to authenticated users, enhancing security and enabling personalized chat histories.

Lessons Learned and Takeaways

Building Grok was an exciting journey into AI agent development and authentication integration. Here are my key insights:

• Challenges Faced: Initially, I struggled with configuring CORS and handling streaming responses from the Gemini API, which required debugging network issues and adjusting the frontend to match backend output. The PDF processing dependency (pdfminer.six) also posed a setup challenge, resolved by ensuring proper package installation.
• AI Agents: I learned that integrating LLMs like Gemini requires careful handling of token limits and response formatting. The switch from streaming to direct API calls simplified the UI but highlighted the trade-off between real-time feedback and simplicity.
• Authentication: Working with Auth0 taught me the importance of secure token management and the flexibility of OAuth flows. The demo mode without custom auth modules was a limitation, but it helped me focus on core functionality before scaling.
• Development Insights: Modularizing the codebase (e.g., UI utilities, event handlers) improved maintainability. Testing across sessions revealed the need for persistent chat storage, a feature I plan to enhance with a real database.
• Advice: For other developers, start with a minimal viable product, test authentication early, and leverage community resources (e.g., Auth0 docs, DEV forums) to troubleshoot. Embrace iterative development to refine AI agent behavior.

This project deepened my understanding of AI agents and secure authentication, and I’m eager to expand Grok with features like multi-model support and persistent storage. Thanks for the opportunity to participate!

Team Submissions: This submission is by me, pratik_12b3f8bf3b50e48bae
*Thanks for participating!

Comments

[roshan sharma]

Nice work! You built a solid, secure AI chat app that actually works end-to-end. I like that you didn’t just stop at integrating Gemini, you also handled file uploads, chat history, and proper authentication with Auth0. Your write-up shows you tackled real-world issues like CORS, streaming responses, and PDF parsing, which is exactly the kind of practical problem-solving that stands out. The modular approach and lessons learned also show you’re thinking ahead about maintainability and scaling. Overall, this is a clean, professional, and thoughtful submission.