Building an AI Devil's Advocate: A Multi-Agent Debate App with LangGraph & Streamlit #ai #python #opensource #showdev

Hey devs!
This is my first post on here, and I am sharing what I built

Have you ever wanted an AI that argues with itself? I recently built a project called "AI Devil's Advocate" — a multi-agent debate application that lets two AI agents argue a topic while a Judge AI delivers the final verdict.

But building it came with a major challenges like: API Rate Limits etc.

Here is how I built it, how it works, and how I solved the rate-limit issue using Automatic Fallback.

What is AI Devil's Advocate?

It's an autonomous multi-agent system where:

1. Agent A (For): Argues in favor of the topic.
2. Agent B (Against): Argues against the topic.
3. Judge AI: Listens to both sides and provides a structured verdict.

It's fully built with Python, LangGraph, and Streamlit, supporting multiple free-tier providers like Groq, Google Gemini, and OpenRouter.

Key Features

1. Multi-Provider Architecture: Switch between Groq, Gemini, and OpenRouter on the fly.
2. Automatic 429 Fallback (The Magic): If your active provider hits a rate limit or quota exhaustion, the app automatically switches to your next configured provider with zero interruption to the debate.
3. Polished UI & Animations: Custom CSS, Google Fonts (Inter/Poppins), fade-in animations, and stylized debater cards.
4. Transcript Exports: Download the entire debate as a styled PDF or Markdown file with 1 click.
5. Session API Call Counter:** Real-time sidebar counter to monitor your free-tier usage.

Tech Stack & Architecture

1. Frontend: Streamlit (with custom CSS for animations)
2. Orchestration: LangGraph (State machine for multi-agent flow)
3. LLM Factory: Custom FallbackLLMWrapper class that handles provider switching.
4. Providers: Groq (ultra-low latency), Google Gemini, OpenRouter.
5. Exports: ReportLab (for PDF generation).

How the Fallback Works
Instead of relying on one API, I built an llm_factory.py that wraps all providers. If the primary LLM throws a 429 Too Many Requests error, the wrapper catches it and immediately routes the prompt to the next available provider in the list. The debate never stops!

Try it Live!

I have deployed it on Streamlit Community Cloud. You can try it right now:

Live Demo Link

Open Source

The project is completely open-source. Feel free to fork it, add new providers, or improve the UI!

GitHub Repository

What I Learned

Building this taught me a lot about:

1. State Management in LangGraph for multi-agent conversations.
2. Error Handling in Production LLMs (the 429 fallback is a lifesaver).
3. UI/UX in Streamlit — you can make it look like a real SaaS product with a bit of custom CSS.

If you have any questions or feedback, drop a comment below! I'm currently exploring more Agentic AI workflows.

And this is my first post, so please let me know if I made any mistakes in writing or anywhere else. Also, if you like the repo, please consider giving it a star..

Happy coding!