Multi-Agent Feedback Loop Web App

Overview

This is a Streamlit-based interactive web application that leverages multiple autonomous AI agents to generate and critique responses to a user's query. The agents work independently and collaboratively in a feedback loop, enabling a richer and more critical multi-perspective analysis.

Features

• Multiple AI agents generate diverse responses
• Peer evaluation system where agents review each other's output
• Modular architecture with extensibility for new tools or agent roles
• Lightweight and easy-to-deploy using Streamlit

Core Concepts

This application implements a multi-agent reasoning loop:

1. User enters a query.
2. Multiple response agents generate answers.
3. One or more evaluation agents assess those answers based on criteria like accuracy, relevance, or creativity.
4. The evaluation helps highlight the most compelling responses or guide further refinement.

Architecture

multi-agent-app/
├── agents/
│   ├── base_agent.py       # Abstract base class for all agents
│   └── evaluator.py        # Agent that evaluates generated responses
├── models/
│   └── model_loader.py     # Loads external LLMs
├── tools/
│   └── web_search.py       # Web search tool for agents needing context
├── main.py                 # Streamlit entry point

External Libraries & Models Used

• OpenAI API (e.g., GPT-4) via openai library
• Streamlit for UI rendering
• LangChain for agent structuring and tools
• Python Standard Libraries: abc, typing, os, dotenv, etc.

Installation & Setup

1. Clone the repository

git clone https://github.com/anshulraj10/multi-llm-agents-feedback.git
cd multi-llm-agents-feedback

2. Set up Python environment

We recommend Python 3.10 or later.

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Add API keys

Create a .env file in the root directory:

HF_ACCESS_TOKEN=your-huggingface-token-here

5. Run the app

streamlit run main.py

Usage

• Open the app in your browser.
• Enter a query like: "What are the pros and cons of AI in education?"
• Review how each agent responds and how they critique one another.
• Use this tool to explore multi-faceted viewpoints or generate collaborative AI-driven content.

Contribution Guidelines

Contributions are welcome! To contribute:

1. Fork the repository
2. Create your feature branch: git checkout -b feature/YourFeature
3. Commit your changes: git commit -m 'Add YourFeature'
4. Push to the branch: git push origin feature/YourFeature
5. Open a Pull Request

License

This project is licensed under the MIT License.

Credits and Acknowledgements

Developed by Anshul Raj

Special thanks to the open-source community behind:

• OpenAI
• LangChain
• Streamlit

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This app demonstrates the power of collaborative reasoning through autonomous agents. Ideal for research, brainstorming, or prototyping decision-making systems.