🚀 Self-Evolving AI Agent

A multi-agent system that autonomously generates, evaluates, and improves solutions using Mistral AI’s powerful LLM in an iterative self-improvement loop.

---

✨ An innovative multi-agent system that learns, critiques, and improves itself — simulating how humans refine ideas through feedback loops.

---

🧠 Overview

Self-Evolving AI Agent is a multi-agent architecture leveraging Mistral AI’s cutting-edge LLM API to autonomously:

• Generate solutions
• Evaluate them critically
• Iteratively improve upon them

This closed-loop system mimics human creativity — producing progressively smarter and higher-quality outputs with minimal human input.

---

⚙️ Features

🤖 Multi-Agent Design

• Executor Agent — Crafts original solutions from user prompts.
• Evaluator Agent — Analyzes and scores each result with detailed feedback.
• Improver Agent — Refines and re-optimizes based on evaluator insights.

🔄 Continuous Self-Improvement

• Agents communicate in feedback loops until the output reaches top quality.

🧩 Tech Stack

• Python, Streamlit, and Mistral AI SDK
• Modular architecture for easy scaling or custom agent addition

---

🚀 Getting Started

🧾 Prerequisites

• Python 3.11+
• Mistral API key → Get one here
• Install dependencies:

  pip install -r requirements.txt

💾 Installation

git clone https://github.com/msravanthi674/self-evolving-ai-agent.git
cd self-evolving-ai-agent
pip install -r requirements.txt

Create a .env file in the root directory:

MISTRAL_API_KEY=your_api_key_here

🖥️ Run Locally

streamlit run app/streamlit_app.py

☁️ Deployment

• Streamlit Cloud → one-click deploy by linking your GitHub repo.
• Supports secure API key management via environment variables.
• Optional: Deploy via Docker, Render, or Heroku for scalability.

---

🗂️ Project Structure

self-evolving-ai-agent/
├── agent_core/         # Core agent logic (executor, evaluator, improver)
├── app/                # Streamlit UI + orchestrator
├── .env                # Environment variables (not committed)
├── requirements.txt    # Dependencies
└── README.md           # Documentation

---

🧩 Example Workflow

• User enters a task description.
• Executor Agent generates an initial solution.
• Evaluator Agent reviews and scores it.
• Improver Agent refines based on feedback.
• The cycle repeats — each iteration gets smarter.

---

💡 Future Enhancements

• 🧬 Reinforcement learning–based improvement scoring
• 🌐 Multi-model orchestration (OpenAI + Mistral hybrid)
• 🧠 Memory-based adaptive agent personalities

---