🤖 NexusAI Multi-Agent System 🚀
⚡ Ultra-Fast Multi-Agent AI System powered by Groq + LangGraph
---🧩 Tech Badges
🚀 Live Demo
👉 Try the App Here
🔗 Frontend (Streamlit): https://multi-agent-system-ai.onrender.com/
📌 Project Overview
NexusAI is a Multi-Agent AI System designed to intelligently process user queries using a structured pipeline of AI agents.
Instead of relying on a single LLM call, this system breaks down tasks into multiple specialized agents, improving accuracy, reasoning, and response quality.
It combines speed + intelligence using Groq’s fast inference with a modular agent architecture.
✨ Key Features
⚡ Ultra-Fast Responses Powered by Groq for low-latency inference
🧠 Multi-Agent Architecture Planner → Worker → Reviewer pipeline
🚀 Smart Routing System Simple queries bypass full pipeline for instant replies
📊 Context-Aware Memory Stores and retrieves past interactions using FAISS
🔧 Tool Integration
- Web Search
- Calculator
- File Reader
🔁 Self-Improving Responses Reviewer agent refines outputs
🛠️ Tech Stack
Technology| Purpose 🐍 Python| Core Programming ⚡ Groq API| Fast LLM Inference 🧠 LangGraph| Agent Workflow 🔗 LangChain| LLM Integration 🎨 Streamlit| Frontend UI 📦 FAISS| Vector Memory 🌐 Tavily| Web Search Tool
🏗️ Project Architecture
NexusAI │ ├── app.py # Streamlit Frontend ├── agents.py # Multi-Agent Logic ├── requirements.txt ├── .env # Environment variables (NOT pushed) └── README.md
⚙️ Installation Guide
1️⃣ Clone Repository
git clone https:https://github.com/hari9618/Multi-Agent_System cd nexusai
2️⃣ Create Virtual Environment
python -m venv venv source venv/bin/activate # Mac/Linux venv\Scripts\activate # Windows
3️⃣ Install Dependencies
pip install -r requirements.txt
4️⃣ Setup Environment Variables
Create a ".env" file:
GROQ_API_KEY=your_api_key_here AGENT_MODEL=gemma2-9b-it
5️⃣ Run the App
streamlit run app.py
🧠 How It Works
1️⃣ User sends a query 2️⃣ Smart Router decides execution path 3️⃣ Planner creates structured steps 4️⃣ Worker generates solution 5️⃣ Reviewer refines output 6️⃣ Final response displayed
📷 Application Preview
📚 What I Learned
✔ Multi-Agent System Design ✔ LangGraph Workflow Engineering ✔ LLM Optimization for Speed ✔ Memory Integration with FAISS ✔ Tool-augmented AI Systems
🎯 Future Improvements
🔹 RAG-based knowledge integration 🔹 Advanced tool chaining 🔹 Real-time collaboration agents 🔹 Voice-based interaction 🔹 UI enhancements
👨💻 Author
Hari Krishna AI Engineer | Multi-Agent Systems Builder
🔗 GitHub https://github.com/hari9618
⭐ Support
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📢 Tags
AI • Multi-Agent • LangGraph • Groq • Streamlit • Python • Generative AI • LLM