🤖 AI Agent

A production-grade autonomous AI agent built with LangGraph, FastAPI, and multi-provider LLM fallback. Capable of real-time web search, live weather, Python code execution with auto-fix, multi-language code generation, math calculations, and currency conversion.

🌐 Live Demo: https://ai-agent-p13l.onrender.com

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✨ Features

• 🔍 Real-time web search — breaking news, current events, live data
• 🌤️ Live weather — any city worldwide
• 🐍 Python code execution — runs code, shows output, auto-fixes errors
• 💻 Multi-language code generation — Python, Java, JavaScript, C++, Go, Rust, SQL, and 15+ more
• 🧮 Math & calculations — percentages, formulas, unit conversions
• 💱 Live currency conversion — real-time exchange rates
• 🧠 Conversation memory — remembers full session context
• ⚡ Multi-provider LLM fallback — Groq → Gemini → OpenRouter, never goes down
• 🌐 Multilingual — responds in Hindi, English, Hinglish, or any language

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🛠️ Tech Stack

Layer	Technology
Backend	FastAPI, Python 3.12
AI Framework	LangGraph, LangChain
LLM Providers	Groq (Llama 3.3 70B), Google Gemini 2.0, OpenRouter
Search	Tavily API
Weather	wttr.in
Currency	ExchangeRate API
Frontend	HTML, CSS, Vanilla JS
Deployment	Render

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🚀 Quick Start

1. Clone the repo

git clone https://github.com/kailashv2/ai-agent.git
cd ai-agent

2. Create virtual environment

python -m venv venv
venv\Scripts\activate  # Windows
source venv/bin/activate  # Mac/Linux

3. Install dependencies

cd backend
pip install -r requirements.txt

4. Set up environment variables

Create a .env file in the root directory:

GROQ_API_KEY=your_groq_key
GEMINI_API_KEY=your_gemini_key
OPENROUTER_API_KEY=your_openrouter_key
TAVILY_API_KEY=your_tavily_key

5. Run locally

uvicorn main:app --reload

Open http://127.0.0.1:8000

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🔑 Getting API Keys (All Free)

Service	Link	Free Tier
Groq	https://console.groq.com	100K tokens/day
Google Gemini	https://aistudio.google.com/apikey	1500 requests/day
OpenRouter	https://openrouter.ai	Multiple free models
Tavily	https://app.tavily.com	1000 searches/month

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📁 Project Structure

ai-agent/
├── backend/
│   ├── agent.py         # LLM provider management, routing, code handling
│   ├── tools.py         # Search, weather, calculator, code executor, currency
│   ├── main.py          # FastAPI server, routes
│   ├── requirements.txt
│   └── static/
│       └── index.html   # Served frontend
├── frontend/
│   └── index.html       # Chat UI source
├── Dockerfile
├── .env.example
└── README.md

🧠 Architecture

User Message
     ↓
  Router
  ├── Code Request? → LLM generates code → Execute → Auto-fix if error → Return output
  ├── Needs Tools?  → LangGraph ReAct Agent → Tools (Search/Weather/Calculator/Currency)
  └── General?      → Direct LLM response (fastest)
     ↓
Multi-Provider LLM Fallback
  Groq (primary) → Gemini (secondary) → OpenRouter (tertiary)

💬 Example Queries

"What is the latest news about AI?"
"Write fibonacci series in Python"
"Weather in Mumbai"
"100 USD to INR"
"Write a REST API in Java"
"What is machine learning?"
"Calculate 18% of 25000"
"Write bubble sort in C++"
"Latest NEET 2026 news"
"What is quantum computing?"

🚀 Deployment

Render (Recommended)

1. Fork this repo
2. Create new Web Service on Render
3. Set Root Directory to backend
4. Build Command: pip install -r requirements.txt
5. Start Command: uvicorn main:app --host 0.0.0.0 --port $PORT
6. Add environment variables
7. Deploy

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📄 License

MIT License — feel free to use, modify, and distribute.

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Built with ❤️ by Kailash