🌤️ Weather Agent powered by Langchain & Langgraph

This project implements an AI-powered weather agent using Langchain and Langgraph, integrated with a FastAPI backend and a simple HTML/CSS/JavaScript frontend. The agent can fetch real-time weather information for specified locations using the OpenWeatherMap API.

✨ Features

• Intelligent Agent: Built with Langchain and Langgraph to understand natural language queries about weather.
• Real-time Weather Data: Integrates with the OpenWeatherMap API to provide current weather conditions.
• FastAPI Backend: Exposes a RESTful API for communication between the frontend and the agent.
• Interactive UI: A simple, modern web interface for users to chat with the weather agent.
• Modular Project Structure: Organized into standard production-grade folders (src/nodes, src/state, src/graph, src/tools, src/llm).
• Environment Variable Management: Secure handling of API keys using .env and .env.example.

🛠️ Technologies Used

• Python 3.x
• uv (Package Manager)
• FastAPI (Web Framework)
• Langchain (LLM Orchestration)
• Langgraph (Agentic Workflow)
• Groq (LLM Provider - qwen-qw-32b model)
• OpenWeatherMap API (Weather Data)
• HTML, CSS, JavaScript (Frontend)

🚀 Getting Started

prerequisites

• Python 3.x installed
• uv package manager installed (pip install uv)
• API keys for Groq and OpenWeatherMap

1. Clone the repository

git clone https://github.com/rishav-learnerml/weather-agent.git
cd weather-agent

2. Set up your virtual environment and install dependencies

uv venv
source .venv/bin/activate
uv pip install -r requirements.txt

3. Configure API Keys

Create a .env file in the root of your project based on .env.example and add your API keys:

GROQ_API_KEY="your_groq_api_key"
LANGCHAIN_PROJECT="BlogGeneratorAgent" # Or your desired project name
LANGCHAIN_API_KEY="your_langchain_api_key"
UNSPLASH_ACCESS_KEY="your_unsplash_access_key" # Not directly used in weather agent, but kept for consistency
OPENWEATHER_API_KEY="your_openweathermap_api_key"

Replace the placeholder values with your actual API keys. You can get them from:

• Groq Console
• OpenWeatherMap API

4. Run the Backend

Ensure your virtual environment is activated:

source .venv/bin/activate
python main.py

The FastAPI application will run on http://127.0.0.1:8000.

5. Run the Frontend

Open the ui/index.html file in your web browser. For local development, it's recommended to use a simple HTTP server (e.g., VS Code Live Server extension or python -m http.server 5500 in the ui directory).

The frontend will typically run on http://127.0.0.1:5500 (or similar, depending on your local server setup).

🧑‍💻 Project Structure

.env               # Environment variables (ignored by Git)
.env.example       # Template for .env
.gitignore         # Git ignore file
main.py            # FastAPI application entry point
pyproject.toml     # Project metadata (for uv)
requirements.txt   # Python dependencies

src/
├── graph/         # Langgraph definitions
│   └── graph.py
├── llm/           # LLM configurations
│   └── llm.py
├── nodes/         # Langgraph nodes (functions/steps in the agent workflow)
│   └── nodes.py
├── state/         # Langgraph state definition
│   └── state.py
└── tools/         # Langchain tools for the agent
    └── tools.py

ui/
├── index.html     # Frontend HTML structure
├── script.js      # Frontend JavaScript for chat interaction
└── style.css      # Frontend CSS for styling