LangGraph Agentic Models Practice
Author: Harsh Srivastava
Handle: horus-bot
Purpose: Practice repository for LangGraph agentic model implementations
📖 Overview This repository contains various implementations of AI agents using LangGraph, demonstrating different patterns and use cases for building intelligent, tool-enabled conversational systems. Each project explores different aspects of agentic AI development, from basic chatbots to sophisticated RAG (Retrieval-Augmented Generation) systems.
🏗️ Project Structure 🚀 Key Features
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Mathematical Tools Agent (toolsChatbot.ipynb) Performs arithmetic operations (addition, subtraction, multiplication) Demonstrates tool calling with LangGraph state management Uses ChatGroq with Llama-3.3-70b-versatile model
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RAG System (rag/ragModel.ipynb) Document-based question answering PDF processing with vector embeddings ChromaDB for persistent vector storage Contextual search with citation support
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Email Automation Agent (emailAgent.ipynb) Automated email composition and sending SMTP integration with Gmail Natural language email generation
4 Conditional Routing (2nd_level_contion.ipynb) Multi-level decision trees Complex workflow orchestration Dynamic routing based on user input
🛠️ Technology Stack Component Technology Framework LangGraph LLM Provider ChatGroq (Llama-3.3-70b-versatile) Vector Database ChromaDB Embeddings HuggingFace (all-MiniLM-L6-v2) Document Processing PyPDF, LangChain Email SMTP, aiosmtplib
📋 Installation Prerequisites Environment Setup Create a .env file in the root directory:
🎯 Quick Start
- Mathematical Tools Agent
- RAG System
- Email Agent 🔧 Core Patterns State Management All agents use consistent state management:
Tool Integration Tools are defined using LangChain's @tool decorator:
Conditional Routing Agents use conditional edges for decision making:
📊 Example Workflows Basic Chatbot Flow Tool-Enhanced Agent Flow RAG System Flow 🎓 Learning Objectives This repository demonstrates:
LangGraph Fundamentals: State graphs, nodes, and edges Tool Integration: Seamless LLM-tool interaction State Management: Message handling and conversation flow Conditional Logic: Dynamic routing and decision making Error Handling: Robust system design RAG Implementation: Document-based AI systems 🔍 Key Concepts Explored State Graphs: Building complex AI workflows Tool Calling: Extending LLM capabilities Conditional Routing: Dynamic decision making Vector Databases: Efficient document retrieval Multi-turn Conversations: Context preservation Error Handling: Graceful failure management
🚨 Common Issues & Solutions Module Import Errors
API Key Issues
Ensure .env file is in the correct location
Verify API key validity
Check environment variable loading
Tool Execution Errors
Verify tool call arguments
🤝 Contributing This is a personal learning repository. Feel free to:
Fork and experiment
Submit improvements
Share feedback and suggestions
Add new agent patterns
📚 Resources LangGraph Documentation LangChain Documentation ChatGroq API ChromaDB Documentation
📄 License Educational and research use. Please follow respective API terms of service.
Happy Coding! 🚀 Building the future of AI agents with LangGraph