🤖 Agents: Advanced AI Agent Implementations with LangGraph

Welcome to the Agents repository! This project is a comprehensive exploration of cutting-edge AI agent architectures, leveraging LangGraph, a powerful framework for building complex agentic workflows. From reasoning agents to advanced retrieval-augmented systems, this repository showcases innovative implementations designed to push the boundaries of AI reasoning, retrieval, interaction, and structured output generation. 🚀

📖 Project Overview

This repository serves as a hub for experimenting with and advancing AI agent technologies. I’ve implemented a diverse set of agent types, each tackling unique challenges in reasoning, retrieval, conversational tasks, and structured data processing. Using LangGraph, these implementations are modular, scalable, and efficient, making them ideal for research, experimentation, or real-world applications. Whether you're interested in agentic reasoning, retrieval-augmented generation (RAG), conversational AI, or structured output generation, there’s something here for you!

🛠️ What’s Implemented So Far

Here’s a rundown of the exciting work completed in this repository:

• ReAct Agents:
  Built ReAct (Reasoning + Acting) agents from scratch, including versions with custom tools and LangGraph integration. These agents iteratively reason about tasks, act on decisions, and incorporate human feedback for improved decision-making (see LangGraph/human_in_the_loop.ipynb).

• Corrective-RAG:
  Developed a Corrective Retrieval-Augmented Generation (RAG) system that enhances retrieval accuracy by iteratively refining retrieved information, ensuring more relevant and precise responses from the language model.

• Self-RAG:
  Implemented a Self-RAG system where the agent autonomously evaluates and improves its retrieval process without relying on a language model for every step, optimizing for efficiency and accuracy.

• Agentic RAG:
  Created an advanced Agentic RAG system that integrates agentic workflows with RAG, enabling dynamic reasoning over retrieved information and informed decision-making.

• Simple LangGraph-Based Agents:
  Designed lightweight agents using LangGraph to perform tasks like task decomposition, state management, and workflow orchestration, serving as a foundation for more complex systems.

• Chatbot with LangGraph:
  Built an interactive chatbot powered by LangGraph (Bot_with_LangGraph/app.py, Bot_with_LangGraph/bot.py), capable of maintaining conversational context, managing multi-turn dialogues, and integrating reasoning capabilities for more intelligent responses.

• Structured Output Generation:
  Developed a system for generating structured outputs (e.g., JSON, tables) from agent workflows, useful for integrating AI outputs into downstream applications.

• Database Integration:
  Integrated LangGraph agents with databases (LG_Projects/employee.db, LG_Projects/SQL_db_agent_pro.ipynb), enabling agents to query and manipulate structured data for tasks like employee management or data analysis.

• Self-Ask Agent:
  Built a self-ask agent that autonomously generates follow-up questions to deepen its understanding and improve task performance.

📂 Repository Structure

The repository is organized into directories and files to make it easy to explore each implementation:

• Bot_with_LangGraph/: Chatbot implementation with LangGraph.
  • app.py: Main application for the chatbot.
  • bot.py: Core chatbot logic.
• LangGraph/: Core LangGraph-based agent implementations.
  • LangGraphWithRAG.ipynb: Agentic RAG implementation.
  • basicLevelWithLLM.ipynb: Corrective-RAG implementation.
  • basicLevelWithoutLLM.ipynb: Self-RAG implementation.
  • human_in_the_loop.ipynb: ReAct agents with human-in-the-loop feedback.
  • llama3.txt: Notes and configurations for LLaMA 3 integration.
• LG_Projects/: Database-integrated LangGraph projects.
  • employee.db: SQLite database for employee data.
  • SQL_db_agent_pro.ipynb: Agent for querying and managing the database.
• RAG_With_LangGraph/: Advanced RAG implementations.
  • AgenticRAG(ARAG).ipynb: Agentic RAG system.
  • CorrectiveRAG.ipynb: Corrective-RAG system.
  • SRAG(SELF-RAG).ipynb: Self-RAG system.
• ReAct_Agent_From_Very_Scratch/: ReAct agent implementations.
  • ReAct_Agent.py: Basic ReAct agent built from scratch.
  • reactAgent_with_LG.py: ReAct agent with LangGraph integration.
  • ReAct_With_CUSTOM_tools.py: ReAct agent with custom tools.
  • SelfAsk.py: Self-ask agent for autonomous question generation.
• structure_output.py: Script for generating structured outputs from agent workflows.

Each file contains detailed code, comments, and explanations to help you understand the implementation and adapt it for your own use cases.

🚀 Future Plans: Multi-AI Agents

The journey doesn’t stop here! I’m actively working on expanding this repository with multi-AI agent systems, including:

• Supervisor Agents: Agents that oversee and coordinate the activities of other agents, ensuring efficient task delegation and execution.
• Network Agents: A network of agents that collaborate and communicate to solve complex problems, leveraging collective intelligence.
• Hierarchical Agents: A hierarchical structure of agents where higher-level agents manage lower-level ones, enabling scalable and organized workflows.

These upcoming implementations aim to tackle advanced challenges in distributed AI systems, multi-agent collaboration, and hierarchical decision-making. Stay tuned for updates! 🌟

🧠 Why This Matters

AI agents are at the forefront of transforming how we interact with technology. By combining reasoning, retrieval, action-taking, and structured output generation, these agents can solve complex problems, automate workflows, and enhance human-AI collaboration. This repository serves as a playground for exploring these concepts, with practical implementations that you can build upon for research, experimentation, or real-world applications.

🛠️ Getting Started

Prerequisites

• Python 3.8+
• Install dependencies:

Running the Code

Clone the repository:

git clone https://github.com/Sunilyadav03/Agents.git
cd Agents

Explore the .ipynb files or run Python scripts (e.g., python Bot_with_LangGraph/app.py) to test the implementations.

🌟 Contributing

Contributions are welcome! If you have ideas for new agent architectures, improvements, or bug fixes, feel free to open an issue or submit a pull request. Let’s build the future of AI agents together! 🤝

📜 License

This project is licensed under the MIT License—see the LICENSE file for details.

📬 Contact

For questions, feedback, or collaboration, reach out via: GitHub Issues ,LinkedIn: Sunil Yadav

Built with 💡 by Sunil Yadav | Last Updated: June 2025