🤖 AI Chatbots and Agents Repository

A comprehensive collection of production-ready AI agents and chatbots built with cutting-edge LLM technologies including Google Gemini, Groq, LangChain, CrewAI, LangGraph, and Embedchain.

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📋 Table of Contents

• Overview
• Agents & Chatbots Directory
• Related Projects
• Getting Started
• Installation
• Configuration
• Usage Examples
• Project Structure
• Contributing
• Author

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<details open> <summary><h2>Overview</h2></summary>

This repository showcases various AI agents and chatbots designed to solve real-world problems across different domains including content generation, document analysis, data extraction, conversational AI, code repository exploration, and academic research. Each project is self-contained with its own dependencies and can be run independently.

Key Features

• 🚀 Production-ready implementations
• 🎨 User-friendly Streamlit interfaces
• 🔧 Modular and extensible architecture
• 📚 Multiple LLM providers (Gemini, Groq)
• 💾 Persistent storage and state management
• 🌐 Multi-modal capabilities (text, images, PDFs)
• 🔍 RAG-based document and code retrieval
• 📖 Academic research paper integration

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<details open> <summary><h2>Agents & Chatbots Directory</h2></summary>

| Agent/Chatbot Name | Use Case | Description | Technologies | Folder Link | |-------------------|----------|-------------|--------------|-------------| | Chat with GitHub Agent | Code Repository Q&A | RAG-based agent that enables conversational interaction with GitHub repositories. Uses embeddings to understand repository structure, code, documentation, and issues. Allows developers to query codebases, understand architecture, find specific implementations, and get context-aware answers about any public GitHub repository. | Embedchain, Groq (GPT-OSS-120B), HuggingFace Embeddings, Chroma VectorDB, Streamlit | Chat with Github Agent/ | | Chat with Research Paper | Academic Research Assistant | AI-powered research assistant that searches and retrieves information from arXiv research papers. Enables researchers and students to query academic literature, get paper summaries, understand complex concepts, and explore research topics through natural language queries. Automatically fetches relevant papers from arXiv database. | Agno, Groq (GPT-OSS-120B), ArxivTools, Streamlit | Chat with Research Paper/ | | Data Analysis Agent | Data Science & Analytics | AI-powered data analysis agent that performs comprehensive exploratory data analysis on CSV files. Automatically generates multiple visualizations including correlation heatmaps, pairplots, violin plots, histograms, box plots, and count plots. Uses Gemini 2.0 Flash vision model to analyze visualizations and provide intelligent insights. Features streaming responses, robust error handling with retry logic, and automatic encoding detection. | Chainlit, Gemini 2.0 Flash, Pandas, Matplotlib, Seaborn | Data Analysis Agent/ | | News Reporter AI Agent | Automated Content Generation | Multi-agent system with researcher and writer agents that collaborate to generate comprehensive tech news articles. Uses web search for current information and produces markdown formatted reports. | CrewAI, Gemini 2.0 Flash, SERP API | News Reporter AI Agent/ | | Resume ATS Analyzer | Recruitment & HR Tech | AI-powered Applicant Tracking System that analyzes resumes against job descriptions, provides match percentages, identifies missing keywords, and offers professional evaluation. Processes PDFs as images using vision-capable LLMs. | Gemini 2.0 Flash (Vision), Streamlit, pdf2image | Resume ATS and Score Analyzer/ | | Text to SQL LLM App | Database Query Interface | Natural language to SQL converter that allows non-technical users to query databases using plain English. Demonstrates prompt engineering for code generation with strict output formatting. | Gemini 2.0 Flash, SQLite, Streamlit | Text to SQL LLM App/ | | MultiPDF Chat Bot | Document Q&A & Research | RAG-based chatbot that enables conversational interaction with multiple PDF documents. Uses vector embeddings for semantic search and retrieves relevant context for accurate answers. | LangChain, Gemini 2.0 Flash, FAISS, HuggingFace Embeddings | Chat with MultiPDF document/ | | Multilanguage Invoice Extractor | Document Processing & Automation | Vision-based AI that extracts information from invoices in any language. Supports multilingual text recognition and structured data extraction without requiring OCR. | Gemini 2.0 Flash (Vision), Streamlit, PIL | Multilanguage Invoice Extractor/ | | YT & Web Summarizer | Content Curation & Analysis | Universal content summarization tool that generates concise summaries from YouTube videos (via transcripts) and web articles. Configurable summary length with consistent formatting. | LangChain, Groq (Llama-3.3-70b), YouTube Transcript API, Streamlit | Langchain - YT & Web Summarizer/ | | LangGraph Chatbot | Conversational AI & Customer Support | Stateful chatbot with persistent conversation memory, multi-thread management, and SQLite-based checkpointing. Supports conversation history, thread switching, and streaming responses. | LangGraph, Groq (Llama-3.3-70b), SQLite, Streamlit | Langgraph- Chatbot/ | | Resume Reviewer Agent | Resume Analysis & Career Development | Production-grade resume review API that provides professional three-paragraph feedback on resumes. Features async job processing with Redis Queue, MongoDB persistence, Docker containerization, and vision-based PDF analysis using Gemini 2.5 Flash through OpenRouter. Analyzes resume strengths, weaknesses, and provides actionable recommendations for improvement. | FastAPI, OpenRouter (Gemini 2.5 Flash), MongoDB, Redis Queue, Docker, pdf2image | Resume Reviewer Agent/ |

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<details open> <summary><h2>Related Projects</h2></summary>

🔗 Customer Support Agent (Separate Repository)

A production-grade, enterprise-level AI-powered customer support automation system with advanced multi-agent workflow orchestration.

Repository: Customer-Support-Agent

Key Features:

• Multi-Agent Workflow - 8-stage intelligent pipeline (Validation → Categorization → Sentiment Analysis → Priority Assignment → Response Generation → Action Suggestions → Re-evaluation → Escalation)
• Dual Frontend Architecture - Separate React portals for customers and employees
• RESTful API Backend - FastAPI with complete CRUD operations
• Real-time Analytics Dashboard - Comprehensive metrics, trends, and complaint tracking
• Automated Email Responses - Context-aware, policy-compliant responses with human escalation
• Persistent Storage - SQLite database with SQLAlchemy ORM

Technology Stack:

• Backend: Python, FastAPI, LangChain, LangGraph, Groq, SQLAlchemy, SQLite, Pydantic
• Frontend: React, Axios
• AI/ML: Multi-agent system with 8 specialized agents for different tasks

Why Separate Repository?

This project is a complete, production-ready customer support solution with complex architecture including multiple frontends, backend APIs, database management, and email integration. It's structured as an enterprise application rather than a standalone agent/chatbot demo.

View Customer Support Agent →

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<details open> <summary><h2>Getting Started</h2></summary>

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)
• Git

Required API Keys

You'll need to obtain API keys for the following services:

1. Google AI Studio (for Gemini)

   • Visit: https://makersuite.google.com/app/apikey
   • Create API key and save as GOOGLE_API_KEY

2. Groq (for Llama models and GPT-OSS)

   • Visit: https://console.groq.com/
   • Create API key and save as GROQ_API_KEY

3. GitHub Token (for GitHub Agent only)

   • Visit: https://github.com/settings/tokens
   • Generate a personal access token with repo scope
   • Save as GITHUB_TOKEN

4. HuggingFace (for embeddings - GitHub Agent only)

   • Visit: https://huggingface.co/settings/tokens
   • Create API token and save as HUGGINGFACE_API_KEY

5. SERPER (for web search - News Agent only)

   • Visit: https://serper.dev/
   • Create API key and save as SERPER_API_KEY

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<details open> <summary><h2>Installation</h2></summary>

Clone the Repository

git clone https://github.com/MasirJafri1/AI-Chatbots-and-Agents.git
cd AI-Chatbots-and-Agents

Install Dependencies for a Specific Agent

Each agent/chatbot has its own requirements.txt file. Navigate to the desired folder and install dependencies:

# Example: Installing dependencies for GitHub Agent
cd "Chat with Github Agent"
pip install -r requirements.txt

System Dependencies

For Resume ATS Analyzer (pdf2image), install Poppler:

Ubuntu/Debian:

sudo apt-get install poppler-utils

macOS:

brew install poppler

Windows: Download and install from: https://github.com/oschwartz10612/poppler-windows/releases/tag/v25.11.0-0

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<details open> <summary><h2>Configuration</h2></summary>

Environment Variables Setup

Create a .env file in each project folder with the required API keys:

# Example .env file for GitHub Agent
GITHUB_TOKEN=your_github_token_here
GROQ_API_KEY=your_groq_api_key_here
HUGGINGFACE_API_KEY=your_huggingface_api_key_here
SERPER_API_KEY=your_serper_api_key_here  # Only for News Reporter Agent

⚠️ Security Note: Never commit your .env file to version control. Each project includes a .gitignore file that excludes .env files.

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<details open> <summary><h2>Usage Examples</h2></summary>

Each agent can be run independently. Here are examples:

1. Chat with GitHub Agent (NEW!)

cd "Chat with Github Agent"
streamlit run app.py
# Enter repository in format: owner/repo (e.g., MasirJafri1/AI-Chatbots-and-Agents)

2. Chat with Research Paper (NEW!)

cd "Chat with Research Paper"
streamlit run app.py
# Enter research topic or specific paper query

3. Data Analysis Agent

cd "Data Analysis Agent"
chainlit run app.py

4. MultiPDF Chat Bot

cd "Chat with MultiPDF document"
streamlit run app.py

5. Resume ATS Analyzer

cd "Resume ATS and Score Analyzer"
streamlit run app.py

6. Text to SQL App

cd "Text to SQL LLM App"
streamlit run app.py

7. News Reporter Agent (CrewAI)

cd "News Reporter AI Agent"
python crew.py  # Modify the topic in crew.py before running

8. LangGraph Chatbot

cd "Langgraph- Chatbot"
streamlit run streamlit_frontend.py

9. YT & Web Summarizer

cd "Langchain - YT & Web Summarizer"
streamlit run app.py

10. Invoice Extractor

cd "Multilanguage Invoice Extractor"
streamlit run app.py

11. Resume Reviewer Agent

cd "Resume Reviewer Agent"
# Using Docker Compose (recommended)
docker-compose -f docker-compose.prod.yaml up
# API will be available at http://localhost:8000
# Upload resume via POST /upload endpoint

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<details open> <summary><h2>Project Structure</h2></summary>

AI-Chatbots-and-Agents/
├── Chat with Github Agent/
│   ├── app.py
│   ├── requirements.txt
│   └── .gitignore
├── Chat with Research Paper/
│   ├── app.py
│   ├── requirements.txt
│   └── .gitignore
├── Data Analysis Agent/
│   ├── app.py
│   ├── requirements.txt
│   └── .gitignore
├── Chat with MultiPDF document/
│   ├── app.py
│   ├── requirements.txt
│   └── .gitignore
├── Langchain - YT & Web Summarizer/
│   ├── app.py
│   ├── requirements.txt
│   └── .gitignore
├── Langgraph- Chatbot/
│   ├── streamlit_frontend.py
│   ├── langgraph_backend/
│   │   ├── __init__.py
│   │   └── chatbot.py
│   ├── requirements.txt
│   └── .gitignore
├── Multilanguage Invoice Extractor/
│   ├── app.py
│   ├── requirements.txt
│   └── .gitignore
├── News Reporter AI Agent/
│   ├── agents.py
│   ├── tasks.py
│   ├── tools.py
│   ├── crew.py
│   ├── requirements.txt
│   └── .gitignore
├── Resume ATS and Score Analyzer/
│   ├── app.py
│   ├── requirements.txt
│   └── .gitignore
├── Text to SQL LLM App/
│   ├── app.py
│   ├── sql.py
│   ├── student.db
│   ├── requirements.txt
│   └── .gitignore
├── Resume Reviewer Agent/
│   ├── app/
│   │   ├── main.py
│   │   ├── server.py
│   │   ├── db/
│   │   ├── queue/
│   │   └── utils/
│   ├── Dockerfile
│   ├── docker-compose.prod.yaml
│   ├── requirements.txt
│   └── .gitignore
└── README.md

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<details open> <summary><h2>Contributing</h2></summary>

Contributions are welcome! Here's how you can contribute:

1. Fork the repository
2. Create a feature branch

   git checkout -b feature/AmazingAgent
   

3. Commit your changes

   git commit -m 'Add some AmazingAgent'
   

4. Push to the branch

   git push origin feature/AmazingAgent
   

5. Open a Pull Request

Adding a New Agent

When adding a new agent to this repository:

1. Create a new folder with a descriptive name
2. Include a requirements.txt with all dependencies
3. Add a .gitignore file (copy from existing projects)
4. Update the Agents & Chatbots Directory table in this README
5. Ensure your code follows the existing structure:
   • Use environment variables for API keys
   • Include error handling
   • Add comments for complex logic
   • Use Streamlit for UI consistency

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Author

Masir Jafri

• GitHub: @MasirJafri1
• Email: [email protected]
• LinkedIn: @MasirAbbas Jafri
• Hashnode: @MasirJafri

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<details open> <summary><h2>Acknowledgments</h2></summary>

• Google AI for Gemini API
• Groq for fast LLM inference
• LangChain community for excellent documentation
• CrewAI for multi-agent framework
• Embedchain for RAG framework
• Agno for research paper integration
• arXiv for open academic research
• Streamlit for amazing UI framework
• Open source community for various libraries and tools

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Repository Stats

• Total Projects: 11 (in this repo) + 1 (linked)
• Created: October 2025
• Last Updated: February 2026
• Language: Python
• Stars: 3 ⭐

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Quick Links

• Report Bug
• Request Feature
• GitHub Profile
• Customer Support Agent Repository

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Star History

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Made By MasirJafri