This project showcases a fully functional customer service AI agent that leverages OpenAI’s Autogen framework and a Streamlit front end. It routes user inquiries through a customizable agent pipeline, maintains conversational context, and generates accurate, context-aware responses. Developers can easily clone the repository, set their OpenAI API key, and launch a web UI to test or extend the bot’s capabilities. The codebase includes clear configuration points for prompt design, response handling, and integration with external services, making it a versatile starting point for building support chatbots, helpdesk automations, or internal Q&A assistants.

LangChain Google Gemini Agent is a Python-based library designed to simplify the creation of autonomous AI agents powered by Google’s Gemini language models. It combines LangChain’s modular approach—allowing prompt chains, memory management, and tool integrations—with Gemini’s advanced natural language understanding. Users can define custom tools for API calls, database queries, web scraping, and document summarization; orchestrate them via an agent that interprets user inputs, selects appropriate tool actions, and composes coherent responses. The result is a flexible agent capable of multi-step reasoning, live data access, and context-aware dialogues, ideal for building chatbots, research assistants, and automated workflows, and supports integration with popular vector stores and cloud services for scalability.

Naxos.ai Voice Assistant revolutionizes the way you browse the web. This powerful tool allows for hands-free control through simple voice commands, providing smart, context-aware responses powered by advanced AI. It offers a personalized browsing experience by allowing customization of its behavior and preferences. Automate repetitive tasks, from opening tabs to conducting searches, effortlessly. Seamlessly integrating with your favorite websites and applications, Naxos.ai enhances productivity and efficiency, making it an essential tool for modern web users.

ReplaiGPT is a Chrome extension designed to streamline your email responses. It employs advanced AI techniques to generate custom replies based on the context you provide. Unlike other tools, ReplaiGPT understands your preferences, background, and tone, allowing for more authentic and personalized communication. This tool integrates seamlessly with Gmail to ensure that your replies not only make sense contextually but also resonate with the recipient, enhancing engagement and professionalism.

SNS-GPT leverages advanced AI to enhance user engagement on social media platforms. This Chrome extension automates the comment creation process by analyzing the content of the post and comments already present. It provides personalized suggestions to help you express your thoughts more effectively, saving time and effort while increasing interaction on your posts. Whether you’re engaging with friends or promoting your brand, SNS-GPT makes your comments stand out with relevant, context-aware responses.

Virtual Scale provides AI-driven virtual teams designed to enhance customer engagement across voice, chat, and various communication channels. Utilizing advanced AI, it handles inbound and outbound calls, intelligent chat interactions, and creates customized conversation flows. With the integration of your business data, Virtual Scale ensures context-aware responses, making customer interactions more efficient and personalized. Ideal for diverse industries like hospitality, education, healthcare, and retail, Virtual Scale's platform is tailored to improve operational efficiency, provide 24/7 support, and scale business capabilities effortlessly.

RepoAgent is an AI framework that transforms any code repository into an interactive knowledge base. It indexes source files, functions, classes, and documentation into a vector store, enabling fast retrieval and context-aware responses. Developers can ask natural language questions about code functionality, architecture, or dependencies. It supports automated code summarization, documentation generation, and test case creation by integrating with LLMs. RepoAgent also analyzes issues, pull requests, and commit history to provide insights on code quality and potential bugs. Its modular design allows customization of retrieval pipelines, model selection, and output formatting. By embedding directly into CI/CD pipelines or IDEs, RepoAgent streamlines development, reduces onboarding time, and boosts team productivity.

AgenticRAG provides a modular architecture for creating autonomous agents that leverage retrieval-augmented generation (RAG). It offers components to index documents in vector stores, retrieve relevant context, and feed it into LLMs to generate context-aware responses. Users can integrate external APIs and tools, configure memory stores to track conversation history, and define custom workflows to orchestrate multi-step decision-making processes. The framework supports popular vector databases like Pinecone and FAISS, and LLM providers such as OpenAI, allowing seamless switching or multi-model setups. With built-in abstractions for agent loops and tool management, AgenticRAG simplifies development of agents capable of tasks like document QA, automated research, and knowledge-driven automation, reducing boilerplate code and accelerating time to deployment.