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  • AI-powered customer service agent built with OpenAI Autogen and Streamlit for automated, interactive support and query resolution.
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    What is Customer Service Agent with Autogen Streamlit?
    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.
    Customer Service Agent with Autogen Streamlit Core Features
    • Integration with OpenAI Autogen for agent orchestration
    • Streamlit-based interactive web UI
    • Dynamic context and state management
    • Customizable prompt templates and handlers
    • Easy local deployment and testing
    • Extendable back-end integration points
  • Agent-Baba enables developers to create autonomous AI agents with customizable plugins, conversational memory, and automated task workflows.
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    What is Agent-Baba?
    Agent-Baba provides a comprehensive toolkit for creating and managing autonomous AI agents tailored to specific tasks. It offers a plugin architecture for extending capabilities, a memory system to retain conversational context, and workflow automation for sequential task execution. Developers can integrate tools like web scrapers, databases, and custom APIs into agents. The framework simplifies configuration through declarative YAML or JSON schemas, supports multi-agent collaboration, and provides monitoring dashboards to track agent performance and logs, enabling iterative improvement and seamless deployment across environments.
  • Agents-Deep-Research is a framework for developing autonomous AI agents that plan, act, and learn using LLMs.
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    What is Agents-Deep-Research?
    Agents-Deep-Research is designed to streamline the development and testing of autonomous AI agents by offering a modular, extensible codebase. It features a task planning engine that decomposes user-defined goals into sub-tasks, a long-term memory module that stores and retrieves context, and a tool integration layer that allows agents to interact with external APIs and simulated environments. The framework also provides evaluation scripts and benchmarking tools to measure agent performance across diverse scenarios. Built on Python and adaptable to various LLM backends, it enables researchers and developers to rapidly prototype novel agent architectures, conduct reproducible experiments, and compare different planning strategies under controlled conditions.
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