GoLC provides developers with a comprehensive toolkit for constructing language model chains and agents in Go. At its core, it includes chain management, customizable prompt templates, and seamless integration with major LLM providers. Through document loaders and vector stores, GoLC enables embedding-based retrieval, powering RAG workflows. The framework supports stateful memory modules for conversational contexts and a lightweight agent architecture to orchestrate multi-step reasoning and tool invocations. Its modular design allows plugging in custom tools, data sources, and output handlers. With Go-native performance and minimal dependencies, GoLC streamlines AI pipeline development, making it ideal for building chatbots, knowledge assistants, automated reasoning agents, and production-grade backend AI services in Go.

Sharp-GPT empowers .NET developers to create robust AI agents by leveraging custom attributes on interfaces to define prompt templates, configure models, and manage conversational memory. It offers streaming output for real-time interaction, automatic JSON deserialization for structured responses, and built-in support for fallback strategies and logging. With pluggable HTTP clients and provider abstraction, you can switch between OpenAI, Azure, or other LLM services effortlessly. Ideal for chatbots, content generation, summarization, classification, and more, Sharp-GPT reduces boilerplate and accelerates AI agent development on Windows, Linux, or macOS.

SuperBot is a comprehensive AI Agent framework enabling developers to deploy autonomous, context-aware assistants via Python and the command line. It integrates OpenAI’s chat models with a memory system, function-calling features, and plugin architecture. Agents can execute shell commands, run code, interact with files, perform web searches, and maintain conversation state. SuperBot supports multi-agent orchestration for complex workflows, all configurable through simple Python scripts and CLI commands. Its extensible design allows you to add custom tools, automate tasks, and integrate external APIs to build robust AI-driven applications.

AI Agent is an open-source Python framework designed to simplify the development of intelligent agents. It supports multi-agent orchestration, seamless integration with external tools and APIs, and built-in memory management for persistent conversations. Developers can define custom prompts, actions, and workflows, and extend functionality through a plugin system. AI Agent accelerates the creation of chatbots, virtual assistants, and automated workflows by providing reusable components and standardized interfaces.

BotFast simplifies the process of building AI-powered Telegram bots by providing developers with a comprehensive Python boilerplate. It encompasses everything needed to create unique bot experiences, including payment integration with Telegram and easy setup for subscription services. With BotFast, users can set up custom AI agents, leverage multimedia capabilities, and utilize a variety of back-end features like MongoDB for data management, making it an all-in-one solution for bot development.

OSU NLP Middleware is a lightweight framework built in Python that simplifies the development of AI agent systems. It provides a core agent loop that orchestrates interactions between natural language models and external tool functions defined as plugins. The framework supports popular LLM providers (OpenAI, Hugging Face, etc.), and enables developers to register custom tools for tasks like database queries, document retrieval, web search, mathematical computation, and RESTful API calls. Middleware manages conversation history, handles rate limits, and logs all interactions. It also offers configurable caching and retry policies for improved reliability, making it easy to build intelligent assistants, chatbots, and autonomous workflows with minimal boilerplate code.

MixAgent provides a plug-and-play architecture that lets developers define prompts, connect multiple LLM backends, and incorporate external tools (APIs, databases, or code). It orchestrates planning and execution loops, manages agent memory for stateful interactions, and logs chain-of-thought reasoning. Users can quickly prototype assistants, data fetchers, or automation bots without building orchestration layers from scratch, accelerating AI agent deployment.

ragChatbot is a developer-centric framework designed to streamline the creation of Retrieval-Augmented Generation chatbots. It integrates LangChain pipelines with OpenAI or other LLM APIs to process queries against custom document corpora. Users can upload files in various formats (PDF, DOCX, TXT), automatically extract text, and compute embeddings using popular models. The framework supports multiple vector stores such as FAISS, Chroma, and Pinecone for efficient similarity search. It features a conversational memory layer for multi-turn interactions and a modular architecture for customizing prompt templates and retrieval strategies. With a simple CLI or web interface, you can ingest data, configure search parameters, and launch a chat server to answer user questions with contextual relevance and accuracy.