VillagerAgent provides a comprehensive toolkit for constructing AI agents that leverage large language models. At its core, developers define modular tool interfaces such as web search, data retrieval, or custom APIs. The framework manages agent memory by storing conversation context, facts, and session state for seamless multi-turn interactions. A flexible prompt templating system ensures consistent messaging and behavior control. Advanced features include orchestrating multiple agents to collaborate on tasks and scheduling background operations. Built in Python, VillagerAgent supports easy installation through pip and integrates with popular LLM providers. Whether building customer support bots, research assistants, or workflow automation tools, VillagerAgent streamlines the design, testing, and deployment of intelligent agents.

HackerGCLASS Agent API is an open-source Python framework that exposes RESTful endpoints to run AI agents. Developers can define custom tool integrations, configure prompt templates, and maintain agent state and memory across sessions. The framework supports orchestrating multiple agents in parallel, handling complex conversational flows, and integrating external services. It simplifies deployment via Uvicorn or other ASGI servers and offers extensibility with plugin modules, enabling rapid creation of domain-specific AI agents for diverse use cases.

Agent Teams is a developer-friendly framework that brings AI-powered conversation, task automation, and knowledge management to Microsoft Teams. Built on the Microsoft Bot Framework, OpenAI GPT models, and LangChain, it supports multi-turn dialogue, retrieval-augmented generation, and customizable workflows. Teams can connect external data sources, define triggers, and deploy bots within their channels. The open-source architecture allows for extensibility via plugins and configuration, making it ideal for building intelligent assistants for customer support, HR inquiries, internal knowledge bases, and more, all within the familiar Teams interface.

IntelliConnect is a versatile AI agent framework that enables developers to build intelligent agents by connecting LLMs (e.g., GPT-4) with various external APIs and services. It supports multi-step reasoning, context-aware tool selection, and error handling, making it ideal for automating complex workflows such as customer support, data extraction from web or documents, scheduling, and more. Its plugin-based design allows easy extension, while built-in logging and observability help monitor agent performance and refine capabilities over time.

LangGraph Agent combines LLMs with a graph-structured memory to build autonomous agents that can remember facts, reason over relationships, and call external functions or tools when needed. Developers define memory schemas as graph nodes and edges, plug in custom tools or APIs, and orchestrate agent workflows through configurable planners and executors. This approach enhances context retention, enables knowledge-driven decision making, and supports dynamic tool invocation in diverse applications.

Lila delivers a complete AI agent framework tailored for multi-step reasoning and autonomous task execution. Developers can define custom tools (APIs, databases, webhooks) and configure Lila to call them dynamically during runtime. It offers memory modules to store conversation history and facts, a planning component to sequence sub-tasks, and chain-of-thought prompting for transparent decision paths. Its plugin system allows seamless extension with new capabilities, while built-in monitoring tracks agent actions and outputs. Lila’s modular design makes it easy to integrate into existing Python projects or deploy as a hosted service for real-time agent workflows.