FlyingAgent provides a modular architecture that leverages large language models to simulate autonomous agents capable of reasoning, planning, and executing actions across various domains. Agents maintain an internal memory for context retention and can integrate external toolkits for tasks like web browsing, data analysis, or third-party API calls. The framework supports multi-agent coordination, plugin-based extensions, and customizable decision-making policies. With its open design, developers can tailor memory backends, tool integrations, and task managers, enabling applications in customer support automation, research assistance, content generation pipelines, and digital workforce orchestration.

MindForge is a robust orchestration framework designed for building and deploying AI-driven agents with minimal boilerplate. It offers a modular architecture comprising a task planner, reasoning engine, memory manager, and tool execution layer. By leveraging LLMs, agents can parse user input, formulate plans, and invoke external tools—such as web scraping APIs, databases, or custom scripts—to accomplish complex tasks. Memory components store conversational context, enabling multi-turn interactions, while the decision engine dynamically selects actions based on defined policies. With plugin support and customizable pipelines, developers can extend functionality to include custom tools, third-party integrations, and domain-specific knowledge bases. MindForge simplifies AI agent development, facilitating rapid prototyping and scalable deployment in production environments.

Multi-Agents System from Scratch provides a comprehensive set of Python modules to build, customize, and evaluate multi-agent environments from the ground up. Users can define world models, create agent classes with unique sensory inputs and action capabilities, and establish flexible communication protocols for cooperation or competition. The framework supports dynamic task allocation, strategic planning modules, and real-time performance tracking. Its modular architecture allows easy integration of custom algorithms, reward functions, and learning mechanisms. With built-in visualization tools and logging utilities, developers can monitor agent interactions and diagnose behavior patterns. Designed for extensibility and clarity, the system caters to both researchers exploring distributed AI and educators teaching agent-based modeling.

Semi Agent provides a modular architecture for building AI agents that can plan, execute actions, and remember context over time. It integrates with popular language models, supports tool definitions for custom functionality, and maintains conversational or task-oriented memory. Developers can define step-by-step plans, connect external APIs or scripts as tools, and leverage built-in logging to debug and optimize agent behavior. Its open-source design and Python basis allow easy customization, extensibility, and integration into existing pipelines.