Goat SDK is designed to simplify the creation and orchestration of AI agents in Go. It provides pluggable LLM integrations (OpenAI, Anthropic, Azure, local models), a tool registry for custom actions, and memory stores for stateful conversations. Developers can define chains, representer strategies, and publishers to output interactions via CLI, WebSocket, REST endpoints, or a built-in Web UI. Goat supports streaming responses, customizable logging, and easy error handling. By combining these components, you can develop chatbots, automation workflows, and decision-support systems in Go with minimal boilerplate, while maintaining flexibility to swap or extend providers and tools as needed.

MCP Agent provides a robust foundation for building intelligent AI-driven assistants by offering modular components for integrating language models, custom tools, and data sources. Its core functionalities include dynamic tool invocation based on user intents, context-aware memory management for long-term conversations, and a flexible plugin system that simplifies extending capabilities. Developers can define pipelines to process inputs, trigger external APIs, and manage asynchronous workflows, all while maintaining transparent logs and metrics. With support for popular LLMs, configurable templates, and role-based access controls, MCP Agent streamlines the deployment of scalable, maintainable AI agents in production environments. Whether for customer support chatbots, RPA bots, or research assistants, MCP Agent accelerates development cycles and ensures consistent performance across use cases.

Multi-Agent Surveillance offers a flexible simulation framework where multiple AI agents act as predators or evaders in a discrete grid world. Users can configure environment parameters such as grid dimensions, number of agents, detection radii, and reward structures. The repository includes Python classes for agent behavior, scenario generation scripts, built-in visualization via matplotlib, and seamless integration with popular reinforcement learning libraries. This makes it easy to benchmark multi-agent coordination, develop custom surveillance strategies, and conduct reproducible experiments.