Agent-Go provides a modular framework for building autonomous AI agents in Go. It integrates LLM providers (such as OpenAI), vector-based memory stores for long-term context retention, and a flexible planning engine that breaks down user requests into executable steps. Developers define and register custom tools (APIs, databases, or shell commands) that agents can invoke. A conversation manager tracks dialog history, while a configurable planner orchestrates tool calls and LLM interactions. This allows teams to rapidly prototype AI-driven assistants, automated workflows, and task-oriented bots in a production-ready Go environment.

Agentfy provides a modular architecture for constructing AI agents by combining LLMs, memory backends, and tool integrations into a cohesive runtime. Developers declare agent behavior using Python classes, register tools (REST APIs, databases, utilities), and choose memory stores (local, Redis, SQL). The framework orchestrates prompts, actions, tool calls, and context management to automate tasks. Built-in CLI and Docker support enable one-step deployment to cloud, edge, or desktop environments.

Fast-LLM-Agent-MCP is a lightweight, open-source Python framework for building AI agents that combine memory management, chain-of-thought reasoning, and multi-step planning. Developers can integrate it with OpenAI, Azure OpenAI, local Llama, and other models to maintain conversational context, generate structured reasoning traces, and decompose complex tasks into executable subtasks. Its modular design allows custom tool integration and memory stores, making it ideal for applications like virtual assistants, decision support systems, and automated customer support bots.

MCP Browser Agent is a browser-based autonomous AI agent framework that leverages large language models to perform web navigation, data scraping, content summarization, form interaction, and automated task sequences. Built as a lightweight JavaScript library, it integrates seamlessly with OpenAI's GPT APIs, allowing developers to programmatically define custom actions, memory stores, and prompt chains. The agent can click links, fill forms, extract table data, and summarize page content on demand. It supports asynchronous execution, error handling, and session persistence via browser storage. With customizable interfaces and extensible action modules, MCP Browser Agent simplifies the creation of intelligent browser assistants to boost productivity, streamline workflows, and reduce manual browsing tasks across diverse web applications.