AgentX provides an extensible architecture for building AI-driven agents that leverage large language models, tool and API integrations, and memory modules to perform complex tasks autonomously. It features a plugin system for custom tools, support for vector-based retrieval, chain-of-thought reasoning, and detailed execution logs. Users define agents through flexible configuration files or code, specifying tools, memory backends like Chroma DB, and reasoning pipelines. AgentX manages context across sessions, enables retrieval-augmented generation, and facilitates multiturn conversations. Its modular components allow developers to orchestrate workflows, customize agent behaviors, and integrate external services for automation, research assistance, customer support, and data analysis.

The LLM Maze Agent framework provides a Python-based environment for building intelligent agents capable of navigating grid mazes using large language models. By combining modular environment interfaces with chain-of-thought prompt templates and heuristic planning, the agent iteratively queries an LLM to decide movement directions, adapts to obstacles, and updates its internal state representation. Out-of-the-box support for OpenAI and Hugging Face models allows seamless integration, while configurable maze generation and step-by-step debugging enable experimentation with different strategies. Researchers can adjust reward functions, define custom observation spaces, and visualize agent paths to analyze reasoning processes. This design makes LLM Maze Agent a versatile tool for evaluating LLM-driven planning, teaching AI concepts, and benchmarking model performance on spatial reasoning tasks.