MCP Ollama Agent leverages the Ollama local LLM runtime to provide a versatile agent framework for task automation. It integrates multiple tool interfaces, including web search via SERP API, file system operations, shell command execution, and Python environment management. By defining custom prompts and tool configurations, users can orchestrate complex workflows, automate repetitive tasks, and build specialized assistants tailored to various domains. The agent handles tool invocation and context management, maintaining conversation history and tool responses to generate coherent actions. Its CLI-based setup and modular architecture make it easy to extend with new tools and adapt to different use cases, from research and data analysis to development support.

Pacman AI offers a fully functional Python-based environment and agent framework for the classic Pacman game. The project implements key reinforcement learning algorithms—Q-learning and value iteration—to allow the agent to learn optimal policies for pill collection, maze navigation, and ghost avoidance. Users can define custom reward functions and adjust hyperparameters such as learning rate, discount factor, and exploration strategy. The framework supports metric logging, performance visualization, and reproducible experiment setups. It is designed for easy extension, letting researchers and students integrate new algorithms or neural network-based learning approaches and benchmark them against baseline grid-based methods within the Pacman domain.