AI-Agents provides a flexible agent architecture that orchestrates language model planners, persistent memory modules, and pluggable toolkits. Developers define tools for HTTP requests, file operations, and custom logic, then configure an LLM planner to decide which tool to invoke. Memory stores context and conversation history. The framework handles asynchronous execution, error recovery, and logging, enabling rapid prototyping of intelligent assistants, data analyzers, or automation bots without reinventing core orchestration logic.

MIDCA is a modular cognitive architecture designed to support the full cognitive loop of intelligent agents. It processes sensory inputs through a perception module, interprets data to generate and prioritize goals, leverages a planner to create action sequences, executes tasks, and then evaluates outcomes through a metacognitive layer. The dual-cycle design separates fast reactive responses from slower deliberative reasoning, enabling agents to adapt dynamically. MIDCA’s extensible framework and open-source codebase make it ideal for researchers and developers exploring autonomous decision-making, learning, and self-reflection in AI agents.