Semi Agent provides a modular architecture for building AI agents that can plan, execute actions, and remember context over time. It integrates with popular language models, supports tool definitions for custom functionality, and maintains conversational or task-oriented memory. Developers can define step-by-step plans, connect external APIs or scripts as tools, and leverage built-in logging to debug and optimize agent behavior. Its open-source design and Python basis allow easy customization, extensibility, and integration into existing pipelines.

Hyperbolic Time Chamber provides a flexible environment for constructing AI agents by offering components for memory management, context window orchestration, prompt chaining, tool integration, and execution control. Developers define agent behaviors via modular building blocks, configure custom memories (short- and long-term), and link external APIs or local tools. The framework includes async support, logging, and debugging utilities, enabling rapid iteration and deployment of sophisticated conversational or task-oriented agents in Python projects.