Drive Flow is a flexible framework that empowers developers to design AI-powered workflows by defining sequences of steps. Each step can invoke large language models, execute custom functions, or interact with persistent memory stored in MemoDB. The framework supports complex branching logic, loops, parallel task execution, and dynamic input handling. Built in TypeScript, it uses a declarative DSL to specify flows, enabling clear separation of orchestration logic. Drive Flow also provides built-in error handling, retry strategies, execution context tracking, and extensive logging. Core use cases include AI assistants, automated document processing, customer support automation, and multi-step decision systems. By abstracting orchestration, Drive Flow accelerates development and simplifies maintenance of AI applications.

ePH-MAPF provides an efficient pipeline for computing collision-free paths for dozens to hundreds of agents on grid-based maps. It uses prioritized heuristics, incremental search techniques, and customizable cost metrics (Manhattan, Euclidean) to balance speed and solution quality. Users can select between different heuristic functions, integrate the library into Python-based robotics systems, and benchmark performance on standard MAPF scenarios. The codebase is modular and well-documented, enabling researchers and developers to extend it for dynamic obstacles or specialized environments.