The deepagents Python package enables users to create advanced agents that can plan and execute complex tasks by utilizing a combination of tools, subagents, and a planning tool. It enhances the capabilities of traditional agents by incorporating features like context management, task decomposition, and long-term memory. This allows for more sophisticated interactions and workflows in applications such as research and data analysis.

A novel model called KITPose has been developed for general mammal pose estimation, focusing on structure-supporting dependencies among keypoints. The model incorporates keypoint-specific clues and introduces techniques such as Generalised Heatmap Regression Loss and adaptive weighting to enhance performance, achieving state-of-the-art results in various datasets.

DeerFlow is a community-driven deep research framework that integrates language models with specialized tools for web search, crawling, and Python code execution. It supports one-click deployment through Volcengine, features a modular multi-agent system for automated research tasks, and includes capabilities like text-to-speech and report generation. Users can explore its functionalities through a web UI and configure various search engines for tailored experiences.