Multi-Agent Visual Tracking implements a distributed tracking system composed of intelligent agents that communicate to improve accuracy and robustness in video object tracking. Agents run convolutional neural networks for detection, share observations to handle occlusions, and adjust tracking parameters through reinforcement learning. Compatible with popular video datasets, it supports both training and real-time inference. Users can easily integrate it into existing pipelines and extend agent behaviors for custom applications.

llm-ReAct implements the ReAct (Reasoning and Acting) architecture for large language models, enabling seamless integration of chain-of-thought reasoning with external tool execution and memory storage. Developers can configure a toolkit of custom tools—such as web search, database queries, file operations, and calculators—and instruct the agent to plan multi-step tasks, invoking tools as needed to retrieve or process information. The built-in memory module preserves conversational state and past actions, supporting more context-aware agent behaviors. With modular Python code and support for OpenAI APIs, llm-ReAct simplifies experimentation and deployment of intelligent agents that can adaptively solve problems, automate workflows, and provide context-rich responses.