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.