🔄 ReAct Pattern
Reasoning and Acting: The synergy that makes AI agents more capable
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0 / 5 completedIntroduction to ReAct
🎯 What is ReAct?
ReAct (Reasoning + Acting) is a paradigm that combines verbal reasoning traces with task-specific actions in an interleaved manner. This approach allows language models to generate reasoning steps while interacting with external environments to gather additional information.
ReAct bridges the gap between reasoning and acting, enabling agents to dynamically adjust their plans based on observations from the environment.
🔄 The ReAct Cycle
ReAct operates in a continuous loop alternating between internal reasoning and external actions.
Generate reasoning step explaining current understanding and next action
Execute specific action (search, calculate, lookup, finish)
Receive feedback from environment based on action taken
Continue cycle until task is complete
✅ Advantages
- •Interpretable reasoning traces
- •Dynamic information gathering
- •Error recovery through reasoning
- •Handles complex multi-step tasks
📊 Use Cases
- •Question answering with research
- •Interactive problem solving
- •Fact verification tasks
- •Multi-tool agent systems
🆚 ReAct vs Other Approaches
Chain of Thought
Pure reasoning without external actions
Action-Only
Direct action selection without reasoning
ReAct
Combines reasoning with actions