An interactive visualization of how AI agents interleave reasoning and action to solve real-world tasks
What is ReAct?
ReAct (Reason + Act) is a prompting framework that synergizes reasoning and acting by interleaving thought steps with action steps in an iterative loop. Instead of just thinking or just acting, the agent alternates between reasoning about what it needs and executing tools to get information, creating a dynamic problem-solving cycle.
🔄
ReAct Loop
🤔
Thought
Plan next action
⚡
Action
Execute tool
👁️
Observation
Process results
Why ReAct is Powerful
🔄 Dynamic Adaptation
The agent adjusts its approach based on what it discovers, enabling flexible problem-solving
🌐 Real-World Access
Overcomes LLM limitations by accessing current data through external tools and APIs
🔍 Transparent Reasoning
Every decision is visible, making it easy to debug and understand the agent's thought process
✅ Reduced Hallucination
Grounds responses in actual data rather than relying on potentially outdated training knowledge