ReAct: Synergizing Reasoning and Acting in Language Models

The Problem: Thinking Alone Isn't Enough

Chain-of-Thought (CoT) prompting showed that LLMs improve when they write out their reasoning step by step. But pure thinking has limits: The model cannot access current information, perform calculations, or query external systems.

Conversely, there are systems that let LLMs use tools (acting), but without explicit reasoning. These often act blindly — without a plan, error analysis, or strategy adjustment.

The ReAct Idea: Thinking AND Acting

ReAct combines both in an alternating loop:

• Thought: The model thinks — analyzes the current situation, plans the next step, interprets previous results.
• Action: The model executes a concrete action — e.g., a web search, calculation, or API call.
• Observation: The result of the action is returned to the model. It serves as input for the next thought.

This cycle repeats until the task is solved.

ReAct in Action: An Example

Question: "In what year was the capital of the country where the Transformer was invented founded?"

Why ReAct Works Better

• Transparency: The thought steps make the reasoning process traceable. You can see why the model chose a particular action.
• Error correction: When an action returns an unexpected result, the model can adjust its approach instead of stubbornly sticking to a wrong strategy.
• Grounding: Through actions (search, computation), answers are based on real data rather than hallucinations.
• Flexibility: The pattern works with any tools — web search, databases, APIs, code execution.

ReAct in Today's Agent Frameworks

ReAct is the basis of virtually all AI agent frameworks today:

• LangChain Agents: Implement the ReAct loop as the default agent type
• Claude Tool Use: Anthropic's function calling follows the Thought-Action-Observation pattern
• AutoGPT / CrewAI: Multi-agent systems where each agent internally uses ReAct
• Claude Code: Also uses the ReAct pattern for code analysis and generation