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Also known as: Reason+Act prompting
Prompting method where language models interleave reasoning steps with tool actions to improve accuracy and task completion.
DESCRIPTION
ReAct combines chain-of-thought reasoning with action execution, letting language models plan and interact with tools in iterative loops. It produces reasoning traces, selects actions like search or code execution, and reflects on outcomes to refine subsequent steps. The approach improves performance on tasks requiring external information, multi-step decision making, or verification. ReAct influenced agentic LLM design and frameworks that orchestrate tools, memory, and control. It raises governance questions around trace logging, tool safety, and reproducibility. Limitations include potential verbosity, error propagation, and the need for reliable tool integrations.
ALTERNATIVE DEFINITIONS
arXiv
We introduce ReAct, a framework that synergizes reasoning and acting for language models, allowing them to interleave thoughts and actions, and learn to act and reason over external sources.
Formal introduction emphasizing interleaving thoughts and actions over tools.
Princeton NLP
ReAct enables language models to generate reasoning traces and task-specific actions in an interleaved fashion, improving performance on tasks that require access to external information and multi-step decision making.
Project page clarifies benefits and targeted task types.
LangChain
The ReAct agent pattern combines chain-of-thought style reasoning with actions (tool calls) to allow LLMs to solve problems that require both reasoning and external tool use.
Developer-focused definition within an implementation framework.
RELATED TERMS
Tool Use in LLMs Agentic LLMs Program-of-Thought Tree-of-Thought Prompt Engineering Retrieval-Augmented Generation MRKL Systems Action Plans Execution Traces
COMPETING TERMS
RELATIONS
- Referenced by: AgentBench
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer MLOps Engineer Data Scientist AI Product Manager
EXTERNAL SOURCES
- Wikipedia - Prompt engineering
- arXiv - Original ReAct research paper
- Princeton NLP - Project site and examples
- LlamaIndex - ReAct agent implementation
- LangChain - ReAct pattern in LangChain
TOP QUESTIONS
What problems does ReAct address in LLMs?
It improves tasks needing multi-step reasoning and external tool usage, reducing hallucinations by verifying steps with actions and evidence.
Category: Relevancy
How is ReAct different from Chain-of-Thought Prompting?
ReAct interleaves reasoning with tool actions, whereas chain-of-thought typically generates reasoning only without executing external tools.
Category: Alternatives
What tools are commonly used with ReAct?
Web search, retrieval APIs, calculators, code execution, and domain-specific services integrated via agent frameworks.
Category: Example
What are risks when applying ReAct in production?
Tool misuse, elevated latency, and trace leakage can occur; robust governance and resource controls are mandatory.
Category: Risks
How do you evaluate ReAct performance?
Use task success rates, step-wise correctness, latency SLOs, and auditability of action traces compared to baseline prompting.
Category: Requirements
What is a common mistake with ReAct prompts?
Insufficient instruction or tool descriptions cause wrong actions; prompts must specify allowed tools, formats, and verification steps.
Category: Mistakes
Is ReAct suitable for safety-critical applications?
Only with strict tool whitelisting, guardrails, and human oversight, given risks of incorrect actions or unexpected tool outputs.
Category: Opinion
What is a hot topic around ReAct today?
Agentic LLM orchestration, better self-verification, and structured traces for governance and reproducibility are actively explored.
Category: Topic
How do you start learning ReAct?
Read the original paper, then experiment with LangChain or LlamaIndex ReAct agents using simple tools like retrieval and calculators.
Category: Example