{
  "id": "react",
  "code": "PTL-0058",
  "term": "ReAct",
  "aliases": [
    "Reason + Act",
    "thought-action-observation loop"
  ],
  "category": "retrieval-tools",
  "definition": "ReAct is a prompting pattern that interleaves reasoning traces (\"Thought\") with actions such as tool calls (\"Action\") and their results (\"Observation\"), letting a model plan, act, and update its plan in a loop.",
  "description": "Yao et al. showed that combining reasoning and acting outperformed either alone on question answering and interactive decision-making tasks. ReAct is the template for most tool-using agent loops.",
  "example": "Thought: I need the population of the capital of France.\nAction: search(\"capital of France\")\nObservation: Paris\nThought: Now find the population of Paris.\n",
  "broader": [],
  "narrower": [],
  "related": [
    "function-calling",
    "chain-of-thought",
    "self-ask",
    "ai-agent",
    "reflexion"
  ],
  "introduced": 2022,
  "sources": [
    {
      "title": "ReAct: Synergizing Reasoning and Acting in Language Models",
      "authors": "Yao et al.",
      "year": 2022,
      "url": "https://arxiv.org/abs/2210.03629"
    }
  ],
  "url": "https://protologue.com/t/react/",
  "citation": "Protologue. (2026). ReAct. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0058). https://protologue.com/t/react/"
}