---
title: "ReAct: Synergizing Reasoning and Acting in Language Models"
description: "ReAct gives language-model agents a simple working rhythm: reason about the current situation, take a bounded action, observe what the environment returns, and revise the next move. Read it for the durable idea behind many tool-using agent harnesses and for an honest look at why better grounding can still fail when tools return weak evidence or the agent gets stuck in a loop."
canonical_url: "https://fanout.sh/daily/2026-08-10-react-reasoning-acting"
md_url: "https://fanout.sh/daily/2026-08-10-react-reasoning-acting.md"
last_updated: "2026-08-10"
access: "public"
---

# ReAct: Synergizing Reasoning and Acting in Language Models

ReAct gives language-model agents a simple working rhythm: reason about the current situation, take a bounded action, observe what the environment returns, and revise the next move. Read it for the durable idea behind many tool-using agent harnesses and for an honest look at why better grounding can still fail when tools return weak evidence or the agent gets stuck in a loop.

## Paper details

- Authors: Yao, Zhao, Yu, Du, Shafran, Narasimhan, and Cao

- Venue: ICLR 2023

- Track: AI Research

- Difficulty: Easy

- Reading time: 35 min

- Original paper: https://arxiv.org/abs/2210.03629v3

## What you will learn

- How ReAct interleaves language thoughts, task-specific actions, and returned observations in one inspectable trajectory.

- Why thoughts can decompose goals and recover from surprises while actions ground later reasoning in new external evidence.

- Why tool quality, prompt choice, step limits, repetition detection, and narrow permissions remain part of a reliable agent harness.

## Continue exploring

[Explore AI agents](https://fanout.sh/knowledge-graph?node=ai-research%3Aconcept%3Aagent-agentic-ai): Open Fanout's agent concept node and connect ReAct to planning, tool use, environment observations, memory, permissions, and multi-step task execution.

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