ReAct 项目分析报告

本报告由 OpenClaw 自动生成

研究日期: 2026-03-31

项目路径: /Users/daoyu/Documents/ai-repo/ReAct

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1. 项目概述

GPT-3 prompting code for ICLR 2023 paper ReAct: Synergizing Reasoning and Acting in Language Models.
To use ReAct for more tasks, consider trying LangChain’s zero-shot ReAct Agent.

Setup

You need to first have an OpenAI API key and store it in the environment variable OPENAI_API_KEY (see here).
Package requirement: openai, and install alfworld following instructions here.

Experiments

Run {hotpotqa,fever,alfworld,webshop}.ipynb. As HotpotQA and FEVER have large validation sets, we only run 500 random examples (see notebooks). We find PaLM and GPT-3 are better at different tasks.

	HotpotQA (500 random dev, EM)	FEVER (500 random dev, EM)	AlfWorld (success rate)	WebShop (success rate)
PaLM-540B (paper)	29.4	62.2	70.9	40
GPT-3 (davinci-002)	30.4	54	78.4	35.8

2. 技术栈

3. 项目结构

./wikienv.py
./wrappers.py

4. 技术实现分析

核心架构

待深入分析

关键模块

待深入分析

设计模式

待深入分析

5. 产品意义

解决的问题

待分析

目标用户

待分析

应用场景

待分析

6. 借鉴点

技术层面

1. 待分析
2. 待分析

产品层面

1. 待分析
2. 待分析

工程实践

1. 待分析
2. 待分析

7. 待深入研究

• 阅读核心源码
• 运行示例
• 分析测试用例
• 研究 API 设计

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