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Decision brief
ReAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3.
Good fit when
- When aiming for better decision-making in HotpotQA, alfworld environments, or WebShop scenarios with GPT-3
- For projects that benefit from detailed action-reasoning sequences as seen in AlfWorld success rates
Avoid when
- If requiring extensive custom task integration beyond provided notebooks, LangChain's zero-shot ReAct agent may be more preferable
- When PaLM outperforms GPT-3 on specific tasks or if an alternative model is preferred
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (923d since push)
- As of 3d
- Provenance
- Not a fork · Personal account
- As of 3d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/ysymyth/ReActHow it fits your stack(4)
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Evidence and technical details
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Overview
A repository containing GPT-3 prompting code for the ReAct method that enhances reasoning and action in large language models through specified tasks such as HotpotQA, FEVER, AlfWorld, and WebShop.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 17, 2026
Categories
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 17, 2026)
To use ReAct for more tasks, consider trying [LangChain's zero-shot ReAct Agent](https://python.langchain.com/docs/modules/agents/agentSource link
Source: README excerpt (regex_v1, Aug 17, 2026)
You need to first have an OpenAI API key and store it in the environment variable ``OPENAI_API_KEY`` (see [here](httSource link
Source: README excerpt (regex_v1, Aug 17, 2026)
Act for more tasks, consider trying [LangChain's zero-shot ReAct Agent](https://python.langchain.com/docs/modules/agents/agent_types/react.html).Source link
Tags
README
ReAct Prompting
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 |
Citation
@inproceedings{yao2023react,
title = {{ReAct}: Synergizing Reasoning and Acting in Language Models},
author = {Yao, Shunyu and Zhao, Jeffrey and Yu, Dian and Du, Nan and Shafran, Izhak and Narasimhan, Karthik and Cao, Yuan},
booktitle = {International Conference on Learning Representations (ICLR) },
year = {2023},
html = {https://arxiv.org/abs/2210.03629},
}
For agents
This page has a .md twin and JSON over the API.