by teddynote-lab · Agent Tool · ★ 51
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🌪️ STORM Research Assistant STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking) - A writing system for generating grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages 📖 Overview STORM Research Assistant is a LangGraph-based implementation of the STORM methodology from Stanford, designed to write grounded and organized long-form articles from scratch. The system models the pre-writing stage by (1) discovering diverse perspectives for researching the given topic, (2) simulating conversations where writers with different perspectives pose questions to a topic expert grounded on trusted Internet sources, and (3) curating the collected information to create an outline before generating the final article. 🎯 Key Features 🔍 Pre-writing Stage Modeling: Comprehensive research and outline preparation before article generation 🤖 Diverse Perspective Discovery: Automatic generation of multiple expert p
| Stars | 51 |
| Forks | 8 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 39.25/100 |
| Last Updated | 2025-06-06 |
| Created | 2025-06-04 |
| Platforms | python |
| Est. Tokens | ~34k |
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STORM-Research-Assistant is 🌪️ AI research assistant that generates Wikipedia-quality articles through multi-perspective analysis. Based on Stanford's STORM methodology.. It is categorized as a Agent Tool with 51 GitHub stars.
STORM-Research-Assistant is primarily written in Python. It covers topics such as ai-research, anthropic, arxiv.
You can find installation instructions and usage details in the STORM-Research-Assistant GitHub repository at github.com/teddynote-lab/STORM-Research-Assistant. The project has 51 stars and 8 forks, indicating an active community.
STORM-Research-Assistant is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to STORM-Research-Assistant on Agent Skills Hub include Agentic-AI-Pipeline, Multi-Agent-RAG-Template, open-extract. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.