modelscope/awesome-vibe-research — explained in plain English
Analysis updated 2026-05-18
Discover AI tools for a specific research stage, like literature review or figure generation.
Find fully automated research agents such as AI-Scientist or STORM.
Contribute a new tool entry to the list for a research stage you know well.
| modelscope/awesome-vibe-research | 0petru/sentimo | 0xblackash/cve-2026-46333 | |
|---|---|---|---|
| Stars | 17 | 17 | 17 |
| Language | — | Python | C |
| Setup difficulty | easy | moderate | moderate |
| Complexity | 1/5 | 3/5 | 4/5 |
| Audience | researcher | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
This is a reference list, not installable software, each linked project has its own setup requirements.
Awesome Vibe Research is a collaboratively maintained list of AI tools, agents, skills, and workflows built to assist with scientific research. It is organized and hosted by ModelScope, a Chinese AI platform, and is primarily documented in Chinese with English terms used throughout for tool names and categories. The list covers the full lifecycle of an academic research project, broken into nine stages. These include: scanning for new ideas and defining research questions, searching and reading the literature, designing experiments, running and analyzing experiments, creating figures and visualizations, writing and editing papers, packaging results for others to reproduce, and tracking how published work spreads and is cited. Each stage has a table of relevant open-source projects with descriptions, star counts, and links to the code or live demos. The tools listed range from fully automated multi-agent systems (systems where AI agents work through the research process with minimal human input) to single-purpose skills (focused tools that do one specific task, such as generating publication-quality figures or verifying citations). Some of the more prominent entries include AI-Scientist (a well-known project from Sakana AI that can generate and review its own research papers), STORM (a Stanford system for generating long-form referenced reports), and RD-Agent (a Microsoft tool for automating data and model experimentation). The repository is intended to grow through community contributions. Readers are invited to add entries directly to the relevant table in the README when they find tools that belong in a given stage. The project grew out of a talk series called "Doing Research Together with Agents" and aims to consolidate practical knowledge from practitioners, users, and open-source communities into one maintained reference.
A curated, community-maintained list of AI tools and agents that help with every stage of academic research, from ideas to publication.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Verify against the repo before relying on details.