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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Scholar All-In-One: A research infrastructure for AI agents
| Date | Stars |
|---|---|
| 2026-07-24 | 553 |
| 2026-07-25 | 553 |
| 2026-07-28 | 553 |
| 2026-07-30 | 553 |
| 2026-07-31 | 553 |
| 2026-08-06 | 553 |
Today
— stars today
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Momentum
15.0
growth rate 0.00%/day
<div align="center">
<!-- TODO: Replace with actual logo when available -->
<!-- <img src="docs/assets/logo.png" width="200" alt="ScholarAIO Logo"> -->
# ScholarAIO
**Scholar All-In-One — an academic harness for AI agents.**
[English](README.md) | [中文](README_CN.md)
[](https://github.com/ZimoLiao/scholaraio/stargazers)
[](LICENSE)
[](https://www.python.org/)
[](.agents/skills/)
</div>
---
Your coding agent already reasons, plans, browses, writes code, and uses tools. ScholarAIO adds the academic harness around it, so the same agent can carry evidence, project state, repeatable workflows, and reviewable outputs across the whole research process.
- Your paper library becomes a reusable knowledge base for the same agent.
- Skills and CLI contracts give the agent stable ways to search, read, organize, cite, write, and verify.
- Optional tools are integrated selectively when they strengthen that workflow and degrade cleanly when unavailable.
Here, **All-in-One means one coherent academic workflow**, not every scientific package in one distribution. The active agent supplies reasoning and orchestration; ScholarAIO supplies the durable academic context and operational contracts around it.
<div align="center">
<img src="docs/assets/scholaraio.gif" width="900" alt="ScholarAIO natural-language research workflow">
</div>
ScholarAIO offers more than search. It gives an AI coding agent a stable academic substrate for evidence, project memory, tool use, research outputs, and verification without trying to replace the agent itself.
```mermaid
flowchart LR
R[Researcher] <--> A[Coding agent]
A <--> H[ScholarAIO academic harness]
H --> E[Evidence and context]
H --> W[Skills, CLI, and workspaces]
H --> O[Outputs and verification]
H -. optional .-> X[Bounded external adapters]
```
## Quick Start
The default and recommended way to use ScholarAIO is simple: install it, configure it once, and open this repository directly with your coding agent.
```bash
git clone https://github.com/ZimoLiao/scholaraio.git
cd scholaraio
pip install -e .
scholaraio setup
```
Then open the repository in Codex, Claude Code, or another supported agent. In this setup, the agent gets the fullest experience: bundled instructions, local skills, the CLI, the repository knowledge map in [`docs/DESIGN.md`](docs/DESIGN.md), and the complete codebase context are all available directly. For Claude Code plugins, Codex/OpenClaw skill registration, and other setup paths, see [`docs/getting-started/agent-setup.md`](docs/getting-started/agent-setup.md).
## Upgrading To 2.0
ScholarAIO 2.0 is a product-boundary and compatibility release; it does not
change the current data layout for 1.4 or 1.5 users. Upgrade the package, run
`scholaraio setup check`, and rebuild indexes when appropriate. Users coming
from 1.3 or earlier must still complete the explicit runtime migration.
See [`docs/getting-started/upgrading-to-2.0.md`](docs/getting-started/upgrading-to-2.0.md)
for removed surfaces, migration guidance, and the 2.x compatibility promise.
## What It Does
| | Feature | Details |
|--|---------|---------|
| **PDF Parsing** | Deep structure extraction | Convert PDFs into structured Markdown while preserving formulas, figures, and layout as much as possible |
| **Not Just Papers** | More than papers | Journal articles, theses, patents, technical reports, standards, and lecture notes — four inbox categories with tailored metadata handling |
| **Hybrid Search** | Keyword + semantic fusion | Combine full-text and vector retrieval, with optional line-addressable evidence chunk search for precise source snippets |
| **Topic Discovery** | See what your libExcerpt of 12,794 characters
Read on GitHub
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3e3499e346710b81, topic:ai-agent, desc:ai agents, readme:ai agents
matched fp:3e3499e346710b81, topic:scientific-computing