Your coding agent already reads code, writes code, and runs experiments. ScholarAIO adds a structured research workspace on top, so the same agent can search literature, cross-check results against papers, use scientific software more accurately, and carry the whole research workflow from one terminal.

• Your paper library becomes a reusable knowledge base for the same agent.
• When scientific software questions come up, the agent can consult official documentation at runtime instead of guessing from prompts.
• The system is built to keep expanding as new tools and workflows become worth supporting.

ScholarAIO offers more than search. It gives an AI coding agent a research workspace that supports natural-language interaction, papers and notes, more reliable use of scientific software, writing and running code, checking results against the literature, and structured academic writing.

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.

git clone https://github.com/ZimoLiao/scholaraio.git
cd scholaraio
pip install -e ".[full]"
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, 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.

What It Does

Works With Your Agent

ScholarAIO is designed to be agent-agnostic, but different agents expose different integration paths. Some work best when you open this repository directly; others are easier to use through plugins.

Skills follow the open AgentSkills.io standard, and .agents/skills/ is a symlink to .claude/skills/ so different agents can discover and reuse the same skills.

Migrating from existing tools? Import directly from Endnote (XML/RIS) and Zotero (Web API or local SQLite), with PDFs, metadata, and references brought over together. More import sources are on the roadmap.

Configuration

Start by opening scholaraio with your agent and let it walk you through the setup. The notes below are only a basic overview.

ScholarAIO works with a minimal setup and can be expanded as needed.

• scholaraio setup walks you through the basics.
• An LLM API key is optional but recommended for more robust metadata extraction and content completion.
• A MinerU token is optional but recommended, and free. You can also deploy MinerU or Docling locally for PDF parsing.
• scholaraio setup check shows what is installed, what is optional, and what is missing.

Full setup and configuration details → docs/getting-started/agent-setup.md, config.yaml

Agent First, CLI Available

ScholarAIO works best through an AI coding agent, but it also provides a CLI for scripting, debugging, and quick queries. For a current command reference aligned with the code, see docs/guide/cli-reference.md.

Project Structure

scholaraio/             # Python package — CLI and all core modules
  ingest/               #   PDF parsing + metadata extraction pipeline
  sources/              #   External source adapters (arXiv / Endnote / Zotero)

.claude/skills/         # Agent skills (AgentSkills.io format)
.agents/skills/         # ↑ symlink for cross-agent discovery
data/papers/            # Your paper library (gitignored)
data/proceedings/       # Proceedings library (gitignored)
data/inbox/             # Drop PDFs here for ingestion
data/inbox-proceedings/ # Drop proceedings volumes here for dedicated ingest

Full module reference → CLAUDE.md or AGENTS.md

Citation

If you use ScholarAIO in your research, please cite:

@software{scholaraio,
  author = {Liao, Zi-Mo},
  title = {ScholarAIO: AI-Native Research Terminal},
  year = {2026},
  url = {https://github.com/ZimoLiao/scholaraio},
  license = {MIT}
}

License

MIT © 2026 Zi-Mo Liao