scholaraio

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.

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.

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 .
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, 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.

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 for removed surfaces, migration guidance, and the 2.x compatibility promise.

What It Does

For writing tasks, start with the router-style writing entry when the deliverable is clear but the workflow is not. The current writing stack is organized around:

• academic-writing: route by deliverable and writing stage
• nature-workflow: bridge to the upstream nature-skills bundle for Nature/high-impact figures, polishing, writing, reviewer critique, citation, Data Availability, paper reading, reviewer response, paper-to-PPT, and academic search; direct upstream skills are preferred when available
• literature-review: long-form review and survey writing
• paper-guided-reading: guided deep reading of a single paper from fuzzy search to full-text analysis
• paper-writing: manuscript sections and paper-focused drafting
• review-response: rebuttal and response-letter workflows
• research-gap: gap analysis and open-question reports
• technical-report: technical briefings and topic reports
• poster: poster-oriented content packaging
• document: final DOCX / PPTX packaging

See docs/guide/writing.md for the full writing map.

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/ and .qwen/skills/ are symlinks to .claude/skills/ so different agents can discover and reuse the same skills. Qwen-specific project context lives in .qwen/QWEN.md.

For reuse from another project, run scholaraio setup agent to preview shell, skill-discovery, and project-wrapper changes; add --apply to perform the automatic steps.

Wrappers created with --target-project include local machine paths; review the managed block before committing those files to a shared repository.

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. If your current network has publisher access, scholaraio fetch-pdf can also pull DOI or landing-page PDFs into the normal ingest flow or refresh canonical PDFs for existing library records.

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 starts with a minimal setup. Install optional capabilities only when a workflow needs them.

• scholaraio setup walks you through the basics.
• scholaraio setup agent configures cross-project agent discovery and CLI runtime wiring.
• 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
  services/ingest/      #   PDF parsing + metadata extraction pipeline
  providers/            #   External source adapters (arXiv / Endnote / Zotero)

.claude/skills/         # Agent skills (canonical source)
.agents/skills/         # ↑ symlink for cross-agent discovery
.qwen/QWEN.md           # ↑ project context for Qwen Code
.qwen/skills/           # ↑ symlink for Qwen agent skill discovery
data/libraries/papers/  # Paper library (fresh default)
data/libraries/proceedings/ # Proceedings library (fresh default)
data/spool/inbox/       # Drop PDFs here for ingestion
data/spool/inbox-proceedings/ # Dedicated proceedings ingest inbox

Upgrading from an older release? See Upgrading To 2.0.

• Agent entry docs: CLAUDE.md or AGENTS.md
• Repository knowledge map: docs/DESIGN.md
• Deep agent reference: docs/guide/agent-reference.md

Citation

If you use ScholarAIO in your research, please cite:

@software{scholaraio,
  author = {Liao, Zi-Mo},
  title = {ScholarAIO: An Academic Harness for AI Agents},
  year = {2026},
  url = {https://github.com/ZimoLiao/scholaraio},
  license = {MIT}
}

License

MIT © 2026 Zi-Mo Liao