CHB-learner

CHB-learner / PaperPilot

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AI 文献检索与综述 Agent：支持多源检索、代码仓库定位、开放 PDF 下载、证据链与中英双语报告。

chb-learner.github.ioPaperPilot academic-research ai-literature-review cli literature-review llm
27
0
100% credibility
Found May 17, 2026 at 28 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

PaperPilot is a research assistant that helps you conduct thorough literature reviews on any academic topic. You describe your research question in plain language, and it searches multiple scholarly databases, filters and ranks papers by relevance, verifies which ones have public code and PDFs, and produces complete bilingual research reports in multiple formats. It also creates an interconnected knowledge wiki that links papers, methods, and claims together for deeper exploration.

How It Works

1
💡 You have a research question

You need to understand a topic thoroughly by reviewing many academic papers and their connections.

2
📦 You install the tool

You download PaperPilot and connect it to an AI service of your choice to help with the research work.

3
⌨️ You type your research topic

In plain language, you describe what you want to explore—like 'RNA sequence design methods with public code.'

4
🔍 The tool searches and organizes papers

PaperPilot automatically queries multiple academic databases, removes duplicates, and classifies papers by relevance.

5
✅ You review the curated results

You see which papers have working code, open PDFs, and strong evidence—sorted by importance to your question.

📄 You receive your complete research report

PaperPilot generates bilingual reports in multiple formats, plus a connected knowledge wiki you can explore.

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AI-Generated Review

What is PaperPilot?

PaperPilot is a Python CLI tool that turns a research topic into a complete, traceable literature review. You give it a keyword like "RNA inverse folding" and it queries multiple academic databases, screens papers by relevance, verifies PDF and code links, synthesizes evidence, and outputs bilingual reports in Markdown, HTML, PDF, or Obsidian Wiki format.

The workflow runs as a state machine: intake, protocol, search, corpus, screening, verification, synthesis, review, report. Each run creates a folder with full state, logs, and intermediate files so you can inspect or resume later. It integrates with OpenAI-compatible LLMs for query understanding, planning, and synthesis, with a fallback to deterministic heuristics when no API key is available.

Why is it gaining traction?

The hook is the end-to-end coverage: most literature tools stop at "return papers." PaperPilot adds verification, evidence ledger tracking, code availability checks, and structured bilingual reporting. The GitHub filter flag (`--github-filter required`) is practical for researchers who want reproducible work with public implementations.

The Obsidian Wiki export is a differentiator. Instead of just dumping a report, it generates a linked knowledge graph with pages for papers, methods, topics, and claims. The source registry supports over a dozen free sources plus optional API keys for paid services like IEEE, Elsevier, and Dimensions.

Who should use this?

Academic researchers writing literature reviews, particularly in AI/ML, biomedicine, and AI for Science. Graduate students doing systematic reviews will benefit from the structured protocol and PRISMA-style accounting. Teams wanting to track code availability for reproducibility will find the GitHub verification useful. The bilingual output serves Chinese-English research groups.

Verdict

PaperPilot solves a real pain point with a well-thought-out workflow, but with only 27 stars and a 1.0% credibility score, it is early-stage software. The documentation is thorough, PyPI packaging exists, and the feature set is ambitious. If you need a scripted literature review pipeline today, try it on a low-stakes query first. For production use, monitor the GitHub for updates and test thoroughly. The concept is solid; the project needs more battle-testing before relying on it for deadline-driven research.

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