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A CLI research agent for AI-related paper search, code discovery, PDF collection, and bilingual reports.

Project description

PaperPilot

A CLI AI literature search agent that follows a v1.0 research workflow:

Intake -> Protocol -> Search -> Corpus -> Screening -> Verification -> Synthesis -> Review -> Report

Quick Start

pip install -e .
PaperPilot

Command mode:

PaperPilot "LLM agent" --auto-confirm --max-papers 30 --since-year 2021
PaperPilot "RNA inverse folding sequence design" --github-filter required --mode apa --quality balanced
PaperPilot inspect runs/<task-id>
PaperPilot resume runs/<task-id>

LLM Config

Recommended:

PaperPilot config set --base-url https://api.deepseek.com --model deepseek-chat
PaperPilot config import ./api.json
PaperPilot config show
PaperPilot config list
PaperPilot config use deepseek

The config is stored at ~/.paperpilot/config.json with file mode 600 where supported. Running PaperPilot without an LLM config starts a setup wizard. The app requires a working LLM configuration before running searches. Inside the interactive shell, use /model to add, import, switch, test, or delete model profiles.

Priority:

  1. Environment variables: OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL
  2. User config: ~/.paperpilot/config.json
  3. Legacy project file: llmapi.txt

Without a working LLM config, PaperPilot will pause and ask you to configure one first.

Outputs

Each run writes:

  • task.json
  • state.json
  • manifest.json
  • query_understanding.md
  • plan.json
  • protocol.json
  • metadata.json
  • corpus.json
  • core_papers.json
  • adjacent_papers.json
  • excluded_papers.json
  • ranked_papers.json
  • verification.json
  • quality_gate.json
  • literature_matrix.json
  • synthesis.json
  • report.canonical.json
  • reflection.json
  • report.zh.md
  • report.en.md
  • report.zh.html
  • report.en.html
  • report.zh.pdf
  • report.en.pdf
  • download_log.json
  • pdfs/
  • fulltext/
  • paper_notes.json

The Chinese and English Markdown, HTML, and PDF reports are rendered from the same report.canonical.json, so paper lists and conclusions stay aligned.

GitHub Filter

literature-agent "retrieval augmented generation" --auto-confirm --github-filter required
  • any: keep all papers and annotate code availability.
  • required: the final report view keeps papers with a detected public code link; the full screened corpus is still saved.
  • none: keep papers without detected public code links.

v1.0 Quality Layer

  • protocol.json records research question, inclusion/exclusion rules, sources, and negative keywords.
  • corpus.json stores every screened paper with core, adjacent, or exclude decisions.
  • verification.json records DOI, URL, PDF, and code confidence status.
  • quality_gate.json emits pass, retry, or needs_user_attention.
  • literature_matrix.json and synthesis.json support APA-style reports with evidence limits and AI disclosure.
  • Reports include a real review narrative: field background, method families, representative paper summaries, method comparison, trends, and open questions.

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