@jerry609/web

{ "name": "web", "version": "0.1.0", "private": true, "scripts": { "dev": "next dev", "build": "next build", "start": "next start", "lint": "eslint", "test": "vitest run", "test:coverage": "vitest run --coverage", "test:e2e": "node ./scripts/run-playwright-e2e.mjs", "test:e2e:ui": "node ./scripts/run-playwright-e2e.mjs --ui", "test:e2e:headed": "node ./scripts/run-playwright-e2e.mjs --headed", "test:e2e:report": "npx playwright show-report" }, "dependencies": { "@ai-sdk/anthropic": "^2.0.56", "@ai-sdk/google": "^2.0.51", "@ai-sdk/openai": "^2.0.88", "@ai-sdk/react": "^2.0.118", "@modelcontextprotocol/sdk": "^1.25.1", "@monaco-editor/react": "^4.7.0", "@radix-ui/react-alert-dialog": "^1.1.15", "@radix-ui/react-avatar": "^1.1.11", "@radix-ui/react-checkbox": "^1.3.3", "@radix-ui/react-dialog": "^1.1.15", "@radix-ui/react-dropdown-menu": "^2.1.16", "@radix-ui/react-label": "^2.1.8", "@radix-ui/react-popover": "^1.1.15", "@radix-ui/react-progress": "^1.1.8", "@radix-ui/react-scroll-area": "^1.2.10", "@radix-ui/react-select": "^2.2.6", "@radix-ui/react-separator": "^1.1.8", "@radix-ui/react-slot": "^1.2.4", "@radix-ui/react-switch": "^1.2.6", "@radix-ui/react-tabs": "^1.1.13", "@radix-ui/react-tooltip": "^1.2.8", "@xyflow/react": "^12.10.0", "ai": "^5.0.116", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "cmdk": "^1.1.1", "framer-motion": "^12.23.26", "lightningcss": "^1.32.0", "lucide-react": "^0.562.0", "next": "16.1.0", "next-auth": "5.0.0-beta.30", "next-themes": "^0.4.6", "radix-ui": "^1.4.3", "react": "19.2.3", "react-dom": "19.2.3", "react-markdown": "^10.1.0", "react-resizable-panels": "^4.0.11", "recharts": "^3.6.0", "remark-gfm": "^4.0.1", "tailwind-merge": "^3.4.0", "undici": "^6.23.0", "xterm": "^5.3.0", "xterm-addon-fit": "^0.8.0", "zustand": "^5.0.9" }, "devDependencies": { "@playwright/test": "^1.58.2", "@tailwindcss/postcss": "^4", "@testing-library/jest-dom": "^6.9.1", "@testing-library/react": "^16.3.2", "@types/node": "^20", "@types/react": "^19", "@types/react-dom": "^19", "@vitest/coverage-v8": "^2.1.4", "babel-plugin-react-compiler": "1.0.0", "eslint": "^9", "eslint-config-next": "16.1.0", "jsdom": "^28.1.0", "tailwindcss": "^4", "tw-animate-css": "^1.4.0", "typescript": "^5", "vitest": "^2.1.4" } }

About

"Oh, God! My idea comes true." is an end-to-end research assistant that automates the paper discovery → analysis → reproduction pipeline. It combines multi-source search, LLM-powered evaluation, scholar tracking, and code generation into a unified workflow with Web, CLI, and API interfaces.

Backend Python + FastAPI (SSE streaming) · Frontend Next.js + Ink CLI · Sources arXiv / Semantic Scholar / OpenAlex / HuggingFace Daily Papers / papers.cool

Screenshots

Current dashboard layout focused on the active research question, the workflow console, and decision-critical alerts.

Research Workspace	AgentSwarm Studio

LLM-as-Judge Radar	Email Push

Features

Discovery & Analysis

• Multi-source search — Aggregate arXiv, Semantic Scholar, OpenAlex, HF Daily Papers, papers.cool with cross-query dedup and scoring
• DailyPaper — Automated daily report generation with SSE streaming, LLM enrichment (summary / trends / insight), and multi-channel push (Email / Slack / DingTalk / Telegram / Discord / WeCom / Feishu)
• LLM-as-Judge — 5-dimensional scoring (Relevance / Novelty / Rigor / Impact / Clarity) with multi-round calibration, automatic filtering of low-quality papers
• Deadline Radar — Conference deadline tracking with CCF ranking and research track matching

Knowledge Management

• Paper Library — Save, organize, and export papers (BibTeX / RIS / Markdown / CSL-JSON / Zotero sync)
• Structured Cards — LLM-extracted method / dataset / conclusion / limitations with DB caching
• Related Work — Draft generation from saved papers with [AuthorYear] citation format
• Memory System — Research memory with FTS5 + BM25 search, context engine for personalized recommendations
• MemoryBench Suite — Retrieval / context / isolation / injection / performance / ROI / effectiveness benchmarks for the memory and Paper2Code stack

Reproduction & Studio

• Paper2Code — Paper → code skeleton (Planning → Analysis → Generation → Verification) with self-healing debugging
• AgentSwarm — Multi-agent orchestration platform with Claude Code integration, Runbook file management, Diff/Snapshot, and sandbox execution (Docker / E2B)
• Scholar Tracking — Multi-agent monitoring with PIS influence scoring (citation velocity, trend momentum)
• Deep Review — Simulated peer review (screening → critique → decision)

Getting Started

Install

# Use python3 for macOS/Linux
python -m venv .venv && source .venv/bin/activate
pip install -e .

Configure

cp env.example .env
# Set at least one LLM key: OPENAI_API_KEY=sk-...

Multiple LLM backends supported via ModelRouter:

DailyPaper supports Email / Slack / DingTalk / Telegram / Discord / WeCom / Feishu push.

Web UI — Configure in the Topic Workflow settings panel (recommended).

Environment variables:

PAPERBOT_NOTIFY_ENABLED=true
PAPERBOT_NOTIFY_CHANNELS=email,slack
PAPERBOT_NOTIFY_SMTP_HOST=smtp.qq.com
PAPERBOT_NOTIFY_SMTP_PORT=587
PAPERBOT_NOTIFY_SMTP_USERNAME=your@qq.com
PAPERBOT_NOTIFY_SMTP_PASSWORD=your-auth-code
PAPERBOT_NOTIFY_EMAIL_FROM=your@qq.com
PAPERBOT_NOTIFY_EMAIL_TO=recipient@example.com

Run

# Database migration (first time)
alembic upgrade head

# API server
# Use python3 for macOS/Linux
python -m uvicorn src.paperbot.api.main:app --reload --port 8000

# Web dashboard (separate terminal)
cd web && npm install && npm run dev

# Background jobs (optional)
arq paperbot.infrastructure.queue.arq_worker.WorkerSettings

CLI Usage

# Daily paper with LLM + Judge + push
python -m paperbot.presentation.cli.main daily-paper \
  -q "LLM reasoning" -q "code generation" \
  --with-llm --with-judge --save --notify

# Topic search
python -m paperbot.presentation.cli.main topic-search \
  -q "ICL compression" --source arxiv_api --source hf_daily

# Scholar tracking
python main.py track --summary

# Paper2Code
python main.py gen-code --title "..." --abstract "..." --output-dir ./output

# Deep review
python main.py review --title "..." --abstract "..."

Architecture

Editable source: Excalidraw · draw.io

Module Status

Full maturity matrix and progress: Roadmap #232

MemoryBench Evaluation

Aligned with LongMemEval (ICLR 2025), LoCoMo (ACL 2024), Mem0, Letta. Full methodology: evals/memory/README.md · Epic #283

Breakdown by LoCoMo question type:

Covers: instruction override, tag escape, special token injection, role hijack, Unicode bypass, privilege escalation.

# Run full MemoryBench suite (~6s, fully offline, no API keys needed)
PYTHONPATH=src pytest -q evals/memory/test_retrieval_bench.py \
  evals/memory/test_scope_isolation.py \
  evals/memory/test_context_extraction.py \
  evals/memory/test_injection_robustness.py -s

Roadmap

Roadmap #232 — Living roadmap organized by functional area, with checkbox tracking and Epic links.

Active Epics:

Contributing

1. Pick an unchecked item from the Roadmap
2. Check the linked Epic for detailed requirements
3. Open a PR targeting dev branch
4. Follow Conventional Commits format

# Run tests
pytest -q

# Format
python -m black . && python -m isort .

Documentation

Acknowledgements

• Qc-TX — Crawler contributions
• BettaFish — Multi-agent collaboration reference
• OpenClaw — Memory architecture reference

License

MIT