Oh, God! My idea comes true.

AI-powered research workflow: paper discovery → LLM analysis → scholar tracking → Paper2Code → multi-agent studio

Getting Started · Features · Roadmap · Architecture · Contributing

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

Web Dashboard

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-...

PAPERBOT_NOTIFY_ENABLED=true
PAPERBOT_NOTIFY_CHANNELS=email,slack
PAPERBOT_NOTIFY_SMTP_HOST=smtp.qq.com
PAPERBOT_NOTIFY_SMTP_PORT=587
[email protected]
PAPERBOT_NOTIFY_SMTP_PASSWORD=your-auth-code
[email protected]
[email protected]

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

# 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