metadata
license: mit
Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework
Komal Kumar1, Aman Chadha2, Salman Khan1, Fahad Shahbaz Khan1, Hisham Cholakkal1
1 Mohamed bin Zayed University of Artificial Intelligence 2 AWS Generative AI Innovation Center, Amazon Web Services
Features
- Paper Discovery — Multi-agent AI search across arXiv, Scopus, and IEEE with hybrid BM25 + TF-IDF ranking and three discovery modes (Stable, Discovery, Balanced)
- Paper Mind Graph — LLM-powered extraction of concepts, methods, and experiments into structured knowledge graphs with interactive Q&A
- Paper Review Generation — Conference-format reviews (ICLR/NeurIPS/ICML style) via multi-agent analysis with lineage extraction
- Paper Lineage — Relationship mapping (extends/applies/evaluates/contradicts/survey/prerequisite) with interactive graph visualization
- Reading Circles — Community-based reading groups with role-based access, session scheduling, RSVP, and discussion threads
Hugging Face Resources
| Resource | Type | Link |
|---|---|---|
| Papers Database | Dataset | ItsMaxNorm/pc-database |
| Papers API | Space | ItsMaxNorm/papercircle-papers-api |
| Benchmark Leaderboard | Space | ItsMaxNorm/pc-bench |
| Benchmark Results | Dataset | ItsMaxNorm/pc-benchmark |
| Research Sessions | Dataset | ItsMaxNorm/pc-research |
Getting Started
Prerequisites
- Node.js >= 18 and Python >= 3.10
- A Supabase project
- An LLM provider: Ollama (local), OpenAI, or Anthropic
Install and Run
git clone https://github.com/MAXNORM8650/papercircle.git
cd papercircle
# Install
npm install
pip install -r backend/requirements-prod.txt
# Configure
cp .env.example .env # Edit with your Supabase & LLM credentials
# Run
npm run dev # Frontend (localhost:5173)
python backend/apis/fast_discovery_api.py # Discovery API (localhost:8000)
python backend/apis/paper_review_server.py # Review API (localhost:8005)
python backend/apis/paper_analysis_api.py # Analysis API (localhost:8006)
See docs/QUICK_START.md for detailed setup and docs/DEPLOYMENT_GUIDE.md for production deployment.
Project Structure
papercircle/
├── src/ # Frontend (React 18 + TypeScript)
│ ├── components/
│ │ ├── Papers/ # Paper discovery, detail, analysis views
│ │ ├── Lineage/ # Paper relationship graph & analysis hub
│ │ ├── Sessions/ # Session scheduling, RSVP, attendance
│ │ ├── Communities/ # Reading circle management
│ │ ├── Dashboard/ # User dashboard
│ │ ├── Auth/ # Authentication modals
│ │ ├── Layout/ # Header, navigation
│ │ ├── Admin/ # Admin panel
│ │ └── Settings/ # LLM & user settings
│ ├── contexts/ # AuthContext, CommunityContext, LineageAnalysisContext
│ ├── lib/ # Supabase client, API helpers, arXiv client
│ └── hooks/ # Custom React hooks
│
├── backend/
│ ├── agents/
│ │ ├── paper_review_agents/ # Multi-agent review generation & benchmarking
│ │ │ ├── orchestrator.py # Agent orchestration pipeline
│ │ │ ├── specialized_agents.py # Critic, Literature, Reproducibility agents
│ │ │ ├── benchmark_framework.py # Review benchmark framework
│ │ │ ├── benchmark_paper_review.py # Benchmark CLI
│ │ │ ├── evaluation_metrics.py # MSE, MAE, correlation, accuracy metrics
│ │ │ └── benchmark_results/ # Cached benchmark outputs
│ │ ├── paper_mind_graph/ # Knowledge graph extraction from PDFs
│ │ │ ├── graph_builder.py # LLM-based concept/method extraction
│ │ │ ├── qa_system.py # Interactive Q&A over papers
│ │ │ ├── ingestion.py # PDF parsing & chunking
│ │ │ └── export.py # JSON/Markdown/Mermaid/HTML export
│ │ ├── discovery/ # Paper discovery agents & ranking
│ │ └── agents/ # Core query & research agents
│ ├── apis/
│ │ ├── fast_discovery_api.py # Discovery API (port 8000)
│ │ ├── paper_review_server.py # Review API (port 8005)
│ │ ├── paper_analysis_api.py # Analysis API (port 8006)
│ │ ├── community_papers_api.py # Community papers API
│ │ ├── research_pipeline_api.py # Research pipeline API
│ │ └── unified/ # Unified Docker API (app.py + routers/)
│ ├── core/ # paperfinder.py, discovery_papers.py
│ ├── services/ # HuggingFace papers client
│ └── utils/ # Storage utilities
│
├── supabase/
│ ├── migrations/ # 55 SQL migrations (schema, RLS, seeds)
│ └── functions/ # Edge functions (arxiv-search)
│
├── api/ # Vercel serverless functions
│ ├── arxiv.js # arXiv CORS proxy
│ ├── community-papers.js # Community papers endpoint
│ └── sync-status.js # Sync status endpoint
│
├── scripts/ # Utility scripts
│ ├── javascript/ # arxiv-proxy, search engine, test scripts
│ ├── shell/ # Start scripts for each API service
│ └── *.py # Dataset builder, sync, DB fixes
│
├── docs/ # Documentation
│ ├── BENCHMARKS.md # Benchmark guide (review + retrieval)
│ ├── QUICK_START.md # Quick start guide
│ ├── DEPLOYMENT_GUIDE.md # Production deployment
│ ├── SECURITY.md # Security guidelines
│ ├── MIGRATION_COMPLETE.md # Serverless migration summary
│ └── PAPER_REVIEW_AGENTS_IMPLEMENTATION.md # Review system implementation
│
├── examples/
│ ├── pc-data/ # Benchmark datasets
│ └── docs/ # Architecture & integration guides
│ ├── ARCHITECTURE_DIAGRAMS.md # System diagrams
│ ├── MULTI_AGENT_PIPELINE_ARCHITECTURE.md
│ ├── ORCHESTRATOR_ARCHITECTURE.md
│ ├── PAPER_MIND_GRAPH_ARCHITECTURE.md
│ ├── AGENT_OPTIMIZATION_GUIDE.md
│ ├── RERANKER_INTEGRATION_SUMMARY.md
│ └── setup/ # Module setup & integration guides
│
├── hf_spaces/ # HuggingFace Spaces (Papers API app)
├── assets/ # Architecture & results figures
└── public/ # Logo and static assets
Benchmarks
Two evaluation suites: Review Quality (AI reviews vs human reviewers) and Retrieval Quality (paper search accuracy).
| Benchmark | Metrics | Conferences | Details |
|---|---|---|---|
| Paper Review | MSE, MAE, Pearson r, Spearman ρ, Accuracy ±0.5/1.0/1.5 | ICLR, NeurIPS, ICML | docs/BENCHMARKS.md |
| Retrieval | Recall@k, MRR, Success Rate | 30+ conferences | docs/BENCHMARKS.md |
# Review benchmark
python backend/agents/paper_review_agents/benchmark_paper_review.py \
--data iclr2024.json --conference iclr --limit 100
# Retrieval benchmark
python benchmark_multiagent.py --queries queries.json --baseline bm25+reranker
Model results: ItsMaxNorm/pc-benchmark Interactive leaderboard: ItsMaxNorm/pc-bench
Citation
If you find PaperCircle useful in your research, please cite our paper:
misc{kumar2026papercircleopensourcemultiagent,
title={Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework},
author={Komal Kumar and Aman Chadha and Salman Khan and Fahad Shahbaz Khan and Hisham Cholakkal},
year={2026},
eprint={2604.06170},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2604.06170},
}
License
MIT License — see LICENSE
Acknowledgments
arXiv • Supabase • smolagents • LiteLLM • Ollama • Hugging Face