scholarx 0.6.0

Universal Research Paper API — single entry point for arXiv, PMC, bioRxiv, medRxiv, PsyArXiv, OSF, and Semantic Scholar

ScholarX 📚 - API | MCP | AgentOS

Version: 1.8.0

Universal Research Paper API — a single entry point for querying, downloading, and ingesting research papers from all major preprint and academic repositories.

Version: 0.6.0

Overview

ScholarX provides a unified interface to search across 7 paper sources simultaneously, with automatic cross-source deduplication, full PDF downloads, and Knowledge Graph integration. It is registered as an Agent OS subsystem in the genius-agent ecosystem.

Supported Sources

Source	API	Auth	Rate Limit
arXiv	Atom/OpenSearch	Free	1 req/3s
PubMed Central	NCBI E-utilities	Optional NCBI_API_KEY	3 req/s (10 with key)
bioRxiv	bioRxiv REST	Free	1 req/s
medRxiv	bioRxiv REST	Free	1 req/s
PsyArXiv	OSF v2	OSF_TOKEN	1 req/s
OSF	OSF v2	OSF_TOKEN	1 req/s
Semantic Scholar	Academic Graph v1	Optional S2_API_KEY	100 req/min

Key Features

• Unified Search — Single SearchQuery model works across all sources
• 3-Tier Deduplication — DOI exact match → cross-ID mapping → fuzzy title+author (Levenshtein ≥ 0.90)
• Full Paper Download — Download and store complete PDFs locally (~/.scholarx/papers/)
• Knowledge Graph Integration — Ingest papers via existing KBIngestionEngine (ArticleNode, SourceNode, PersonNode)
• RLM Auto-Trigger — Large papers (>50K chars) automatically route through Recursive Language Model decomposition
• Per-Source Rate Limiting — Token-bucket rate limiter in the abstract provider base class
• Configurable Watchlists — Register custom research topics as MaintenanceCron tasks

Installation

# Core (API client only)
pip install scholarx

# With MCP server
pip install scholarx[mcp]

# With agent server
pip install scholarx[agent]

# Everything
pip install scholarx[all]

Quick Start

Python API

import asyncio
from scholarx.api_client import ScholarXClient
from scholarx.models import SearchQuery, PaperSource

async def main():
    client = ScholarXClient()

    # Search across all sources
    result = await client.search(SearchQuery(
        query="multi-agent orchestration",
        categories=["cs.AI", "cs.MA"],
        max_results=10,
    ))

    for paper in result.papers:
        print(f"[{paper.source}] {paper.title}")
        print(f"  Authors: {', '.join(paper.authors[:3])}")
        print(f"  DOI: {paper.doi}")
        print()

    # Download a paper
    if result.papers:
        path = await client.download_paper(result.papers[0])
        print(f"Downloaded to: {path}")

asyncio.run(main())

CLI

ScholarX includes a rich CLI with progress bars for paper discovery, relevance scoring, and PDF downloads.

# Scan for recent AI papers across 7 CS categories
scholarx scan --query "artificial intelligence" --output-dir ./papers

# Customize categories and result count
scholarx scan --categories cs.AI,cs.LG,cs.CL --max-results 30 --output-dir ./papers

# Use a custom relevance taxonomy
scholarx scan --query "knowledge graphs" --taxonomy custom_taxonomy.json --output-dir ./papers

# Auto-trigger comparative analysis on high-confidence papers
scholarx scan --analyze --output-dir ./papers

# Show stored paper library status
scholarx status

Relevance Scoring

The CLI scores each paper's abstract against a 9-domain weighted keyword taxonomy:

Domain	Weight	Focus
Orchestration	3.0	Multi-agent, workflow, task decomposition
Knowledge Graph	3.0	Ontology, OWL, entity relations, graph reasoning
Planning & Reasoning	2.5	Chain-of-thought, MCTS, deliberation
Memory & Retrieval	2.5	RAG, episodic memory, continual learning
Tool Use	2.0	Function calling, MCP, code generation
Evaluation & Safety	2.0	Benchmarks, red teaming, hallucination
Swarm & Evolution	2.0	Evolutionary methods, stigmergy, biomimicry
LLM Architecture	1.5	Transformers, MoE, distillation
Human-AI	1.0	Human-in-the-loop, decision support

Papers are classified into three tiers:

• ✅ Relevant (score ≥ 3.0) — Direct value for the target domain
• 🟡 Marginal (score 1.0–2.9) — Potential indirect value
• ❌ Irrelevant (score < 1.0) — Filtered out

Deduplication

ScholarX prevents duplicate downloads through two mechanisms:

1. Cross-source deduplication (deduplication.py): 3-tier matching removes duplicates when the same paper appears across multiple sources:

   • Tier 1: DOI exact match
   • Tier 2: Cross-ID mapping (arXiv ID ↔ S2 corpus ID via metadata)
   • Tier 3: Normalized title + first-author last name (Levenshtein ≥ 0.90)

2. Storage deduplication (paper_storage.py): Before downloading, PaperStorage.download_paper() checks if the paper ID's metadata hash already exists in ~/.scholarx/papers/.metadata/. Already-downloaded papers are skipped instantly.

MCP Server

# Start in stdio mode (for agent integration)
scholarx-mcp --transport stdio

# Start in HTTP mode
scholarx-mcp --transport streamable-http --host 0.0.0.0 --port 9600

MCP Tools

Tool	Description
search_papers	Multi-source search with deduplication
get_paper	Single paper by source + ID
search_by_author	Author-based search
get_recent_papers	Papers from last N days
list_sources	Available sources and status
list_categories	Categories per source
download_paper	Download full PDF
get_stored_papers	List locally stored papers

MCP Prompts

Prompt	Purpose
agent_utilities_enhancement_scan	Scan CS/AI papers for AU concept enhancement opportunities
biomimicry_innovation_scan	Scan biology papers for biomimetic agent patterns

Docker

# Build and run
docker compose up -d

# Debug mode (mounts local source)
docker compose -f compose.yml up --build

Environment Variables

# API Keys (all optional for basic functionality)
OSF_TOKEN=              # OSF/PsyArXiv API token
S2_API_KEY=             # Semantic Scholar (higher rate limits)
NCBI_API_KEY=           # PubMed Central (higher rate limits)

# MCP Server
TRANSPORT=stdio         # stdio | streamable-http
HOST=0.0.0.0
PORT=9600

# Tool Toggles
SEARCHTOOL=True
DISCOVERYTOOL=True
STORAGETOOL=True

# Paper Storage
SCHOLARX_STORAGE_DIR=   # Default: ~/.scholarx/papers/

Architecture

User/Agent
    │
    ▼
┌─────────────────────────┐
│  ScholarX MCP Server    │  12 tools + prompts
│  (mcp_server.py)        │
└────────┬────────────────┘
         │
         ▼
┌─────────────────────────┐
│  ScholarXClient         │  Unified API
│  (api_client.py)        │
└────────┬────────────────┘
         │
    ┌────┼────┬────┬────┬────┬────┐
    ▼    ▼    ▼    ▼    ▼    ▼    ▼
  arXiv PMC bioRx medRx PsyAr OSF  S2    ← Per-source rate limiting
    │    │    │    │    │    │    │
    └────┴────┴────┴────┴────┴────┘
         │
         ▼
┌─────────────────────────┐
│  Deduplication Engine   │  DOI → cross-ID → fuzzy title
│  (deduplication.py)     │
└────────┬────────────────┘
         │
         ▼
┌─────────────────────────┐
│  Paper Storage          │  Full PDF download
│  (~/.scholarx/papers/)  │
│         │               │
│         ▼               │
│  KBIngestionEngine      │  → ArticleNode + PersonNode
│  (KG auto-ingest)       │     + SourceNode + KBConceptNode
│         │               │
│    RLM (AU-007)         │  Auto-triggers for >50K char papers
└─────────────────────────┘

Agent OS Subsystem

ScholarX is registered as an Agent OS subsystem alongside:

Subsystem	Role
container-manager-mcp	Infrastructure provisioning
systems-manager	Host/OS operations
tunnel-manager	Network tunneling
repository-manager	Git/repo operations
scholarx	Research intelligence

Maintenance Cron

A SIX_HOURLY maintenance task (scholarx_paper_discovery) automatically:

1. Checks for new papers across configured categories
2. Evaluates relevance to Knowledge Graph concepts
3. Ingests high-relevance papers (score > 0.6)
4. Produces actionable research digests

Custom watchlists can be added via MaintenanceCron.add_task() or the create_research_watchlist MCP tool.

License

MIT

MCP Configuration Examples

1. Standard IO (stdio) Deployment

{
  "mcpServers": {
    "scholarx": {
      "command": "uv",
      "args": [
        "run",
        "scholarx-mcp"
      ],
      "env": {
        "AGENT_DESCRIPTION": "<YOUR_AGENT_DESCRIPTION>",
        "AGENT_SYSTEM_PROMPT": "<YOUR_AGENT_SYSTEM_PROMPT>",
        "DEFAULT_AGENT_NAME": "<YOUR_DEFAULT_AGENT_NAME>",
        "DISCOVERYTOOL": "True",
        "SEARCHTOOL": "True",
        "STORAGETOOL": "True"
      }
    }
  }
}

2. Streamable HTTP (SSE) Deployment

{
  "mcpServers": {
    "scholarx": {
      "command": "uv",
      "args": [
        "run",
        "scholarx-mcp",
        "--transport",
        "http",
        "--host",
        "0.0.0.0",
        "--port",
        "8000"
      ],
      "env": {
        "AGENT_DESCRIPTION": "<YOUR_AGENT_DESCRIPTION>",
        "AGENT_SYSTEM_PROMPT": "<YOUR_AGENT_SYSTEM_PROMPT>",
        "DEFAULT_AGENT_NAME": "<YOUR_DEFAULT_AGENT_NAME>",
        "DISCOVERYTOOL": "True",
        "SEARCHTOOL": "True",
        "STORAGETOOL": "True"
      }
    }
  }
}