deepxiv-sdk

DeepXiv is an agent-first paper search and progressive reading tool.

Install it with pip, start using it immediately, and let the CLI auto-register a token on first use. No extra setup is required before your first query.

• 📚 API Documentation: https://data.rag.ac.cn/api/docs
• 🎥 Demo Video:
• 📄 Technical Report:
• 📖 中文文档: README.zh.md

What DeepXiv Does

DeepXiv is built around two core workflows that matter for agents:

1. Search + Progressive Content Access
2. Trending + Popularity signals

Instead of blindly loading full papers, DeepXiv lets agents read in layers, based on token budget and task value.

Quick Start

pip install deepxiv-sdk

On first use, deepxiv automatically registers a free token and saves it to ~/.env:

deepxiv search "agentic memory" --limit 5

If you want the full stack including MCP and the built-in research agent:

pip install "deepxiv-sdk[all]"

CLI-First Workflow

The CLI is the primary interface. DeepXiv is designed so agents can work like researchers: search first, judge quickly, then read only the most valuable parts.

deepxiv search "agentic memory" --limit 5
deepxiv paper 2603.21489 --brief
deepxiv paper 2603.21489 --head
deepxiv paper 2603.21489 --section Analysis

Three commands matter most for progressive reading:

• --brief: decide whether a paper is worth deeper reading
• --head: inspect structure, sections, and token distribution
• --section: read only the most valuable parts such as Introduction, Method, or Experiments

This is the core DeepXiv idea: agents should not load full papers unless they truly need them.

CLI Features

1. Paper Search and Reading

deepxiv search "transformer" --limit 10
deepxiv paper 2409.05591 --brief
deepxiv paper 2409.05591 --head
deepxiv paper 2409.05591 --section Introduction
deepxiv paper 2409.05591

2. Trending and Popularity

Research is not only about what exists, but what is worth reading now.

deepxiv trending --days 7 --limit 30
deepxiv paper 2409.05591 --popularity

• trending finds the hottest recent papers from social signals
• --popularity gives paper-level propagation metrics such as views, tweets, likes, and replies

3. Web Search

deepxiv wsearch "karpathy"
deepxiv wsearch "karpathy" --json

Notes:

• deepxiv wsearch calls the DeepXiv web search endpoint
• each wsearch request costs 20 limit
• a registered token gets 10,000 limit per day, so this is roughly 500 web searches per day

4. Semantic Scholar Metadata by ID

deepxiv sc 258001
deepxiv sc 258001 --json

deepxiv sc fetches metadata using a Semantic Scholar paper ID.

Notes:

• this is useful when your workflow already has Semantic Scholar IDs
• DeepXiv will soon provide a Semantic Scholar search service that returns Semantic Scholar IDs directly

5. Biomedical Papers

deepxiv pmc PMC544940 --head
deepxiv pmc PMC544940

Example Agent Workflows

Workflow 1: Review recent hot papers

deepxiv trending --days 7 --limit 30 --json

Then an agent can:

1. run --brief for each paper
2. run --head for the most promising ones
3. read only key sections
4. produce a report without manually opening dozens of papers

This workflow has already been written as a reusable skill. See skills/deepxiv-trending-digest/SKILL.md.

Workflow 2: Enter a new research topic

deepxiv search "agentic memory" --date-from 2026-03-01 --limit 100 --format json

Then an agent can:

1. batch-brief the results
2. prioritize papers with GitHub links
3. inspect experiments via --head
4. read Experiments / Results
5. turn datasets, metrics, and scores into a baseline table

This workflow is also available as a reusable skill. See skills/deepxiv-baseline-table/SKILL.md.

Built-in Deep Research Agent

If you do not want to compose the workflow manually, the CLI already includes a built-in research agent.

pip install "deepxiv-sdk[all]"
deepxiv agent config
deepxiv agent query "What are the latest papers about agent memory?" --verbose

If you already have your own agent stack, you can also just plug in the DeepXiv CLI skill and keep your own orchestration.

Agent Integration

DeepXiv is designed to work well inside Codex, Claude Code, OpenClaw, and similar agent runtimes.

MCP Server

Add to Claude Desktop MCP config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "deepxiv": {
      "command": "deepxiv",
      "args": ["serve"],
      "env": {
        "DEEPXIV_TOKEN": "your_token_here"
      }
    }
  }
}

CLI Skill

mkdir -p $CODEX_HOME/skills
ln -s "$(pwd)/skills/deepxiv-cli" $CODEX_HOME/skills/deepxiv-cli

For frameworks without native skill support, load skills/deepxiv-cli/SKILL.md as operating instructions.

Python Usage

from deepxiv_sdk import Reader

reader = Reader()

results = reader.search("agent memory", size=5)
brief = reader.brief("2409.05591")
head = reader.head("2409.05591")
intro = reader.section("2409.05591", "Introduction")

web = reader.websearch("karpathy")
sc_meta = reader.semantic_scholar("258001")

Roadmap

DeepXiv is moving toward an academic paper data interface at 100M+ scale.

The roadmap is:

1. Full arXiv coverage with T+1 automatic updates
2. anyXiv coverage, including bioRxiv, medRxiv, and similar repositories
3. Full open-access literature coverage

The metadata backbone will increasingly rely on Semantic Scholar metadata as the base layer, while continuously expanding coverage and enrichment quality.

Current Coverage

• ✅ arXiv - current primary source
• ✅ PubMed Central (PMC) - biomedical and life sciences
• 🔄 Semantic Scholar metadata integration - expanding as the metadata foundation

DeepXiv focuses on open-access literature so agents can work on unrestricted paper data instead of getting blocked by subscription barriers.

Complete API Reference

Search and Query

reader.search(query, size=10, search_mode="hybrid", categories=None, min_citation=None)
reader.websearch(query)            # Web search (20 limit per request)
reader.semantic_scholar(sc_id)     # Metadata lookup by Semantic Scholar ID
reader.head(arxiv_id)              # Paper metadata and sections overview
reader.brief(arxiv_id)             # Quick summary (title, TLDR, keywords, citations, GitHub URL)
reader.section(arxiv_id, section)  # Read specific section
reader.raw(arxiv_id)               # Full paper
reader.preview(arxiv_id)           # Paper preview (~10k characters)
reader.json(arxiv_id)              # Complete structured JSON

PMC (Biomedical Papers)

reader.pmc_head(pmc_id)            # PMC paper metadata
reader.pmc_full(pmc_id)            # Complete PMC paper JSON

Agent (Optional)

from deepxiv_sdk import Agent

agent = Agent(api_key="your_openai_key", model="gpt-4")
answer = agent.query("What are the latest papers about agent memory?")
print(answer)

Token Management

deepxiv supports 4 ways to configure tokens:

1. Auto-registration (Recommended) - Automatically creates and saves on first use

deepxiv search "agent"

2. Using config command

deepxiv config --token YOUR_TOKEN

3. Environment variable

export DEEPXIV_TOKEN="your_token"

4. Command-line option

deepxiv paper 2409.05591 --token YOUR_TOKEN

Increase daily limit: Default is 10,000 requests/day. For higher limits, email your name, email, and phone to [email protected].

Free Test Papers

These papers can be accessed without a token:

arXiv: 2409.05591, 2504.21776 PMC: PMC544940, PMC514704

MCP Tools

Available tools when using MCP Server:

Tool	Description
search_papers	Search arXiv papers
get_paper_brief	Quick summary
get_paper_metadata	Full metadata
get_paper_section	Read specific section
get_full_paper	Complete paper
get_paper_preview	Paper preview
get_pmc_metadata	PMC paper metadata
get_pmc_full	Complete PMC paper

Agent Usage (Optional)

The built-in ReAct agent can automatically search papers, read content, and perform multi-turn reasoning:

from deepxiv_sdk import Agent

agent = Agent(
    api_key="your_deepseek_key",
    base_url="https://api.deepseek.com/v1",
    model="deepseek-chat"
)

answer = agent.query("Compare key ideas in transformers and attention mechanisms")
print(answer)

Or via CLI:

deepxiv agent config  # Configure LLM API
deepxiv agent query "What are the latest papers about agent memory?" --verbose

Error Handling

deepxiv provides specific exception types:

from deepxiv_sdk import (
    Reader,
    AuthenticationError,  # 401 - Invalid or expired token
    RateLimitError,       # 429 - Daily limit reached
    NotFoundError,        # 404 - Paper not found
    ServerError,          # 5xx - Server error
    APIError              # Other API errors
)

try:
    paper = reader.brief("2409.05591")
except AuthenticationError:
    print("Please update your token")
except RateLimitError:
    print("Daily limit reached")
except NotFoundError:
    print("Paper not found")
except APIError as e:
    print(f"API error: {e}")

Troubleshooting

Q: Do I need a token to use? A: No. Some papers are free to access. Search and some content require a token, but it's auto-created on first use.

Q: What's the maximum search results? A: 100 per request. Use offset parameter for pagination.

Q: How to handle timeouts? A: Reader automatically retries (max 3 times) with exponential backoff. You can customize:

reader = Reader(timeout=120, max_retries=5)

Q: Can I cache paper content? A: Yes. After getting content with reader, cache locally to database or file system.

Q: Which LLMs does the agent support? A: Any OpenAI-compatible API (OpenAI, DeepSeek, OpenRouter, local Ollama, etc.).

Examples

See examples/ directory:

• quickstart.py - 5-minute quick start
• example_reader.py - Basic Reader usage
• example_agent.py - Agent usage
• example_advanced.py - Advanced patterns
• example_error_handling.py - Error handling examples

License

MIT License - see LICENSE file

Support

• 🐛 GitHub Issues: https://github.com/qhjqhj00/deepxiv_sdk/issues
• 📚 API Documentation: https://data.rag.ac.cn/api/docs
• 📧 Higher Limits: Email with your name, email, and phone to [email protected]