研究论文AI对话助手 是 AI Skill Hub 本期精选AI工具之一。综合评分 8.2 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
研究论文AI对话助手 是一款基于 Python 开发的开源工具,专注于 论文解读、AI对话、工作流自动化 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
研究论文AI对话助手 是一款基于 Python 开发的开源工具,专注于 论文解读、AI对话、工作流自动化 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 方式一:pip 安装(推荐)
pip install deepxiv_sdk
# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install deepxiv_sdk
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/DeepXiv/deepxiv_sdk
cd deepxiv_sdk
pip install -e .
# 验证安装
python -c "import deepxiv_sdk; print('安装成功')"
# 命令行使用
deepxiv_sdk --help
# 基本用法
deepxiv_sdk input_file -o output_file
# Python 代码中调用
import deepxiv_sdk
# 示例
result = deepxiv_sdk.process("input")
print(result)
# deepxiv_sdk 配置文件示例(config.yml) app: name: "deepxiv_sdk" debug: false log_level: "INFO" # 运行时指定配置文件 deepxiv_sdk --config config.yml # 或通过环境变量配置 export DEEPXIV_SDK_API_KEY="your-key" export DEEPXIV_SDK_OUTPUT_DIR="./output"
```
---
pip install deepxiv-sdk
On first use, deepxiv auto-registers a free anonymous token (1,000 requests/day) and saves it to ~/.env:
deepxiv search "agentic memory" --limit 5
For the full stack (MCP server + built-in research agent):
pip install "deepxiv-sdk[all]"
See examples/: quickstart.py, example_reader.py, example_agent.py, example_advanced.py, example_error_handling.py.
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 an API token on first use — no setup needed before your first query.
### 🚦 Service status — live status page - 🟢 arXiv retrieval & reading — online. We aim for a T+1 sync with arXiv (subject to arXiv's own ~1-day API latency). - 🔴 bioRxiv / medRxiv — temporarily down due to a server-side issue. We're working to restore it as soon as possible. Related commands return 503 in the meantime. - 🔑 Lost your token? Recover it at data.rag.ac.cn/token-lookup (Google sign-in supported). - ℹ️ Data processing is currently trying a broader mix of models. If a TLDR looks off (e.g. truncated thinking content), please open an issue — we'll fix it.
<p align="center"> <img src="./assets/demo.gif" width="60%"> </p>
🚀 Live Demo: built on the deepxiv CLI in ~1 hour with vibe coding — try the DeepResearch demo. A full-stack research platform is on the way.
---
```python from deepxiv_sdk import Reader
reader = Reader()
results = reader.search("agent memory", size=5) for paper in results["result"]: print(paper["arxiv_id"], paper["score"], paper["title"])
web = reader.websearch("karpathy") sc_meta = reader.semantic_scholar("258001") ```
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.
Two ready-to-use workflows ship as reusable skills:
Review recent hot papers → skills/deepxiv-trending-digest/SKILL.md
```bash deepxiv trending --days 7 --limit 30 --json
DeepXiv works well inside Codex, Claude Code, OpenClaw, and similar agent runtimes.
--offset / offset= to paginate.--date-* + --min-citations constraints can over-narrow the result set.Reader(timeout=120, max_retries=5).Reasoning content is only supported as the last assistant message? Thinking/reasoning models (MiMo, DeepSeek-R1, …) need thinking disabled for multi-round tool use. Use deepxiv agent query "…" --disable-thinking, or in Python Agent(..., enable_thinking=False) (equivalently extra_body={"enable_thinking": False}).Agent(..., max_consecutive_failures=N) (0 disables it).agent.add_paper() on a brand-new paper? It returns False (instead of raising) when the paper isn't found or isn't indexed yet — very recent papers (<1–3 days old) often aren't. Genuine errors (auth, rate limit, 5xx) still raise. To handle the exception directly: from deepxiv_sdk import NotFoundError (also available as from deepxiv_sdk.exceptions import NotFoundError).503? Known outage — see the status page.创意十足的科研工具,Agent架构设计合理。Star数增长稳定,社区活跃度高。实用性强,对学术工作者价值明显。
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建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,研究论文AI对话助手 在AI工具赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | deepxiv_sdk |
| 原始描述 | 开源AI工作流:Talk to research papers like talking to authors - Python package with AI agent f。⭐686 · Python |
| Topics | 论文解读AI对话工作流自动化Python SDK科研辅助 |
| GitHub | https://github.com/DeepXiv/deepxiv_sdk |
| License | MIT |
| 语言 | Python |
收录时间:2026-05-22 · 更新时间:2026-05-22 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。