Event Deep Research alternatives for AI agents.

Tongyi Deep Research, the Leading Open-source Deep Research Agent

Academic Research Skills for Claude Code: research → write → review → revise → finalize

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

Run autonomous deep research over web and local sources

Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复

Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.

Codex-native Academic Research Skills suite for human-in-the-loop academic research workflows

Draft, restructure, or plan Nature-style manuscript sections and initial-submission materials from author-provided claims, results, figures, notes, or Chinese drafts. Use for abstracts, introductions, related work, methods, Results or experiments, discussions, conclusions, titles, full manuscript arguments, and first-submission packages such as cover letters, title pages, highlights, author contributions, availability or declaration text, and reviewer suggestions. Also use to classify Results evidence, decide what belongs in main text, captions, Methods or source data, or Supplementary Information, compress Results to the shortest sufficient evidence chain, prevent revision accretion, and audit paragraph necessity or claim repetition. Trigger on drafting a paper or section, structuring a manuscript, academic writing, first submission, 投稿材料、首次投稿、投稿信、标题页、亮点、作者贡献、数据可用性声明、推荐审稿人.

LLM驱动的 A/H/美股智能分析：多数据源行情 + 实时新闻 + LLM决策仪表盘 + 多渠道推送，零成本定时运行，纯白嫖. LLM-powered stock analysis system for A/H/US markets.

Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1

An open source, privacy focused alternative to NotebookLM for teams with no data limits. Join our Discord: https://discord.gg/ejRNvftDp9

Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. 🔗https://aka.ms/RD-Agent-Tech-Report

An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.

Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.

TradingAgents: Multi-Agents LLM Financial Trading Framework

Python tool for converting files and office documents to Markdown.