AI agent skills and MCP tools for deep research, multi-source investigation, literature review, fact-checking, and automated report generation.
AI Deep Research tools are AI-powered software designed to help developers and teams tackle ai deep research-related tasks more efficiently. These tools are typically published as open-source projects on GitHub and can be integrated into existing workflows via MCP (Model Context Protocol), Claude Skills, or standalone agent frameworks. On Agent Skills Hub, we index 25 quality-scored ai deep research tools across languages including Python, TypeScript, JavaScript.
In 2026, the AI agent ecosystem is maturing rapidly. AI Deep Research tools can significantly boost development efficiency by automating repetitive tasks, reducing human error, and providing intelligent suggestions. The top 3 tools — hyperresearch, gigaxity-deep-research, de-anthropocentric-research-engine — have earned an average of 3,532 GitHub stars, reflecting strong community validation. 25 of the listed tools come with clear open-source licenses, ensuring freedom to use and modify.
When choosing a ai deep research tool, consider these factors: 1) Community activity — GitHub stars and recent commit frequency indicate reliability; 2) Integration method — check if it supports MCP, Claude, or your preferred agent framework; 3) Language compatibility — the most common language in this list is Python; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with hyperresearch — it ranks highest in both star count and quality score.
Convert Claude Code or Codex into the most intelligent Deep Research Agent. Collect, search, and synthesize web research into a persistent, searchable wiki that builds on itself.
Open-source deep research MCP. Qwen3-30B-A3B-Thinking via OpenRouter, cited web synthesis for Claude Code, Codex, Cursor, Hermes and any MCP-compatible agent.
A 267-skill research graph in pure markdown — 51 research operations built from 216 single-purpose steps, composed in any order with explicit backtracking. One npx install, no runtime, no MCP bindings. The AI is the researcher; you set the direction.
De-Anthropocentric Research Engine — AI-powered academic research automation with deep literature survey, gap analysis, idea generation, experiment design & execution. Combines iterative deep research, adversarial debate, evolutionary generation, and distributed GPU execution.
Hands-on workshop: Build a multi-agent AI system from scratch — Deep Research Agent + Writing Workflow served as MCP servers. Includes code, slides, and video
Autonomous AI backend for deep research AI applications.
A curated hub of autonomous-research skills & agents — from idea to paper, on autopilot. | 自主科研技能与智能体精选库 —— 从灵感到论文全文，全程自动完成。
Zotero AI plugin Research assistant for Zotero 10. Chat with your library, run federated scholarly search, RAG, OCR, systematic reviews, and manage cloud storage. Includes standalone MCP, Agentic capabilities, and skills library.
~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.
Use any LLMs (Large Language Models) for Deep Research. Support SSE API and MCP server.
Structured deep research skill for Claude Code/Open Code/Codex with human-in-the-loop control
📚🔭 Your personal research radar — an LLM-powered tool that auto-aggregates the latest papers for your keywords across arXiv / Crossref / Semantic Scholar / GitHub / RSS.
MCP server for the OpenAlex API — search 240M+ scholarly works, analyze citations, track research trends, and map collaboration networks
Claude Code–style dynamic workflows for Pi: code-mode subagents with real model routing, journaled resume, git-worktree isolation, cost accounting, an interactive /workflows TUI, an /ultracode standing opt-in, and deep research.
A Model Context Protocol (MCP) server for ATLAS, a Neo4j-powered task management system for LLM Agents - implementing a three-tier architecture (Projects, Tasks, Knowledge) to manage complex workflows. Now with Deep Research.
MCP server for enabling LLM applications to perform deep research via the MCP protocol
Open-source, local-first social media research agent for Instagram, TikTok, and LinkedIn. Jev routes read-only steps; socai CLI captures cited browser evidence.
AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
Cerno is a local-first research platform that leverages agentic AI to break down complex queries into verifiable, multi-step workflows. Switch seamlessly between cloud LLMs and self-hosted models, track every reasoning step, and optimize cost and tokens—all while keeping your data on your machine.
Academic literature discovery as a Skill — Claude Code · Codex · any agent that loads SKILL.md. Five sources · four tiers · single-file Shadcn report.
Universal SKILL.md catalog for research workflows: literature review, research design, project memory, manuscript writing, and cross-agent delegation for Claude Code, Codex, Gemini, Cursor, OpenClaw, and generic AI clients.
DOI → PDF resolver with 7-source fallback (Unpaywall, S2, arXiv, PMC, bioRxiv, publisher, Sci-Hub). Multi-agent, zero-deps Python.
Pure instruction-pack skill wrapping Ai2's Asta MCP server (Semantic Scholar). Intent-to-tool routing, safe defaults, multi-agent.
面向中文用户的学术论文与科研 Agent Skill 每日排行榜 · 自动搜索、过滤并排名 GitHub 上的 Claude Code / Codex / OpenCode 科研 Skill 仓库
Schema-Guided Reasoning (SGR) has agentic system design created by neuraldeep community
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| hyperresearch | ★ 3.7k | Python | MIT | 74 |
| gigaxity-deep-research | ★ 61 | Python | MIT | 64 |
| de-anthropocentric-research-engine | ★ 504 | — | Apache-2.0 | 62 |
| De-Anthropocentric-Research-Engine | ★ 229 | TypeScript | Apache-2.0 | 58 |
| designing-real-world-ai-agents-workshop | ★ 487 | Python | MIT | 63 |
| af-deep-research | ★ 95 | Python | Apache-2.0 | 58 |
| Auto-Research-Skills | ★ 182 | Python | CC0-1.0 | 60 |
| seerai | ★ 80 | TypeScript | MIT | 67 |
| local-deep-research | ★ 9.1k | Python | MIT | 72 |
| deep-research | ★ 4.7k | JavaScript | MIT | 73 |
| Deep-Research-skills | ★ 2.0k | Python | MIT | 74 |
| auto-paper-collecter | ★ 63 | Python | MIT | 70 |
| openalex-research-mcp | ★ 55 | TypeScript | MIT | 70 |
| pi-dynamic-workflows | ★ 545 | TypeScript | MIT | 70 |
| atlas-mcp-server | ★ 478 | TypeScript | Apache-2.0 | 49 |
| gptr-mcp | ★ 370 | Python | MIT | 57 |
| jev-social | ★ 142 | JavaScript | MIT | 65 |
| last30days-skill | ★ 63.3k | Python | MIT | 80 |
| Cerno-Agentic-Local-Deep-Research | ★ 84 | Python | MIT | 45 |
| paper-search-pro | ★ 172 | HTML | Apache-2.0 | 71 |
| ai-research-skills | ★ 297 | Python | MIT | 66 |
| paper-fetch | ★ 174 | Python | MIT | 80 |
| asta-skill | ★ 179 | — | MIT | 75 |
| awesome-academic-research-skills | ★ 123 | Python | MIT | 70 |
| sgr-agent-core | ★ 1.1k | Python | MIT | 70 |
The top ai deep research tools in 2026 are hyperresearch, gigaxity-deep-research, de-anthropocentric-research-engine. Agent Skills Hub ranks 25 options by GitHub stars, quality score (6 dimensions including completeness, examples, and agent readiness), and recent activity. The list is rebuilt every 8 hours from live GitHub data.
hyperresearch (3.7k stars) is the most adopted choice for general ai deep research workflows, written in Python. gigaxity-deep-research (61 stars) is a strong alternative. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with hyperresearch — it has the deepest community and the most examples online.
Avoid pre-built ai deep research tools when (1) your use case requires deep customization that the tool's plugin system doesn't support, (2) you have strict compliance requirements that ban third-party dependencies, (3) the tool's maintenance is inactive (last commit >6 months ago), or (4) your data volume is small enough that a 50-line custom script is cheaper than learning the tool. For most production workflows above 100 requests/day, the time savings from a maintained tool outweigh the customization loss.
AI Deep Research focuses specifically on ai agent skills and mcp tools for deep research, multi-source investigation, literature review, fact-checking, and automated report generation. Semantic Search is a related but distinct category — see https://agentskillshub.top/best/semantic-search/ for those tools. The two often appear in the same agent pipeline but solve different problems: choose ai deep research when your primary goal is the specific task, and semantic search when the workflow is broader.
For most teams, yes. hyperresearch has 3.7k stars worth of community testing, handles edge cases you haven't thought of, and ships with documentation. Build your own only when (1) your requirements are deeply non-standard, (2) you have a security/compliance reason to avoid OSS dependencies, or (3) the maintenance burden is small enough (<200 lines of code) that you'll save time long-term. The break-even point is usually around 2-3 weeks of dev time saved.
Most ai deep research tools listed are open source under permissive licenses (MIT, Apache 2.0). A handful offer paid managed/cloud versions on top of free self-hosted core. Always check the LICENSE file on each tool's GitHub repository before commercial use — some use AGPL or non-commercial restrictions that may not fit your deployment model.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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