open-research-ANA: An open-source, AI agent-native research canvas application that performs real-time search with HITL

by CopilotKit · Agent Tool · ★ 414

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-nativeaianthropiccanvaschatbotchatgptcopilotkithitlhuman-in-the-looplangchain

Quick Facts

Stars414
Forks112
LanguageTypeScript
CategoryAgent Tool
Quality Score66/100
Open Issues3
Last Updated2026-10-10
Created2024-11-04
Platformsnode
Est. Tokens~14k

About open-research-ANA

open-research-ANA 🔍 This demo showcases ANA (Agent Native Application), a research canvas app that combines Human-in-the-Loop capabilities with Tavily's real-time search and CopilotKit's agentic interface. Powered by LangGraph, it simplifies complex research tasks, making them more interactive and efficient.

Compatible Skills

These tools work well together with open-research-ANA for enhanced workflows:

  • fullstack-langgraph-nextjs-agent — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (52%)
  • interactive-mcp — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • flock — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)
  • openpencil — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (50%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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.

Sources & who's responsible:

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