No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by CopilotKit · Agent Tool · ★ 406
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is open-research-ANA safe to install? View the security audit →
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. Explore the CopilotKit docs » Quick Start 🚀 Prerequisites This projects uses the following tools: pnpm Docker Langgraph CLI API Keys Needed Running locally, you'll need the following API keys: OpenAI Tavily LangSmith CopilotKit Start the Agent There are two main components to this project: the agent and the frontend. First, we'll start the agent. bash cd agent Create and populate .env cat .env OPENAIAPIKEY=yourkey TAVILYAPIKEY=yourkey LANGSMITHAPIKEY=yourke
| Stars | 406 |
| Forks | 111 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 66.2237200498523/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-14 |
| Created | 2024-11-04 |
| Platforms | node |
| Est. Tokens | ~14k |
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open-research-ANA is 🤖 An open-source, AI agent-native research canvas application that performs real-time search with HITL (Human in The Loop) capabilities, powered by CopilotKit, Tavily and LangGraph. It is categorized as a Agent Tool with 406 GitHub stars.
open-research-ANA is primarily written in TypeScript. It covers topics such as agent-native, ai, anthropic.
You can find installation instructions and usage details in the open-research-ANA GitHub repository at github.com/CopilotKit/open-research-ANA. The project has 406 stars and 111 forks, indicating an active community.
The top alternatives to open-research-ANA on Agent Skills Hub include flock, argo, ai-playground. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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.
Sources & who's responsible: