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 shouzhuoshouzhuo · Agent Tool · ★ 76
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is FloatTrip safe to install? View the security audit →
✈️ AI 旅游规划助手 一句话描述需求，自动生成带时刻表的多日旅游计划 📖 简介 AI 旅游规划助手是一个基于 LangGraph 多 Agent 流水线 的旅游行程生成工具。只需输入一句话的出行需求（目的地、日期、偏好），系统即可自动完成： 🔍 意图识别（快速失败） — 从自然语言抽取目的地、日期、偏好；支持"明天开始3日游"等相对时间 + 天数表达式；缺目的地/日期时立即返回，不触发后续 LLM 调用 🌤️ 天气感知 — 自动拉取出行日期天气预报，雨雪天优先安排室内景点，超出预报范围时降级提示 🗺️ 景点搜索 — 调用高德地图 API 获取真实景点候选池，按评分过滤 🧭 智能规划 — Planner/Reviewer 多轮循环优化，共享对话记忆确保紧急问题优先修复 🍜 餐饮推荐 — 基于每天动线搜索周边餐厅，按天并行调用 LLM 推荐午晚餐 🗺️ 地图可视化 — 前端接入高德 JS API，在半屏地图上逐天绘制行程动线与景点标注 ✍️ 手动编辑 — 对生成的行程可拖拽换序、搜索换点/换餐厅、改时段，带撤销/重做栈；保存时服务端重算逐段距离 🔌 多 LLM 提供商 — 通过 一键切换 DeepSeek / 豆包，统一工厂函数封装，缺 Key 时给出明确报错 所有景点与餐厅数据均来自高德真实 POI，不会凭空捏造地点。 🧪 这个项目有Agent评测体系：基于 Anthropic《Demystifying Evals for AI Agents》方法论，对核心 Agent（Planner / Reviewer）建立了可复现的定量评估框架，包括代码打分器 G1–G7 + LLM 评委 + Reviewer 可靠性指标，指标覆盖 pass@k、pass^k、收敛轮次、误放行率、planner 反驳率。详见 。 🎬 演示 新建规划页 <p al
| Stars | 76 |
| Forks | 15 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 57.4445378066453/100 |
| Last Updated | 2026-09-19 |
| Created | 2026-05-21 |
| Platforms | python |
| Est. Tokens | ~16k |
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FloatTrip is A travel planning Agent powered by LangGraph, Amap POI, route clustering and LLM-based itinerary generation.. It is categorized as a Agent Tool with 76 GitHub stars.
FloatTrip is primarily written in Python. It covers topics such as ai-travel-planner, amap, itinerary-planner.
You can find installation instructions and usage details in the FloatTrip GitHub repository at github.com/shouzhuoshouzhuo/FloatTrip. The project has 76 stars and 15 forks, indicating an active community.
The top alternatives to FloatTrip on Agent Skills Hub include Upwork-AI-jobs-applier, palico-ai, 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: