FloatTrip: A travel planning Agent powered by LangGraph, Amap POI, route clustering and LLM-based itinerary generation

by shouzhuoshouzhuo · Agent Tool · ★ 79

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

Security audit verdict: SAFE · quality 57/100

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.

ai-travel-planneramapitinerary-plannerlangchainlanggraphllm-agentpoiroute-optimizationtravel-agent

Quick Facts

Stars79
Forks17
LanguagePython
CategoryAgent Tool
Quality Score57/100
Last Updated2026-09-26
Created2026-05-21
Platformspython
Est. Tokens~16k

About FloatTrip

✈️ 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 —...

Compatible Skills

These tools work well together with FloatTrip for enhanced workflows:

  • Fast-LLM-Agent-MCP — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)

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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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