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✈️ About the Project “Your AI Travel Agent” is a voice-powered mobile application that plans and books your perfect trip — all through simple spoken prompts. Whether you're organizing a quick weekend escape or an elaborate multi-city vacation, the app delivers a fully personalized itinerary that you can freely modify — and when you're ready, book your entire trip all at once with just one click. 💡
Inspiration
The idea was born out of the hassle of traditional trip planning — searching endlessly for flights, hotels, and activities, or relying on expensive travel agencies. We wanted to transform that process into a simple voice conversation. Instead of filling out forms or clicking through pages, imagine just saying, “Plan a 3-day trip from San Francisco to Vegas with a $3000 budget,” and getting a complete itinerary in seconds — that’s the future we’re building. 🛠️ How We Built It Our system is made up of several intelligent components that work together: 🎙 Vapi to capture the user’s voice, transcribe it into text, and handle all audio interactions. 🧠 Gemini (Google’s LLM) to generate a detailed, day-by-day travel itinerary based on the voice prompt. 🤖 Agentic AI / Micro-APIs to fetch real-time data — including hotel prices, flight availability, and attractions — tailored to the user’s preferences. 🧩 Backend API to coordinate all services: receiving the voice input, managing AI prompts, querying external APIs, and returning a coherent response. 📱 Mobile App Frontend to deliver both a spoken and visual summary of the trip, complete with editable options and one-tap booking. 🧠 What We Learned How to chain multiple AI systems (Vapi → Gemini → custom agents) to produce dynamic, real-world travel experiences from just a single spoken prompt. The importance of designing flexible, schema-driven prompts for consistent LLM outputs. Building a backend that is modular, fast, and capable of orchestrating several services in real-time. Handling latency and rate-limiting issues when working with multiple external APIs — especially when fetching live data. 🚧 Challenges We Faced Getting bots to fetch live booking information reliably was challenging due to API access limits and anti-scraping protections. Integrating multiple distinct technologies (voice input, LLMs, booking APIs, and frontend UI) into one smooth pipeline took careful engineering. Ensuring the voice-to-response experience felt natural, fast, and cohesive, especially when calling several APIs behind the scenes. 🌟 Final Thoughts This isn’t just a travel planner — it’s your AI-powered, voice-based travel concierge. With just your voice, you can plan, tweak, and book your next trip in a matter of seconds. It’s highly customizable, intuitive, and designed to eliminate the friction from travel planning. The future of trips starts with a conversation — and ends with a single tap to book it all.
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Analysis
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Metric
- 3
- 3
- 1
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- FastAPIIn code
- JavaScriptIn code
- PythonIn code
- ReactIn code
- TypeScriptIn code
- Google GeminiClaimed
5 of 6 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
- Claude CodeCommits
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
111 KB
Source files
26
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
aadityad12/AutoTrip
38 files · 505 KB · @ d08eb8a
Structure
Interface
8 files · 21%Screens, components and styles rendered to the user.
Application logic
11 files · 29%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- TypeScript65%
- Markdown18%
- Python12%
- Shell4%
- JavaScript1%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
mobile/package.json
npm · 18- @expo/vector-icons
- @react-navigation/native
- @react-navigation/stack
- expo
- expo-av
- expo-constants
- expo-status-bar
- react
- react-native
- react-native-gesture-handler
- react-native-reanimated
- react-native-safe-area-context
- react-native-screens
- react-native-svg
- react-native-vector-icons
- +3 more
backend/travel_planner_api/requirements.txt
pypi · 3- fastapi
- python-multipart
- uvicorn[standard]
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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