Travel Agent
A 14-node LangGraph multi-agent system that turns "I want a week in Tuscany" into a fully booked itinerary — no tabs, no comparison hell.
The Problem
Planning a trip means juggling flights, hotels, restaurants, activities, and logistics across 10+ tabs. Users spend hours comparing options, and the result is still a messy spreadsheet. Travel agents are expensive. Existing AI tools give generic suggestions with no real booking capability.
The Approach
I designed a LangGraph state machine with 14 nodes and 9 specialized agents. The routing agent classifies intent. Context analysis extracts constraints (budget, dates, preferences). Search planning parallelizes queries across providers. A scoring agent ranks results, then a critic-debate loop between two agents challenges weak picks before they reach the user.
The key insight was the critic-debate pattern — instead of trusting a single ranking pass, two agents argue about each recommendation. This caught edge cases that a linear pipeline would miss.
System Performance
14 nodes orchestrating 9 agents with sub-second routing decisions. Weaviate vector store handles thousands of travel entities with semantic retrieval. The system is still in active development with a paying client.
My Role
Solo architect and builder. Designed the agent topology from scratch, built the FastAPI backend, configured the Weaviate vector store, and built the Next.js frontend.