DOCUMENTATION
Comprehensive technical guide to WayVify's multi-agent architecture, supervisor routing algorithms, live MCP tool adapters, and human-in-the-loop state interrupts.
What is WayVify?
WayVify is an autonomous multi-agent travel orchestration framework engineered with LangGraph and the Model Context Protocol (MCP). Unlike traditional single-prompt chatbot wrappers, WayVify operates as an intelligent multi-agent network: a central Supervisor Agent decomposes incoming user queries, enforces safety and domain guardrails, dynamically selects specialized domain agents (flights, hotels, weather, budget), and aggregates results into a structured draft itinerary for human review before finalization.
Evolution: From TripGenie to WayVify
WayVify evolved from my earlier TripGenie project—a React Native mobile application for AI travel planning—expanding the concept into a full-fledged multi-agent orchestration architecture featuring dynamic supervisor routing, live MCP server integration, and stateful human-in-the-loop approval checkpoints.
How WayVify Works
WayVify converts natural language travel requests into personalized itineraries through a stateful execution pipeline:
Autonomous Agent Network
WayVify distributes tasks across dedicated specialist agents running within a unified state graph:
Supervisor & Guardrail Agent
Validates query safety and relevance, extracts travel constraints (origin, destination, budget, style), and determines optimal routing to specialist sub-agents.
Flight Specialist Agent
Interfaces via MCP to retrieve live airport metadata, airline routes, direct/connecting leg options, peak season alerts, and currency-adjusted airfare estimates.
Hotel Discovery Agent
Executes web search tool calls to query curated stays, safety assessments, luxury vs budget accommodations, and neighborhood location advice.
Weather Specialist Agent
Queries custom OpenWeather MCP servers for real-time temperatures, five-period forecasts, seasonal advice, and packing suggestions.
Budget Analyst Agent
Evaluates financial feasibility, identifies risk areas (overpriced stays, surge transport), recommends cost savings, and computes total estimates in preferred currency.
Itinerary Aggregator Agent
Synthesizes flight, hotel, weather, and budget data into a cohesive day-by-day travel plan, resolving scheduling conflicts and optimizing flow.
Human-in-the-Loop Agent
Leverages LangGraph state interrupts (`interrupt()`) to pause execution. Allows travelers to approve the draft or submit revision feedback before finalizing.
Engineering Architecture & Integrations
Built for high performance, state resilience, and strict domain control:
- LangGraph State Graphs: Manages stateful cyclic transitions, checkpointer persistence (PostgreSQL / MemorySaver), and dynamic graph routing.
- Model Context Protocol (MCP): Standardized client adapters (`MultiServerMCPClient`) connecting agents asynchronously to live Tavily, AviationStack, and custom FastMCP servers.
- FastAPI & REST Endpoints: Exposes clean REST endpoints (`/api/travel`, `/api/travel/approve`, `/health`) with async execution handling.
- Llama-3.3-70B via Groq: High-speed inference engine providing structured JSON extraction and rapid multi-turn reasoning.
Engineering Decisions & Safeguards
Key architectural choices behind WayVify's production design:
- Stateful Graph Interruption: Pausing execution via `interrupt()` preserves full conversation state and past tool results across user approval cycles.
- MCP Tool Decoupling: Tool servers (OpenWeather, AviationStack, Tavily) run as isolated FastMCP microservices, isolating core agent logic from third-party API changes.
- Multi-Currency Normalization: Automatic currency conversion layer standardizes budget outputs across INR (₹), USD ($), EUR (€), and GBP (£).
- Input Guardrailling: Front-line validation agent filters non-travel queries before invoking downstream agent networks, saving compute and preventing hallucinations.