# Kompass App: Product & Feature Status (Post-Building) This document tracks the actual implementation status of product features outlined in the PRD as of Phase 10. --- ## 1. Feature Status Summary The application has completed **Phases 1–10**: a multi-turn conversational travel agent (Phase 1–2) with SQLite persistence and a global user profile (Phase 3), live flight data via a flights MCP server plus a grounded web-search sub-agent (Phase 4), side-by-side scenario comparison with stress scoring and a full scenario detail modal incl. day-by-day plans (Phase 5), live accommodation data via an accommodations MCP server (Phase 6), multi-modal ground transport (train/bus/ferry) via grounded research with realistic connection sequencing (Phase 7), and an interactive Google Map split-panel that visualizes the active scenario's route with mode-colored legs and stop/stay markers (Phase 8). Phase 9 added streaming UX polish (animated search-progress pills, a styled reasoning bubble, candy skeletons, and graceful error cards/toasts). Phase 10 closed the "last 20%": an editable global-profile settings modal, an interactive human-in-the-loop clarifying-question card, prompt-driven manual overrides and targeted scenario refinement, a full responsive (mobile bottom-sheet / off-canvas drawer) pass, and an automated test suite (pytest, Vitest, Playwright). Agent runs are traced to Langfuse. --- ## 2. Feature Implementation Tracker ### A. Smart Date, Budget & Accommodation Optimization * **Seasonality Awareness:** Implemented (System prompt instructs seasonality reasoning; the agent injects the current date and can verify peaks via `search_web`). * **Date Recommendations:** Implemented (`find_cheapest_dates` tool + grounded `search_web` surface cheap date windows, rendered in the cheapest-dates card). * **Live Price Integration:** Implemented. Structured flight prices/dates come from the remote **Kiwi.com MCP** (no API key) and lodging from the **SerpApi Google Hotels** provider (accommodations MCP); the Gemini-grounded `search_web` sub-agent covers destination research and acts as the price fallback. Lodging requires `SERPAPI_API_KEY`; degrades to `search_web` when absent (or runs deterministic mock data when `MCP_MODE=mock`). * **Comprehensive Cost Tracking:** Implemented (Domain `CostBreakdown` provides transportation/accommodation subtotals and a grand total; the scenario tool recomputes totals from parts; accommodation costs now come from live `search_accommodations` total rates). * **Hotel Availability Checking:** Implemented (Phase 6: `search_accommodations` returns live hotels/rentals with nightly + total rates, ratings, amenities, and booking links for the stay dates; `min_rating`/`max_price` honor quality/budget constraints). ### B. Intelligent Transportation Logistics * **Multi-Modal Transport:** Implemented (Phase 7: `TransportMode` enum + `Leg` model support flight/train/bus/ferry/car; `search_ground_transport` provides grounded train/bus/ferry routing for non-flight legs). * **Convenience Filtering:** Implemented (`direct_flights_only` and preferred transit modes are gathered and honored by the flight tools). * **Detailed Route Sequencing:** Implemented (Phase 7: system prompt sequences ground legs after flights, aligns onward `departure_time` to the prior `arrival_time` plus a realistic transfer buffer, and flags tight/overnight connections). * **User Preference Gathering:** Implemented (Agent gathers preferences via the interactive `ask_clarifying_question` human-in-the-loop card — one focused question with concrete options + free-text "Other" — then calls `gather_preferences`. Baseline preferences are also editable up front in the global settings modal). * **Transit Synchronization:** Implemented (Phase 7: connection-buffer alignment in the system prompt; tight/overnight connections surfaced in stress factors and timeline chips). ### C. Scenario Comparison & Scoring * **Side-by-Side Scenario Generation:** Implemented (Phase 5: `generate_scenarios` tool + `scenario-comparison-card.js` render 2-3 cards side-by-side with best-value / lowest-stress badges; input validation enforces 2-3 scenarios and a complete day-by-day plan). * **Qualitative "Stress" Scoring:** Implemented (1–5 `stress_score` backed by structured `StressFactors`, displayed as a 5-pip gauge with a label and highlight chips). ### D. Activity & Destination Discovery * **Vibe-Based Recommendations:** Implemented (Agent gathers vibe tags through clarifying questions). * **Manual Overrides:** Implemented (Phase 10: system-prompt directive 9 "Respecting Explicit Constraints" — when the traveler has already arranged a piece ("I already have flights"), the agent skips that data tool, zeroes its cost, plans only the rest, and honors scope-limiting + hard constraints). * **Iterative Refinement:** Implemented (Phase 10: the Scenario Comparison directive supports targeted refinement — refine one scenario by its `comparison_label` and re-call `generate_scenarios` once with the full set, others carried over unchanged). ### E. User Interface & Output Formats * **Visual & Textual Summaries:** Implemented (CopilotKit v2 conversational text + generative-UI cards for preferences, clarifying questions, flights, dates, accommodations, research, ground transport, and scenario comparison). * **Trip Vibe Background Image:** Implemented (a fire-and-forget sub-agent — [image_agent.py](../backend/app/agent/image_agent.py) — turns the conversation + vibe tags into a destination scene once a destination is known, renders it with a Gemini image model, and stores it per-trip; [trip-backdrop.js](../frontend/app/components/trip-backdrop.js) fades it in behind the trip-sidebar row and the scenario-detail modal header. The traveler can explicitly ask to change it via the `set_background_image` tool. Optional — `BACKGROUND_IMAGE_ENABLED=false` disables it). * **Detailed Views / Tables:** Implemented (Phase 5: the scenario detail modal shows cost breakdown, stress factors, a per-direction travel timeline, accommodation list, and an expandable **day-by-day itinerary** with time-blocked schedules; Phase 10 adds a day count + Expand all/Collapse all control). * **Interactive Clarifying Questions:** Implemented (Phase 10: `ask_clarifying_question` is a frontend human-in-the-loop tool — the agent pauses the run on a card offering concrete option buttons plus an always-present "Other" free-text field, then resumes with the chosen answer; the answer rehydrates on resume). * **Streaming UX Polish:** Implemented (Phase 9: animated candy search-progress pills inside loading tool cards, a styled collapsible reasoning bubble from streamed `REASONING_*` events, shimmer scenario skeletons, and rose-tinted error cards + a dismissible run-failure toast). * **Responsive Design:** Implemented (Phase 10: desktop keeps the 3-pane split; below `lg` the chat is full-width, the trip sidebar becomes an off-canvas drawer via a header hamburger, and both the settings and scenario-detail modals become mobile bottom-sheets). * **Interactive Map:** Implemented (Phase 8: a persistent split-panel plots the active scenario's route — numbered transit stops, accommodation markers, and mode-colored legs — auto-fitting the viewport; see [User Story 11](user-stories.md#e-user-interface--output-formats)). ### F. Session Management & Memory * **Iterative Workflow:** Implemented (Agent runs each turn and appends to persisted history). * **Trip-Specific Memory:** Implemented (Phase 3: SQLite `Trip`/`Message` persistence; full AG-UI `message_history` is persisted so generative-UI cards rehydrate on resume; trips listed/resumed via the sidebar and REST endpoints). * **Global User Profile:** Implemented (Phase 3: singleton `UserProfile` persists preferences across trips, loaded as the baseline each run; Phase 10 adds an editable settings modal — gear button in the header — to view/edit home city, direct-flights toggle, transit modes, hotel class, vibe tags, and currency via `GET/PUT /api/profile`). * **Saved Scenarios:** Implemented (the "Save this trip" action persists a bookmarked scenario; the sidebar **Saved** tab lists and reopens them). ### G. Observability * **Agent Tracing:** Implemented (Langfuse v4 / OpenTelemetry traces every PydanticAI run — LLM requests, tool calls, inputs/outputs — grouped per trip thread via `session_id`; no-op when keys are absent). ### H. Quality & Testing * **Automated Tests:** Implemented (Phase 10: backend `pytest` covers domain, DI, persistence, agent, and REST routes — `/health`, `/api/profile`, `/api/trips`, `/api/saved-scenarios` — against an isolated SQLite DB via `app.dependency_overrides`; frontend **Vitest** unit tests cover the `lib/itinerary.js` helpers; **Playwright** e2e specs cover API, persistence, saved scenarios, and settings flows). * **LLM Agent Eval Suite:** Mostly implemented (`backend/evals/`: a 30-case golden dataset across 7 categories run headless against the real agent with deterministic fakes for flights/hotels/research; 10 evaluators — 7 deterministic + 3 LLM-as-judge on a decoupled `gemini-2.5-flash-lite` — score tool selection, constraint adherence, anti-fabrication, HITL behavior, and vibe/recommendation quality; a stdlib live dashboard visualizes report trends. Remaining: a clean variance run to lock in CI thresholds, then wiring into CI. Details: [`docs/evals.md`](evals.md) / [`docs/evals.html`](evals.html)).