Lotara
Agenda
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February, 2026
Team members
Pain points
Solutions
Challenges
Demo
Next plan
Team members
Khoi Nguyen
Leader, Backend Developer
Man Duong
Frontend Developer
Hung Tran
AI Developer
Phuc Tran
Data Analysis
February, 2026
One-Size-Fits-All Itineraries
Tourists take a boat tour while visiting Hoi An City in Quang Nam Province, central Vietnam. Photo: Nam Tran / Tuoi Tre
February, 2026
Tour packages treat every traveler the same, with fixed schedules and generic activities that ignore personal interests and pace. Travelers end up rushing through places they love and spending time on experiences they don’t care about.
Solution
Lotara is a personality-driven travel platform that uncovers hidden local gems through data and helps travelers plan affordable, meaningful trips.
Comet Opik
Next plan
Optimize waiting time
More features
Enhance search
Thank
you
Pain points
Overcrowded, Repetitive Destinations
Hidden Gems Are Hard to Find
One-Size-Fits-All Itineraries
February, 2026
Travel companies promote the same famous places, leading to crowded and predictable trips.
Authentic local spots are scattered across the internet, outdated, or difficult to access.
Tour packages ignore personal interests, pace, and travel style.
Challenge
Multiagent architecture
RAG
Request timeout
Outline what needs to be done to prevent orchestration loops and conflicts.
Irrelevant document chunks occasionally overwhelmed the context window, leading to decreased response precision.
The cumulative time of sequential LLM reasoning steps frequently exceeded the standard roughly 150-second gateway threshold.
Difficulty in tracing errors across non-linear workflows when multiple agents operate concurrently.
Managing handoffs between specialized agents led to redundant tool calls and inconsistent state management.
Standard embedding models struggled to capture the specific domain nuances required for highly accurate grounding.
Challenges in maintaining a real-time vector index as the underlying source data was updated.
System performance degraded under high load, causing API timeouts during peak multi-agent processing.
Heavy prompt engineering and complex system instructions increased token processing time beyond acceptable UX limits.
Technology
Frontend: NextJS
Backend: NestJS, FastAPI
Agent Development Kit: Google ADK
Database: Postgres
Vector Database: Milvus
Model: Gemini
Google Gemini
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Gemini 2.5 acts as the core "brain" for our multi-agent system, providing the reasoning depth and context window (1M+ tokens) required to plan and execute complex, multi-step workflows. It transitions the system from simple responses to autonomous task completion.
Deploy Gemini 2.5 Flash as the master orchestrator to manage state across 1M+ tokens, ensuring no information is "lost" during long agent conversations
Offload sub-tasks (like travel inspiration, planning or repetitive tool calls) to Gemini 2.5 Flash to achieve sub-second latency and reduce operational costs.
Tasked with managing API rate limits (up to 2,000 RPM for Flash) and ensuring high availability of the multi-agent system.
Gemini 2.5 serves as the central engine within the ADK framework, allowing specialized agents to learn and collaborate in real-time to synthesize a single, cohesive response.
Comet Opik
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Key performance indicator
Key performance indicator
To implement a robust observability layer that moves beyond "vibe checks" to data-driven agent optimization. We will use Comet Opik to trace multi-agent reasoning, monitor token costs, and manage prompt iterations in a unified library.
Use Opik’s Trace and Span structure to reconstruct the full decision path. A Trace represents the entire user request, while Spans break down individual tool calls, retrieval steps, and agent-to-agent handoff.
Monitor total token usage broken down by phase (planning vs. tool-use vs. final response), source (system prompt vs. RAG context), and model tier (Gemini model)
Observe the failure rate (null or error responses) and set up alerts for agent loops—where agents repeat the same tool call with identical parameters
Directly visualize agent trajectories—showing exactly how an agent planned, which tools it chose, and the reasoning behind each decision.
Timeline
Week 1:Getting Started
Week 2: Halfway There!
Week 3: Keep Building
Week 4: Submission Time
Build team
Brainstorming ideas
Collect and clean destinations
Core features implemented: User onboarding, Recommend places (without AI)
RAG with vector DB
Prepare demo / documentation