Projects

Researchers and developers studying multi-agent systems lacked a unified platform to compare different agent architectures (rule-based, RL, LLM) under controlled game-theoretic conditions. Existing tools were either too academic (no visualization) or too narrow (single agent type).

Built an event-driven simulation engine in Python with a plugin architecture allowing any agent type to be registered. Added real-time WebSocket streaming so simulations can be watched live. Created a React frontend with D3.js for interactive visualization of agent behaviors, payoffs, and strategy evolution across runs.

Event-driven simulation engine with a Python backend serving WebSocket connections for real-time streaming. React frontend with D3.js visualizations renders live agent interactions and analytics dashboards. Plugin architecture allows custom agent types (rule-based, RL, LLM) with a unified interface across 5+ LLM providers. Simulation state is managed server-side with event sourcing for replay capability.

Published as open-source on PyPI with 15+ built-in game scenarios. Supports OpenAI, Anthropic, Gemini, DeepSeek, and Ollama as LLM backends. Used for research comparisons and educational demonstrations of game theory concepts.

Travel planning involves coordinating dozens of decisions across flights, accommodations, activities, dining, and logistics. Existing AI travel tools either produced generic suggestions or couldn't handle the complexity of multi-day, multi-city itineraries with real constraints like budgets and preferences.

Designed a multi-agent system where specialized agents each own a domain (flights, hotels, activities, dining). Agents run in parallel via async Python and coordinate through a central orchestrator. MCP tool integration enables agents to query real services. A conversational interface lets users iteratively refine the plan.

Multi-agent orchestration system where 10+ specialized agents run in parallel via async Python. FastAPI backend on GCP Cloud Run handles agent coordination and MCP tool integration for external service calls. React frontend provides conversational chat with streaming responses, interactive map rendering via Google Maps API, and itinerary visualization. Langfuse integration provides full observability into agent decisions and tool usage.

Generates complete day-by-day itineraries with activities, photos, maps, and budgets. Conversational refinement allows natural back-and-forth. Full agent observability through Langfuse traces every decision and tool call.

People relocating to a new city struggle to evaluate neighborhoods objectively. Information is scattered across forums, real estate sites, and local knowledge. There was no tool that scored neighborhoods across multiple lifestyle dimensions with a consistent methodology worldwide.

Built an AI scoring pipeline that aggregates data from multiple sources and applies weighted scoring across dimensions (safety, affordability, commute, lifestyle). Created interactive maps with filterable overlays so users can visually explore results. Added SEO-optimized city pages to capture organic search traffic from people researching relocations.

AI scoring pipeline that aggregates neighborhood data from multiple sources, processes it through weighted scoring models, and serves results via FastAPI. React frontend renders interactive maps with filterable overlays using Google Maps API. PostgreSQL stores neighborhood scores and metadata. SEO-optimized static pages are generated for 100+ cities with structured data markup. Langfuse provides observability into AI scoring decisions.

Covers 100+ cities worldwide with neighborhood-level scoring. Interactive maps allow filtering by any combination of lifestyle factors. SEO-optimized pages rank for neighborhood-related search queries. Supports both long-term and short-term rental perspectives.