Demo Detail
LiveA flagship demo designed to showcase multi-agent orchestration, retrieval strategies, visible progress states, and dynamic UI updates within a production-style copilot experience.
LangGraph
A visible workflow, not a hidden prompt chain
The Realtor Copilot uses LangGraph to coordinate the assistant workflow as an explicit graph rather than treating the whole interaction as one opaque model call.
The graph separates planning, progress tracking, specialist worker tasks, error reporting, and finalisation. That makes the demo useful as a real systems example: the user can see progress while the workflow is running, and the application has defined places to handle failures rather than leaving the UI stuck or unclear.
CopilotKit
A chat experience with application UI around it
The frontend uses CopilotKit v2 API components to make the assistant feel like part of the application rather than a detached chatbot bolted onto the page.
In practice, that means the demo can combine conversational interaction with richer UI moments such as progress cards, listing results, and structured updates while the LangGraph workflow is running.
What this demonstrates
The point is the pattern, not real estate alone
Real estate is useful here because it combines structured data, business knowledge, retrieval, user preferences, and guided decision-making in a way that is easy to understand quickly.
The same patterns apply to internal copilots, operational assistants, sales support tools, customer service workflows, and knowledge-driven business applications.
Want to see it in practice?
This demo reflects how I build real production-minded AI systems, not just prototypes.
