Why We Built This
Real estate sales teams often struggle with fragmented data and low-quality leads, wasting valuable time on repetitive tasks. ReOwls solves this by combining accurate property data and multi-agent LLM workflows to streamline lead qualification, allowing agents to spend more time on what matters—closing deals.
What It Does Best
-
Knowledge-Base Q&A (GraphRAG): Ensures accurate property details are available on-demand, preventing hallucinated responses and keeping compliance intact.
-
Multi-Agent Workflows (LangChain & Agno): Originally prototyped with LangChain; now moving towards Agno to simplify scaling to larger multi-agent interactions and making complex flows easier to manage.
-
Deep Chat Frontend: Implemented using Deep Chat, a highly customizable, framework-agnostic web component optimized for fast response streaming and flexibility in UX design.
-
Unified Analytics with Postgres: Centralizes event data in a robust relational database, providing a single source of truth for effective operational insights and sales coaching.
My Role & Stack Choices
As the sole developer behind ReOwls, I chose a combination of proven, efficient technologies tailored specifically to solve real-world pain points in real estate.
Backend & APIs:
- FastAPI and Uvicorn for lightweight, performant, type-safe APIs
- Good development experience with life code reloading
Data Management:
- Chose Postgres from the outset due to superior concurrency support and easier analytics compared to file-based options like SQLite. It keeps data clean, consistent, and easily queryable.
Agent Orchestration:
- Initial rapid prototyping with LangChain allowed me to quickly validate concepts.
- Transitioning to Agno is currently underway—not for performance benchmarks—but because Agno provides a straightforward approach for scaling complex multi-agent scenarios.
Frontend Chat:
- Selected Deep Chat as the chat widget due to its agnostic nature, ease of customization, and clean UX without framework lock-in. This allows the chat experience to remain snappy and adaptable, especially crucial on lower-end mobile devices.
Deployment:
- Dockerized deployments ensure consistency between local and production environments.
- Currently running on simple bare-metal hosting, prioritizing simplicity and speed of iteration over complexity or premature optimization.
Behind the Tech Choices (and why simple wins)
I deliberately chose technologies and frameworks that balance ease of use with long-term scalability. FastAPI and Postgres form a solid, predictable backend that won’t easily break or surprise. GraphRAG was crucial, as it naturally models the interconnected nature of real estate data, significantly improving response accuracy and quality.
Switching from LangChain to Agno wasn’t about chasing performance stats, but about preparing the project to gracefully scale multi-agent complexity without adding layers of unnecessary abstraction. Likewise, Deep Chat resonated perfectly with my goal to maintain a snappy, intuitive user experience without embedding large frameworks or complicating the frontend build.
Simplicity lets me iterate quickly, and that’s a big deal—especially since I’m managing everything from frontend UI to database design myself. Avoiding complexity now makes future growth more manageable and development more enjoyable.
Got questions, ideas, or just want to check it out yourself? Visit ReOwls →