A while back, I built a simple app to track stocks. It pulled market data and generated daily reports based on my risk tolerance. Basically a personal investment assistant. It worked well enough that I kept going.
Now, the same framework helps me with real estate: comparing neighborhoods, checking flood risk, weather patterns, school zones, old vs. new builds, etc. It’s a messy, multi-variable decision—which turns out to be a great use case for AI agents.
Instead of ChatGPT or Grok 4, I use mcp-agent, which lets me build a persistent, multi-agent system that pulls live data, remembers my preferences, and improves over time.
Key pieces:
• Orchestrator: picks the right agent or tool for the job
• EvaluatorOptimizer: rates and refines the results until they’re high quality
• Elicitation: adds a human-in-the-loop when needed
• MCP server: exposes everything via API so I can use it in Streamlit, CLI, or anywhere
• Memory: stores preferences and outcomes for personalization
It’s modular, model-agnostic (works with GPT-4 or local models via Ollama), and shareable.
Let me know what you all think!
Now, the same framework helps me with real estate: comparing neighborhoods, checking flood risk, weather patterns, school zones, old vs. new builds, etc. It’s a messy, multi-variable decision—which turns out to be a great use case for AI agents.
Instead of ChatGPT or Grok 4, I use mcp-agent, which lets me build a persistent, multi-agent system that pulls live data, remembers my preferences, and improves over time.
Key pieces: • Orchestrator: picks the right agent or tool for the job • EvaluatorOptimizer: rates and refines the results until they’re high quality • Elicitation: adds a human-in-the-loop when needed • MCP server: exposes everything via API so I can use it in Streamlit, CLI, or anywhere • Memory: stores preferences and outcomes for personalization
It’s modular, model-agnostic (works with GPT-4 or local models via Ollama), and shareable.
Let me know what you all think!