Architected a 19-node stateful LangGraph system that separates live conversational memory extraction, critique-driven planning loops, and an automated nightly dossier pipeline into distinct tracks.
Case Study / Agentic AI system
Co-Founder Memory
Most AI assistants answer the current prompt but lose long-term builder context, project momentum, and evolving preferences.
LangGraph orchestration backed by Supabase pgvector with HNSW indexing, with Self-Corrective RAG verification gating what reaches the response path.
Built a daily catch-up scheduler that reconstructs project momentum by correlating engineering logs, GitHub activity, and targeted web search, looping an auto-reviewer node back to search until the dossier passes quality checks.
Technical highlights
What to inspect.
01
19 nodes routing across conversational, planning, memory extraction, and retrieval tracks.
02
Grades retrieved documents and generated answers before accepting the response path.
03
Nightly dossier loop that searches, reviews, summarizes, and updates memory without prompting.