Standard vector-only RAG (`pgvector` / Pinecone) excels at finding semantically similar paragraphs, but struggles with multi-hop relational questions like "Which microservices owned by Team Alpha depend on the deprecated billing database?" Answering relational questions accurately requires combining vector embeddings with a Property Knowledge Graph (FalkorDB / Neo4j) and structured long-term agent memory.

If you want to hire a freelance Knowledge Graph, FalkorDB/Neo4j, and long-term AI memory programmer, I am Aldian Fazrihady.

GraphRAG & Dual-Memory Architectures I Build

1. Dual-Memory Agent Orchestration (Short-Term Context + Knowledge Graph)

I design dual-memory systems (The Dual-Memory Architecture: Short-Term Context & Knowledge Graphs) that fuse fast working-memory buffers with persistent Cypher graph entities and edges.

2. Asynchronous Fact Extraction via NATS & FalkorDB

To keep user-facing chat responses sub-second, I decouple LLM entity/relationship extraction into background NATS workers (Asynchronous Memory Curation with NATS and FalkorDB) that resolve entity coreferences and update temporal graph edges.

Hire a Freelance Knowledge Graph & AI Memory Programmer

About the Author — Hire Freelance Programmer Aldian Fazrihady

Aldian Fazrihady is a Top-Rated Senior Freelance Programmer, Freelance Full Stack Developer, and AI Application Engineer with 15+ years of production software engineering experience and Google Cloud Professional Cloud Architect & Data Engineer certifications. He specializes in building custom LLM & autonomous AI agents, RAG pipelines, Next.js/React PWAs, Python (Django/FastAPI), Go microservices, and serverless cloud infrastructure on Google Cloud Run and AWS.

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