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jinsoo96/README.md

Hi, I'm Jinsoo Kim

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Building multi-agent AI systems with cognition, memory, and ontology-grounded retrieval.

Python Claude RAG GraphRAG Multi-Agent Ontology Harness Engineering

Gmail KHU GitHub


Focus

Multi-agent systems: agent cognition (persona, emotion, memory, theory-of-mind), harness execution engines, and forge engineering — agents that rewrite their own harness under benchmark-gated control.
RAG and knowledge graphs: one-shot GraphRAG, ontology build + search toolkits, ontology-grounded retrieval, document AI pipelines.
Open source and research: xgen-ontology / xgen-omnifuse / xgen-harness / Agethos on PyPI, multi-award academic publications.


Featured Projects

Project Description Links
xgen-omnifuse Backend-agnostic one-shot GraphRAG — fuses vector + graph (label / class enumeration / relation) seeds with MMR diversity into a single synthesis; zero-infra (in-memory BM25) or any SPARQL/Fuseki. Plus Vault, an omnifuse-native memory (fuse / surface). Extracted from production ontology GraphRAG PyPI
xgen-ontology Backend-agnostic ontology / knowledge-graph toolkit — build a clean KG from documents or tables (extract / entity-resolution / dedup / is-a induction / quality), then search it with one-shot GraphRAG. Zero infra (in-memory), any SPARQL store. The BUILD half to omnifuse's search PyPI
xgen-harness Declarative LLM agent execution engine (harness engineering) — declare a HarnessConfig, get a 10-stage pipeline. Multi-provider, capability-based tool matching, compile workflows to installable MCP wheels PyPI
JINXUS Hyper-personalized multi-agent AI assistant — 28 agents, virtual pixel office, 225 tools, autonomous execution Python FastAPI Next.js
Agethos A brain for AI agents — OCEAN personality, PAD emotion, memory stream, Hebbian learning, vicarious learning, cross-platform export PyPI
forge-engineering Forge Engineering — a meta-engineering discipline above harness engineering: agents that rewrite their own harness under versioned, benchmark-gated control Python

Awards

Year Award Conference
2025 Excellence Paper Award Korea Society of Electronic Commerce & Smart Media Society
2024 Excellence Paper Award Korea Society of IT Services
2024 Best Paper Award Korea Intelligent Information Systems Society
2024 Excellence Paper Award Korean Academy of Management
2023 Excellence Paper Award Korea Society of Information Systems
2023 Best Paper -- Honorable Mention KHU Big Data Graduate Student Conference
2023 Grand Prize Korea Knowledge Management Society -- Idea Competition

Background

Education M.S. Big Data Analytics — Kyung Hee University / B.S. Statistics — Jeonbuk National University
Experience Plateer — AI/LLM Engineer (current), building agent harness engines & ontology GraphRAG Shaveron — FDE (Forward Deployed Engineer) LG CNS SINGLEX Strategy/Operations Team @ LG Science Park — RAG Development
Interests Multi-Agent AI Systems, Agent Cognition, RAG, NLP, Time Series Forecasting

Tech Stack

Languages

Python TypeScript JavaScript SQL R

AI & Data

Anthropic LangChain PyTorch scikit-learn Pandas

Backend & Frontend

FastAPI Next.js React TailwindCSS

DevOps & Infra

Docker Jenkins GitLab Redis Cloudflare Linux Git


GitHub Stats

 



GitHub Contribution Snake

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  1. agethos agethos Public

    Empowering agents with a unique persona and ethical cognitive intelligence.

    Python 7

  2. JINXUS JINXUS Public

    A hyper-personalized multi-agent AI assistant with a virtual pixel office.

    Python 3

  3. forge-engineering forge-engineering Public

    Forge Engineering — Meta-engineering discipline above Harness Engineering. Agents that rewrite their own harness with versioned, benchmark-gated control.

    Python

  4. xgen-harness-executor xgen-harness-executor Public

    Python

  5. xgen-omnifuse xgen-omnifuse Public

    OmniFuse : backend-agnostic one-shot GraphRAG: fuse vector + graph(label/class/relation) seeds with MMR diversity into one synthesis. Zero-infra default.

    Python

  6. xgen-ontology xgen-ontology Public

    Backend-agnostic ontology / knowledge-graph toolkit — build a clean KG from documents or tables, then search it with one-shot GraphRAG. Zero infra, any graph DB.

    Python