Work

GameMind Agent

LangGraph
RAG
PostgreSQL
Python

Conversational AI platform for game developers using LangGraph with PostgreSQL vector embeddings. Boosts idea recall accuracy by 65% through semantic memory retrieval and automated visualization. Built at Schoene Neue Kinder GmbH (Munich, remote).

GameMind Agent - conversational AI for game developers with LangGraph and semantic memory

GameMind Agent is a conversational AI platform I developed at Schoene Neue Kinder GmbH for game developers. It uses LangGraph to orchestrate multi-step conversations and PostgreSQL with vector embeddings for semantic memory, so users can recall and refine game ideas across sessions.

What I built

  • Semantic memory: Stored and retrieved game-design concepts via embeddings so the agent could reference past ideas accurately.
  • Automated visualization: Turned high-level ideas into structured outputs that supported quick iteration.
  • LangGraph flows: Designed the graph so the agent could switch between recall, generation, and clarification steps.

This work improved idea recall accuracy by about 65% compared to non-memory baselines, and is used internally by the team for concept development.