Show HN: Visual knowledge graphs using Ollama and Embeddings Morten Punnerud-Engelstad released mpe-lkg, a local knowledge graph tool that visualizes a language model's step-by-step reasoning using Ollama and embeddings, with blue edges for embedding similarity and green edges for exact evaluations via MPEqs. The tool, published on PyPI, runs entirely on the user's machine and supports modes like explore and settle, with RDF export. Benchmarking shows qwen3:4b-instruct-2507 answers 82.5% of arithmetic questions versus llama3.2:3b's 40%. Ask a local model a question, watch it reason step by step, and see the steps drawn as a graph. Blue edges are embedding similarity — association. Green is what an exact evaluator settled — sums and unit conversions, in fractions, via mpeqs https://github.com/punnerud/MPEqs . Everything runs on your machine. pip install mpe-lkg mpe-lkg Open http://localhost:5100 http://localhost:5100 . It talks to a local model through Ollama https://ollama.com and will tell you what is missing and the one command that fixes it. The model matters more than anything else here — measured, qwen3:4b-instruct-2507 answers 82.5% of the arithmetic battery against llama3.2:3b 's 40%: ollama pull qwen3:4b-instruct-2507-q4 K M Ask harder questions with mode=explore each sub-question answered by its own run or mode=settle explored twice, finished only when two independent runs agree , and take any run as RDF: curl -sX POST localhost:5100/jobs -H 'content-type: application/json' \ -d '{"query":"How many seconds are there in 23 weeks?","mode":"settle"}' curl -s localhost:5100/jobs/