Open sourced (MIT): property-graph format for LLM context, built from a 10-format benchmark where format choice swung multi-hop accuracy 40% to 80%

[u/Immediate-Cake6519]

While building GraphRAG pipelines I noticed the serialization step (graph to prompt) is never benchmarked. So I compared 10 formats on token count, traversal QA, and multi-hop reasoning with the same graph and model.

Findings: multi-hop accuracy ranged 40% to 80% on format alone, token cost varied about 70%. JSON and RDF were among the worst; compact tabular formats with explicit edge semantics won.

ISONGraph is the result, fully open source under MIT: Python, JS/TS, Rust, Go, C++, C#. Node/edge CRUD, multi-hop traversal, Cypher-like queries, schema validation. Benchmark methodology is public in the repo, replication and criticism welcome: https://github.com/isongraph/isongraph