Built and open sourced from India: a property-graph format for LLMs that cuts context tokens by 70% (benchmarked against 9 formats)

I am an AI architect building open source infrastructure for agentic AI at AroorA AI Labs. While working on GraphRAG pipelines I found that nobody benchmarks the serialization step, the part where your retrieved knowledge graph gets dumped into the prompt as JSON.

So I compared 10 formats on token count, traversal QA, and multi-hop reasoning. Same graph, same model: multi-hop accuracy ranged from 40% to 80% on format choice alone, with about 70% token savings for the most compact format. That matters even more when you are paying for tokens in dollars and building on a budget in rupees.

The result is ISONGraph, MIT licensed, available in Python, JS/TS, Rust, Go, C++ and C#: https://github.com/isongraph/isongraph - benchmark methodology is public. Happy to answer questions about the benchmark or about building open source from India.