Memory graphs are only as good as the format you feed them to the model in: 10-format benchmark, 40% to 80% multi-hop swing

Most agent memory systems store knowledge as a graph: entities, relationships, episodes. But at recall time that graph gets serialized into the context window, and the format is usually whatever json.dumps produces. Nobody measures what that step costs.

I benchmarked 10 graph serialization formats on token count, traversal QA, and multi-hop reasoning. Same memory graph, same model: multi-hop accuracy ranged from 40% to 80% on format alone, with about 70% token spread. For memory systems this compounds - a compact format means you can recall 3x more history per query at higher reasoning accuracy.

The winning format is open source (MIT, 6 languages): https://github.com/isongraph/isongraph - methodology in the repo. Curious how folks here serialize recalled memories today, and whether anyone has measured the impact on their own stack.