How to Evaluate Agent Memory with MemoryBench

MemoryBench is a shared harness for evaluating agent-memory providers on benchmark datasets, measuring answer correctness, retrieved evidence, latency, and context use, while emphasizing that products also need targeted tests for their own users and failure modes. Evaluations should begin with a small, inspectable run using the official repository and documented configuration, with approved data and awareness of service costs, before drawing conclusions from larger score tables. Results should report accuracy alongside denominators, failures, unanswered requests, run configurations, and the separate dimensions of MemScore rather than treating performance as a single reliability metric. Reliable comparisons require identical datasets, questions, models, judges, scoring rules, and documented provider settings, while pipeline inspection should distinguish ingestion, indexing, retrieval, readiness, prompting, and reasoning failures. Product-specific regression suites should test relevant behaviors such as prior support attempts, codebase changes, evidence provenance, uncertainty, data isolation, and deletion, including cases where agents should decline or seek clarification. Evaluation findings should guide controlled experiments that modify one part of the pipeline at a time, preserve baselines, and verify both improvements and regressions across broader test sets.

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