Agent Memory Benchmark Model Card

这是可索引项目报告证据页：它保留 Agent Memory Benchmark Model Card 的源材料入口、机制线索和限制提醒；正文仍需 reader/editor 与 academic public-copy review 后才能当作最终结论引用。

Agent Memory Benchmark Model Card

1. Role in Self Evolve

vectorize-io/agent-memory-benchmark is a benchmark harness for comparing agent memory strategies on multi-step tasks where retrieval quality, latency, and token cost all matter. It matters because self-evolving agents need inspectable memory, skill, harness, benchmark, and runtime substrates before improvement claims become trustworthy.

2. Working Principle

ingest documents and traces -> retrieve candidate context -> generate agent answer -> judge accuracy and cost -> compare memory strategies across datasets and modes

3. Evidence Path

web-observed GitHub page showed 46 stars, 17 forks, 29 commits, 6 open issues, 4 pull requests, and a benchmark surface spanning ingest, retrieve, generate, judge, outputs, and a bundled viewer. This iteration keeps freshness honest: the snapshot comes from the public GitHub page observed on 2026-06-04, while shell GitHub API access remained blocked in this workspace.

4. Teaching Use

Use this card to explain Agent Memory Benchmark: it shows how raw public GitHub evidence becomes a project-facing teaching artifact inside the self-evolving-agent pipeline.

5. Limits

The repository was not cloned in this iteration; no benchmark run, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page signals unless independently revalidated later.