Mem0 Memory Benchmarks Model Card
这是可索引项目报告证据页：它保留 Mem0 Memory Benchmarks 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。
Mem0 Memory Benchmarks Model Card
| Field | Value |
|---|---|
| Repository | mem0ai/memory-benchmarks |
| Category | Memory Benchmark Suite |
| Stars / forks snapshot | 38 / 12 |
| Commits / issues / PRs snapshot | 15 / 2 / 2 |
| Language | Python/TypeScript |
| License | Apache-2.0 |
| Raw capture | raw-github/mem0ai_memory-benchmarks.md |
| Updated by | hourly public metadata update, 2026-06-04 21:38 +0800 |
1. Role in Self Evolve
mem0ai/memory-benchmarks 是 memory-augmented LLM systems 的公开评测套件，覆盖 LOCOMO、LongMemEval 和 BEAM，并同时支持 Mem0 cloud 与 OSS self-hosted pipeline。 It matters because self-evolving agents need explicit memory, harness, benchmark, and safety substrates before their improvement claims become trustworthy.
2. Working Principle
memory benchmark dataset -> ingest/search/evaluate pipeline -> answerer/judge scoring -> UI/results comparison
3. Evidence Path
web-observed GitHub page showed 38 stars, 12 forks, 2 issues, 2 pull requests, 15 commits, Apache-2.0 license, LOCOMO/LongMemEval/BEAM coverage, OSS+cloud paths, and published benchmark result tables. 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 Memory Benchmark Suite: it shows how memory systems, harness maps, benchmark suites, and outer-loop evaluators connect to the broader 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.