Memaware

MemAware

Benchmark for **proactive memory retrieval** in AI agents — the ability to surface relevant past context without being asked, as opposed to reactive retrieval (RAG) which only searches when prompted.

Why This Benchmark Exists

Every existing memory benchmark asks the same question: *"Can your system find the answer in past conversations?"*

[LoCoMo](https://snap-research.github.io/locomo/) gives you a long conversation and asks factual questions about it. [LongMemEval](https://github.com/xiaowu0162/LongMemEval) does the same with multi-session histories. [MemoryAgentBench](https://github.com/HUST-AI-HYZ/MemoryAgentBench) tests retrieval, learning, understanding, and forgetting. These are all useful benchmarks — but they all test the **search engine**, not the **memory system**.

Here's the gap: in real multi-session agent work, the hardest memory problem isn't finding something you're looking for. It's **knowing that something relevant exists when nobody asked about it.**

The problem, illustrated

Here are real questions from MemAware. In each case, the user makes a request. Somewhere in 3 months of conversation history, there's context the agent should proactively surface — but the user never mentions it.

**Easy** — keyword overlap exists, search could find it:

**User:** "Luna keeps scratching the new sofa. Can you suggest some deterrents or training techniques?"

**What the agent should recall:** The user's cat is named Luna.

**Why it matters:** The agent should recognize Luna as the user's cat and tailor advice accordingly — not ask "is Luna a cat or a dog?"

A keyword search for "Luna scratching sofa" might find past sessions mentioning Luna. This tier confirms that memory-aware systems perform at least as well as search-only approaches.

**Medium** — same domain, different words. Search returns noise:

**User:** "I'm trying to plan my morning routine so I can arrive at the office by 8:30 AM. What time should I set my alarm?"