Reproducing results across AI agent memory systems is hard, different LLMs, embeddings, token budgets, and scoring methods make comparisons almost meaningless.
We built MemEval, an open-source benchmark that evaluates memory systems under standardized conditions and tracks token efficiency. While benchmarking, we discovered recurring failure modes, which led to PropMem, a factual memory system designed to address them efficiently.
Both projects are Open Source: ready for evaluation, extension, or collaboration.
I built AMB, an open and preregistered benchmark for memory layers in coding agents.
Most memory benchmarks test whether a system retrieves a relevant chunk. AMB tests whether that memory actually helps an agent complete real coding work, or causes a worse change.
The agent runs Claude Code inside a sandboxed repository. Executable tests grade the resulting artifact. The benchmark is adversarially constructed with absent, stale, superseded, contradictory, adjacent, and irrelevant memories.
The current public feed contains 195 pre-authored session transcripts. A separate hard corpus record scales to 4,900 documents, including 196 real documents and 4,704 synthetic distractors, with roughly 143,000 chunks.
I am inviting small memory vendors to test their systems. There is no participation fee. Vendors can review the adapter contract, run with their own credentials, and publish their configuration and results. Negative results are welcome.
The current suite mainly measures the read path. It does not yet measure whether a memory layer learns from the agent’s own work across sessions, and no multi product ranking has been published.
Repository:
I would especially like feedback from maintainers of memory layers, MCP servers, agent frameworks, and retrieval systems.
What would you want this benchmark to measure before trusting its results?