# YourMemory — Agentic AI memory with Ebbinghaus forgetting curve decay

## Metadata

| Field | Value |
|---|---|
| Institute | Molodetz Institute — Research & Retrieval Division |
| Subject | agent-memory-recency-tracking |
| Document type | Source Dossier |
| Tier | 1 |
| Source title | YourMemory: Agentic AI memory with Ebbinghaus forgetting curve decay |
| Source URL | https://github.com/sachitrafa/YourMemory |
| Source publisher / author | sachitrafa (GitHub, independent/open-source project) |
| Source type | repository |
| Source date (published) | Unknown — not stated |
| Date researched | 2026-10-09 |
| Researched by | tai (agent), model: Claude Sonnet 5, on behalf of retoor |
| Research method | web search synthesis |
| Confidence | Medium |
| Status | Active |

## Summary

An open-source, smaller competitor project to Mem0 that implements agent memory aging explicitly on an Ebbinghaus forgetting-curve model, rather than Mem0's simpler linear-ish 0.3x–1.5x recency multiplier — and claims a measurable recall improvement over Mem0 on a named benchmark.

## Key findings

- Every memory is described as aging on an Ebbinghaus forgetting curve: stale, unused facts fade; important and frequently-recalled ones persist.
- The project claims "+16pp better recall than Mem0 on LoCoMo" (a named long-context-memory benchmark).

## Pros / strengths

- Represents a concrete, implemented alternative to Mem0's simpler decay curve, grounded in an actual cognitive-science forgetting model rather than an ad hoc multiplier range — useful as a second data point on "how might decay actually be shaped" if tai ever builds a ranking-integration phase.

## Cons / limitations / caveats

- The "+16pp better recall than Mem0" figure is the competing project's own self-reported claim, on its own chosen benchmark, with no independent verification found during this research pass. It should be treated as a marketing/positioning claim, not a verified result, until independently reproduced.
- Smaller, less established project than Mem0 or Letta — lower confidence that its described behavior matches real-world production experience at scale.

## Relevance to tai

A second, independent example (alongside Mem0) that agent-memory products are converging on recency-as-decay-curve for ranking rather than for eviction — reinforces, but does not change, the subject's conclusion that tai's current proposal should stop at raw tracking and defer any decay-curve decision.

## Changelog

| Date | Change |
|---|---|
| 2026-10-09 | Initial dossier created. |