Files

T

retoorandClaude Sonnet 5 9dbd1fe667 Add plexer subsystem, container test infra, and last-resort opencode box backend

- Plexer: always-active terminal multiplexing. NativePlexerController/
  TmuxPlexerController share one interface, chosen by which env var is
  already set (TAI_PLEXER_SOCKET vs TMUX_PANE); NativePlexerBackend is
  the pty.fork()-based wrapper (raw mode, SIGWINCH resize forwarding,
  a Unix-socket control protocol, continuous session logging, a
  Ctrl+A prefix key distinct from tmux's own Ctrl+B). Auto-wraps new
  interactive shells via a .bashrc hook mirroring the existing
  command-not-found install pattern, gated on $TMUX/$TAI_PLEXER_ACTIVE/
  a $HOME/.tai-no-plexer bypass. New plexer_send/read/type/status
  tools, env-gated on a detected controller.
- tests/container.py + docker-compose.yml + tests/docker/Dockerfile:
  a disposable podman/docker image for the tests that must never touch
  a real machine (installing the plexer hook into a real ~/.bashrc, a
  live tmux session). Required suite (test_seal.py, test_tai.py,
  tests/plexer.py, tests/bashrc.py) gates the result; tests/rag.py
  runs as best-effort since its real-LLM dependency can't be satisfied
  inside a disposable container with no nested engine.
- tests/bashrc.py: real end-to-end hook install + bash -i auto-wrap,
  anti-recursion, tmux-active skip, non-interactive skip, a live
  TmuxPlexerController test, and an install/uninstall round-trip.
- tests/plexer.py: NativePlexerBackend under a real pty, plus a
  dedicated robustness pass (file permissions, >64KB payload framing,
  a malformed control request, a genuine self-deadlock fix in the
  write-to-child path).
- tai_test.py: the shared async process-driving framework (pipe/pty
  transport, group-wide kill-and-reap, a StdinBridge for --interactive)
  both test files build on.
- ChatClient last-resort backend: when there's no local opencode CLI
  but the sandbox box is already running, install opencode into it
  (official Node tarball, not apt -- apt's nodejs/npm pull in
  webpack/babel/jest as transitive deps) and route through it instead
  of giving up. Only reachable after the first retry round already
  failed everything else; never creates the box itself. TmuxPlexerController.status()
  fixed to use `list-panes` instead of `display-message -p`, which
  silently fell back to the default pane instead of erroring against
  a dead target.
- Fix 4 pre-existing test_tai.py bugs surfaced by running it in a
  clean environment for the first time: two stale mock signatures
  (subprocess.run's stdout= kwarg, Chat.complete's deadline= kwarg),
  one $TMUX-dependent catalog assertion, one tty-dependent color
  assertion.
- research/: a sourced research archive (Source Dossier/Citation
  Card/Conclusion tiers) backing the above design decisions, with its
  own audit.py consistency checker.
- .gitignore: fix a blanket *.md rule that was about to silently drop
  the entire research/ archive from version control; add __pycache__/
  and the one-off debug transcript this session started from.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

2026-10-10 04:36:05 +02:00

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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.

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