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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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Memory eviction and forgetting in AI agents

Metadata

Field	Value
Institute	Molodetz Institute — Research & Retrieval Division
Subject	agent-memory-recency-tracking
Document type	Source Dossier
Tier	1
Source title	Memory eviction and forgetting in AI agents
Source URL	https://mem0.ai/blog/memory-eviction-and-forgetting-in-ai-agents
Source publisher / author	Mem0 (vendor blog)
Source type	blog post / vendor docs
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

Mem0's broader guidance on eviction/forgetting beyond the Memory Decay feature specifically: it distinguishes soft re-ranking (decay) from real pruning, and recommends TTL policies plus explicit deletion APIs for genuine forgetting, with tiered memory lifetimes by scope.

Key findings

Session memory is described as persisting minutes-to-hours and being cleared explicitly; user- and organization-level memory are long-lived by contrast.
For episodic data specifically, the guidance is to use timestamps plus explicit TTL policies rather than relying on decay alone.
The source explicitly states memory systems need deletion APIs and UX patterns that expose what is remembered — i.e. forgetting should be an observable, intentional operation, not an opaque side effect.
Decay (see mem0-introducing-memory-decay.md) is characterized here as "decays low-relevance entries over time, freeing up space and attention" — a description, not a documented formula.

Pros / strengths

Clearly separates three distinct concerns that are easy to conflate: ranking (decay), lifecycle/expiry (TTL), and user-visible deletion (explicit API) — a useful three-way framework for tai to keep in mind even though only the tracking substrate for the first concern is being built right now.

Cons / limitations / caveats

Lacks a documented formula for its own decay mechanism (consistent with the companion decay-announcement post); this source is guidance-level, not implementation-level detail.
Vendor content, not independently verified.

Relevance to tai

Useful for correctly scoping what tai's last_used/use_count proposal is and is not: it is tracking substrate that could later feed a decay-style ranking signal (per Mem0's own distinction), but it is explicitly not a TTL/expiry mechanism and not a deletion mechanism — those would be separate, later decisions if ever made.

Changelog

Date	Change
2026-10-09	Initial dossier created.

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