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@caix84476-netizen caix84476-netizen released this 31 Aug 00:33
· 82 commits to main since this release
v2.1.0
0a48f4e

Personal Understanding v2.1.0

Verbatim-first, evidence-chain personal memory for AI agents. One folder, Python stdlib only, fully local. Ships as a skill + stdio MCP server (11 tools) with a local audit dashboard. Bilingual: English (SKILL.md) and 中文 (SKILL.zh-CN.md).

Highlights

  • Verbatim-first capture — every personal message saved word-for-word (SHA-256, timestamped, session-tagged) before any summarizing; every derived record links back to the exact quotes
  • Derivation closure + hard session gate — the agent cannot claim "memory updated" unless structural validation, closure, and v2 integrity checks pass with exit code 0
  • Human-like three-layer recall — survey (compact routing map) → probe (entities, context cards, time neighbors) → deep (verbatim only); cold-recall ladder for "I forget…"
  • Causal hypothesis layer — claim / mechanism / supports / counterexamples / alternatives / scope, always candidate, never silently promoted to fact
  • Entity profiles + cross-entity context cards — people, schools, places, objects, works, games, concepts, environments
  • Proactive follow-ups — "let's see in a few days" becomes a tracked loop that checks back with context
  • Guided starters — new users who don't know what to share get one warm, concrete question from the archive's real gaps
  • Audit dashboard — local read-only panel: counts, validation state, full chain from any event back to the original words
  • Zero dependencies — Python stdlib only, no embeddings service, no API keys, no telemetry

v2.1.0 changes (2026-08-29)

  • Correctness: fixed a salience-decay data-corruption bug; unified follow-up-due semantics; atomic writes everywhere; tolerant load of corrupt manifests; dead code removed
  • Product: trigger-scenario description; low-signal fast path; survey phase bucketing (~230 ms); evidence_fidelity counts in probe
  • Loops: persisted retrieval decision traces; redesigned backup model (snapshots + rclone mirror); install_mcp.py across clients; references consolidated 26 → 14
  • Includes the full hardening history from earlier rounds (v2.0.0 architecture leap: memory/v2/, immutable captures, salience axis, context cards, hypotheses, three-state validation, dashboard)

See the full CHANGELOG.

  • MIT License
  • Install: git clone into your client's skills dir → python scripts/init_archive.py → python scripts/install_mcp.py --auto
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