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openclaw-mem v2.0.0

Release date: 2026-07-17

v2.0.0 is the v2 architecture and governance upgrade: stable core APIs, governed database evolution, converged agent-facing commands, seven local harness installers, policy-shared MCP tools, exact local vector acceleration, and a full taxonomy/lifecycle/scoring/decay system.

Upgrade posture

The Python distribution remains openclaw-context-pack; the CLI remains openclaw-mem. Existing databases and legacy commands are preserved through explicit compatibility contracts:

  • old command families remain callable through --help-all with additive deprecation guidance
  • v1.9.26 and v1.9.31 database fixtures remain readable
  • DB migration is explicit, dry-runnable, backup-first, hash-bound, and rollbackable
  • the default install remains local SQLite-only; sqlite-vec, NumPy, FastEmbed, and Qdrant are optional/fail-open according to their receipts

Existing local agents should follow Upgrade an existing local agent to openclaw-mem v2 before resuming writers.

Highlights

Agent-facing workflow

  • Primary help now centers recall, store, curate, sync, graph, and db.
  • init creates a stamped database and fill-only configuration without overwriting operator choices.
  • install --harness and doctor --harness support Claude Code, Codex, OpenClaw, generic hosts, Gemini CLI, Cursor, and Windsurf with dry-run, atomic writes, backups, and verification.

Retrieval and MCP

  • Unified fail-open lexical/vector/hybrid/graph recall emits routing and fallback evidence.
  • MCP adds policy-shared mem_recall/mem_pack and read-only graph neighbor, path, and impact tools.
  • Persisted sqlite-vec indexes carry freshness metadata; automatic selection falls back to NumPy then Python without hidden writes.

Memory governance

  • Six lifecycle states and eight deterministic bilingual kinds.
  • Category-aware pack quotas, composite score evidence, citation-only use tracking, protected decay tiers, and reversible soft archive.
  • Composite became the default after the 50-case gate preserved Recall@5 at 1.000 and improved MRR from 0.740 to 0.990 (+33.78%).

Performance

Final fixed-seed absolute SLO receipts are committed for 10k and 100k rows.

100k lane Target p95 Result
stamped connect <30 ms 10.754 ms pass
lexical recall <50 ms 26.805 ms pass
hybrid recall <200 ms 75.397 ms pass
graph-auto pack <300 ms 62.160 ms pass
sqlite-vec exact search <30 ms 23.030 ms pass

Final verification fixed a use-tracking regression in which any observation UPDATE invalidated the graph-scope cache and forced a 100k-row scope rescan. Invalidation now occurs only for insert/delete or a real scope change.

Install v2.0.0

From PyPI:

python -m pip install --upgrade "openclaw-context-pack==2.0.0"

Directly from the GitHub tag:

python -m pip install --upgrade \
  "openclaw-context-pack @ git+https://github.com/phenomenoner/openclaw-mem.git@v2.0.0"

Repository checkout:

git fetch --tags origin
git checkout v2.0.0
uv sync --locked

Verification

  • local v2.0.0 full suite: 1175 passed, 3 skipped, 5 expected compatibility warnings, and 87 unittest subtests
  • independent Ubuntu GitHub Actions: both push and PR test jobs passed
  • surface/alias/MCP/golden slice: 123 passed
  • strict MkDocs build and docs/skill tests passed
  • wheel and sdist built; both openclaw-mem and openclaw-mem-mcp started from an isolated wheel installation
  • tag CI, main CI, GitHub Pages deployment, and trusted PyPI publication passed
  • public diff scan found no high-confidence secrets or private absolute paths

Still gated

  • live memory-owner cutover in an operator environment
  • public LongMemEval/LoCoMo product claims
  • Qdrant L3 promotion beyond the optional read-index/cache posture