# openclaw-mem v1.2.0 — Public release kit

Date: 2026-03-25

This file is a **public-facing** announcement kit for the v1.2.0 release.

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## Headline options

1) Trust-aware context packing for OpenClaw (with receipts)
2) openclaw-mem v1.2.0: smaller prompt packs, explicit trust tiers, and traceable selection
3) Keep agent memory useful: trust tiers + context pack receipts for OpenClaw
4) A practical memory layer: prove it locally, run sidecar by default, promote the engine only when needed
5) Make memory inspectable: local-first recall + trust-aware packing for OpenClaw
6) When “memory” goes wrong, it’s often trust: openclaw-mem v1.2.0

## TL;DR (short summary)

**openclaw-mem is a local-first memory layer for OpenClaw that helps you pack the right durable facts into context—without dragging stale, untrusted, or hostile content into every prompt.**

v1.2.0 tightens the rollout path (prove locally → sidecar default → optional engine), strengthens trust-aware packing surfaces (with trace receipts you can inspect), and expands the optional engine’s “docs memory” lane so you can ingest/search your repos and notes with scoped, receipt-backed retrieval.

If you’ve ever had a long-running agent slowly drift because “retrieved” content got treated as “trusted,” this release is about making that behavior **testable, auditable, and rollbackable**.

## Release announcement

### openclaw-mem v1.2.0 — Trust-aware context packing for OpenClaw

Long-running agents don’t only forget. They also **admit**.

Over time, stale notes, weak assumptions, scraped suggestions, or even hostile instructions can quietly leak into future prompts—especially when “whatever retrieved well” gets treated as “safe to inject.”

**openclaw-mem** is built to make that failure mode visible and controllable:
- keep prompt packs **small**
- keep selection **inspectable**
- keep trust tiers and provenance **explicit**
- keep “quarantined” or suspect material visible *without* silently injecting it

This release keeps the default posture practical:
1) **Prove it locally (5 minutes)** using a synthetic fixture you can run and inspect.
2) **Run sidecar by default** on an existing OpenClaw install (low risk, easy rollback).
3) **Promote to the optional engine** only when hybrid recall + policy controls are clearly worth it.

### What shipped in v1.2.0

- **Local recall loop:** `search → timeline → get` for fast, inspectable lookup.
- **Trust-aware context packing with receipts:** `pack` + `--trace` plus policy surfaces that show inclusion/exclusion decisions.
- **Graph + provenance surfaces (inspectable):** topology refresh/query, drift checks, and provenance gating for graph-derived candidates.
- **Action plane stays optional and write-gated:** recommendation-only review queues (no silent writeback).
- **Optional Mem Engine upgrades:** hybrid recall controls and a **Docs Memory cold lane** for ingest/search over repos/docs with scoped retrieval and explicit receipts.

### Proof links (start here)

- Canonical proof: `docs/showcase/trust-aware-context-pack-proof.md`
- Metrics artifact: `docs/showcase/artifacts/trust-aware-context-pack.metrics.json`
- Companion demo: `docs/showcase/inside-out-demo.md`

### Where to start

- Docs index: `docs/index.md`
- Quickstart: `docs/quickstart.md`
- Install paths: `docs/install-modes.md`
- GitHub releases: https://github.com/phenomenoner/openclaw-mem/releases

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## Social posts

### Short post #1 (very short)

openclaw-mem v1.2.0 is out: trust-aware context packing for OpenClaw.
Smaller prompt packs, explicit trust tiers, and trace receipts you can inspect.
Proof-first: `docs/showcase/trust-aware-context-pack-proof.md`

### Short post #2 (short, slightly more context)

If your agent runs for days, “memory” failures are often *trust* failures.
openclaw-mem v1.2.0 helps keep context packs small + inspectable, with explicit trust tiers and receipts.
Start with the synthetic proof, then run sidecar by default.

### Longer post (LinkedIn / newsletter style)

Long-running agents don’t only forget—they also admit.

Over time, stale notes and weak assumptions can sneak into future prompts, and “retrieved” content starts being treated like “trusted context.” When that happens, debugging gets expensive: you can’t tell what entered the prompt, why it entered, or what should have been excluded.

openclaw-mem v1.2.0 is a practical answer to that:
- trust-aware context packing (small packs, explicit trust tiers)
- citations + trace receipts so selection is inspectable
- a rollout path that stays rollbackable: **prove locally → sidecar default → optional engine**

If you want the core value fast, start with the reproducible proof artifact:
`docs/showcase/trust-aware-context-pack-proof.md`

(Repo/releases: https://github.com/phenomenoner/openclaw-mem/releases)
