Quickstart¶
This is the fastest local proof for openclaw-mem.
Goal: show that the same query can produce a smaller, safer, cited pack once trust policy is enabled.
If you are still deciding how to adopt it, read Choose an install path first.
Prerequisites¶
- Python 3.10+ (recommended: Python 3.13)
pipfor the packaged CLI path- uv only if you want to run the repository proof fixture directly
1) Install the packaged CLI¶
python -m venv .venv
. .venv/bin/activate
pip install openclaw-context-pack
openclaw-mem --db /tmp/openclaw-mem-quickstart.sqlite status --json
The PyPI distribution is openclaw-context-pack; it installs the openclaw_mem Python package and the openclaw-mem console command.
If you want to run the bundled synthetic fixture from this repository instead, clone the repo too:
git clone https://github.com/phenomenoner/openclaw-mem.git
cd openclaw-mem
uv sync --locked
2) Ingest the synthetic proof fixture¶
DB=/tmp/openclaw-mem-quickstart.sqlite
openclaw-mem ingest \
--db "$DB" \
--json \
--file ./docs/showcase/artifacts/trust-aware-context-pack.synthetic.jsonl
What this gives you: - six synthetic rows with trust tiers, importance labels, and provenance refs - no private or user data - a reproducible basis for pack before/after comparison
3) Build the pack without trust gating¶
openclaw-mem pack \
--db "$DB" \
--query "trust-aware context packing prompt pack receipts hostile durable memory provenance" \
--limit 5 \
--budget-tokens 500 \
--trace
Expected shape:
- a compact bundle_text
- items[] + citations[]
- trace.kind = openclaw-mem.pack.trace.v1
In the synthetic proof, this ungated pack still admits one quarantined row because it matches the query text.
4) Build the pack with trust gating on¶
openclaw-mem pack \
--db "$DB" \
--query "trust-aware context packing prompt pack receipts hostile durable memory provenance" \
--limit 5 \
--budget-tokens 500 \
--trace \
--pack-trust-policy exclude_quarantined_fail_open
What changes:
- the quarantined row is excluded
- a trusted row takes its place
- the pack gets smaller
- trust_policy, policy_surface, and lifecycle_shadow explain exactly what happened
5) Inspect the proof artifact¶
What to do next¶
If the local proof was enough¶
- move to Deployment guide
- add the sidecar to your existing OpenClaw install
If you are integrating with OpenClaw agents¶
- read the Agent memory skill (SOP)
- review Context pack
- review Mem Engine reference
If you want the detailed source-checkout walkthrough¶
- GitHub quickstart: https://github.com/phenomenoner/openclaw-mem/blob/main/QUICKSTART.md