🧠Mnemon OpenClaw Plugin & Skill | ClawHub
Looking to integrate Mnemon into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate ai & llms tasks instantly, without having to write custom tools from scratch.
What this skill does
Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
Install
openclaw skills install @grivn/mnemonnpx clawhub@latest install mnemonFull SKILL.md
Open original| name | description |
|---|---|
| mnemon | Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle. |
SKILL.md content below is scrollable.
mnemon
Install & Configure
1. Install the binary
Homebrew (macOS / Linux):
brew install mnemon-dev/tap/mnemon
Go install:
go install github.com/mnemon-dev/mnemon@latest
2. Set up OpenClaw integration
mnemon setup --target openclaw --yes
This single command deploys all components:
- Skill →
~/.openclaw/skills/mnemon/SKILL.md - Hook →
~/.openclaw/hooks/mnemon-prime/(agent:bootstrap — injects behavioral guide) - Plugin →
~/.openclaw/extensions/mnemon/(remind, nudge, compact hooks) - Prompts →
~/.mnemon/prompt/(guide.md, skill.md)
Restart the OpenClaw gateway to activate.
3. Customize (optional)
Edit ~/.mnemon/prompt/guide.md to tune recall/remember behavior.
Plugin hooks are configured in ~/.openclaw/openclaw.json:
{
"plugins": {
"entries": {
"mnemon": {
"enabled": true,
"config": {
"remind": true,
"nudge": true,
"compact": false
}
}
}
}
}
| Hook | Default | Description |
|---|---|---|
remind |
on | Recall relevant memories + remind agent on each message |
nudge |
on | Suggest remember sub-agent after each reply |
compact |
off | Save key insights before context compaction |
4. Uninstall
mnemon setup --eject --target openclaw --yes
Workflow
- Remember:
mnemon remember "<fact>" --cat <cat> --imp <1-5> --entities "e1,e2" --source agent- Diff is built-in: duplicates skipped, conflicts auto-replaced.
- Output includes
action(added/updated/skipped),semantic_candidates,causal_candidates.
- Link (evaluate candidates from step 1 — use judgment, not mechanical rules):
- Review
causal_candidates: does a genuine cause-effect relationship exist?causal_signalis regex-based and prone to false positives — only link if the memories are truly causally related. - Review
semantic_candidates: are these memories meaningfully related? Highsimilarityalone is not sufficient — skip candidates that share keywords but discuss unrelated topics. - Syntax:
mnemon link <id> <candidate> --type <causal|semantic> --weight <0-1> [--meta '<json>']
- Review
- Recall:
mnemon recall "<query>" --limit 10
Commands
mnemon remember "<fact>" --cat <cat> --imp <1-5> --entities "e1,e2" --source agent
mnemon link <id1> <id2> --type <type> --weight <0-1> [--meta '<json>']
mnemon recall "<query>" --limit 10
mnemon search "<query>" --limit 10
mnemon forget <id>
mnemon related <id> --edge causal
mnemon gc --threshold 0.4
mnemon gc --keep <id>
mnemon status
mnemon log
mnemon store list
mnemon store create <name>
mnemon store set <name>
mnemon store remove <name>
Guardrails
- Use the
exectool to run mnemon commands. - Do not store secrets, passwords, or tokens.
- Categories:
preference·decision·insight·fact·context - Edge types:
temporal·semantic·causal·entity - Max 8,000 chars per insight.