mnemon

name	mnemon
description	Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
metadata	{"openclaw":{"emoji":"🧠","requires":{"bins":"[Truncated]"},"install":["[Truncated]","[Truncated]"]}}

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

1. 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.
2. Link (evaluate candidates from step 1 — use judgment, not mechanical rules):
   • Review causal_candidates: does a genuine cause-effect relationship exist? causal_signal is regex-based and prone to false positives — only link if the memories are truly causally related.
   • Review semantic_candidates: are these memories meaningfully related? High similarity alone 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>']
3. 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 import --dry-run <file>
mnemon import <file>
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>

Import Historical Chats

When the user asks to import old chats, notes, or exported context, create a memory_draft.json with schema_version: "1", insights entries containing content, category, importance, tags, entities, and optional created_at, plus optional edges using source_index, target_index, edge_type, weight, and reason. Run mnemon import --dry-run <file>, then run mnemon import <file> only after validation passes. After import, verify with mnemon status and a focused mnemon search or mnemon recall. Check the output errors field because imports can partially succeed.

Guardrails

• Use the exec tool 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.