---
name: Mnemon
slug: mnemon-3
category: AI Engineering
description: Mnemon is a persistent memory CLI for LLM agents to store facts, recall past knowledge, and link related memories. Use it to import context, manage memory lifecycle, and reduce lost continuity across tasks.
github: "https://github.com/mnemon-dev/mnemon/tree/master/internal/memory/setup/assets/codex"
language: Go
stars: 523
forks: 65
install: "npx degit https://github.com/mnemon-dev/mnemon/tree/master/internal/memory/setup/assets/codex ~/.claude/skills/codex"
installs_to: ~/.claude/skills/codex
source_path: internal/memory/setup/assets/codex/SKILL.md
collection_size: 16
category_size: 2451
collection_url: "https://dirskills.com/collections/mnemon-dev/mnemon"
added: 2026-08-26T05:12:09.244Z
last_synced: 2026-08-26T05:12:09.244Z
canonical_url: "https://dirskills.com/skills/mnemon-3"
---

# Mnemon

Mnemon is a persistent memory CLI for LLM agents to store facts, recall past knowledge, and link related memories. Use it to import context, manage memory lifecycle, and reduce lost continuity across tasks.

**Install:**

```bash
npx degit https://github.com/mnemon-dev/mnemon/tree/master/internal/memory/setup/assets/codex ~/.claude/skills/codex
```

## README

# mnemon

## Workflow

1. **Remember**: `mnemon remember "<fact>" --cat <cat> --imp <1-5> --entities "e1,e2" --source agent`
   - Diff is built in: duplicates are skipped, conflicts are auto-replaced.
   - Output includes `action` (added/updated/skipped), `semantic_candidates`, and `causal_candidates`.
2. **Link** (evaluate candidates from step 1 using judgment):
   - Review `causal_candidates`: link only when the memories are genuinely causally related.
   - Review `semantic_candidates`: high `similarity` alone is not enough; skip unrelated keyword matches.
   - Syntax: `mnemon link <id> <candidate> --type <causal|semantic> --weight <0-1> [--meta '<json>']`
3. **Recall**: `mnemon recall "<query>" --limit 10`

## Commands

```bash
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 memory only when it can materially improve continuity or task quality.
- Do not store secrets, passwords, tokens, private keys, or short-lived operational noise.
- Categories: `preference` · `decision` · `insight` · `fact` · `context`
- Edge types: `temporal` · `semantic` · `causal` · `entity`
- Max 8,000 chars per insight.