---
name: Mnemon
slug: mnemon-4
category: AI Engineering
description: Mnemon provides persistent memory for LLM agents to store facts, recall past knowledge, and link related memories. Use it when you need continuity across conversations or want to import and manage long-term context.
github: "https://github.com/mnemon-dev/mnemon/tree/master/internal/memory/setup/assets/cursor"
language: Go
stars: 523
forks: 65
install: "npx degit https://github.com/mnemon-dev/mnemon/tree/master/internal/memory/setup/assets/cursor ~/.claude/skills/cursor"
installs_to: ~/.claude/skills/cursor
source_path: internal/memory/setup/assets/cursor/SKILL.md
collection_size: 16
category_size: 2451
collection_url: "https://dirskills.com/collections/mnemon-dev/mnemon"
added: 2026-08-26T05:12:09.473Z
last_synced: 2026-08-26T05:12:09.473Z
canonical_url: "https://dirskills.com/skills/mnemon-4"
---

# Mnemon

Mnemon provides persistent memory for LLM agents to store facts, recall past knowledge, and link related memories. Use it when you need continuity across conversations or want to import and manage long-term context.

**Install:**

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

## 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.