version: "1.0.0" name: mnemon description: Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.

mnemon

Workflow

1. Remember: mnemon remember "<fact>" --cat <cat> --imp <1-5> --entities "e1,e2" --source agent

• Only exact content repeats are skipped; distinct content is stored and diff suggestions are advisory.
• To retire a superseded memory, store and verify the new fact, then explicitly run mnemon forget <old-id>.
• Output includes action (added/skipped), semantic_candidates, and causal_candidates.

1. 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>']

1. Recall: mnemon recall "<query>" --limit 10

Recall Intent

Keep focused queries and memories in their original language. When the user's meaning is clear, choose --intent WHY (reasons), --intent WHEN (timing), --intent ENTITY (what/who), or --intent GENERAL (neutral retrieval). For example: mnemon recall "<query>" --intent WHY. The override works in any language; --verbose reports meta.intent and meta.intent_source (auto or override).

Automatic cues cover some forms in English, Mandarin Chinese (simplified and traditional), Hindi (Devanagari), Spanish, Modern Standard Arabic, French, Bengali (Bengali script), Portuguese, Indonesian (Latin script), Russian (Cyrillic), and German. Unrecognized forms use GENERAL; conflicting cues involving additional languages also use GENERAL. English/Chinese-only scoring is preserved. This is a lexical heuristic, not full language understanding. See the supported forms, script variants, and limits.

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 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.