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Mnemon — Usage & Reference

You don't run mnemon commands yourself — the agent does, driven by hooks and guided by the skill file. This document is a reference for understanding what the agent can do, for debugging, and for advanced manual operation.

Global Flags

These flags are available on every command:

Flag Default Description
--store <name> (auto) Named memory store (overrides MNEMON_STORE and active file)
--data-dir <path> ~/.mnemon Base data directory
--version Print version and exit

Setup

Deploy mnemon into LLM CLI environments. This is the first command to run after installation.

# Interactive: detect environments and install (project-local)
mnemon setup

# User-wide install (all projects)
mnemon setup --global

# Non-interactive: specific target only
mnemon setup --target claude-code
mnemon setup --target openclaw
mnemon setup --target pi
mnemon setup --target nanobot --global
mnemon setup --target hermes

# Auto-confirm all prompts (CI-friendly)
mnemon setup --yes

# Remove mnemon integrations
mnemon setup --eject
mnemon setup --eject --target claude-code
Flag Default Description
--global false Install to user-wide config instead of project-local (recommended for Nanobot: installs to ~/.nanobot/workspace/; Pi installs to ~/.pi/agent/; Hermes installs to ~/.hermes/)
--target <name> (auto-detect) Target environment: claude-code, codex, openclaw, nanobot, pi, or hermes
--eject false Remove mnemon integrations
--yes false Auto-confirm all prompts

CLI Commands

Core

# Remember — store a new insight (built-in diff: duplicates skipped, conflicts auto-replaced)
mnemon remember "Chose Qdrant over Milvus for vector search" \
  --cat decision --imp 5 --entities "Qdrant,Milvus" --tags "architecture,search" --source agent

# Skip duplicate/conflict detection
mnemon remember "Raw note" --no-diff

# Recall — intent-aware graph-enhanced retrieval (default: compact output)
mnemon recall "vector database" --limit 10

# Recall with full verbose output (signals, meta, timestamps)
mnemon recall "vector database" --verbose

# Recall with explicit intent override
mnemon recall "why did we choose Qdrant" --intent WHY

# Recall with category/source filter
mnemon recall "auth" --cat decision --source agent

# Simple SQL LIKE matching (faster, no graph traversal)
mnemon recall "auth" --basic

# Search — token-scored keyword search
mnemon search "authentication" --limit 10

# Import — bulk-import a memory draft file (see docs/IMPORT.md for schema and LLM prompt)
mnemon import memory_draft.json
mnemon import --dry-run memory_draft.json   # validate without writing
mnemon import --no-diff memory_draft.json   # skip deduplication

# Forget — soft-delete an insight
mnemon forget <id>

Remember flags:

Flag Default Description
--cat general Category: preference, decision, fact, insight, context, general
--imp 3 Importance: 1–5
--tags Comma-separated tags
--entities Comma-separated entities (merged with auto-extraction)
--entity-mode merge Entity handling: merge (provided + auto), provided (only --entities), auto (only auto-extraction)
--source user Source: user, agent, external
--no-diff false Skip duplicate/conflict detection

Recall flags:

Flag Default Description
--limit 10 Max results
--intent (auto-detect) Override intent: WHY, WHEN, ENTITY, GENERAL
--cat Filter by category
--source Filter by source
--basic false Use simple SQL LIKE matching instead of smart recall
--verbose false Output full recall response (signals, meta, timestamps)

The default compact output is optimized for LLM/agent consumption. It includes id, content, category, importance, intent, matched_via, confidence, and score. Use --verbose to restore the full payload with signals, traversal metadata, and timestamps. The confidence label is only emitted in compact mode; verbose payloads return the raw score for callers that prefer their own thresholds.

Graph Operations

# Link — create a typed edge
mnemon link <source_id> <target_id> --type semantic --weight 0.85
mnemon link <source_id> <target_id> --type causal --weight 0.8 \
  --meta '{"sub_type":"causes","reason":"..."}'

# Related — BFS traversal from an insight
mnemon related <id> --edge causal --depth 2

Lifecycle Management

# GC — view low-retention candidates
mnemon gc --threshold 0.5 --limit 20

# GC keep — boost an insight's retention
mnemon gc --keep <id>

Store Management

Mnemon supports named stores for data isolation. Each store has its own independent database.

# List all stores (* marks the active one)
mnemon store list

# Create a new store
mnemon store create work

# Switch the default active store
mnemon store set work

# Remove a store (cannot remove the active store)
mnemon store remove old-project

Store resolution priority (highest to lowest):

  1. --store <name> CLI flag
  2. MNEMON_STORE environment variable
  3. ~/.mnemon/active file
  4. Falls back to "default"

Different agents or processes can use different stores via the MNEMON_STORE environment variable — no global state contention. Legacy databases (~/.mnemon/mnemon.db) are automatically migrated to ~/.mnemon/data/default/ on first run.

Observability

mnemon status              # memory statistics
mnemon log                 # operation log (default: last 20)
mnemon log --limit 50      # show more entries
mnemon receipt             # JSON receipt with hashed recent operations
mnemon receipt --limit 50  # include more operations in the receipt

mnemon receipt is for sharing or archiving memory-boundary evidence without publishing raw memories, recall queries, paths, or operation details. It emits operation names, timestamps, and SHA-256 hashes for identifiers/details so a team can prove that remember, recall, forget, or GC activity happened without exposing the underlying content.

Example shape:

{
  "schema": "mnemon.memory.receipt.v1",
  "privacy": {
    "raw_detail_included": false,
    "hash_algorithm": "sha256"
  },
  "events": [
    {
      "event_name": "mnemon.memory.operation.observed",
      "operation": "remember",
      "detail_present": true,
      "detail_hash": "..."
    }
  ]
}

Visualization

Export the knowledge graph for visual exploration:

# DOT format — render with Graphviz (brew install graphviz)
mnemon viz --format dot -o graph.dot
dot -Tpng graph.dot -o graph.png

# Interactive HTML — open directly in the browser (vis.js, no install needed)
mnemon viz --format html -o graph.html
open graph.html

Nodes are colored by category (decision, fact, insight, preference, context); edges are colored by type (temporal, semantic, causal, entity).

Configuration

Variable Default Description
MNEMON_DATA_DIR ~/.mnemon Base data directory
MNEMON_STORE default Active named store
MNEMON_EMBED_ENDPOINT http://localhost:11434 Ollama API endpoint
MNEMON_EMBED_MODEL nomic-embed-text Ollama embedding model
MNEMON_EMBED_DIMENSIONS (native) Embedding dimensions; set to truncate (e.g., 256 for Matryoshka models)

Embedding Support (Optional)

Mnemon works fully without Ollama — all core features (remember, recall, link, graph traversal) function out of the box. Adding Ollama enhances recall precision through vector similarity, but is never required.

What changes with and without embeddings

Capability Without Ollama With Ollama
Recall anchors Keyword + recency Keyword + vector + recency (RRF hybrid)
Semantic edges Token overlap (coarser) Cosine similarity ≥ 0.50 (precise)
Traversal scoring Pure structural Structural + semantic
Rerank weights Keyword 45%, Entity 25%, Graph 30% Keyword 30%, Entity 15%, Similarity 35%, Graph 20%

When Ollama is unavailable, the reranking system automatically redistributes similarity weight to keyword and graph signals — no configuration needed, no degraded mode flag. The system detects Ollama availability at runtime with a 2-second timeout.

Setup

brew install ollama              # or see https://ollama.ai
ollama pull nomic-embed-text     # download the embedding model

Verify with:

mnemon embed --status
{
  "total_insights": 87,
  "embedded": 87,
  "coverage": "100%",
  "ollama_available": true,
  "model": "nomic-embed-text"
}

Backfilling existing insights

If you install Ollama after already using mnemon, existing insights won't have embeddings. Backfill them in one command:

mnemon embed --all

This generates embeddings for all un-embedded insights and automatically creates semantic edges. You can check coverage before and after with mnemon embed --status.

Architecture

┌──────────────────┐     CLI commands      ┌──────────────────┐
│   LLM Agent      │ ───────────────────── │     Mnemon       │
│ (Claude Code,    │  remember, recall,    │                  │
│  Cursor, etc.)   │  link, forget, gc     │  SQLite (WAL)    │
└──────────────────┘                       │  ┌────────────┐  │
                                           │  │ Insights   │  │
        The LLM decides WHAT               │  ├────────────┤  │
        to remember and link.              │  │ 4 Edge     │  │
                                           │  │ Types:     │  │
        Mnemon handles HOW                 │  │ temporal   │  │
        to store, index, and               │  │ entity     │  │
        retrieve.                          │  │ causal     │  │
                                           │  │ semantic   │  │
      ┌──────────────────┐                 │  ├────────────┤  │
      │  Ollama          │  (optional)     │  │ Embeddings │  │
      │  nomic-embed-text│ ◄───────────── │  └────────────┘  │
      └──────────────────┘                 └──────────────────┘

Inspired by MAGMA four-graph model. See Design & Architecture for the full deep dive.