Version 3.2.0 - Jungian Intelligence Layer

Released: March 2026

Overview

Version 3.2.0 introduces the Jungian Intelligence Layer, four psychology-inspired subsystems that transform raw memory data into meaningful, context-aware intelligence. Built on top of the concentric memory rings (v3.1.0) and Collective Best Practices, this release adds temporal pattern detection, archetype-driven pattern delivery, automatic memory consolidation, and per-profile maturity scoring.

The Jungian Intelligence Layer is named after Carl Jung’s analytical psychology. Each subsystem maps to a Jungian concept: Archetypes classify patterns, Synchronicity discovers meaningful coincidences, Dreams consolidate memories, and Individuation measures growth toward self-realization.

Key Features

1. Synchronicity Detection

Discovers temporal co-occurrence patterns across anonymized profiles using pure SQL analysis — no LLM calls required. Three analysis types operate on the temporal_events table:

Co-occurrence: Tools that are frequently used together within a 5-minute window
Failure Correlation: Tools that tend to fail at specific times (day-of-week and hour-of-day clustering)
Emergent Workflows: Three-tool sequential patterns that recur across 3+ profiles

Patterns that pass the k-anonymity threshold (k >= 3 unique profiles) are stored in gut_patterns with pattern_type='synchronicity' and embedded for vector search. Privacy: Only profile_hash (HMAC-SHA256) is stored in temporal events — raw UUIDs never touch the table. Events are retained for 90 days by default.

2. Archetype-Driven Pattern Delivery

Wraps the existing CBP injection engine with Jungian archetype filtering. Each pattern is classified into one of four archetypes:

Archetype	Label	Pattern Types	When Dominant
Shadow	Shadow Warning	`anti_pattern`, `security_warning`, `gotcha`	Error/failure contexts
Sage	Sage Advice	`best_practice`, `error_solution`, `performance_tip`, `migration_note`	Default (knowledge sharing)
Hero	Hero Path	`workflow`, `tool_sequence`, `migration_note`	Workflow/tool contexts
Trickster	Trickster Insight	`gotcha`, `compatibility`, `error_recovery`	2+ consecutive failures

Archetype selection is fully deterministic — context fields (outcome, observation type, consecutive failures) are mapped to weights without any LLM call. Patterns are then sorted by archetypeWeight * confidence * similarity and capped at 2 per archetype.

3. Dream Processing (Memory Consolidation)

Discovers clusters of semantically similar memories and consolidates them into single unified memories via LLM, reducing token consumption while preserving all key insights. Three-phase pipeline:

Cluster Discovery (no LLM): Uses vector embeddings and Union-Find to identify connected components of memories with cosine similarity >= 0.75
LLM Consolidation: Each cluster is fed to a compression-tuned LLM that merges redundant content into one coherent memory (max 300 output tokens)
Transactional Storage: New consolidated memory, dream record, and source cluster marking all happen in a single database transaction

Source memories are not deleted — they continue to decay naturally through the existing decay engine. Dream processing is integrated into the decay cron; no separate job is needed.

4. Individuation Scoring

Per-profile maturity scoring with four components (each 0-25, total 0-100), calculated via pure SQL with in-memory caching:

Component	What It Measures	Max
Memory Depth	Ring diversity + decay survival + shock recovery	25
Learning Velocity	Weekly observation rate + classification confidence + reinforcement	25
Collective Contribution	CBP patterns promoted + pattern diversity + feedback given	25
Self-Awareness	Memory search usage + memory engagement + dream consolidation count	25

Five maturity levels:

Level	Score Range	Description
Nascent	0-20	Just getting started
Developing	21-40	Building initial patterns
Established	41-60	Consistent memory usage
Mature	61-80	Deep, diverse knowledge base
Individuated	81-100	Full self-realization

Daily snapshots are saved automatically at session start for trend tracking. Weekly trends are calculated as accelerating, stable, or decelerating by comparing recent vs. older snapshot averages.

Ecosystem Updates

New Database Tables

Table	Purpose
`temporal_events`	Anonymized tool usage events for synchronicity detection
`dream_consolidations`	Records of cluster-to-memory consolidations
`individuation_snapshots`	Daily per-profile individuation score snapshots

New API Endpoints

Endpoint	Method	Description
`/api/memory/temporal-events`	POST	Batch write temporal events
`/api/memory/temporal-events/cleanup`	POST	Cleanup events older than retention period
`/api/memory/sync/detect`	POST	Trigger synchronicity detection
`/api/memory/sync/patterns`	GET	Retrieve detected synchronicity patterns
`/api/memory/dream/process`	POST	Trigger dream processing for current profile
`/api/memory/dream/history`	GET	Get dream consolidation history
`/api/memory/archetype/inject`	POST	Archetype-enhanced pattern search
`/api/memory/individuation`	GET	Get individuation score (with optional `?history=true&days=30`)

New SDK Methods

Available in all three official SDKs (JavaScript, Python, Go):

Method	Description
`searchWithContext(query, toolName?, outcome?)`	Archetype-aware memory search
`getIndividuationScore()`	Get current individuation score and maturity level
`getIndividuationHistory(days?)`	Get score history for trend visualization
`getSynchronicityPatterns()`	Retrieve detected synchronicity patterns
`getDreamHistory()`	Get dream consolidation history

New MCP Tools

Tool	Description
`pluggedin_memory_search_with_context`	Search memories with archetype-aware context (tool name, outcome)
`pluggedin_memory_individuation`	Get individuation score with maturity level and improvement tips

Environment Variables

All new settings have sensible defaults and are optional:

# Synchronicity Detector
SYNC_RETENTION_DAYS=90
SYNC_COOCCURRENCE_WINDOW_DAYS=30
SYNC_COOCCURRENCE_GAP_MINUTES=5
SYNC_FAILURE_WINDOW_DAYS=90
SYNC_WORKFLOW_WINDOW_DAYS=30
SYNC_WORKFLOW_GAP_MINUTES=15
SYNC_MIN_EVENTS_THRESHOLD=10
SYNC_CRON_ENABLED=true

# Dream Processing
DREAM_ENABLED=true
DREAM_MIN_CLUSTER_SIZE=3
DREAM_SIMILARITY_THRESHOLD=0.75
DREAM_MAX_CLUSTERS_PER_RUN=10
DREAM_CONSOLIDATION_MAX_INPUT_TOKENS=1500
DREAM_COOLDOWN_DAYS=7

# Archetype Router
ARCHETYPE_ENABLED=true
ARCHETYPE_MAX_PATTERNS_PER_TYPE=2
ARCHETYPE_SHADOW_BOOST=1.2
ARCHETYPE_SAGE_BOOST=1.1

# Individuation Metrics
INDIVIDUATION_ENABLED=true
INDIVIDUATION_CACHE_TTL_MINUTES=60
INDIVIDUATION_HISTORY_DAYS=90

Migration Notes

Upgrade Steps

# Pull latest and install
git pull origin main
pnpm install

# Generate and apply migrations
pnpm db:generate
pnpm db:migrate

# No additional setup required -- all features are enabled by default

Breaking Changes

None. All new features are additive and backward compatible. The existing memory API, MCP tools, and injection engine continue to work unchanged. Archetype routing enriches existing patterns without altering their underlying data.

Security

Temporal events store only profile_hash (HMAC-SHA256), never raw UUIDs
Synchronicity patterns enforce k-anonymity (k >= 3) before surfacing
Advisory locks prevent concurrent synchronicity detection or dream processing runs
Dream consolidation prompt includes explicit anti-injection instructions
Individuation scores are profile-scoped and cached in-memory (not shared)

What’s Next

Jungian Intelligence UI dashboard with archetype visualizations
Cross-agent synchronicity detection
Dream processing analytics and token savings reports
Individuation leaderboards (opt-in, anonymized)

For questions or issues, please visit our GitHub Discussions or contact us at team@plugged.in.

​Version 3.2.0 - Jungian Intelligence Layer

​Overview

​Key Features

​1. Synchronicity Detection

​2. Archetype-Driven Pattern Delivery

​3. Dream Processing (Memory Consolidation)

​4. Individuation Scoring

​Ecosystem Updates

​New Database Tables

​New API Endpoints

​New SDK Methods

​New MCP Tools

​Environment Variables

​Migration Notes

​Upgrade Steps

​Breaking Changes

​Security

​What’s Next

Version 3.2.0 - Jungian Intelligence Layer

Overview

Key Features

1. Synchronicity Detection

2. Archetype-Driven Pattern Delivery

3. Dream Processing (Memory Consolidation)

4. Individuation Scoring

Ecosystem Updates

New Database Tables

New API Endpoints

New SDK Methods

New MCP Tools

Environment Variables

Migration Notes

Upgrade Steps

Breaking Changes

Security

What’s Next