See how each retrieval channel contributes to results.
Knowledge Clusters
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Learned Patterns
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Memory Creation Timeline
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How Learning Works
SuperLocalMemory learns from your usage patterns to improve retrieval ranking over time. This happens automatically as you use slm recall or search via MCP.
0-20 signals: Baseline phase (collecting data)
20+ signals: Rule-based ranking adjustments
200+ signals: ML model trained on your patterns
Each recall query generates a signal. Keep using SLM and this tab will populate automatically.
RANKING PHASE
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FEEDBACK SIGNALS
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ENGAGEMENT HEALTH
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Adaptive Ranking Progress
Baseline (0)Rule-Based (20+)ML Model (200+)
What SuperLocalMemory Learned About You
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Tech Preferences
Layer 1
Patterns detected after using recall with feedback
Workflow Patterns
Layer 3
Sequences detected after 30+ memories
Source Quality
Per-Tool Scoring
Quality scores computed after feedback signals
Privacy & Data
GDPR Compliant
Learning DB--
Patterns learned0
Models trained0
Sources tracked0
TelemetryNone
Deletes learning.db. Memories preserved.
Live Event Stream
Real-time memory operations as they happen
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Total Events
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Last 24h
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Listeners
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In Buffer
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Events will appear here in real-time as memories are created, updated, or deleted.
Connected Agents
AI tools that interact with your memory
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Total Agents
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Active (24h)
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Total Writes
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Total Recalls
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Trust Scoring Silent Collection
Trust scores are being collected silently. No enforcement in v2.5 — scores will affect recall ranking in v2.6.
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Trust Dashboard
Bayesian trust scoring per agent and per fact.
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Avg Trust Score
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Burst Alerts
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Agent/Fact
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Status
Memory Lifecycle
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State Distribution
Average Memory Age by State
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Recent Transitions
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Compaction Preview
Behavioral Learning
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How Behavioral Learning Works
This tab tracks how memories are used in practice — which recalls led to successful outcomes (code written, decisions made, bugs fixed) and which didn't.
Report outcomes using the form below, or via MCP: report_outcome. Patterns emerge after 10+ outcomes.
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Successes
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Failures
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Partial
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Patterns Learned
Report Outcome
Learned Patterns
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Cross-Project Transfers
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Recent Outcomes
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Compliance & Audit
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Compliance Overview
Set retention policies and access controls for your memories. In Mode A, all data stays on your device — EU AI Act compliant by default.
Create a retention policy below to start managing memory lifecycle automatically.
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Audit Events
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Retention Policies
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Access Policies
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Active Retention Policies
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Audit Trail
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Health Monitor
Process health, RSS budget, worker heartbeat — real-time
Worker Processes
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Budget Rules
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Ingestion Status
Gmail, Calendar, Transcript adapters — all opt-in
Recent Ingestion Log
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Entity Explorer
Compiled truth per entity — PageRank + Louvain communities
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Skill Evolution
Track skill performance, evolution history, and health status
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Mesh Peers
P2P agent communication network — real-time status
Connected Peers
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Recent Events
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Shared State
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Mathematical Layer Health
Status of Fisher-Rao, sheaf cohomology, and Langevin dynamics.
IDE Connections
Manage IDE integrations. Connect new IDEs or check connection status.
IDE
Installed
Config Path
Action
V3 Configuration
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Step 2: LLM Configuration
Saved locally in ~/.superlocalmemory/
Auto-Capture
Auto-Recall
Migration & Database
Database: ~/.superlocalmemory/memory.db
Backup: Check with slm migrate --rollback
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Engine: V3 Active
Profile Management
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Auto-Backup
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Backup Configuration
Cloud Backup
Sync your memory backups to Google Drive or GitHub. Credentials are stored securely in your OS keychain.
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Learning Data
SuperLocalMemory learns from your usage patterns to improve recall results.
All learning data is stored locally in ~/.claude-memory/learning.db.
Reset clears all learned preferences, feedback signals, and patterns. Your memories are preserved.