All notable changes to SuperLocalMemory V3 will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- License: Changed from Elastic-2.0 to AGPL-3.0-or-later to protect research IP
- Recall cold-start eliminated. Embedding + reranker workers stay warm for 30 minutes by default instead of 2 minutes, so bursts of recalls no longer pay a 30-60 second model-load tax on every other query.
SLM_EMBED_IDLE_TIMEOUT— seconds to keep the embedding worker warm (default 1800). Set to 120 to restore pre-v3.4.19 behavior.SLM_RERANKER_IDLE_TIMEOUT— same, for the cross-encoder reranker (default 1800).
- pip and npm installs now ship identical functionality. Semantic search and cross-encoder reranking work out of the box on pip (previously required
pip install superlocalmemory[search]). - First pip run auto-installs Claude Code hooks when Claude Code is detected, matching the npm postinstall experience.
- Entity Explorer no longer stuck on "No entities found" after switching operating modes.
- Engine-backed routes (entity, ingest, recall, remember, list) auto-recover after mode changes — no daemon restart required.
- Mode change audit log at
~/.superlocalmemory/logs/mode-audit.log. - Mode C now requires an explicit API key via Settings to prevent accidental cloud-mode writes.
Varun Pratap Bhardwaj Solution Architect
SuperLocalMemory V3 - Intelligent local memory system for AI coding assistants.
- Excessive memory usage during rapid file edits — auto-observe now reuses a single background process instead of spawning one per edit. Rapid multi-file operations (parallel agents, branch switching, batch edits) no longer risk high memory usage.
- Observation debounce — rapid-fire observations are batched and deduplicated within a short window, reducing redundant work.
- Memory-aware worker management — new safety check skips heavy processing when system memory is low.
| Variable | Default | Description |
|---|---|---|
SLM_OBSERVE_DEBOUNCE_SEC |
3.0 |
Observation batching window |
SLM_MIN_AVAILABLE_MEMORY_GB |
2.0 |
Min free RAM for background processing |
- Langevin dynamics now active — positions were never initialized at store time, causing the entire Langevin lifecycle system to be inert (0 positioned facts). New facts now receive near-origin positions (Strategy A).
- Backfill for existing facts — maintenance now initializes unpositioned facts using metadata-aware equilibrium seeding (Strategy B) followed by 50-step burn-in (Strategy C). Old, rarely-accessed facts land in their correct lifecycle zones immediately.
- Maintenance returns
langevin_backfilledcount for observability - Health check now reports positioned facts accurately after backfill
- Adaptive Memory Lifecycle — memories naturally strengthen with use and fade when neglected. No manual cleanup needed.
- Smart Compression — embedding precision adapts to memory importance, achieving up to 32x storage savings on low-priority memories.
- Cognitive Consolidation — automatic pattern extraction from clusters of related memories. Your knowledge graph self-organizes.
- Pattern Learning — auto-learned soft prompts injected into agent context at session start. The system teaches itself what matters.
- Hopfield Retrieval — 6th retrieval channel for vague or partial query completion. Ask half a question, get the whole answer.
- Process Health — automatic detection and cleanup of orphaned SLM processes. No more zombie workers.
slm decay— run memory lifecycle reviewslm quantize— run smart compression cycleslm consolidate --cognitive— extract patterns from memory clustersslm soft-prompts— view auto-learned patternsslm reap— clean orphaned processes
forget— programmatic memory archival via lifecycle rulesquantize— trigger smart compression on demandconsolidate_cognitive— extract and store patterns from memory clustersget_soft_prompts— retrieve auto-learned patterns for context injectionreap_processes— clean orphaned SLM processesget_retention_stats— memory lifecycle analytics
- 7 new API endpoints for lifecycle stats, compression stats, patterns, and process health
- New dashboard tabs: Memory Lifecycle, Compression, Patterns
- Mode A/B memory usage reduced from ~4GB to ~40MB (100x reduction)
- Embedding migration on mode switch (auto-detects model change)
- Forgetting filter in retrieval pipeline (archived memories excluded from results)
- 6-channel retrieval (was 5)
- Fully backward compatible with 3.2.x
- New tables created automatically on first run
- No manual migration needed
- Performance improvements for retrieval pipeline
- New memory management capabilities with configurable lifecycle controls
- Enhanced dashboard with 3 additional monitoring tabs
- 9 new API endpoints for configuration and status
- 5 new MCP tools for proactive memory operations
- 5 new CLI commands for configuration management
- Internal retrieval architecture optimized with additional search channel
- Schema extensions for improved data management (9 new tables)
- Memory surfacing engine with multi-signal scoring
- Significant latency reduction in recall operations (vector-indexed retrieval)
- Idle-time memory optimization for large stores
- Reduced memory footprint for long-running sessions
- Windows
slm --version/slm -v—.batand.cmdwrappers now intercept--version/-vdirectly (fast path, no Python needed) and setPYTHONPATHto the npm package'ssrc/directory before launching Python. Previously, Windows users hittingslm.batinstead of the Node.js wrapper gotunrecognized arguments: --versionbecause Python resolved an older pip-installed version without the flag. - Unix bash wrapper (
bin/slm) — now setsPYTHONPATHand intercepts--version/-v, matching the Node.js wrapper's behavior. Previously relied on npm's shim always routing toslm-npm. postinstall.js— now runspip install .to install thesuperlocalmemoryPython package itself (not just dependencies). Prevents stale pip-installed versions from shadowing the npm-distributed source. Falls back to--userfor PEP 668 environments.preuninstall.js— corrected version string from "V2" to "V3".- Windows Python detection — added
py -3(Python Launcher for Windows) as a fallback candidate inslm.bat. - Environment parity — all three entry points (
slm-npm,slm,slm.bat) now set identical PyTorch memory-prevention env vars (PYTORCH_MPS_HIGH_WATERMARK_RATIO,TORCH_DEVICE, etc.).
slm doctorcommand — comprehensive pre-flight check: Python version, all dependency groups, embedding worker functional test, Ollama connectivity, API key validation, disk space, database integrity. Supports--jsonfor agent-native output.slm hooks installlisted in CLI reference and README.- Dashboard, learning (lightgbm), and performance (diskcache, orjson) dependencies now install automatically during
npm install.
- Warmup reliability — increased subprocess timeout from 60s to 180s for first-time model download. Added step-by-step progress output and direct in-process import diagnostics when worker fails.
- Mode B default model — changed from
phi3:minitollama3.2to matchprovider_presets()and reduce first-time setup friction. - postinstall.js — now installs all 5 dependency groups (core, search, dashboard, learning, performance) with clear status messages per group.
- Error messages — all embedding worker failures, engine fallbacks, and dashboard errors now suggest
slm doctorfor diagnosis. - pyproject.toml — added
diskcacheandorjsonto core dependencies; aligned optional dependency versions with core.
- Profile switching and display uses correct identifiers
- Profile sync across CLI, Dashboard, and MCP — all entry points now see the same profiles
- Profile switching now persists correctly across restarts
- Resolve circular import in server module loading
- Environment variable support across all CLI tools
- Multi-tool memory database sharing
- Paweł Przytuła (@pawelel) - Issue #7 and PR #8
- Windows installation and cross-platform compatibility
- Database stability under concurrent usage
- Forward compatibility with latest Python versions
- Full Windows support with PowerShell scripts for all operations
slm attributioncommand for license and creator information
- Overall reliability and code quality
- Dependency management for reproducible installs
- Windows compatibility for repository cloning (#7)
- Updated test assertions for v2.8 behavioral feature dimensions
Release Type: Major Feature Release — "Memory That Manages Itself"
SuperLocalMemory now manages its own memory lifecycle, learns from action outcomes, and provides enterprise-grade compliance — all 100% locally on your machine.
- Memory Lifecycle Management — Memories automatically organize themselves over time based on usage patterns, keeping your memory system fast and relevant
- Behavioral Learning — The system learns what works by tracking action outcomes, extracting success patterns, and transferring knowledge across projects
- Enterprise Compliance — Full access control, immutable audit trails, and retention policy management for GDPR, HIPAA, and EU AI Act
- 6 New MCP Tools —
report_outcome,get_lifecycle_status,set_retention_policy,compact_memories,get_behavioral_patterns,audit_trail - Improved Search — Lifecycle-aware recall that automatically promotes relevant memories and filters stale ones
- Performance Optimized — Real-time lifecycle management and access control
- Enhanced ranking algorithm with additional signals for improved relevance
- Improved search ranking using multiple relevance factors
- Search results include lifecycle state information
- Configurable storage limits prevent unbounded memory growth
- Documentation organization and navigation
- Per-profile learning — each profile learns its own preferences independently
- Thumbs up/down and pin feedback on memory cards
- Learning data management in Settings (backup + reset)
- "What We Learned" summary card in Learning tab
- Smarter learning from your natural usage patterns
- Recall results improve automatically over time
- Privacy notice for all learning features
- All dashboard tabs refresh on profile switch
- Enhanced trust scoring accuracy
- Improved search result relevance across all access methods
- Better error handling for optional components
- Learning Dashboard Tab — View your ranking phase, preferences, workflow patterns, and privacy controls
- Learning API — Endpoints for dashboard learning features
- One-click Reset — Reset all learning data directly from the dashboard
Release Type: Major Feature Release — "Your AI Learns You"
SuperLocalMemory now learns your patterns, adapts to your workflow, and personalizes recall. All processing happens 100% locally — your behavioral data never leaves your machine.
- Adaptive Learning System — Detects your tech preferences, project context, and workflow patterns across all your projects
- Personalized Recall — Search results automatically re-ranked based on your learned preferences. Gets smarter over time.
- Zero Cold-Start — Personalization works from day 1 using your existing memory patterns
- Multi-Channel Feedback — Tell the system which memories were useful via MCP, CLI, or dashboard
- Source Quality Scoring — Learns which tools produce the most useful memories
- Workflow Detection — Recognizes your coding workflow sequences and adapts retrieval accordingly
- Engagement Metrics — Track memory system health locally with zero telemetry
- Isolated Learning Data — Behavioral data stored separately from memories. One-command erasure for full GDPR compliance.
- 3 New MCP Tools — Feedback signal, pattern transparency, and user correction
- 2 New MCP Resources — Learning status and engagement metrics
- New CLI Commands — Learning management, engagement tracking, pattern correction
- New Skill — View learned preferences in Claude Code and compatible tools
- Auto Python Installation — Installer now auto-detects and installs Python for new users
- Interactive Knowledge Graph — Fully interactive visualization with zoom, pan, and click-to-explore
- Mobile & Accessibility Support — Touch gestures, keyboard navigation, and screen reader compatibility
Release Type: Security Hardening & Scalability — "Battle-Tested"
- Rate Limiting — Protection against abuse with configurable thresholds
- API Key Authentication — Optional authentication for API access
- CI Workflow — Automated testing across multiple Python versions
- Trust Enforcement — Untrusted agents blocked from write and delete operations
- Advanced Search Index — Faster search at scale with graceful fallback
- Hybrid Search — Combined search across multiple retrieval methods
- SSRF Protection — Webhook URLs validated against malicious targets
- Higher memory graph capacity with intelligent sampling
- Hardened profile isolation across all queries
- Bounded resource usage under high load
- Optimized index rebuilds for large databases
- Sanitized error messages — no internal details leaked
- Capped resource pools for stability
Release Type: Framework Integration — "Plugged Into the Ecosystem"
- LangChain Integration — Persistent chat history for LangChain applications
- LlamaIndex Integration — Chat memory storage for LlamaIndex
- Session Isolation — Framework memories tagged separately from normal recall
Release Type: Major Feature Release — "Your AI Memory Has a Heartbeat"
SuperLocalMemory transforms from passive storage to active coordination layer. Every memory operation now triggers real-time events.
- Reliable Concurrent Access — No more "database is locked" errors under multi-agent workloads
- Real-Time Events — Live event broadcasting across all connected tools
- Subscriptions — Durable and ephemeral event subscriptions with filters
- Webhook Delivery — HTTP notifications with automatic retry on failure
- Agent Registry — Track connected AI agents with protocol and activity monitoring
- Memory Provenance — Track who created or modified each memory, and from which tool
- Trust Scoring — Behavioral trust signals collected per agent
- Dashboard: Live Events — Real-time event stream with filters and stats
- Dashboard: Agents — Connected agents table with trust scores and protocol badges
- Refactored core modules for reliability and performance
- Dashboard modernized with modular architecture
- Profile isolation bug in dashboard — graph stats now filter by active profile
- Hierarchical Clustering — Large knowledge clusters auto-subdivided for finer-grained topic discovery
- Cluster Summaries — Structured topic reports for every cluster in the knowledge graph
Release Type: Profile System & Intelligence
- Memory Profiles — Single database, multiple profiles. Switch instantly from any IDE or CLI.
- Auto-Backup — Configurable automatic backups with retention policy
- Confidence Scoring — Statistical confidence tracking for learned patterns
- Profile Management UI — Create, switch, and delete profiles from the dashboard
- Settings Tab — Backup configuration, history, and profile management
- Column Sorting — Click headers to sort in Memories table
--fullflag to show complete memory content without truncation- Smart truncation for large memories
- CLI
getcommand now retrieves memories correctly
- ChatGPT Connector — Search and fetch memories from ChatGPT via MCP
- Streamable HTTP Transport — Additional transport option for MCP connections
- Dashboard Enhancements — Memory detail modal, dark mode, export, search score visualization
- Security improvement in dashboard event handling
Release Type: Universal Integration
SuperLocalMemory now works across 16+ IDEs and CLI tools.
- Auto-Configuration — Automatic setup for Cursor, Windsurf, Claude Desktop, Continue.dev, Codex, Copilot, Gemini, JetBrains
- Universal CLI —
slmcommand works in any terminal - Skills Installer — One-command setup for supported editors
- Tool Annotations — Read-only, destructive, and open-world hints for all MCP tools
Release Type: Feature Release — Advanced Search
- Advanced Search — Faster, more accurate search with multiple retrieval strategies
- Query Optimization — Spell correction, query expansion, and technical term preservation
- Search Caching — Frequently-used queries return near-instantly
- Combined Search — Results fused from multiple search methods for better relevance
- Fast Vector Search — Sub-10ms search at scale (optional)
- Local Embeddings — Semantic search with GPU acceleration (optional)
- Modular Installation — Install only what you need: core, UI, search, or everything
Release Type: Major Feature Release — Universal Integration
- 6 Universal Skills — remember, recall, list-recent, status, build-graph, switch-profile
- MCP Server — Native IDE integration with tools, resources, and prompts
- Attribution Protection — Multi-layer protection ensuring proper credit
- 11+ IDE Support — Cursor, Windsurf, Claude Desktop, Continue.dev, Cody, Aider, ChatGPT, Perplexity, Zed, OpenCode, Antigravity
SuperLocalMemory V3 represents a complete architectural rewrite with intelligent knowledge graphs, pattern learning, and enhanced organization.
- 4-Layer Architecture — Storage, Hierarchical Index, Knowledge Graph, Pattern Learning
- Automatic Entity Extraction — Discovers key topics and concepts from your memories
- Intelligent Clustering — Automatic thematic grouping of related memories
- Pattern Learning — Tracks your preferences across frameworks, languages, architecture, security, and coding style
- Storage Optimization — Progressive compression reduces storage by up to 96%
- Profile Management — Multi-profile support with isolated data
We use Semantic Versioning:
- MAJOR: Breaking changes (e.g., 2.0.0 → 3.0.0)
- MINOR: New features (backward compatible, e.g., 2.0.0 → 2.1.0)
- PATCH: Bug fixes (backward compatible, e.g., 2.1.0 → 2.1.1)
Current Version: v3.3.0
Website: superlocalmemory.com
npm: npm install -g superlocalmemory
SuperLocalMemory V3 is released under the Elastic License 2.0.
100% local. 100% private. 100% yours.