SuperLocalMemory v2.7: Your AI Learns You — Adaptive Learning That Runs Locally

The Problem We Solved

You have been using SuperLocalMemory for three months. You have stored thousands of memories across multiple projects — architecture decisions, coding patterns, deployment notes, debugging discoveries. The system works. Persistence works. Cross-tool sharing works.

But search is getting noisy.

When you recall “database configuration,” you get fifty results. Some are from your current project. Some are from a project you finished six weeks ago. Some are from a quick experiment you abandoned after an hour. The system treats them all equally because, until now, it had no way to know which results matter most to you in this context.

This is the scaling problem every memory system hits. Raw storage and retrieval work fine at low volume. At high volume, you need intelligence — not just search, but understanding of what is relevant based on how you actually work.

SuperLocalMemory v2.7 solves this with adaptive learning. The system now observes which memories you use, learns your patterns across projects, and re-ranks search results to surface the most relevant content first. All of it runs locally. No cloud. No telemetry. No data leaving your machine.

How It Works

The v2.7 learning system operates in three layers, each building on the one below it.

Layer 1: Cross-Project Technology Preferences

The first layer tracks your technology preferences across all projects and profiles. When you consistently choose TypeScript over JavaScript, prefer PostgreSQL over MySQL, or reach for server components over client components, the system records these signals.

These preferences are transferable. Start a new project and your technology affinities carry over. The system does not dictate choices — it adjusts relevance weights so that memories matching your established preferences rank higher in search results.

Layer 2: Project Context Detection

The second layer understands project boundaries. It uses multiple signals — active directory, recent memory tags, time-of-day patterns, and explicit profile selection — to determine which project you are working on right now.

This matters because a memory about “API authentication” means something different when you are working on a Node.js backend versus a Python data pipeline. Project context lets the ranker prioritize memories from the relevant project without you having to specify it every time.

Layer 3: Workflow Pattern Mining

The third layer discovers sequential patterns in how you work. If you consistently recall deployment notes after modifying CI configuration, or if you always check database schema docs before writing migration files, the system learns these sequences.

Over time, it can anticipate what you will need next based on what you just did. This is not prediction in the speculative sense — it is pattern recognition grounded in your actual recorded behavior.

Adaptive Re-Ranking

Sitting on top of all three layers is an adaptive re-ranker. It takes the raw search results from SuperLocalMemory’s existing search engine and re-orders them based on everything the learning layers know about you.

The ranker starts with a rule-based system on day one. Using your existing memories, it creates personalized ranking from the moment you upgrade — no cold-start degradation. As real behavioral signals accumulate over days and weeks of use, the system progressively improves. You never need to configure anything — it learns in the background.

Privacy First

Every byte of behavioral data stays on your machine. This is not a policy decision — it is an architectural guarantee.

Separate database. All learning data lives in a dedicated local database, completely isolated from your memory store. Your memories and your behavioral patterns are never co-mingled.

Zero telemetry. No usage data, no analytics pings, no crash reports, no phone-home of any kind. The learning system has no network code. It cannot send data anywhere because it has no capability to do so.

GDPR-ready by design. Export all your behavioral data with a single command. Delete it with another. The separate database makes this trivial — reset your learning data and your behavioral footprint is gone. Your memories remain untouched.

No cloud dependencies. The ML model trains locally on your machine. There is no external inference API. The model file lives next to your database, versioned and portable.

Compare this with cloud-based alternatives where your behavioral data — what you search for, what you use, how you work — flows through third-party servers. With SuperLocalMemory, that data never leaves your file system.

Novel Contribution

To our knowledge, SuperLocalMemory v2.7 is the first system to implement fully local, zero-communication, adaptive re-ranking for personal AI memory. The architecture draws from published research in information retrieval, privacy-preserving feedback mechanisms, and sequence mining — adapted for the unique constraints of a system that can never communicate with any external server.

New Tools

MCP Tools (for IDE integrations)

memory_used — The primary feedback channel. When your AI tool uses a recalled memory in its response, it calls memory_used to signal that the memory was relevant. This is the strongest learning signal available and it happens automatically through MCP.

get_learned_patterns — Retrieve your learned technology preferences, project contexts, and workflow patterns. Useful for inspecting what the system has learned and verifying it matches your expectations.

correct_pattern — Override or correct a learned pattern. If the system inferred that you prefer Redux but you have actually switched to Zustand, this tool lets you correct the record. The system adjusts its model accordingly.

CLI Commands

# View your learned technology preferences
slm patterns list

# See what the system knows about your current project context
slm patterns context

# Export all behavioral data (GDPR export)
slm learning export

# Delete all behavioral data
slm learning reset

# Check learning system status
slm learning status

Getting Started

Upgrading to v2.7 takes under two minutes.

# Update to v2.7
npm update -g superlocalmemory

# Verify the version
superlocalmemory --version
# Expected: 2.7.x

# Check that the learning system initialized
slm learning status

The learning system activates automatically on first use after upgrade. It creates a dedicated database alongside your existing data and begins collecting signals immediately. No configuration required.

Optional ML dependencies are installed automatically if you use the npm package. If you installed from source, run:

pip install -r requirements-learning.txt

For the full migration guide, including what changes for existing users and how to verify the upgrade, see the Upgrading to v2.7 wiki page.

For a deep dive into the three-layer architecture, visit the Learning System documentation or explore the interactive learning diagram.

What is Next

The next release focuses on multi-agent collaboration — enabling multiple AI agents to share context through a single memory layer. Think of it as giving your entire AI toolkit a shared brain.

More details will be published as the design solidifies. For now, v2.7 makes your personal AI memory meaningfully smarter. Install it, use it for a week, and notice the difference in search relevance.

Your AI is learning you. Locally. Privately. Starting today.