Memory for
AI Reliability
Engineering
The memory substrate of the Qualixar AI Reliability Engineering platform. Mathematical foundations, backed by 3 peer-reviewed papers. 74.8% on LoCoMo with data staying local. EU AI Act compliant. Open source AGPL v3.
V3.3: The Living Brain Evolves
Adaptive lifecycle. Smart compression. Cognitive consolidation. Memory that grows with you.
Memory That Adapts, Compresses, and Learns
Adaptive Memory Lifecycle
Memories strengthen when used and fade when neglected. The system self-organizes around what matters most to your workflow.
Smart Compression
Up to 32x storage savings. Precision adapts to importance — critical memories stay full-fidelity while cold ones compress automatically.
Cognitive Consolidation
Automatically extracts patterns from related memories and synthesizes higher-level insights. Your knowledge base refines itself over time.
6th Retrieval Channel
Partial queries complete themselves. Start typing a fragment and the system infers your full intent across six parallel retrieval channels.
Pattern Learning
Soft prompts injected into agent context automatically. The system learns your patterns and proactively surfaces relevant knowledge.
100x RAM Reduction
Dramatically lower memory footprint in Mode A and Mode B. Run on resource-constrained machines without sacrificing capability.
Process Health
Automatic orphan cleanup and self-healing. The system detects and resolves inconsistencies without manual intervention.
Open source, AGPL v3. A Qualixar Research Initiative.
The Context Persistence Problem
Session Reset
No Persistence.
Current AI assistants lack persistent memory across sessions. Context accumulated during a session is discarded at termination.
Context Loss
Re-initialization Required.
Domain-specific patterns and decisions require re-initialization each session. Learned preferences do not transfer.
Architecture Trade-offs
External Dependencies.
Centralized memory introduces external data dependencies and privacy considerations for sensitive development contexts.
9 Layers Deep
Each layer handles one responsibility. Together, they give your AI persistent, intelligent memory.
Neural Capabilities
Every feature designed to make your AI smarter, faster, and completely private.
See It Think
Three commands. That's all it takes to give your AI persistent memory.
Measured Performance
Evaluated on the LoCoMo benchmark (Long Conversation Memory). Mode A Retrieval achieves 74.8% — the highest score reported without cloud dependency.
LoCoMo Benchmark: Competitive Landscape
Full comparison →
Mode A Retrieval (74.8%) is the highest score achieved without cloud dependency during retrieval.
Mode A Raw (60.4%) uses no LLM at any stage — a first in the field.
All other systems require cloud LLM for core operations.
Everywhere You Code
One memory layer. Every IDE and AI tool you use.
Frequently Asked Questions
What is SuperLocalMemory?
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Which AI tools does SuperLocalMemory work with?
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Is it open source?
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How does the local-first approach differ?
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How do I install it?
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Does SuperLocalMemory send data externally?
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Does SuperLocalMemory work with CI/CD pipelines and agent frameworks?
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What is the --json flag?
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Installation
$ npm install -g superlocalmemory $ slm setup $ slm remember "Alice works at Google as Staff Engineer" $ slm recall "What does Alice do?" AGPL v3 • Local-first architecture