SuperLocalMemory
Governed Memory for AI Agents
SuperLocalMemory 4.0 is a governed, local-first memory operating system for AI agents. It combines durable memory transactions, scoped access, evidence-led retrieval, auditable operations, and CLI, MCP, dashboard, and framework-adapter surfaces.
The Problem
Every AI agent memory system in production relies on cosine similarity over embeddings. At scale, this fails: flat similarity loses discriminative power, pairwise contradiction checking grows O(n²), and hardcoded lifecycle thresholds break on non-average workloads. Cloud-dependent architectures compound this with data residency risk and an August 2026 EU AI Act compliance deadline that engineering alone cannot resolve.
Key Capabilities
Reliability Spine
Generation-fenced admission, a policy registry, verifiable memory transactions, per-projection apply/verify/compensate/erase ownership, and hash-checkable completion manifests.
Evidence-Led Retrieval
Dense, BM25 lexical, temporal, associative, and spreading-activation candidate producers fuse into a single retrieval path when their declared dependencies are healthy.
Scoped Memory and Governance
Personal, named-profile, shared, and global scopes with role-aware access, provenance, retention, export, verified erasure, and an auditable control plane.
Three Operating Modes
Mode A keeps the core local, Mode B adds a local model, and Mode C deliberately uses an external provider. Network behavior is explicit rather than hidden in the memory path.
CLI, MCP, Dashboard, and Adapters
Operate the same memory system from the command line, Model Context Protocol tools, the dashboard, and framework adapters for agent workflows.
Learning, Cache, and Compression
Feedback-driven learning, reusable retrieval/cache paths, and context compression help agents carry forward useful context without surrendering local control.
SkillFortify
Supply Chain Security for AI Agent Skills
Static analysis, behavioral sandboxing, and cryptographic attestation for the AI agent skill ecosystem. Detects malicious skills before they execute.
AgentAssert
Design-by-Contract for AI Agents
Formal specification and runtime enforcement of behavioral contracts for autonomous AI agents. Prevents drift, ensures compliance, enables composition.
AgentAssay
Regression Testing for Non-Deterministic AI Agents
Token-efficient stochastic behavioral testing framework purpose-built for non-deterministic AI agent workflows. Part of the Qualixar AI Reliability Engineering platform.