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llms.txt Content

# SuperLocalMemory > Governed, local-first memory control plane for AI agents with MCP, an agent-native CLI, and auditable operations. SuperLocalMemory stores core memory state in a configurable local data root. Mode A does not require a cloud model provider for core memory operations. Optional providers, connectors, backup, proxies, model downloads, and dependency acquisition have separate network behavior. ## Current product contract - SuperLocalMemory current product generation: 4.0. - Core memory is SQLite-backed; configuration, logs, queues, models, derived indexes, and optional backend state also live in the data root. - Recall uses five candidate producers followed by fusion, optional reranking, and graph-based score enhancement. - Dense candidate generation uses cosine similarity; Fisher-derived terms can inform later scoring. - Exact caching is available only when Optimize/proxy caching is enabled. - Safe compression preserves JSON and code; measured reduction varies by content and can be zero. Aggressive prose compression is experimental and lossy. - SQLite and sqlite-vec are canonical. Optional CozoDB and LanceDB projections remain explicit, parity-gated paths; they are not automatic replacements for the canonical store. - V4 governs each canonical write through admission and policy control, durable per-store obligations, and a hash-sealed completion manifest. A partial operation is surfaced as degraded rather than silently treated as complete. - Run `slm connect --list` for documented clients. A client is verified only when it passes the release integration matrix. - SuperLocalMemory is AGPL-3.0-or-later. The score contract separates `relevance_score`, internal `ranking_score`, `memory_confidence`, and `trust_score`. Retrieval ordering is not answer probability. Until a held-out calibrator is frozen, canonical responses report `calibration_status: "uncalibrated"`, `calibration_id: null`, and `answer_confidence: null`. ## Research status SuperLocal