SuperLocalMemory V2

What it is

SuperLocalMemory (SLM) keeps agent memory on your own machine in SQLite with sqlite-vec, and exposes it over MCP, a CLI, a dashboard and IDE integrations. Recall is not one vector lookup: semantic, BM25, temporal, Hopfield and spreading-activation candidate channels are fused, and every memory carries ingestion timing and provenance so an agent can ask what changed as well as what is true.

What you get

• A profile switch that decides how much tool surface reaches your client — the README sizes the profiles at core 16 tools, code 31, mesh 8, full 49, power 61 and whole 94 (every registered tool), while the docs and README together name 78 of them one by one — set through SLM_MCP_PROFILE, and an unknown name stops startup rather than silently selecting another set
• Writes that return a durable receipt with operation_id, fact_ids and materialization_state, plus an idempotency_key so a retry is not a duplicate — remember
• Natural-language recall whose scores are explicitly separated: relevance_score for query relevance, ranking_score for diagnostic ranking, memory_confidence for the stored assertion, and answer_confidence left null because retrieval scores are not answer probabilities — recall, search, fetch
• Scoped memory — personal, shared with named profiles, or global — with cross-profile recall default-deny
• Code-graph tools for review and change work: blast radius, review context, architecture overview, dead-weight hunting — build_code_graph, get_blast_radius, get_review_context, get_architecture_overview, find_large_functions
• Explicit context optimisation rather than silent compression — slm_compress, slm_cache_set, slm_cache_get, slm_optimize_stats
• Lifecycle and governance surfaces: retention policy, consolidation, audit trail, health and a recall trace that shows which channels contributed — set_retention_policy, compact_memories, audit_trail, recall_trace, consistency_check
• Correction handling that keeps a human in the loop — correct_pattern, review_correction, list_corrections

Requirements

Python 3.11 or higher, into a virtual environment: python -m pip install superlocalmemory gives you the slm CLI and the importable SDK. Two transports expose the same tools — HTTP against one shared daemon at http://127.0.0.1:8765/mcp/, or stdio via slm mcp. The default local runtime needs no Docker, no separate graph database and no API key; provider-backed enrichment, cloud backup and connectors are separate choices you make. Licensed AGPL v3.

Setup effort

One command — npm install -g superlocalmemory

Tools you get

78 tools named in the project's documentation.

Requires

Node 18+ with npm (npm global CLI path), or Python 3.11+ in a virtual environment (pip path)