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amanasmueiMCP SERVER

amem

Memory for AI coding tools with 33 tools, local embeddings and a reranker — nothing leaves your machine, no API key.

by amanasmuei ↗Agent MemoryCommunity

LABSCO SUMMARY

Local retrieval that publishes its numbers, and a compact mode that matters.

Two things stand out. The retrieval is a real pipeline rather than a keyword search — a bi-encoder plus an int8 cross-encoder reranker, both local, with the reranker taking about three quarters of the roughly 14ms median latency. And recall defaults to compact mode, returning enough to choose from and leaving the full content behind memory_detail, which is the difference between a memory tool that saves context and one that eats it. Benchmark figures come from the amem-core engine on LongMemEval-S.

What it is

An MCP server that gives every AI coding tool the same persistent memory, backed by one SQLite file at ~/.amem/memory.db. The retrieval engine lives in a separate library, amem-core, so recall improvements and MCP tool changes ship independently. Everything runs locally: no cloud, no API keys, no vector database.

What you get

  • Core memory work — memory_store with a type, tags and confidence (auto-redacting private content and auto-expiring contradictions), memory_recall for semantic search in a compact mode, memory_detail for the full content of one result, memory_context to load everything relevant to a topic under a token budget, memory_extract for batch saves, memory_forget, and memory_inject to surface corrections, decisions and graph neighbours before coding starts
  • Precision and history: memory_patch for a field-level edit with an automatic snapshot, memory_versions to view or restore any earlier version, memory_search for exact full-text search via FTS5, memory_since for temporal queries in natural ranges like 7d or 2w, and memory_relate to build a typed knowledge graph
  • Advanced retrieval and lifecycle — memory_multi_recall fusing semantic, full-text, graph and temporal search; memory_tier to move memories between core, working and archival; memory_expire; memory_summarize and memory_history for session summaries
  • memory_reflect, a reflection engine that clusters your memories, detects contradictions, identifies synthesis candidates and surfaces knowledge gaps, returning a report with suggested actions and a health score
  • Six memory types ranked by priority, with corrections at the top so an agent's hard constraints always surface first: correction, decision, pattern, preference, topology, fact
  • Three memory tiers — Core, always injected at session start; Working, session-scoped; Archival, searchable but not auto-injected — and temporal validity, where superseded facts are expired rather than deleted so you can still ask what was true in March
  • Admin, reminders and maintenance: memory_doctor and memory_repair for the database, memory_config, memory_sync to import and export against Claude auto-memory and Copilot instructions, four reminder tools, and memory_log, memory_stats, memory_export, memory_import and memory_consolidate

Requirements

No account, no API key, no cloud. Node 18 or newer. In Claude Code and GitHub Copilot CLI it installs as a plugin from the repository's marketplace; for Cursor, Windsurf or any other MCP client, npm install -g @aman_asmuei/amem then amem-cli init detects and configures the tools you have installed, and amem-cli rules writes extraction rules so the assistant reaches for memory on its own. amem-cli stats confirms it is live. The npm package is @aman_asmuei/amem, run over stdio.

Setup effort

One command — npm install -g @aman_asmuei/amem

See installation options