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AI Memory

A persistent memory store any MCP client can share — local SQLite or Postgres, relevance-ranked recall, tiers with expiry, and a small default tool set with the rest loaded on demand.

by alphaonedev ↗Agent MemoryCommunity

LABSCO SUMMARY

Start on `--profile core`; the full surface is a context-window decision, not a feature decision.

Seven tools cover storing, recalling and listing, which is what a memory layer is for day to day — and eager-loading clients pay for every schema they are shown, which is why the profile exists. Ramp up to --profile full when you actually need the archive, link-graph or coordination tooling. Worth knowing before you deploy over HTTP: unsigned direct writes to the HTTP API are rejected by default, while the MCP store path stays permissive.

What it is

A memory substrate for AI assistants, written in Rust. Memories are stored locally, ranked by relevance on recall rather than returned in insertion order, tiered so short-lived notes expire while important knowledge is promoted to permanent storage, and linked to each other with typed relations. It runs on two production backends behind one identical API — embedded SQLite with FTS5, and PostgreSQL with Apache AGE — and any MCP-capable assistant can read the same store.

What you get

  • The everyday surface: store with deduplication by title and namespace, relevance-ranked recall, exact keyword search, filtered listing and get-by-id — memory_store, memory_recall, memory_search, memory_list, memory_get
  • Two always-on loaders and a bootstrap tool that reports the active feature tier and what else is reachable — memory_load_family, memory_smart_load, memory_capabilities
  • Lifecycle control: partial update, delete, promotion to long-term with expiry cleared, bulk forget by pattern, namespace or tier, and consolidation of several memories into one summary — memory_update, memory_delete, memory_promote, memory_forget, memory_consolidate
  • Typed links between memories and the store's own statistics — memory_link, memory_get_links, memory_stats
  • An archive that is separate from deletion: list, restore, purge and archive statistics — memory_archive_list, memory_archive_restore, memory_archive_purge, memory_archive_stats
  • LLM-assisted helpers at the smart tier and above: query expansion, auto-tagging and contradiction detection between two memories — memory_expand_query, memory_auto_tag, memory_detect_contradiction
  • A profile switch that decides how much of this the model sees: by the README's count, 7 tools at --profile core and 101 at --profile full; its docs name 23 of them

Requirements

No account and no key for the default local setup. Install via the project's shell installer, cargo install from the repository (Rust 1.96+), the container image ghcr.io/alphaonedev/ai-memory, or the published packages — ai-memory on crates.io, @alphaone/ai-memory on npm, ai-memory-mcp on PyPI. The client runs ai-memory --db <path> mcp --tier semantic over stdio; AI_MEMORY_DB sets the database path if you prefer an environment variable, and the compiled default is relative to the current directory. The smart and autonomous tiers call an LLM backend, configured once in the [llm] section of ~/.config/ai-memory/config.toml with api_key_env naming the environment variable rather than holding the key. PostgreSQL with Apache AGE is the alternative backend behind the same API.

Setup effort

Follow the published steps — curl -fsSL https://raw.githubusercontent.com/alphaonedev/ai-memory-mcp/main/install.sh | sh

See installation options