memloom

First release

18 July 2026v0.1.0

Today, we ship the first release of memory engine for AI agents.

Key details:

  • Memory write path: save -> semantic dedup funnel (exact/near-duplicate /contradiction via LLM classification) -> human-in-the-loop (HITL) conflicts. Contradictions keep both memories active and queue a conflict; resolutions (keep new/keep existing/keep both/merge) are durable and reversible.
  • Node versioning: every belief is a version chain: restating or editing a fact appends a new version (shared root_id, prior version staled but kept), and resolving a contradiction is a version step. history() shows how a belief changed; recall returns only the current version. memloom update <id> <text> and memloom history <id>.
  • Hybrid retrieval: vector + keyword + entity-graph arms fused with reciprocal-rank fusion in a single SQL call (memloom_fuse), over memories and context chunks together. A separate date arm answers "what did I plan on Tuesday": recall scoped to a calendar day, ranked by similarity within it.
  • Context connector: memloom context add ingests .md/.txt/.pdf into the same recall with section + page citations, from a path, a browser upload, or a chat attachment. Markdown chunks at headings; plain text and PDFs chunk along their outline (ALL-CAPS titles, numbered points; one point per chunk). PDF text is rebuilt from glyph geometry (reading order for equation-heavy documents, 2-up duplicate-column collapse). Documents are mirrors: unchanged files no-op by content hash, changed files replace their chunks transactionally; extractor pipeline versions are salted into the hash so improvements re-ingest automatically.
  • Pluggable extractor registry: a file format is one registered object (kind, extensions, version, chunker, extract()); see CONTRIBUTING.md.
  • Entity graph with a reviewed vocabulary: schema-constrained LLM extraction over memories and chunks. The vocabulary is data (system seeds, your entries, LLM proposals): unknown types and predicates are held out as proposals with their evidence, and approving one links the held-out finds into the graph immediately, no re-index. Typed relationships carry confidence and provenance (a removed document takes its claims with it). Entity corrections built in: rename, retype, merge, delete. Documents roll up chunk mentions into weighted document→entity edges.
  • Indexing you can watch: memloom index streams per-item progress, every run is logged to the store (session-grouped, survives restarts, CLI runs show in the viewer Console), and auto-index quietly indexes new memories and files in the background (opt out with MEMLOOM_AUTO_INDEX=off).
  • Assistant: chat grounded in your store (memloom ui → assistant tab). Two-stage turn: tool rounds gather memories and passages, then one streaming answer with numbered source citations. Sessions persist and are searchable; files attached to a chat are scoped to that chat and die with it.
  • Single-owner daemon: memloom serve owns the store: HTTP API on 4319 (zod-validated, fast 503 when a Postgres wire client holds the lock), Postgres wire on 54329 (pglite-socket, for psql/Drizzle Studio), embedded viewer. CLI/MCP/viewer are all HTTP clients; any command auto-starts the daemon.
  • Viewer: memloom ui: the living memory graph (deterministic layout, document chunk blooms), assistant, memories and documents with edit/history/delete, schema review queue, conflict review with undo, indexing console.
  • MCP server: @memloom/mcp (stdio): save_memory, recall_memory (memories + files, with sources), read_passage, memory_history, conflict list/resolve, and schema enable/disable/delete, so an agent can use the memory and you stay in control of the vocabulary.
  • Storage tiers: embedded PGLite by default (a folder on disk, no Docker); set MEMLOOM_PG_URL and the same daemon runs on any Postgres with pgvector (Docker, Supabase, managed) over a pooled connection. Same schema, same SQL, both tiers. The embedding-fingerprint guard refuses reopening a store with a mismatched embedding configuration, and memloom reembed migrates a store to a new embedding config in place, resumable if interrupted.
  • Providers: offline hashing mode (no key needed) or OpenRouter cloud mode (qwen3-embedding-8b @ 1024 dims pinned to Nebius, gemini-2.5-flash for classification and chat).