FactMem MCP server

Facthouse

Facthouse is a local memory engine for AI tools. Most “memory” products index chat logs. Facthouse takes agent activity - messages, tool use, and other MCP traffic - and applies neuroscience-inspired consolidation so it moves through Data (what happened in the session) → Information (extracted facts) → Knowledge (integrated beliefs on an entity graph). During this process, Facthouse links entities, drops duplicates, reconciles conflicts, and supersedes what is out of date. Vector embeddings add optional semantic search on top of that graph. The store is a SQLite file on your disk.

Quick Start

Needs Node 22.5 or 24+.

npx -y @facthouse/mcp
facthouse init

facthouse init --web is the same setup as a browser form — it prints a 127.0.0.1 URL and does not open a browser.

Press Enter to accept each default (copy = Claude Code or Cursor session logs on disk; type record if the assistant should save facts). If you picked copy, init asks whether to copy existing logs, then whether to extract and integrate. Init prints an MCP snippet as soon as the store is written — add it to the client's MCP config while copy/extract run. Restart the client when init finishes.

In the client, state something durable in ordinary conversation — there is no remember command.

Ask it back in the next session, or facthouse search. That is the store.

Copy from Claude Code or Cursor logs, or record from any MCP client: How conversations get in. Replay: facthouse.dev/demo.html. CLI: below.

What you get

• Local SQLite. Optional Postgres. Isolation is the directory, not a column.
• Entity graph. People, organisations, projects, places, products — extracted, typed and linked.
• Hybrid search. BM25 + structured domain + entity-graph paths, merged via Reciprocal Rank Fusion. An embedding provider adds meaning as a fourth list; off by default.
• In-session memory. getsessioncontext is the same briefing as memory://briefing. Tools-only clients should call it at session start.
• Immutable history. Facts are never deleted, only superseded.

How it works

One SQLite database. Three tables in it, not three databases: Data (what happened in the session) → Information (extracted facts) → Knowledge (integrated beliefs).

• D (session_events) — what was said (copied transcripts, or what the assistant records)
• I (sessionfacts) — what was just extracted, or capturefact
• K (facts) — integrated knowledge

FTS5 (words) and optional embeddings (meaning) are indexes of K. They are not a second store. Semantic search is off unless you turn it on: search "shellfish" finds a shellfish fact, search "food" does not, until you choose an embedding model — a model is an opinion about what “similar” means.

Two speeds. Copy tails named transcripts into Data. Extract turns new transcript lines into self-contained facts (D→I). Integrate fits them into what the store already knows: domains, entities, duplicates, contradictions, the graph (I→K). consolidate is the umbrella: copy, extract, and integrate together. Extract is capped at 50 lines per run, so a first backfill is never spent on the lot; when extract runs, it takes the oldest 50 lines. Consolidation does not invent a sentence nobody said.

Shortened. The full README is on GitHub.