Memory Model

Every memory in Open Brain is a row in PostgreSQL with a dense vector embedding, structured metadata, and quality signals.

Schema

CREATE TABLE memories (
    id              SERIAL PRIMARY KEY,
    content         TEXT NOT NULL,
    embedding       VECTOR(768),          -- or 1024/1536 depending on model
    metadata        JSONB DEFAULT '{}',
    created_at      TIMESTAMPTZ DEFAULT NOW(),
    project         TEXT DEFAULT '',
    annotation      TEXT DEFAULT '',
    access_count    INTEGER DEFAULT 0,
    last_accessed   TIMESTAMPTZ,
    upvotes         INTEGER DEFAULT 0,
    downvotes       INTEGER DEFAULT 0
);

Metadata Fields

The metadata JSONB column contains:

Field	Type	Description
`type`	string	One of: `decision`, `idea`, `meeting`, `person`, `insight`, `task`, `journal`, `reference`, `note`
`people`	string[]	Names extracted from content (`@mentions` or LLM-detected)
`topics`	string[]	Key subjects (capitalized nouns or LLM-extracted)
`action_items`	string[]	Sentences containing "need to", "must", "follow up", etc.
`tags`	string[]	Explicit `#hashtag` markers
`source`	string	Which agent captured it (`claude`, `windsurf`, `cursor`, etc.)
`auto_captured`	boolean	`true` if from `capture_context`, `false` if from `remember`

Memory Types

Type	When to use
`decision`	Architectural choice, tooling selected, approach taken
`idea`	Brainstorm, "what if", proposal
`meeting`	Conversation notes, standup, call
`person`	Information about someone (role, preferences)
`insight`	Learned fact, discovery, "aha" moment
`task`	Action item, follow-up, reminder
`journal`	Reflection, personal log
`reference`	Link, article, resource
`procedural`	Workflow rules, how-to knowledge, step-by-step conventions, non-negotiables
`episodic`	Specific past events, "last time X happened", session recollections
`note`	Anything else (default)

Quality Signals

Ratings

Call rate(id, "up") or rate(id, "down") after using a memory. The score (upvotes - downvotes) appears in search results, surfacing the most useful memories over time.

Access Tracking

Every recall call bumps access_count and updates last_accessed. This data powers prune. Memories that are old and never accessed can be cleaned up.

Annotations

Attach persistent notes to existing memories with annotate. Use this for corrections, gotchas, or warnings that should surface alongside the original memory.

Deduplication

On every remember and capture_context call, Open Brain checks for near-duplicates:

Embed the new content
Find the closest existing memory by cosine similarity
If similarity >= threshold (default 0.92):
- If the new content is longer (more detailed), update the existing memory
- Otherwise, skip. The memory already exists

This prevents the same decision or fact from being stored dozens of times across sessions.

Indexes

Index Type	Column	Purpose
HNSW (m=16, ef=64)	`embedding`	Fast approximate nearest-neighbor search
GIN	`metadata`	Filter by type, people, topics
B-tree	`created_at DESC`	Time-range queries
B-tree	`project`	Project-scoped filtering
B-tree	`last_accessed`	Identify stale memories for pruning