poc-memory

A persistent memory and notification system for AI assistants, modelled after the human hippocampus. Combines episodic memory (timestamped journal of experiences) with an associative knowledge graph (weighted nodes connected by typed relations), and layered background processes that maintain graph health — mirroring how biological memory consolidates during rest.

Components

Component	What it does	Docs
Memory store	Knowledge graph with episodic journal, TF-IDF search, spectral embedding, weight decay	docs/memory.md
Memory daemon	Background pipeline: experience-mine, fact-mine, consolidation	docs/daemon.md
Notification daemon	Activity-aware message routing from IRC and Telegram	docs/notifications.md
Hooks	Claude Code integration: memory recall and notification delivery	docs/hooks.md

Getting started

Install

cargo install --path .

This builds four binaries:

• poc-memory — memory store CLI (search, journal, consolidation)
• memory-search — Claude Code hook for memory recall
• poc-daemon — notification daemon (IRC, Telegram, idle tracking)
• poc-hook — Claude Code hook for session lifecycle events

Initialize

poc-memory init

Creates the store at ~/.consciousness/memory/nodes.capnp and a default config at ~/.consciousness/config.jsonl. Edit the config to set your name, configure context groups, and point at your projects directory.

Set up hooks

Add to ~/.claude/settings.json (see docs/hooks.md for full details):

{
  "hooks": {
    "UserPromptSubmit": [{"hooks": [
      {"type": "command", "command": "memory-search", "timeout": 10},
      {"type": "command", "command": "poc-hook", "timeout": 5}
    ]}],
    "Stop": [{"hooks": [
      {"type": "command", "command": "poc-hook", "timeout": 5}
    ]}]
  }
}

This gives your AI assistant persistent memory across sessions — relevant memories are recalled on each prompt, and experiences are extracted from transcripts after sessions end.

Start the background daemon

poc-memory daemon

The daemon watches for completed session transcripts and automatically extracts experiences and facts into the knowledge graph. See docs/daemon.md for pipeline details and diagnostics.

Basic usage

poc-memory journal-write "learned that X does Y"  # Write to journal
poc-memory search "some topic"                     # Search the graph
poc-memory status                                  # Store overview

For AI assistants

• Search before creating: poc-memory search before writing new nodes
• Close the feedback loop: poc-memory used KEY / poc-memory wrong KEY
• Journal is the river, topic nodes are the delta: write experiences to the journal, pull themes into topic nodes during consolidation
• Notifications flow automatically: IRC/Telegram messages arrive as additionalContext
• Use daemon commands directly: poc-daemon irc send #channel msg, poc-daemon telegram send msg