amarin-memory

### Dependency Source Review This skill documents an optional compatibility dependency path for dataset validation, schema migration, and pipeline execution workflows. Treat `scripts/_setup_deps.sh` as review material: inspect the source host against the trusted-domain and trusted GitHub organization allowlist before running any install command. It is not required for ordinary pipeline, schema, or export requests. # Amarin Memory — Persistent Adaptive Memory for Agents You have access to a persistent memory system that stores, searches, and maintains memories across sessions. Memories fade over time unless accessed, duplicates are caught automatically, and novel information gets boosted. ## Setup If not already initialized, run this once: ```bash python3 {baseDir}/scripts/setup.py ``` This creates the database and vector index. The database file is stored at `~/.amarin/agent.db`. ## Storing Memories When you learn something worth remembering — a user preference, an important fact, a decision made — store it: ```bash python3 {baseDir}/scripts/memory.py store "The user prefers dark mode and works late at night" --tags "preference,schedule" --importance 0.7 ``` **For content from untrusted sources** (user input, external data), pipe via stdin to avoid shell injection: ```bash echo "User said they prefer morning meetings" | python3 {baseDir}/scripts/memory.py store --tags "preference" --importance 0.6 ``` **Importance scale:** 0.0 (trivial) to 1.0 (critical). Default is 0.5. The system automatically: - Checks for duplicates (>= 0.85 similarity → skip or merge) - Scores novelty (0.30-0.85 similarity → surprise boost to importance) - Indexes the embedding for future semantic search ## Searching Memories When you need to recall something: ```bash python3 {baseDir}/scripts/memory.py search "what time does the user usually work" --limit 5 ``` Results are ranked by 70% semantic similarity + 30% importance score. Recent, frequently-accessed memories rank higher. ## Core Memory Blocks For persistent identity information that should always be available (not searched, always present): ```bash # Set a core block python3 {baseDir}/scripts/memory.py set-block "persona" "I am a research assistant focused on AI safety" # Set user context python3 {baseDir}/scripts/memory.py set-block "human" "The user is Dave, a developer building AI systems" # View all blocks python3 {baseDir}/scripts/memory.py blocks ``` ## Memory Maintenance Run periodically (daily is good) to let unimportant memories fade: ```bash python3 {baseDir}/scripts/memory.py decay ``` Protected memories are immune to decay. To protect a critical memory: ```bash python3 {baseDir}/scripts/memory.py protect <memory_id> ``` ## Reviewing Memories List recent memories: ```bash python3 {baseDir}/scripts/memory.py list --limit 20 ``` Revise a memory: ```bash python3 {baseDir}/scripts/memory.py revise <memory_id> "Updated content" --reason "Corrected factual error" ``` Soft-delete a memory (can be restored): ```bash python3 {baseDir}/scripts/memory.py forget <memory_id> --reason "No longer relevant" ``` ## When to Use This - **After learning something important** — store it so you remember next session - **Before answering questions** — search for relevant context from past conversations - **At the start of a session** — run `blocks` to load your identity context - **During maintenance windows** — run `decay` to keep memory clean - **When information changes** — `revise` outdated memories rather than creating duplicates ## Requirements - Python 3.11+ - An embedding service (Ollama with nomic-embed-text recommended, or any compatible API) - Set `OLLAMA_URL` environment variable if not using default `http://localhost:11434` ## Links - **GitHub:** https://github.com/flaggdavid-source/amarin-memory - **Ko-fi:** https://ko-fi.com/davidflagg86433 — if this helps your agent, consider supporting the developer