| name | amarin-memory |
| description | Persistent adaptive memory for AI agents. Store memories that fade naturally over time (temporal decay), deduplicate automatically (0.85 cosine threshold), score novel information higher (surprise scoring), and search semantically via sqlite-vec KNN. Use when: you need long-term memory across sessions, memory that adapts to what matters, persistent identity blocks, multi-agent memory isolation, or memory that works without cloud services. NOT for: simple key-value storage, ephemeral session context, or when you need a full vector database like Qdrant/Pinecone. Runs entirely on SQLite — no external database server needed. |
| homepage | https://github.com/flaggdavid-source/amarin-memory |
| metadata | {"openclaw":{"emoji":"🧠","requires":{"bins":"[Truncated]"},"install":["[Truncated]"]}} |
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:
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:
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:
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:
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):
python3 {baseDir}/scripts/memory.py set-block "persona" "I am a research assistant focused on AI safety"
python3 {baseDir}/scripts/memory.py set-block "human" "The user is Dave, a developer building AI systems"
python3 {baseDir}/scripts/memory.py blocks
Memory Maintenance
Run periodically (daily is good) to let unimportant memories fade:
python3 {baseDir}/scripts/memory.py decay
Protected memories are immune to decay. To protect a critical memory:
python3 {baseDir}/scripts/memory.py protect <memory_id>
Reviewing Memories
List recent memories:
python3 {baseDir}/scripts/memory.py list --limit 20
Revise a memory:
python3 {baseDir}/scripts/memory.py revise <memory_id> "Updated content" --reason "Corrected factual error"
Soft-delete a memory (can be restored):
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
