tonitangpotato/engram-ai/clawdbot-skill

pip install neuromemory-ai

# Add a memory
neuromem add "User prefers dark mode" --type preference --importance 0.8
 
# Recall memories
neuromem recall "user preferences"
 
# View statistics
neuromem stats
 
# Run consolidation (like sleep)
neuromem consolidate
 
# Prune weak memories
neuromem forget --threshold 0.01
 
# List memories
neuromem list --limit 20
 
# Show Hebbian links
neuromem hebbian "dark mode"
 
# Use specific database
neuromem --db ./custom.db add "Memory"

from engram import Memory
from engram.config import MemoryConfig
# Create/open memory database
mem = Memory("./agent.db")  # or use preset:
# mem = Memory("./agent.db", config=MemoryConfig.personal_assistant())
# Add memories with type and importance
mem.add("User prefers concise answers", type="relational", importance=0.8)
mem.add("Always check calendar before scheduling", type="procedural", importance=0.9)
# Recall (ranked by ACT-R activation)
results = mem.recall("user preferences", limit=5)
for r in results:
    print(f"[{r['confidence_label']}] {r['content']}")
# User feedback shapes memory
mem.reward("Great answer!")  # strengthens recent memories
mem.reward("That's wrong")   # suppresses recent memories
# Daily maintenance
mem.consolidate()  # transfer working → core memory
mem.forget()       # prune weak memories

from engram.config import MemoryConfig
MemoryConfig.chatbot()           # High replay, slow decay
MemoryConfig.task_agent()        # Fast decay, procedural focus
MemoryConfig.personal_assistant() # Long-term, relationships matter
MemoryConfig.researcher()        # Never forget, archive everything

# No need to manually tag entities
mem.add("Python is great for ML")
mem.add("PyTorch is my favorite framework")
mem.add("TensorFlow has better production support")
# Query multiple times
for _ in range(3):
    mem.recall("machine learning", limit=3)
# Now querying "Python" will surface PyTorch/TensorFlow via Hebbian links
results = mem.recall("Python tools", graph_expand=True)

python -m engram.mcp_server --db ./agent.db