engramai

name	engramai
description	Cognitive memory powered by neuroscience — ACT-R activation, Hebbian learning, Ebbinghaus forgetting. Replaces flat memory with a brain-like system that learns, forgets, and discovers associations.
version	1.0.0
metadata	{"hermes":{"tags":["memory","cognitive-science","neuroscience","ai-agents"],"category":"memory","emoji":"🧠","requires":{"bins":["python3"],"packages":{"pip":["engramai"]}}}}

Engram — Cognitive Memory for Hermes Agent 🧠

When to Use

• When you need to remember something important across sessions
• When recalling past conversations, preferences, or facts
• When the built-in MEMORY.md (2,200 chars) is full or insufficient
• When you want to find connections between memories you didn't explicitly link
• Automatically: store important facts after every conversation, recall relevant memories before answering

What It Is

Engram is a neuroscience-grounded memory system implementing:

• ACT-R activation (Anderson 1993) — memories that are used more often and more recently are easier to recall
• Hebbian learning (Hebb 1949) — memories recalled together become linked ("neurons that fire together wire together")
• Ebbinghaus forgetting curve (1885) — unused memories naturally decay, keeping retrieval clean
• Memory consolidation — important working memories graduate to durable core storage

Procedure

Store a memory

python3 -c "
from engram import Memory
mem = Memory('~/.hermes/engram.db')
mem.add('CONTENT_HERE', type='TYPE', importance=0.7)
"

Types: semantic, episodic, procedural, causal, relational Importance: 0.0 (trivial) to 1.0 (critical)

Recall memories

python3 -c "
from engram import Memory
mem = Memory('~/.hermes/engram.db')
results = mem.recall('QUERY_HERE', limit=5)
for r in results:
    print(f'[{r[\"memory_type\"]}] (score: {r[\"score\"]:.2f}) {r[\"content\"]}')
"

Run consolidation (periodic maintenance)

python3 -c "
from engram import Memory
mem = Memory('~/.hermes/engram.db')
mem.consolidate()
print('Consolidation complete')
"

Get memory stats

python3 -c "
from engram import Memory
mem = Memory('~/.hermes/engram.db')
stats = mem.stats()
for k, v in stats.items():
    print(f'{k}: {v}')
"

Best Practices

1. Store after every meaningful conversation — preferences, facts, corrections, project context
2. Recall before answering — check if you already know something relevant
3. Use appropriate types — episodic for events, semantic for facts, procedural for how-tos, relational for connections between things
4. Set importance — 0.9+ for critical facts (allergies, passwords, deadlines), 0.3-0.5 for general context
5. Run consolidation daily — strengthens important memories, lets unimportant ones decay
6. Don't duplicate built-in memory — use Engram for overflow and long-term storage, MEMORY.md for quick-access essentials

How It's Different from Built-in Memory

Feature	Built-in MEMORY.md	Engram
Capacity	2,200 chars	Unlimited
Search	None (full inject)	FTS5 + Hebbian + optional embeddings
Forgetting	Manual delete	Automatic decay (Ebbinghaus)
Associations	None	Automatic Hebbian links
Activation	None	ACT-R (recency × frequency)
Cross-session	Yes (frozen snapshot)	Yes (live query)

Pitfalls

• Don't store trivial info (agent will remember "user said hi" — not useful)
• Don't store raw data dumps (large code blocks should go in files, not memory)
• Consolidation is needed periodically or memory quality degrades
• First recall in a new session is slightly slower (DB warm-up)

Verification

After storing: recall with a related query and confirm the memory appears. Check stats to see total memories, types distribution, and Hebbian link count.