hippocampus-sharp-memory 1.0.2

Brain-inspired memory for AI agents. Ebbinghaus forgetting + Kanerva SDM + spaced recall. Sub-microsecond semantic lookup. Only remembers what matters.

hippocampus-sharp-memory

Brain-inspired memory for AI agents. Adaptive retention + Kanerva SDM + locality-sensitive hashing. Sub-microsecond semantic lookup at 46M memories. Only remembers what matters.

pip install hippocampus-sharp-memory

from hippocampus_sharp_memory import create_memory

mem = create_memory()
mem.remember("important fact", salience=80.0)    # survives ~368 ticks
mem.remember("trivial noise", salience=1.0)       # gone after ~5 ticks
mem.advance(100)
print(mem.recall("important", top_k=1))           # still here

Why This Exists

Every AI agent framework stores chat history in a list. That's a to-do app pretending to be a brain.

Real brains don't work that way. They:

• Prioritize — low-value information fades, critical knowledge stays sharp
• Strengthen memories accessed repeatedly (spaced repetition)
• Associate related memories into webs (Kanerva SDM)
• Amplify emotional/critical events with higher salience
• Consolidate frequently-accessed memories during "sleep" cycles

This library does all of that in Rust, exposed to Python via zero-copy PyO3 bindings.

How Salience Works (Read This First)

Salience controls how long a memory survives. The formula is simple:

Retention = e^(-age / salience)
Lifetime  ≈ salience × 4.6 ticks    (time until <1% retention)

You decide what a "tick" means in your app. Call mem.tick() once per second, once per message, once per API call — whatever makes sense. Then pick salience values based on how many ticks you want the memory to survive:

Salience	Lifetime	Use it for
1	~5 ticks	Throwaway context ("user said hi")
10	~46 ticks	Session-level context (current topic)
50	~230 ticks	Important facts (user preferences)
100	~460 ticks	Business-critical (complaints, errors)
200	~920 ticks	Persistent knowledge (learned patterns)
500	~2,300 ticks	Near-permanent (compliance events)

Example: If you tick once per second, salience=60 means the memory survives ~4.6 minutes. If you tick once per message, it survives ~276 messages.

Emotional tags multiply salience automatically:

Tag	Multiplier	Effect
emotional_tag=0 (neutral)	1.0x	No change
emotional_tag=1 (positive)	1.2x	20% longer
emotional_tag=2 (negative)	1.5x	50% longer
emotional_tag=3 (critical)	3.0x	3x longer

So salience=20, emotional_tag=3 becomes effective salience 60 (survives ~276 ticks instead of ~92).

Spaced repetition: Every recall() multiplies salience by 1.3x. A memory recalled 5 times has its salience boosted 3.7x — it effectively becomes permanent.

Quick Start

from hippocampus_sharp_memory import create_memory

mem = create_memory()

# Pick salience based on how long you want memories to last
mem.remember("user prefers dark mode", salience=50.0)                         # ~230 ticks
mem.remember("billing complaint about invoice #4821", salience=100.0)          # ~460 ticks
mem.remember("CRITICAL: database at 95% capacity", salience=100.0, emotional_tag=3)  # 3x → ~1380 ticks
mem.remember("user said hello", salience=1.0)                                  # ~5 ticks (throwaway)

# Advance time — this is how forgetting happens
# The "hello" decays away; the critical alert stays sharp
mem.advance(100)

# Semantic recall — finds related memories, not just keyword matches
results = mem.recall("database storage issue", top_k=3)
for r in results:
    print(f"  [{r.retention*100:.0f}%] {r.content}")

# "user said hello" is already forgotten. The critical alert persists.

The Recall-Before-LLM Pattern

The killer use case. Save 90% on LLM costs:

def handle_alert(alert_text: str, mem, llm_client):
    # Step 1: Check if we've explained this before
    cached = mem.recall(alert_text, top_k=1)
    if cached and cached[0].retention > 0.3:
        return cached[0].content  # Free! No LLM call needed

    # Step 2: Only call LLM for genuinely new situations
    explanation = llm_client.explain(alert_text)

    # Step 3: Cache the expensive response (survives ~460 ticks)
    mem.remember(
        f"Explanation: {explanation}",
        salience=100.0,
        source="llm_cache",
    )
    return explanation

Why retention > 0.3? At 30% retention, the memory is fading but still relevant. Lower thresholds catch older matches; higher thresholds only return fresh ones. Tune to your domain — 0.3 is a good starting point.

Recurring alerts get answered from memory. Novel situations still go to the LLM. Adaptive retention naturally phases out stale explanations as they age.

Architecture

┌──────────────────────────────────────────────────┐
│                  Python API                       │
│  create_memory() → HippocampusEngine              │
├──────────────────────────────────────────────────┤
│                 Rust Core (PyO3)                  │
│  ┌─────────┐  ┌─────────┐  ┌──────────────────┐ │
│  │ SimHash  │→│ LSH     │→│ Context Scorer    │ │
│  │ 1024-bit │  │ 16 tables│  │ sim+recency+sal  │ │
│  │ address  │  │ O(1)    │  │ +emotion weighting│ │
│  └─────────┘  └─────────┘  └──────────────────┘ │
│  ┌─────────────┐  ┌──────────────────────────┐   │
│  │ Adaptive  │  │ Kanerva SDM              │   │
│  │ Retention │  │ Consolidated Long-Term   │   │
│  └─────────────┘  └──────────────────────────┘   │
│  ┌──────────────────────────────────────────┐    │
│  │ Deduplication (LSH exact-match)          │    │
│  │ Identical content → salience boost       │    │
│  └──────────────────────────────────────────┘    │
├──────────────────────────────────────────────────┤
│          Optional Disk Persistence                │
│  mmap'd records + quota enforcement + compaction  │
└──────────────────────────────────────────────────┘

API Reference

Factory Functions

from hippocampus_sharp_memory import create_memory, create_persistent_memory

# In-memory (fast, ephemeral) — default 500K capacity
mem = create_memory()

# High-capacity for long-running processes — 1M capacity
mem = create_persistent_memory()

Parameter	Default	What it controls
capacity	500K / 1M	Max episodes before oldest are evicted
consolidation_interval	100	Ticks between sleep-replay cycles
recall_reinforcement	1.3	Salience boost per recall (1.0 = no boost)

Core Operations

Method	Description
mem.remember(content, salience, source="", emotional_tag=0)	Store a memory. Duplicates auto-merge.
mem.recall(query, top_k=5)	Semantic recall. Returns List[RecallResult].
mem.tick()	Advance clock by 1. Triggers decay + consolidation.
mem.advance(n)	Advance clock by n ticks at once.
mem.relate(id_a, id_b)	Create associative link between memories.
mem.recall_related(id, depth=1)	Follow relationship web.
mem.recall_between(start, end, top_k=10)	Temporal range query.
mem.stats()	Returns HippocampusStats snapshot.
mem.consolidate_now()	Force a sleep-replay consolidation cycle.

RecallResult Fields

Field	Type	Description
content	str	The memory text
source	str	Origin tag
salience	float	Current importance (grows with recalls)
retention	float	0.0–1.0, how well-retained right now
age_ticks	float	Ticks since creation
recall_count	int	Times this memory was recalled
consolidated	bool	Promoted to long-term (Kanerva SDM) storage

Performance

Benchmarked on a single core (Intel i7-12700K):

Scale	remember()	recall()	Memory
1K memories	2 us	8 us	~1 MB
10K memories	2 us	20 us	~8 MB
100K memories	3 us	50 us	~80 MB
1M memories	3 us	120 us	~800 MB
46M memories	4 us	2 us (LSH)	~37 GB

The LSH index provides O(1) query time regardless of memory count at scale. At 46M memories, recall is actually faster than at 1M because the LSH buckets are more selective.

Advanced Usage

Spaced Repetition

mem = create_memory()
mem.remember("important pattern", salience=20.0)   # survives ~92 ticks

# Each recall boosts salience by 1.3x
mem.recall("important pattern", top_k=1)  # salience → 26  (~120 ticks)
mem.recall("important pattern", top_k=1)  # salience → 34  (~156 ticks)
mem.recall("important pattern", top_k=1)  # salience → 44  (~202 ticks)
# After 5 recalls: salience ~74, effectively permanent

Automatic Deduplication

mem.remember("server alert: CPU at 95%", salience=20.0)
mem.remember("server alert: CPU at 95%", salience=20.0)  # same content
mem.remember("server alert: CPU at 95%", salience=20.0)  # again!

assert mem.episode_count == 1  # Only 1 episode stored
# Salience was boosted, not duplicated

Relationship Graphs

mem.remember("billing complaint", salience=50.0)       # id=0
mem.remember("escalation to manager", salience=50.0)    # id=1
mem.remember("legal threat received", salience=100.0)   # id=2

mem.relate(0, 1)  # complaint → escalation
mem.relate(1, 2)  # escalation → legal

# Follow the chain
related = mem.recall_related(0, depth=2)
# Returns: [escalation, legal threat]

Consolidation (Sleep Replay)

Memories that are both high-retention AND frequently recalled (2+ times) get consolidated into the permanent Kanerva SDM store. This happens automatically every consolidation_interval ticks, or on demand:

# Automatic: happens during tick() / advance()
mem.advance(200)

# Manual: force it now
report = mem.consolidate_now()
print(report)  # "Consolidation: evicted=12, consolidated=3, surviving=85"

Consolidated memories are harder to lose — they're stored redundantly in both the hippocampal buffer and the Kanerva SDM.

Part of the Ebbiforge Ecosystem

hippocampus-sharp-memory is the standalone memory engine extracted from Ebbiforge — a full AI agent framework with:

• 100M-agent swarm simulation (Rust tensor engine)
• Compliance & PII redaction (OWASP, rate limiting, audit trails)
• Self-evolution (Darwinian agent selection, metacognition)
• Latent world model (predictive planning, diffusion predictor)

If you need just memory → pip install hippocampus-sharp-memory If you need the full stack → pip install ebbiforge

License

MIT