kore-memory 0.2.0

The memory layer that thinks like a human: remembers what matters, forgets what doesn't, and never calls home.

Kore Memory

The memory layer that thinks like a human: remembers what matters, forgets what doesn't, and never calls home.

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Why Kore?

Every AI agent memory tool out there has the same problem: they remember everything forever, require cloud APIs, or need an LLM just to decide what's worth storing.

Kore is different.

Feature	Kore	Mem0	Letta	Memori
Runs fully offline	✅	❌	❌	❌
No LLM required	✅	❌	❌	✅
Memory Decay (Ebbinghaus)	✅	❌	❌	❌
Auto-importance scoring	✅ local	✅ via LLM	❌	❌
Memory Compression	✅	❌	❌	❌
Semantic search (50+ langs)	✅ local	✅ via API	✅	✅
Timeline API	✅	❌	❌	❌
Access reinforcement	✅	❌	❌	❌
Install in 2 minutes	✅	❌	❌	❌

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How It Works

Kore models memory the way the human brain does:

1. Save — store a memory with optional category and importance
2. Auto-score — Kore calculates importance locally using content analysis (no API calls)
3. Decay — memories fade over time using the Ebbinghaus forgetting curve
4. Reinforce — retrieving a memory resets its clock and boosts its score
5. Compress — similar memories are automatically merged to keep the DB lean
6. Search — semantic search in any language, filtered by relevance and freshness

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Quickstart

pip install kore-memory
pip install kore-memory[semantic]   # + multilingual embeddings (50+ languages)

kore                                # starts server on http://localhost:8765

Save a memory

curl -X POST http://localhost:8765/save \
  -H "Content-Type: application/json" \
  -d '{"content": "User prefers concise responses", "category": "preference"}'
# → {"id": 1, "importance": 4, "message": "Memory saved"}
# importance was auto-scored: "preference" category + keyword "prefers" → 4

Search (any language)

# English query finds Italian content, French content, etc.
curl "http://localhost:8765/search?q=user+preferences&limit=5"

Run decay pass (call daily via cron)

curl -X POST http://localhost:8765/decay/run
# → {"updated": 42, "message": "Decay pass complete"}

Compress similar memories

curl -X POST http://localhost:8765/compress
# → {"clusters_found": 3, "memories_merged": 7, "new_records_created": 3}

Timeline: what did I know about X over time?

curl "http://localhost:8765/timeline?subject=project+alpha"

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Memory Decay

Kore uses the Ebbinghaus forgetting curve to assign each memory a decay_score between 0.0 and 1.0:

decay = e^(-t * ln(2) / half_life)

Where:

• t = days since last access
• half_life = base days before 50% decay, adjusted by importance level

Importance	Half-life	Meaning
1 (low)	7 days	Casual notes
2 (normal)	14 days	General context
3 (important)	30 days	Project info
4 (high)	90 days	Critical decisions
5 (critical)	365 days	Passwords, rules, never forget

Every time a memory is retrieved, its access_count increases and its half-life is extended by 15% — just like spaced repetition in human learning.

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Auto-Importance Scoring

When you save a memory without an explicit importance level, Kore scores it automatically:

• Category baseline — preference starts at 4, finance at 3, general at 1
• Keyword signals — words like password, token, urgente → importance 5
• Content length — detailed content gets a small boost

Zero LLM calls. Zero API costs.

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API Reference

Method	Endpoint	Description
POST	/save	Save a memory
GET	/search	Semantic search (any language)
GET	/timeline	Chronological history for a subject
DELETE	/memories/{id}	Delete a memory
POST	/decay/run	Update all decay scores
POST	/compress	Merge similar memories
GET	/health	Health check + capabilities

Full interactive docs: http://localhost:8765/docs

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Categories

general · project · trading · finance · person · preference · task · decision

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Requirements

• Python 3.11+
• SQLite (built into Python)
• Optional: sentence-transformers for semantic search

No PostgreSQL. No Redis. No Docker. No API keys.

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License

MIT — use it, fork it, build on it.

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Built with ❤️ for AI agents that deserve better memory.