lorien-memory 0.2.0

Local-first personal knowledge graph for AI agents — ontology, causal chains, contradiction detection

🌳 lorien

Local-first personal knowledge graph for AI agents.
What to believe, why, and what conflicts — structured memory that Mem0 can't do.

pip install lorien-memory           # core (KuzuDB + CLI)
pip install "lorien-memory[vectors]"  # + semantic search

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Why lorien?

Feature	Mem0	lorien
Conversation memory	✅	✅
Semantic vector search	✅	✅
Local (no server)	❌	✅
Cost	$249/mo	$0
Priority rule system	❌	✅
Causal reasoning (CAUSED)	❌	✅
Auto contradiction detection	❌	✅

lorien stores structured knowledge — not just flat strings. Every fact has a source, every rule has a priority, and contradictions are detected automatically.

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Quickstart

from lorien import LorienMemory

mem = LorienMemory(enable_vectors=True)

# Add a conversation
mem.add([
    {"role": "user",      "content": "I have a severe shellfish allergy. Oysters send me to the ER."},
    {"role": "assistant", "content": "Noted — I'll never recommend shellfish."},
], user_id="alice")

# 3 months later — new conversation
mem.add([
    {"role": "user",      "content": "Where should I eat tonight?"},
    {"role": "assistant", "content": "The new oyster bar on Main St is great!"},
], user_id="alice")

# Semantic search — finds allergy even without exact keywords
results = mem.search("seafood restrictions", user_id="alice")
# → [{"memory": "User has severe shellfish allergy...", "score": 0.82}]

# Auto-detected contradiction
contradictions = mem.get_contradictions()
# → [{"fact_a": "shellfish allergy...", "fact_b": "oyster bar recommendation..."}]

# Hard rules with priority
rules = mem.get_entity_rules("alice")
# → [{"text": "Never recommend shellfish to alice", "priority": 100}]

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Schema

lorien uses KuzuDB — an embedded graph database (like SQLite, but for graphs).

Entity ─── HAS_RULE ───► Rule
  │
ABOUT
  │
  ▼
Fact ─── CAUSED ──► Fact
  │
CONTRADICTS
  │
  ▼
Fact

3 node types:

• Entity — people, organizations, topics (canonical_key = "type:name")
• Fact — statements about entities (subject → predicate → object)
• Rule — constraints with priority 0–100 (100 = absolute prohibition)

5 edge types: ABOUT, HAS_RULE, RELATED_TO, CAUSED, CONTRADICTS

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CLI

# Initialize
lorien init

# Check status
lorien status

# Ingest a file (MEMORY.md, notes, etc.)
lorien ingest MEMORY.md
lorien ingest MEMORY.md --model haiku   # LLM extraction via OpenClaw

# Query the graph
lorien query "MATCH (e:Entity) RETURN e.name LIMIT 10"

# Show entity details
lorien show "alice"

# List contradictions
lorien contradictions

# Conversation memory for a user
lorien memory alice

# Web visualization (vis.js, no extra deps)
lorien serve

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Contradiction Detection

After every fact is ingested, lorien automatically checks for semantic contradictions:

1. Vector similarity — find facts with similar meaning (threshold 0.55)
2. Heuristic check — negation pair patterns (허용↔금지, always↔never, must↔must not, ...)
3. LLM confirmation (optional) — yes/no question to any OpenAI-compatible model
4. CONTRADICTS edge — auto-created in the graph for later querying

detector = ContradictionDetector(
    store=store,
    vector_index=vi,
    llm_model="gpt-4o-mini",
    api_key="sk-...",
    similarity_threshold=0.55,
)
n = detector.check_and_record(new_fact_id, new_fact_text)

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OpenClaw Integration

lorien auto-detects the OpenClaw gateway when available:

lorien ingest MEMORY.md --model haiku   # routes through OpenClaw → Anthropic
lorien ingest notes.md  --model flash   # routes through OpenClaw → Gemini

No API key needed when OpenClaw gateway is running locally.

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Installation

# Core only (graph + CLI, no LLM, no vectors)
pip install lorien-memory

# With semantic search
pip install "lorien-memory[vectors]"

# With OpenAI-compatible LLM extraction
pip install "lorien-memory[llm]"

# Everything
pip install "lorien-memory[all]"

Requirements: Python 3.12+, no server, no Docker.
DB stored at ~/.lorien/db. Vectors at ~/.lorien/vectors.db.

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Roadmap

• v0.1 — Core graph schema (Entity, Fact, Rule + 5 edge types)
• v0.1 — LLM ingest via OpenClaw gateway
• v0.1 — Mem0-compatible LorienMemory API
• v0.2 — Vector semantic search (sentence-transformers, multilingual)
• v0.2 — Automatic contradiction detection
• v0.2 — PyPI release (pip install lorien-memory)
• v1.0 — Web graph visualization
• v1.0 — LangChain adapter

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vs Mem0

Mem0 answers: "What did the user say?"
lorien answers: "What should I believe, why, and does it contradict anything?"

Use both. They're complementary, not competitors.

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MIT License · GitHub