loremem-ai 1.2.2

  "I live in Amsterdam and work at Google"
           │                        │
           ▼                        ▼
   (user, live_in, Amsterdam)    (user, work_at, Google)

Parses English by grammar position — pronouns, copulas, prepositions, and a few irregular verbs are recognised; the rest is structural. Raw text is always FTS5-indexed as a fallback. A sentence-transformer is loaded for retrieval and write-time safety classification, not for extraction.

Weights adapt automatically through feedback.

m.feedback(results[0].id, helpful=True)   # adapt channel weights
m.consolidate()                           # decay + replay + archive

m = Memory(user_id="alice", org_id="acme", data_dir="~/.lore-memory")

m.store(text, scope="private")             # Store from natural language
m.query(query, limit=10)                   # 7-channel retrieval
m.forget(memory_id=...)                    # Soft-delete by ID
m.forget(subject="alice", hard=True)       # GDPR-style hard erase + VACUUM
m.forget_all(hard=True)                    # Remove entire user DB file
m.export_all()                             # Every row (incl. deleted) as dicts
m.export_to_jsonl("alice.jsonl")           # Portable audit dump
m.close()                                  # Persist and close

# Retrieval results that flowed through the injection classifier:
for r in m.query("who am I?"):
    r.is_suspicious          # True if write-time classifier flagged it
    r.to_llm_context()       # wraps suspicious results in untrusted delimiters

# Single-answer query with a margin-based certainty signal:
r = m.query_one("where do I live?")
if r.needs_clarification:
    ask_user(r.alternatives)   # top-1 is within 15% of top-2
else:
    use(r.answer.text)         # certainty: fraction top-1 leads top-2

m.store_triple("alice", "works_at", "Google", confidence=0.9)
m.profile()                            # All facts by predicate
m.profile_compact(max_tokens=200)      # Token-budgeted LLM context
m.feedback(memory_id, helpful=True)    # Drive adaptive learning
m.consolidate()                        # Decay + replay + archive
m.stats()                              # Memory counts by scope

with Memory(user_id="alice") as m:
    m.store("I live in Amsterdam")
    results = m.query("where do I live?")

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")

m = Memory(user_id="alice", embedding_dims=384, embed_fn=model.encode)

alice = Memory(user_id="alice")
bob   = Memory(user_id="bob")

alice.store("I work at Google")
bob.query("where does alice work?")  #> [] — fully isolated

Shared org memories:

alice = Memory(user_id="alice", org_id="acme")
alice.store("Our mission is to democratize AI", scope="shared")

bob = Memory(user_id="bob", org_id="acme")
bob.query("what is our mission?")  #> Returns shared memory

lore store "I work at Google"
lore query "where do I work?"
lore list
lore stats
lore forget --id <id>
lore serve --port 8420     # REST API
lore mcp                   # MCP server

pip install loremem-ai[api]
lore serve --port 8420

# Store
curl -X POST localhost:8420/memory \
  -H "Content-Type: application/json" \
  -d '{"user_id":"alice","text":"I prefer dark mode"}'

# Query
curl "localhost:8420/memory?user_id=alice&query=preferences"

docker build -t loremem -f docker/Dockerfile .
docker run -p 8420:8000 -v lore_data:/data loremem