sibyl-memory-client 0.4.14

Local-first agentic memory SDK. SQLite-backed five-tier hierarchical schema, FTS5 search, multi-tenant, with self-learning skill detection and local memory linter. Foundation of the Sibyl Memory Plugin family.

sibyl-memory-client

Local-first agentic memory SDK. The foundation of the Sibyl Memory Plugin family.

A small Python library that gives any AI agent durable memory across sessions, stored in a SQLite database on the user's own computer. No round-trip to anyone's cloud. Organized by what kind of thing it is, not one fuzzy similarity bucket.

pip install sibyl-memory-client

Quickstart

from sibyl_memory_client import MemoryClient

memory = MemoryClient.local("~/.sibyl-memory/memory.db")

# Remember a fact (entity)
memory.set_entity("project", "atlas", {"status": "active", "owner": "jane"})

# Recall it
print(memory.get_entity("project", "atlas"))

# Record what happened (journal)
memory.write_event(acted=["deployed atlas v1.2 to staging"])

# Search across everything
results = memory.search_entities("atlas")

Why this exists

Most agent-memory products store everything on someone else's servers, treat every piece of information the same way, and quietly forget the important things when you need them most. This SDK solves all three:

• Local-first. Memory lives in a SQLite database in ~/.sibyl-memory/. No cloud round-trip for any operation.
• Organized by kind. Five separate tiers: state, entities, journal, reference, archive: each recalled the way it should be recalled.
• Benchmarked. The Sibyl Memory Plugin (built on this SDK) sits at #2 globally on the LongMemEval Oracle benchmark when paired with Claude Opus 4.6. Methodology open at blog.sibylcap.com/longmemeval-v2.

The five tiers

Intent	Tier	API
What you're working on right now	HOT state	set_state(key, body) / get_state(key)
Things the agent knows about	WARM entities	set_entity(kind, name, body) / get_entity
What happened, in time order	COLD journal	write_event(...) / read_events(...)
Documents you look up by name	REFERENCE	set_reference(key, body) / get_reference
Frozen things, kept but out of the way	ARCHIVE	archive_entity(kind, name)
Search across everything	FTS5	search_entities(query)

Forgetting vs deleting

archive_entity(kind, name) is recoverable: it moves the entity into the archived_entities table (stored plaintext at rest), out of the active set but still on disk. delete_entity(kind, name) is a permanent hard delete that removes the row outright. Use archive to declutter, delete to truly forget.

What's in v0.4.x

• The full five-tier memory model and the API surface above.
• Cross-tier FTS5 search across entities, state, reference, and journal tiers.
• Multi-tenant isolation: one machine can hold separate memory for separate identities.
• Self-learning module (paid-tier): the agent watches your patterns and proposes reusable skills.
• Memory linter (paid-tier): a health check on the local database.
• Tier gating: free-tier callers get clear errors pointing at the upgrade page; paid-tier callers get full access.
• Uniform millisecond timestamp precision across all tiers (v0.4.3).

Tier model

Free tier is generous on purpose. You can build real things with it. Paid plans add self-learning, the linter, and remove the 2 MB local cap. Full plan comparison at docs.sibyllabs.org/memory/tiers.

Documentation

Full docs: docs.sibyllabs.org/memory/. Install guide: docs.sibyllabs.org/memory/install.

License

MIT. Published on PyPI at pypi.org/project/sibyl-memory-client.