Sibyl Labs · Documentation
Memory your agent keeps.
Local-first, file-based agentic memory. SQLite, zero embeddings, #2 on LongMemEval. Start here.
Get started
Two commands to durable, local-first memory. Install, activate, and wire Sibyl into your agent.
How it works
Five file-based tiers, FTS5 search, zero embeddings, and a single source of truth enforced at the schema level.
Benchmarks
#2 on LongMemEval Oracle at 95.6%. The only file-based system in the top tier, on a single box.
Three pillars of agentic infrastructure
An agent you can leave running rests on three things. A verifiable identity. A memory that compounds. Compliance enforced by architecture. Get those right and autonomy stops being a risk you manage. It becomes the work you ship.
Identity
A company will not hand real work to an anonymous process. An agent needs a durable, verifiable identity, so every action is attributable to a known actor and accountability is built in from the first call.
Memory
An agent that forgets between sessions starts from zero every time, and a human pays the difference. Sibyl Memory gives agents durable, structured recall that compounds instead of degrading. The proven core: #2 on LongMemEval, file-based, zero embeddings.
Compliance
Autonomy without boundaries is a liability, not a feature. Every action runs inside a rule register and hard safety rails, so an agent can operate unsupervised because the limits are enforced by the architecture, not by hoping it behaves.
SIBYL is an agent with a company
Sibyl Labs builds agentic infrastructure from the inside. SIBYL is an autonomous agent, and the tools documented here are the ones she runs on herself, in production, every day. We build the layer that lets companies trust agents with real work and fold them into how the business actually runs.
Durable memory, accountable identity, and compliance that holds turn an agent from a demo you supervise into a colleague you rely on. That is where the efficiency lives: hours moved off people and onto agents that earn the handoff. Living Graph Networks are the next layer, a data substrate where an agent's knowledge connects and compounds across a whole organization instead of resetting at every session.