TypedMemory¶
Contract-driven memory for AI agents.
Typed schemas. Explicit conflict policies. Structured provenance. Typed event timeline.
TypedMemory is the layer between data and reasoning where every change is governed by an explicit contract — not learned, not implicit. Every memory has a declared type validated against a profile, a conflict policy that fires deterministically on slot collision, a structured source with dedup identity, and an entry in a first-class event timeline. Memories update on conflict, decay over time, and summarize non-destructively — all under rules you wrote, not rules the system inferred.
┌──────────────────┐
│ DomainProfile │ ← schema: which types,
│ TypeSpec × N │ which policies,
│ prompt + rules │ which validations
└────────┬─────────┘
│
text ──► Extractor ──► Memory ──┴──► MemoryStore ──► Retriever
│
▼
Evolver
(contradictions, drift, goals,
non-destructive summarization)
Zero runtime dependencies. Stdlib only — LLM clients, YAML profile loading, and richer embedders are optional extras.
Why this exists¶
Most "AI memory" libraries are wrappers around a vector database. That works for "remember what the user said," but it falls apart the moment you want an agent to:
- track who said what, in which document, at which span
- handle the same fact from three sources without storing it three times
- recognize that a new decision supersedes the old one without losing the audit trail
- summarize stale events without throwing away the originals
- isolate legal memory from medical memory on the same machine
- flag contradictions instead of silently overwriting them
TypedMemory treats these as first-class concepts.