jeevesagent.memory.inmemory

Dict-backed memory for tests and tiny demos.

Recall is naive: filter episodes by substring + recency. Production deployments use PostgresMemory (Phase 4) which uses pgvector for semantic search and tracks bi-temporal facts.

Classes

InMemoryMemory

Dict-backed implementation of Memory.

Module Contents

class jeevesagent.memory.inmemory.InMemoryMemory(*, consolidator: jeevesagent.memory.consolidator.Consolidator | None = None, fact_store: jeevesagent.memory.facts.FactStore | None = None)

Dict-backed implementation of Memory.

async append_block(name: str, content: str) → None

async consolidate() → None

Process unconsolidated episodes through the configured Consolidator, appending facts to self.facts.

async recall(query: str, *, kind: str = 'episodic', limit: int = 5, time_range: tuple[datetime.datetime, datetime.datetime] | None = None, user_id: str | None = None) → list[jeevesagent.core.types.Episode]

async recall_facts(query: str, *, limit: int = 5, valid_at: datetime.datetime | None = None, user_id: str | None = None) → list[jeevesagent.core.types.Fact]

async remember(episode: jeevesagent.core.types.Episode) → str

async session_messages(session_id: str, *, user_id: str | None = None, limit: int = 20) → list[jeevesagent.core.types.Message]

Return user/assistant pairs from prior runs of this session.

Materialises each persisted Episode for the given session_id (within the user_id partition) into a [USER input, ASSISTANT output] pair, ordered oldest-first and capped at limit turns total — i.e. up to limit / 2 Q/A exchanges. Tool-call traces are not replayed; the final assistant text per turn is sufficient context for follow-ups.

snapshot() → dict[str, Any]

async update_block(name: str, content: str) → None

async working() → list[jeevesagent.core.types.MemoryBlock]

facts: jeevesagent.memory.facts.FactStore