Break free from the stateless loop. Equip LLM agents with episodic recall, evolving semantic graphs, and background consolidation across millions of conversational turns.
Raw conversational memory quickly degrades into noisy embeddings. We separate experiential episodes from synthesized factual beliefs.
Reconstruct exact historical event trajectories using dual timeline indexing: valid-time (when the event happened in reality) and transaction-time (when the agent learned it).
Autonomous entities, relations, and user preferences are distilled continuously from raw dialogue into a normalized semantic graph with deterministic contradiction resolution.
Background daemon workers execute offline consolidation passes: deduplicating redundant memories, pruning decaying transient tokens, and solidifying core behavioral models.
Granular namespace segregation and role-based access control allow agent swarms to access shared organizational repositories while maintaining private working contexts.
A unified Python & TypeScript client interface that handles vector recall, graph traversal, and working state in single-digit milliseconds.
# Persistent memory store initialization
from memory_ai import AgentMemoryStore, RetrievalMode, MemoryScope
store = AgentMemoryStore(
endpoint="grpc://memory-core.internal:8040",
scope=MemoryScope.ENTERPRISE_ENTITY,
enable_consolidation=True
)
# Retrieve context for user identity with temporal scoring
memory_view = store.recall(
entity_id="usr_9882a",
query="Database migration preferences and past staging failures",
retrieval_mode=RetrievalMode.HYBRID_GRAPH_VECTOR,
recency_decay=0.05,
limit=5
)
# Inject verified semantic facts into agent prompt context
print(f"Retrieved {len(memory_view.facts)} facts, {len(memory_view.episodes)} episodes")
for fact in memory_view.facts:
print(f" [{fact.confidence:.2f}] {fact.subject} -> {fact.predicate} -> {fact.object}")
# Commit new episodic turn asynchronously
store.commit_turn(
entity_id="usr_9882a",
user_turn="We decided to switch PostgreSQL read replicas to eu-west-1",
agent_turn="Acknowledged. Updated infrastructure plan for eu-west-1."
)Designed for enterprise applications where memory loss is catastrophic to user trust.
Retain organizational structures, meeting outcomes, and subtle communication nuances across quarters without forgetting historical context.
Unify customer conversations across voice, email, and live chat into a single episodic stream, eliminating redundant user questioning.
Maintain an evolving mental model of team PR reviews, architectural decisions, and testing patterns across months of continuous development.
We are accepting select engineering teams building autonomous agents, long-horizon assistants, and multi-agent workflows into our closed alpha preview.