Agent memory

Memory that stays with your agents.

Keep useful context between tasks, with scoped recall and deliberate deletion.

Build agent memory

Remember

Store useful facts and lessons in isolated Nuclei.

Recall

Permissioned Scopes return relevant context within a token budget.

Forget

Deletion creates tombstones to keep forgotten memory out of retrieval.

Memory concepts and comparisons

The four layers of useful agent memory

Reliable memory is more than storing text. It preserves meaning, relationships, time, and evidence while limiting what can enter a model’s context and why.

Working context

The bounded instructions, retrieved facts, and current task data placed in a model request.

Durable, typed memory

Preferences, entities, relationships, decisions, procedures, outcomes, and lessons that remain useful across sessions.

Temporal knowledge

Versioned entities and relationships that recover what is true now and what was known when an earlier decision was made.

Context and learning

Policy-aware retrieval that combines eligible memory, graph state, decisions, outcomes, and reviewed learning for one task.

AI memory vs chat history, vector databases, and RAG

Approach	Best at	Boundary
Chat history	Conversation continuity	Usually session-shaped; grows quickly and mixes relevant with irrelevant turns.
Vector database	Semantic similarity search	Stores and finds vectors, but application policy, identity, lifecycle, and prompt packing remain external.
RAG pipeline	Retrieving source knowledge	Grounds a response in documents; it does not by itself model durable agent experience or deletion policy.
Knowledge and decision platform	Persistent, scoped intelligence	Combines memory, temporal relationships, context, decisions, outcomes, permissions, deletion, audit, and reviewed learning.

Neutron AI

Memory that connects to knowledge and decisions

A Neutron Nucleus is an isolated memory universe. Scopes divide it by workspace, user, project, agent, session, policy, or another application boundary. Memory Cells hold minimised facts and lessons; recall and Context Capsules return only eligible, relevant material within a token budget.

Semantic retrieval is one signal, not the whole answer. Neutron can combine task and entity relevance with graph proximity, time, confidence, importance, previous decisions, observed outcomes, and provenance. Deletion creates tombstones so queues, archives, compaction, and cache refresh cannot silently restore forgotten memory.

Temporal entities and first-class relationships show how knowledge changes across organisational, operational, spatial, ownership, dependency, or domain-defined graph dimensions. Decision workflows add objectives, constraints, options, Pareto trade-offs, structured consequences, approvals, observations, and reviewed lessons. They support judgement with uncertainty; they do not expose hidden chain-of-thought or guarantee an outcome.

Where persistent AI memory helps

Coding agents that remember repository architecture, team standards, past incidents, and verified test commands.
Customer agents that retain consented preferences and support history without exposing another customer’s data.
Operational agents that compare new actions with prior decisions, observed outcomes, and bounded consequences.
Industry agents that retrieve governed domain knowledge alongside organisation-specific policy and evidence.
Regional or jurisdictional workloads that separate memory by Nucleus, Scope, placement profile, and retention rule.

Try the public demo

Try a public demo. Demo datasets are not customers or endorsements.