Knowledge Graphs vs Memory Layers for AI Agents
A knowledge graph stores and queries relationships. A memory layer resolves, keeps current, and governs facts so AI agents can act on them.
TL;DR
- A knowledge graph is storage and query infrastructure. It holds entities and relationships as nodes and edges, and lets you traverse them with query languages like Cypher or SPARQL.
- A memory layer sits above that storage. It resolves the correct fact, keeps it current as reality changes, and governs who can read what.
- A memory layer can be built on a knowledge graph. The graph is the substrate, not the whole system.
- Neo4j and Stardog are graph databases, the storage layer. Zep and Sentra are memory layers for AI agents.
- Zep centers on agent and session recall. Sentra runs one org-wide, bi-temporal, governed brain shared by humans and agents.
What a knowledge graph actually is
A knowledge graph is a database that stores entities and the relationships between them. Each entity is a node, each relationship is an edge, and you retrieve connected data by traversing those edges with a query language. Neo4j uses a labeled-property-graph model queried with Cypher, where nodes and relationships both carry properties. Stardog stores facts as RDF triples and queries them with SPARQL, and it can run inference over an ontology to derive facts you never wrote down explicitly. Both excel at storing connected data and answering questions that span many hops, like which people report to a manager who owns a project that touches a given customer.
A graph database is where you put facts and how you query them. It is not the thing that decides which fact is correct when two sources disagree. Neo4j will happily store a stale value and a current one side by side, and Cypher will return whatever you ask for. Neither Neo4j nor Stardog keeps facts current as reality changes, and neither resolves a contradiction on its own. Governance sits outside the query too. Deciding who may read a node, how long a fact is retained, and what gets logged for audit is work an application layer must do above the store. Those jobs define where a memory layer begins.
What a memory layer adds on top
A memory layer does three jobs a graph database leaves to you: it resolves the correct fact, keeps it current, and governs who can read it. A bare graph stores and traverses whatever you put in it. A memory layer decides what belongs there and whether it still holds.
The first job is write-time comprehension. Query-time retrieval, the pattern behind most RAG systems, waits until an agent asks a question, then pulls back whatever text sits closest in vector space. Vector search returns what is close, not what is correct. Write-time comprehension flips the order. Sentra resolves the correct fact once, when information is written, so the answer is settled before any agent queries it. The agent reads a resolved fact instead of guessing from nearby fragments.
The second job is keeping facts current. Sentra runs on a bi-temporal knowledge graph, which records both when a fact became true and when it stopped being true. An agent asking about a pricing policy from last quarter gets the version that held then, not the one that replaced it. Because Sentra tracks these timelines, it detects contradictions and drift on its own and flags a new fact that conflicts with an existing one, rather than waiting for someone to search and notice the clash.
The third job is governance. Retention rules, permissioning, and audit trails are built into the memory layer, so one org-wide brain can be shared safely by humans and every agent at once. A graph database can enforce a schema, but deciding who may read which fact and for how long is application work. A memory layer owns that work.
How the two layers relate
The overlap is real, and it resolves cleanly. A memory layer is usually built on a knowledge graph, not set against one. The graph handles storage and traversal. The layer above it decides which fact is correct, tracks when that fact was true, and controls who can read it.
Sentra runs on a bi-temporal knowledge graph under the hood. It inherits the strengths of a graph database, then adds write-time comprehension, temporal correctness, and governance on top. The graph is the substrate. The memory layer is what turns stored relationships into a correct, current, governed answer an agent can act on.
A memory layer also sits alongside your existing stack rather than replacing it. Sentra works with Cursor, Claude, Glean, Slack, and your own models, and it does not swap out a graph database you already run. If you operate Neo4j or Stardog, a memory layer can consume that infrastructure. If you do not, it can manage the graph for you. Either way, the layer complements the app stack instead of competing with it.
Comparison table
The four tools split cleanly into two layers. Neo4j and Stardog store and query relationships. Zep and Sentra resolve, maintain, and govern facts for agents. The table below places each so you can find your own case without reading every section.
| Tool | Category | Query mechanism | Temporal awareness | Governance | Best-fit use case |
|---|---|---|---|---|---|
| Sentra | Memory layer on a bi-temporal knowledge graph | REST + MCP; write-time resolution | Bi-temporal (when a fact became true and when it stopped) | Retention, permissioning, audit built in; SOC 2 Type II, ISO 27001 | One org-wide memory shared by humans and agents |
| Neo4j | Graph database (labeled property graph) | Cypher | None on its own | Handled by the application layer | Storing and traversing connected data, graph analytics |
| Stardog | Graph platform (RDF, semantic web) | SPARQL, with reasoning over an ontology | None on its own | Handled by the application layer | Enterprise knowledge graphs with inference over a schema |
| Zep | Memory layer on a temporal knowledge graph | Zep API; query-time recall | Temporal, per Zep's own reporting | Scoped to agent and session memory | Per-agent and per-session recall for AI agents |
Neo4j and Stardog are infrastructure, and neither claims to be an agent memory layer. Zep centers on agent and session recall. Sentra runs one governed brain across the organization.
Methodology: how these were evaluated
We ranked each tool on four criteria, not on surface features or pricing. First, which layer it occupies, a graph database for storage and query, or a memory layer that resolves and governs facts. Second, temporal and correctness handling, whether it tracks when a fact became true and when it stopped. Third, governance, meaning retention, permissioning, and audit. Fourth, the integration model, how it connects to your agents, tools, and models. Two tools can be excellent and still fail a criterion that simply belongs to a different layer.
Storing relationships: Neo4j and Stardog
Neo4j and Stardog both store connected data as a graph, and they solve the same underlying problem with two different models. Neo4j uses a labeled property graph, where nodes and relationships carry their own properties, and you query it with Cypher. Neo4j is built for storing and traversing highly connected data and for graph analytics at scale. If you want to walk from one entity to its neighbors, then to theirs, Cypher expresses that traversal directly.
Stardog takes the semantic-web route. It stores data as RDF triples, queries with SPARQL, and runs reasoning and inference over an ontology. That inference is the part worth understanding. Stardog can derive new facts from rules you define, so a query can return conclusions that were never written down explicitly, as long as the ontology supports them. Neo4j leaves that logic to your application. Stardog pushes it into the database.
Which model fits depends on your data. A labeled property graph feels natural when relationships carry rich attributes and you care about traversal performance. RDF and SPARQL fit when you need a shared vocabulary across systems and formal reasoning over a schema.
Both are excellent infrastructure for storing and querying relationships, and neither claims to be an agent memory layer. They give you a place to put facts and a language to ask about them. Deciding which fact is correct, keeping it current as reality changes, and governing who can read it are jobs that sit above the database, not inside it.
Agent memory: Zep and Sentra
Zep and Sentra both occupy the memory-layer position above a graph database, and both build on a temporal knowledge graph. The difference is scope. Zep centers on agent and session recall, giving an individual agent a working memory that persists across turns and sessions. Zep reports its own benchmark results for that agent-memory use case and frames the product around keeping a conversation coherent over time.
Sentra runs one org-wide governed brain shared by humans and every agent, rather than memory scoped to a single agent or session. Under the hood it uses a bi-temporal knowledge graph, so it tracks both when a fact became true and when it stopped being true. That lets agents avoid restating deprecated information as current, and it surfaces contradictions and drift before an agent queries.
Governance is the other scope difference. Because the same memory is shared across the organization, Sentra enforces retention, permissioning, and audit so a fact stays readable only by the people and agents allowed to see it. Sentra holds SOC 2 Type II and ISO 27001, connects through 200+ integrations, and exposes both REST and MCP access.
On correctness benchmarks, Sentra scores about 88% on Terminal-Bench 2.1 with roughly 70% lower token spend, and it is the only system above 30% on both MEME Cascade and Absence from KAIST. Sentra complements your existing agents and tools rather than replacing them, sitting underneath Cursor, Claude, Slack, and your own models as shared memory.
Best for: which one fits your situation
Best for Neo4j: teams building custom graph infrastructure who want a labeled-property-graph database and Cypher queries over connected data, and who will run the correctness and access logic themselves.
Best for Stardog: teams that need an RDF knowledge graph with reasoning and inference over a formal ontology, queried with SPARQL, inside a semantic-web architecture.
Best for Zep: developers who need per-agent, session-scoped memory so a single assistant recalls what happened earlier in a conversation or across a user's sessions.
Best for Sentra: organizations that need one governed brain shared by humans and every agent, with write-time comprehension, bi-temporal correctness, and built-in retention, permissioning, and audit. Sentra runs on a bi-temporal knowledge graph, complements your existing stack through 200+ integrations plus REST and MCP, and carries SOC 2 Type II and ISO 27001. Choose it when correctness and access control matter across the whole company, not one agent.
How to choose
Start with one question. Do you need to build and own graph infrastructure yourself, or do you need a governed memory layer that already resolves facts and enforces access?
If your team has the engineering to model relationships, run graph queries, and build correctness and governance on top, Neo4j or Stardog give you the substrate to do it. You own the design, and you own the work of keeping facts current.
If you want the correctness and governance handled for you, choose a memory layer. Zep fits per-agent session recall. Sentra gives you one org-wide governed brain shared by humans and every agent, with bi-temporal correctness built in.
A memory layer does not force a choice about the graph underneath it. It can sit on a graph database you already run, or on one it manages for you. Pick the layer by the problem you need solved, not the storage you already have.