RAG vs Knowledge Graph for AI Memory: What's the Difference?

TL;DR

• RAG chunks your documents, embeds them, and retrieves the closest matches at query time. It returns what is close, not what is correct, and it breaks on multi-hop questions that span separate documents.
• A knowledge graph resolves entities and relationships at write time, so facts from different sources connect before any query arrives. It wins on multi-hop reasoning and explainability.
• GraphRAG layers graph structure on top of RAG to fix its multi-hop blind spot. The two are complementary, not competing, and the hybrid costs more to build and run.
• Sentra adds bi-temporal awareness, tracking when each fact became true and when it stopped. That makes it the memory layer for agents reasoning over changing, org-wide state.

What RAG Actually Does (and Where It Breaks)

Retrieval-augmented generation works by turning documents into searchable chunks and pulling the closest matches at query time. The pipeline splits source text into pieces of roughly 256 tokens, converts each into a vector embedding, and stores them. When a question arrives, RAG encodes it as a vector, runs a similarity search, and returns the top-k chunks (often ten) as context for the model (arxiv.org/html/2502.11371v3). The system is stateless and document-oriented, scanning broadly across text rather than reasoning over connections.

The core limitation follows from how similarity works. RAG returns chunks that are close to the query in vector space, not chunks that are correct. A passage can sit near your question semantically while answering a different one, and the model has no way to tell the difference. Tune the retrieval window too low and you miss the fact you need. Tune it too high and you flood the prompt with noise that raises cost and confuses the answer.

The weakness becomes concrete on multi-hop questions, where the answer lives across separate documents. Ask "Did any former OpenAI employees start their own company?" and the system has to connect an employment record in one document to a founding record in another (neo4j.com/blog/genai/knowledge-graph-llm-multi-hop-reasoning/). Similarity search cannot reliably bridge that gap. The top results often repeat references to the same entity while ignoring the second document entirely, because nothing in the index records that the two facts relate.

None of this makes RAG useless. A controlled benchmark across NQ and HotPotQA found RAG outperforms graph approaches on single-hop, detail-oriented factual questions (arxiv.org/html/2502.11371v3). RAG breaks when the answer depends on relationships the index never stored.

What a Knowledge Graph Does Differently

A knowledge graph resolves meaning when documents arrive, not when a question shows up. RAG stores raw embeddings at write time and guesses structure at query time, so every request has to crawl Slack, email, and docs to rediscover what a term means. A graph runs an extraction pipeline at ingestion, identifying entities and the typed relationships between them. By the time you ask a question, the structure already exists.

That timing shift solves the multi-hop problem directly. Consider the question "Did any former OpenAI employees start their own company?" A vector search retrieves chunks similar to the query, but the answer lives in two separate records that never mention each other. A graph stores the employment fact from one document and the founding fact from another as connected nodes the moment each arrives (neo4j.com). Traversing the link between them is a structured lookup, not a similarity gamble.

Resolution also fixes identity. RAG reads "Sarah Chen in HubSpot, S. Chen in Gmail, @schen in Slack" as three different people, because three strings produce three different embeddings. A graph collapses them into one confidence-scored entity, so a fact recorded against any handle attaches to the same node.

Connection alone does not keep a graph correct, because facts expire. A bi-temporal graph records two timestamps on every fact, when it became true and when it stopped being true. Old facts are invalidated, not deleted, so an agent can tell what was true last quarter from what holds today. RAG keeps a flat haystack of embeddings where old facts sit next to new ones, equally weighted, ready to restate yesterday as today. The two timestamps are what let a graph reason about change instead of just storing connections.

RAG vs. Knowledge Graph: Side-by-Side

The two approaches diverge on five dimensions that decide which one your AI needs. The table below shows where each one earns its place, drawn from a systematic benchmark across NQ, HotPotQA, and MultiHop-RAG datasets (arxiv.org).

The benchmark authors found that RAG and graph methods show complementary behaviors rather than a consistent winner. RAG wins on detail-oriented factual questions, and graphs win on multi-hop reasoning. Neither approach is universally better, so the real question is what your AI has to reason over. Stable documents with single-hop answers favor RAG. Changing, connected, org-wide state favors a graph.

When to Use RAG

Reach for RAG when your knowledge lives in stable documents and your questions are answered by a single passage. A product manual, a policy library, or an API reference fits this pattern well. You chunk the text, embed it, and retrieve the closest match at query time. Setup is cheap, and you skip the entity extraction and graph construction that a knowledge graph demands.

RAG holds up best on single-hop, detail-oriented factual questions. A systematic benchmark from researchers at Michigan State, Oregon, UT Arlington, Meta, and IBM found that RAG outperforms GraphRAG on single-hop, detail-oriented factual QA across NQ, HotPotQA, and similar datasets. When the answer sits in one place and you just need to find it, similarity search does the job without extra machinery.

Three conditions tell you RAG is enough. Your documents change infrequently, so stale retrieval is rarely a problem. Your scope stays narrow, so one query rarely needs to span many sources. And your questions stop at one hop, so no answer requires connecting a fact in Document A to a fact in Document B. Meet all three, and a knowledge graph adds cost without buying you anything. Break any one of them, and similarity search starts returning text that is close but wrong.

When to Use a Knowledge Graph

Choose a knowledge graph when your AI has to reason over org-wide state that keeps changing. The clearest case is an agent answering questions that no single document holds. "Which customers did the engineer who closed last quarter's biggest deal also support?" forces the system to connect records across separate sources, and similarity search cannot reliably bridge those hops because chunks lack the cross-document references the question depends on (neo4j.com). A graph links those entities at ingestion time, so the path already exists before the query arrives.

A graph also wins when the same person or account appears under different names across your tools. RAG reads "Sarah Chen in HubSpot, S. Chen in Gmail, @schen in Slack" as three separate people. Write-time identity resolution merges them into one node, so an agent reasons about one Sarah rather than three fragments.

Reach for a graph when facts expire and stale answers cause real harm. Bi-temporal invalidation records when a fact became true and when it stopped, so a deprecated price or a closed ticket never gets restated as current. RAG's flat embeddings weight yesterday and today equally.

The last condition is shared context. When many agents and teams need the same memory, one org-wide graph gives every reader the same resolved truth, instead of per-agent stores that drift apart over time.

GraphRAG: What the Hybrid Actually Buys You

GraphRAG borrows graph structure to fix RAG's multi-hop blind spot, and it works by traversing connected relationships after an initial vector or full-text search instead of relying on similarity alone. Researchers from Michigan State, Meta, and IBM identify four variants. KG-based GraphRAG extracts a knowledge graph and retrieves triplets through neighborhood traversal. Community-based GraphRAG, used in Microsoft's implementation, clusters the graph into hierarchical communities for local and global search. Text-centric methods like HippoRAG2 keep original chunks as the primary unit and let the graph guide scoring. Hierarchical summary methods like RAPTOR build multi-level summaries with no explicit graph at all.

A systematic benchmark across NQ, HotPotQA, and MultiHop-RAG found that the two approaches behave as complements rather than competitors. RAG wins on single-hop, detail-oriented factual questions. GraphRAG wins on multi-hop reasoning. Routing queries by type and combining evidence from both yields consistent gains over either alone, which is why GraphRAG belongs in your toolkit alongside RAG, not as a replacement for it.

The hybrid is not free. GraphRAG carries higher graph construction cost, more retrieval latency, and a larger storage footprint than standard RAG. Its accuracy also depends on the quality of the model that builds the graph. You buy multi-hop reasoning, and you pay for it in setup overhead and per-query expense.

How Sentra's Bi-Temporal Graph Works as a Memory Layer

Sentra sits underneath the tools you already run. It does not replace Cursor, Claude, Slack, or Glean. It gives them a shared memory through a single API, REST or MCP, so every agent and every teammate reads and writes to the same organization-wide graph instead of rebuilding context per session.

That memory is organized into three coordinated layers, and each one answers a question RAG cannot. Factual memory tracks what is true, where it came from, and when it changed. Action memory holds promises made, blockers, and follow-ups owed. Interaction memory records who said what and which perspective shaped a decision. Sentra resolves all three at write time, against a per-organization ontology, so meaning is settled before any query arrives rather than guessed at retrieval.

Identity resolution proves the write-time comprehension is operational, not theoretical. A RAG index reads "Sarah Chen" in HubSpot, "S. Chen" in Gmail, and "@schen" in Slack as three separate people. Sentra runs continuous, confidence-scored resolution across names, emails, handles, phone numbers, and internal IDs, so those three references collapse into one entity the moment they enter the graph.

Bi-temporal design is what keeps that resolved memory correct over time. Every fact carries two timestamps, when it became true and when it stopped being true. Old facts are invalidated, not deleted, so an agent can answer what is true now and still trace what was true last quarter. A flat vector store weights yesterday and today equally, which is exactly how a model restates a deprecated price or a cancelled deadline as current.

The KAIST MEME benchmark measures this directly, and the field is failing it. The average score sits at 3% on Cascade and 1% on Absence, the two categories the benchmark calls unsolved at practical cost. Sentra scores 40% on Cascade and 43% on Absence, the only system above 30% on both. The same write-time structure cuts retrieval work, which is how Sentra reaches roughly 88% on Terminal-Bench 2.1 while spending about 70% fewer tokens than agents that re-crawl context at query time.

RAG vs. Knowledge Graph vs. Sentra: Full Comparison

Read this as reference, not verdict. RAG suits stable document Q&A. A static graph adds structured reasoning. Sentra adds the temporal and identity layers that agents need to reason over changing org-wide state, and it sits underneath the tools you already run.

How to Choose

Match the architecture to what your AI reasons over, not to which approach sounds more advanced.

Choose RAG when your content sits in stable documents and your questions stay single-hop. A product manual, a policy archive, or a support knowledge base updates infrequently and rarely requires connecting facts across files. RAG answers these well at low setup cost, and a graph would be overkill.

Choose a knowledge graph when your questions span multiple documents and demand structured reasoning. Connecting employee records, tracing supply chains, or answering "which customers use feature X and renewed last quarter" needs the typed relationships a graph resolves before the query arrives.

Choose Sentra when agents must reason over changing, org-wide state. Support, sales, and engineering agents share one bi-temporal graph that knows when each fact became true and when it stopped, so none of them restate a deprecated price or a closed deal as current. Identity resolution links the same person across Slack, Gmail, and HubSpot, and write-time comprehension means meaning is settled at ingestion.

Sentra runs underneath the tools you already use. It connects to Claude, Cursor, ChatGPT, and 200+ sources through a single REST or MCP endpoint, serving as the memory layer for your agents rather than a replacement for them.

FAQ

Is GraphRAG better than RAG?

GraphRAG outperforms RAG on multi-hop, reasoning-intensive questions, while RAG wins on single-hop factual lookups, according to a systematic benchmark across NQ, HotPotQA, and MultiHop-RAG. The two behave as complements, not competitors. Routing queries by type or combining their evidence beats either alone.

Can you use RAG and a knowledge graph together?

Yes. GraphRAG does exactly this, starting retrieval with vector or full-text search and then traversing graph relationships to gather connected context. Production systems often compose RAG, a graph, and a persistent memory layer, since the failure mode is usually the quality of the underlying data, not the choice of component.

What is bi-temporal memory?

Bi-temporal memory tracks two timestamps for every fact: when it became true and when it stopped being true. Sentra invalidates old facts rather than deleting them, so an agent never restates a deprecated price or owner as current. A flat store of embeddings cannot do this, because old facts sit next to new ones with equal weight.

When does RAG fail?

RAG fails on questions that require connecting facts across separate documents, because similarity search retrieves text that is close to the query rather than the chain of facts that answers it. It also fails on freshness, since embeddings carry no notion of when a fact changed. A query like "did any former OpenAI employees start their own company?" breaks this way.

What makes Sentra different from a standard knowledge graph?

Sentra resolves entities and relationships at write time against a per-organization ontology, then adds bi-temporal invalidation and confidence-scored identity resolution across Slack, Gmail, HubSpot, and more. It scored 40% on MEME Cascade and 43% on Absence (KAIST, 2026), the only system above 30% on both. One graph serves every team and agent through a single REST or MCP interface.