Knowledge Graphs for LLMs: How They Work Together and When You Need One

Search for "knowledge graph LLM" and you will find two camps talking past each other. One says LLMs make knowledge graphs obsolete: why maintain a schema when a model can read everything? The other says LLMs are useless without a graph: how else do you stop them making things up? Both miss the point. The two technologies have opposite strengths, and most production systems that work well use them together.

What Each One Is Good At

A knowledge graph represents information as entities (people, products, documents, concepts) connected by typed relationships (works at, depends on, supersedes). It is explicit, inspectable, and queryable: you can ask it exactly which services depend on a library and get a complete, correct list.

An LLM represents information implicitly in its weights and reasons over whatever text is in its context. It is flexible and fluent: it can read a messy document and explain it, or draft a query it has never seen before. But it has no reliable store of specific facts, no notion of what is current, and no way to show its work.

Read the table one way and the graph is the memory; read it the other way and the LLM is the interface and the builder. That is the whole relationship.

Four Ways Knowledge Graphs and LLMs Work Together

1. The LLM builds the graph

Historically the hard part of knowledge graphs was construction: someone had to define a schema and populate it. LLMs changed that. Given a document, a model can extract entities, classify them, and propose relationships, turning unstructured text into graph triples at scale. Combined with an ontology that constrains what types and relationships are allowed, this produces graphs that are consistent enough to query and cheap enough to keep current.

This is how cognee builds memory: remember chunks the input, extracts entities and relationships with an LLM, validates them against an optional ontology, and writes nodes and edges to the graph alongside embeddings for every chunk and entity.

2. The graph grounds the LLM

The reverse direction is grounding. Instead of asking the model to answer from its weights, the system retrieves the relevant subgraph — the entities the question mentions and their neighbors — and puts that in the prompt. The model's job shrinks from "know the answer" to "explain these facts," which is what it is good at. Answers become traceable to specific nodes and edges, and when the graph is updated the answers update with it.

3. GraphRAG

GraphRAG is grounding made systematic. Standard RAG embeds text chunks and retrieves the ones most similar to the query. It works for questions whose answer sits in one passage and fails for questions that require connecting several: "which customers were affected by the outage caused by the change Maya shipped?" needs three hops that no single chunk contains.

GraphRAG retrieves along relationships as well as similarity. A query finds its entry points by vector search, then expands through the graph to pull in connected context, and the combined result is ranked and handed to the model. The difference shows up most on multi-hop questions and on questions about things that changed over time.

4. The graph as memory

For agents, the knowledge graph becomes long-term memory. Every session adds facts and relationships; contradictions are detected and resolved, so the graph reflects what is currently true while keeping the history; and retrieval is scoped to what the current task needs. This is the pattern behind agent memory systems, and it is why vector databases vs graph databases is a live question for anyone building one: vectors find similar text, but only a graph can represent that one fact supersedes another.

When You Need a Knowledge Graph With Your LLM

Not every LLM application needs a graph. The signals that you do:

• Multi-hop questions. Answers that require joining facts from several documents or sessions.
• Facts that change. Prices, owners, statuses, decisions. A graph can mark what is current; a pile of chunks cannot.
• Many entities with many relationships. Codebases, organizations, product catalogs, research corpora.
• Explainability. You need to show why an answer was given, not just that it was.
• Memory across sessions and agents. Anything that accumulates knowledge over time.

If your use case is single-document Q&A over static text, a vector index alone is simpler and sufficient. The graph earns its cost when the questions get connected.

What It Looks Like in Practice

With cognee, the extraction, grounding, and memory patterns are one pipeline:

import cognee

# LLM extracts entities and relationships into the graph
await cognee.remember(documents, dataset_name="engineering")

# Retrieval combines graph traversal and vector search, then grounds the answer
results = await cognee.recall(
    query_text="Which services depend on the auth library and who owns them?",
    datasets=["engineering"]
)

The graph lives in your choice of store — Kuzu, Neo4j, Neptune, or Postgres — and the same memory serves applications, agents, and coding tools through the MCP server. Benchmarks comparing graph-backed retrieval with plain vector RAG are in knowledge graph memory benchmarks.

Can an LLM replace a knowledge graph?+

No. An LLM stores facts implicitly and approximately and cannot reliably answer exact, complete, or multi-hop questions about specific data. It can, however, build and maintain a knowledge graph, which is the more useful relationship.

What is the difference between GraphRAG and a knowledge graph?+

A knowledge graph is the data structure. GraphRAG is a retrieval method that uses the graph, alongside vector search, to find connected context for an LLM to answer from.

How does an LLM build a knowledge graph?+

The model reads text in chunks and extracts entities, their types, and the relationships between them, often constrained by an ontology. The output is written as nodes and edges. cognee's remember runs this pipeline automatically.

Do I need a graph database to use a knowledge graph with an LLM?+

Not necessarily. cognee supports dedicated graph databases such as Neo4j and Kuzu, and can also run the full graph, vector, and relational memory on a single Postgres instance.