Graphiti/Zep, Mem0 Graph Memory, and Cognee are the clearest documented options for connecting entities and relationships in AI-agent memory. They do not simply replace vector search: each combines graph structures with other memory or retrieval approaches, but the way those relationships affect what an agent retrieves differs.
What graph-based memory adds to vector search
A vector-only memory system finds stored items by embedding similarity: it returns memories that resemble a query in meaning. Graph-based memory also represents explicit entities and links between them—for example, which person belongs to an organization, who met whom, or how an event relates to a project. An agent can then use those connections to retrieve context that may not be expressed in wording similar to the query.
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The practical distinction is not “graph or vectors.” The platforms discussed here retain vector search in some form. Compare how each builds relationships, handles changing facts, and uses graph data during retrieval.
How the platforms differ
| Platform | How graph information is used | Deployment and storage |
|---|---|---|
| Graphiti / Zep | Temporal context graph; retrieval combines vector similarity, full-text search, and graph traversal. | Graphiti is an open-source framework with several listed graph backends. Zep also offers a separate managed service. |
| Mem0 Graph Memory | Graph-derived relations are returned alongside vector-search results; the documented behavior does not automatically reorder vector hits. | Graph nodes and edges use a graph backend, while embeddings use a configured vector database; several graph backends are listed. |
| Cognee | Knowledge graph is presented as a central structure for agent memory. | Documentation describes a self-hosted Python library and Cognee Cloud, as well as API and MCP access. |
Graphiti and Zep: temporal relationships and traversal
Graphiti, an open-source framework originated by Zep, turns conversations, business data, and documents into temporal context graphs containing entities, relationships, and timelines. Its description says new facts can invalidate outdated ones while retaining historical information. Retrieval combines vector similarity, full-text search, and graph traversal in a ranked answer.
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Graphiti lists Neo4j, FalkorDB, and Amazon Neptune as backends and describes an MCP server for MCP-compatible clients. The framework should be distinguished from Zep’s managed Context Lake, a commercial service that Zep says runs on Graphiti and its proprietary Konig graph database service. Zep also describes governance, SOC 2, HIPAA, and BYOC; those are vendor statements, so organizations considering a deployment should check current terms and documentation for their requirements.
Zep reports the following benchmark results on its product page; the page does not state a year for these figures:
| Benchmark | Accuracy | Retrieval latency | Context size |
|---|---|---|---|
| LoCoMo | 94.7% | 155 ms | 5,760 tokens |
| LongMemEval | 90.2% | 162 ms | 4,408 tokens |
These are Zep-reported results, not a neutral head-to-head ranking of the platforms in this article. Consult the methodology and full results linked from Zep’s page before interpreting the figures; the reviewed materials do not establish a common independent comparison across vendors. A 2025 Zep paper describes the temporal knowledge-graph approach for integrating conversations and business data while retaining historical relationships. It is an architecture and research source, not proof that all current managed-service behavior or performance remains unchanged.
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Mem0’s Graph Memory documentation describes extracting entities and relationships when memories are written. It stores embeddings in a configured vector database and graph nodes and edges in a graph backend. The documented retrieval flow uses vector search to narrow candidates, then returns related graph context alongside results.
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A key implementation detail: Mem0 says graph relations do not automatically reorder vector hits. That makes its documented approach useful for enriching retrieved memories with connected context, but it should not be described as graph-ranked search. The docs also describe scoping graph data with user, agent, and run identifiers, and allow graph behavior to be disabled for individual operations.
Supported graph-backend choices named in the documentation include Neo4j, Memgraph, Amazon Neptune, Kuzu, and Apache AGE. The embeddings remain associated with a configured vector database, so teams need to account for both storage roles.
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Cognee: a knowledge-graph memory with hosted and self-hosted paths
Cognee’s documentation describes turning documents and conversations into agent memory and identifies a knowledge graph as its central memory structure. It documents a self-hosted Python library and Cognee Cloud, with HTTP API and MCP access. TypeScript support and an experimental Rust SDK are also described.
The self-hosted path is intended for running locally or on a team’s infrastructure; Cloud is the managed option. Product packaging and SDK or hosting availability can change, so check the current documentation when selecting a deployment.
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Where Letta fits—and where the evidence stops
Letta’s documentation describes stateful agents with persisted state, editable memory blocks, and stored messages that remain retrievable beyond the context window. That is a useful contrast: persistent agent memory does not, by itself, establish graph-based concept association. The reviewed documentation does not establish graph association as a core Letta feature, so it should not be treated as a confirmed graph-memory platform on that basis.
How to choose a graph-memory layer
- Relationship construction: Check what the system extracts from conversations or documents, and how it represents entities and edges.
- Retrieval behavior: Determine whether graph traversal contributes to ranking, or whether relationships are returned as additional context alongside vector hits.
- Changing facts: If facts evolve, look for explicit handling of timelines, superseded facts, and historical state.
- Deployment and data control: Separate self-hosted framework options from a vendor-managed service; confirm current controls and terms for your environment.
- Backend flexibility: Compare supported graph stores with your existing infrastructure and account for the separate vector-storage role where applicable.
- Evidence quality: Treat vendor feature descriptions and benchmarks as vendor-reported. Benchmark figures are meaningful only with their named tests, methodology, and comparable conditions.
On the documented feature set, Graphiti/Zep is the strongest fit when temporal facts and graph traversal are central; Mem0 is explicit about adding related graph context beside vector hits; and Cognee presents a knowledge graph as its central memory structure with hosted and self-hosted deployment paths. The best fit depends on how the application needs relationships to influence retrieval and where its data must run.
Quick Recap
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