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Graphiti, Mem0 Graph Memory, and Cognee are the clearest documented options for AI agent memory that connects entities and relationships, rather than relying on vector similarity alone. They still use vector retrieval in some form, but differ in how they build graph data and use it to retrieve context. Graphiti emphasizes temporal facts and graph traversal; Mem0 adds related graph context alongside vector results; Cognee centers its memory engine on a knowledge graph and documents both self-hosted and cloud deployment.
What graph-based concept association adds
A vector-only memory system finds stored items whose embeddings are semantically similar to a query. A graph-based layer also represents explicit entities and relationships: for example, who met whom, which person belongs to an organization, or how an event connects to a project. That structure can help an agent retrieve connected context that may not be expressed in the same words as the prompt.
In practice, these platforms are generally hybrid systems, not vector-search replacements. The meaningful distinctions are how they extract and update relationships, whether retrieval traverses those links, how they handle changing facts, and where the data runs.
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| Platform | How graph data is used | Deployment and storage notes |
|---|---|---|
| Graphiti / Zep | Graphiti describes combining vector similarity, full-text search, and graph traversal for retrieval, with temporal context and historical relationships. | Graphiti is an open-source framework and lists Neo4j, FalkorDB, and Amazon Neptune. Zep’s separate managed Context Lake is a commercial service built on Graphiti and its proprietary Konig graph database service. |
| Mem0 Graph Memory | Graph relations are returned as related context alongside vector-search results; the documentation says they do not automatically reorder vector hits. | Documentation names Neo4j, Memgraph, Amazon Neptune, Kuzu, and Apache AGE as graph-backend options. |
| Cognee | Cognee describes a knowledge graph as a central structure for turning documents and conversations into agent memory. | Documentation describes a self-hosted Python library and Cognee Cloud, with HTTP API and MCP access; TypeScript and an experimental Rust SDK are also documented. |
| Letta (contrast) | Documentation focuses on persisted agent state, editable memory blocks, and retrievable stored messages; it does not establish graph-based concept association as a core feature. | Persistent memory is not, by itself, evidence of graph retrieval. |
Graphiti and Zep: temporal context and traversal
Graphiti is an open-source framework originated by Zep. Its product description says it turns conversations, business data, and documents into temporal context graphs of entities, relationships, and timelines. It describes new facts invalidating outdated ones while retaining historical information, which is relevant when an agent must distinguish what was true before from what is true now. Its documented retrieval combines vector similarity, full-text search, and graph traversal.
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Graphiti is distinct from Zep’s managed Context Lake. Zep describes the latter as a commercial service running on Graphiti and its proprietary Konig graph database service. Its page also mentions governance, SOC 2, HIPAA, and BYOC; these are vendor statements, so verify current terms and deployment documentation before relying on them for compliance or procurement decisions. The same page lists Neo4j, FalkorDB, and Amazon Neptune as Graphiti backends and describes an MCP server for compatible clients.
Zep reports the following benchmark figures on its product page; the page does not state a year for them:
| Benchmark | Zep-reported accuracy | Zep-reported retrieval latency | Zep-reported context size |
|---|---|---|---|
| LoCoMo | 94.7% | 155 ms | 5,760 tokens |
| LongMemEval | 90.2% | 162 ms | 4,408 tokens |
These are vendor-reported results, not a neutral head-to-head ranking: the reviewed sources do not establish a common independent comparison across the platforms here. Zep’s page links to its methodology and full results; consult them before interpreting the figures. The 2025 Zep paper describes the temporal knowledge-graph approach, but an architecture paper does not establish that every current managed-service behavior or performance claim remains unchanged. Read the 2025 paper.
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Mem0 Graph Memory: relationship context beside vector hits
Mem0’s Graph Memory documentation describes extracting entities and relationships when memories are written, keeping embeddings in a configured vector database, and storing graph nodes and edges in a graph backend. The documented backend choices include Neo4j, Memgraph, Amazon Neptune, Kuzu, and Apache AGE.
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On retrieval, vector search narrows candidates and graph memory supplies related context alongside the results. The distinction matters: Mem0’s documentation explicitly says graph relations do not automatically reorder vector hits. It also describes scoping graph data with user, agent, and run identifiers, and allowing graph behavior to be disabled for individual operations. This makes the documented design a way to enrich vector results with associations, not a claim that graph edges control ranking.
Cognee: knowledge-graph memory with hosted and self-hosted paths
Cognee’s documentation describes turning documents and conversations into memory for agents, with a knowledge graph as a central memory structure. Its documented deployment choices include a self-hosted Python library and Cognee Cloud; the docs also describe HTTP API and MCP access. TypeScript and an experimental Rust SDK are listed as well.
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The key practical distinction is where the system runs: the documentation describes local or team-infrastructure deployment separately from the managed cloud service. Confirm the current packaging and SDK or hosting availability in Cognee’s documentation before choosing an integration path, since these options can change.
How to choose a graph-memory layer
- Choose by retrieval behavior: If the design needs graph traversal integrated with vector and full-text retrieval, Graphiti’s documented approach is the closest match. If the requirement is to attach related graph context to vector hits without automatically reordering them, Mem0 documents that behavior directly.
- Prioritize changing facts: Graphiti explicitly describes temporal relationships, invalidation of outdated facts, and historical context.
- Check deployment and data control: Graphiti is presented as an open-source framework, Zep as a separate managed service, and Cognee documents both self-hosted and cloud paths. Review each product’s current operational and contractual terms.
- Check graph-backend fit: Graphiti and Mem0 document multiple graph backends, which may matter if you already operate a graph database. Confirm compatibility and versions in the current documentation.
- Evaluate evidence carefully: Vendor feature descriptions explain intended behavior, not independent validation. Zep publishes benchmark figures, but the reviewed sources do not show a shared independent test across these platforms.
Is Letta a graph-memory platform?
Not on the evidence in the reviewed documentation. Letta’s documentation describes stateful agents with persisted state, editable memory blocks, and stored messages that remain retrievable beyond the context window. Those are persistent-memory capabilities, but the documentation does not establish graph-based concept association as a core feature. Treat it as a contrast rather than a confirmed graph-memory option.
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