Best AI Agent Memory Frameworks in 2026: Mem0 vs. Zep vs. Letta vs. Gateway-Native Memory

Persistent memory is presented as a core requirement for production AI agents because language models otherwise lose context between sessions, struggle with changing facts, and force users to repeat information. The comparison distinguishes Mem0’s vector-first retrieval and optional graph capabilities, which emphasize benchmarked accuracy and token efficiency; Zep’s Graphiti temporal knowledge graph, which tracks both when facts were true and when they were learned; Letta’s Git-backed, agent-managed filesystem memory within a broader agent runtime; and MintMCP’s gateway-native approach, which integrates memory with enterprise identity, access control, audit, and governance systems. Vendor benchmark results, including accuracy and latency claims, are not directly comparable unless models, datasets, retrieval settings, and evaluation methods are aligned. The discussion also emphasizes memory scoping for private, team, organizational, and customer contexts, along with security concerns such as poisoning, exfiltration, and corruption. For enterprise deployments, the selection should balance retrieval and temporal-reasoning needs against operational complexity, portability, versioning, auditability, compliance, and the ability to isolate memory across users and tenants.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.