MIND vs Zep

A context-engineering platform for AI agents built on a temporal knowledge graph — automated context assembly from chat history and business data, with sub-200ms retrieval and enterprise deployment options.

A context-engineering platform for AI agents built on a temporal knowledge graph — automated context assembly from chat history and business data, with sub-200ms retrieval and enterprise deployment options.

Zep's pitch is context engineering, not memory storage: the problem it solves is assembling the right context for an agent's next LLM call, fast, from a temporal knowledge graph that tracks not just facts but when those facts were true. That temporal layer is genuinely differentiated — most memory tools store the current state of a fact and overwrite it; Zep, via the Graphiti engine underneath it, keeps the history, so an agent can reason about what used to be true and what changed. For agent builders who need low-latency, structured context at inference time, that's a real technical edge.

The credit-based pricing is worth reading carefully before comparing cost to MIND: Zep meters ingestion by byte count, roughly one credit per 350 bytes, which means the cost scales with how much you feed it, not with how much a person actually cares about. MIND doesn't meter your own memory that way — you're not billed per byte of what you tell it. The deeper difference, though, is who Zep is for: it's built for a team shipping an agent that needs fast, structured context, not for a person who wants one memory that follows them between Claude, ChatGPT, and whatever they use next year. Zep doesn't have a consumer surface at all; MIND's graph is the same one powering a phone app and a web app a person opens directly.

If you're already deep in agent infrastructure and specifically need temporal reasoning — what did we believe last month versus now — Zep and its open-source engine, Graphiti, are the more mature tool for that exact problem than MIND is. MIND doesn't market temporal fact-versioning as a feature; it markets being the memory a person and every model they use can share.

It's also worth being specific about the deployment tiers, since 'enterprise' means something concrete here rather than a vague upsell: BYOC on Zep's Enterprise plan means the graph itself runs inside a customer's own cloud account, which is the deployment model regulated industries typically require and which MIND's own enterprise/air-gapped offering targets from a different angle — MIND deploys the whole graph engine into a customer's perimeter rather than letting a customer host Zep's software on their own infrastructure. Both are real answers to a data-residency requirement; they're just structured differently.

Zep's context graph data isn't designed for bulk export into a general memory layer — it's built to be queried by an agent at inference time, not carried between systems. In practice, teams keep Zep, or self-host its open-source engine Graphiti, for an agent's real-time context needs and use MIND separately for the memory a human wants to review and direct across models.