For developers building AI agents, Zep is a memory layer built on a temporal knowledge graph that gives agents accurate, long-term recall of user context and past interactions.
Presence & Market Position
Tracked · below panel floor — earned mentions are accruing toward the next scored window.
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Zep is positioning itself as the context layer for agents that need persistent, time-aware memory instead of a rolling chat transcript. Its product agenda centers on temporal knowledge graphs, lineage, retrieval quality, and shared memory across custom and off-the-shelf agents. Broader conversation is early and currently partnership-led, focused on connecting agent memory to surrounding workflow infrastructure. That gives Zep a defined technical position in a category still forming around how agents retain business facts, handle changing information, and carry useful context between working environments.
Deepline
ColdIQ
Turbopuffer
Clearskies
CogneeSamsung, HoneyBook, Axtria, Torq, Twin Health, Thrive AI Health, Praktika.ai, Harper
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Business Profile
Founded in 2023, Zep is an independent, eight-person agent-infrastructure company with $0.5 million in funding. The company reached $1 million in ARR in 2024 and was valued at $2.3 million in April 2025. Its business is built around the memory and context problem inside agent applications, ingesting conversations and business data into a temporal graph and returning relevant history and current facts for each interaction.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Zep’s core product is agent infrastructure. Its API and SDKs support Python, TypeScript, and Go implementations, while its Memory MCP Server lets external agents retrieve and write project-scoped memory. Recent releases extend that surface with a coding-agent plugin that combines documentation access and implementation guidance. The product’s temporal graph and lineage model give agents a way to work with changing facts rather than static retrieved text.
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