Problem

Zep AI addresses the context gap that makes AI agents forgetful, generic, and unreliable across multiple interactions. Agents need to combine chat history with business data, documents, and user behavior, but putting an entire conversation into every prompt breaks prompt caching and causes the full conversation to be re-billed on each turn. Static retrieval also struggles with facts that change over time, creating stale or contradictory answers and forcing developers to trade off accuracy, latency, and token cost.

The clearest killer use case is a production customer-facing agent—such as a support, sales, or personal assistant agent—that must remember a user’s preferences, prior decisions, and relationship with a business while responding quickly and using the latest information. Zep’s value is greatest where personalization and continuity directly affect conversion, resolution quality, or user trust.

Product / Service

Zep provides agent-memory infrastructure through a managed cloud product with free and credit-based paid plans, including Flex, Flex Plus, and Enterprise offerings. Its Context Graph ingests chat, business data, documents, JSON, and other events, then constructs a temporal knowledge graph of relevant entities, relationships, and facts. When information changes, Zep preserves the old fact as history while identifying what is currently true.

The system assembles prompt-ready Context Blocks containing the facts, summaries, and higher-level observations most relevant to the current task, rather than returning an indiscriminate document dump. Zep says retrieval remains below 200 milliseconds at large graph sizes, while its open-source Graphiti engine provides the underlying temporal context-graph framework. The intended benefit is more accurate and personalized agents with lower token usage, faster responses, and better governance across many agents and data sources.

Market

Zep competes in the emerging agent-memory and context-engineering infrastructure market: a layer between foundation models and agent applications that makes agents stateful across conversations, users, and business systems. S&P Global Market Intelligence describes the subsector as small but accelerating, with few pure-play vendors and limited standards. Mem0 is identified as the highest-profile direct competitor; other alternatives include Letta and Cognee, while Google Vertex AI Memory Bank and AWS AgentCore Memory represent platform-bound hyperscaler alternatives. Zep differentiates itself through enterprise focus, graph-native semantics, temporal reasoning, and neutrality across model ecosystems.

The company was founded in 2023 and entered YC’s Winter 2024 batch. Public traction includes the open-source Graphiti project reaching 20,000 GitHub stars in under 12 months, Zep’s reported use by teams from startups through Fortune 500 companies, and claimed Fortune 500 production deployments. A third-party directory estimates approximately $1 million of 2024 revenue, although that figure is not corroborated by an audited filing in the available evidence; the stronger conclusion is that Zep is beyond the prototype or clearly pre-revenue stage, with commercial plans and early enterprise adoption but limited publicly verified financial disclosure.