Zep AI logo

Zep AI

Emerging

SF AI context engineering platform providing agent memory and Graph RAG for persistent LLM recall across sessions; YC W24 $3.3M reaching $1M revenue competing with Mem0 and LangChain for AI agent memory infrastructure.

Best for: Cloud ServicesEmerging, rapid growth
26
AI Score
Grade D↑ Trending
AI Visibility Score (Beta)
Cloud & InfrastructureCloud ServicesWebsiteUpdated March 2026

Brand Intelligence Graph

Capabilities
Cloud Services

Company Overview

About Zep AI

Zep AI is a San Francisco-based AI context engineering platform — backed by Y Combinator (W24) with $3.3 million raised from Engineering Capital, Step Function, and investors from Vercel and Google — providing AI application developers with agent memory, graph-based retrieval, and context assembly tools that enable AI agents to remember user history, recall relevant facts from previous interactions, and maintain long-term persistent state across multi-session workflows. Founded in 2023 and generating $1 million in revenue by June 2024 with a 5-person team, Zep AI addresses the fundamental challenge in production AI agent deployment: LLMs are stateless (each conversation starts fresh with no memory of prior interactions) while users expect agents to remember preferences, past decisions, and conversation history across sessions.

Business Model & Competitive Advantage

Zep AI's context engineering platform provides three core capabilities: Agent Memory (storing structured facts extracted from conversations — user preferences, decisions made, key entities mentioned — in a graph database that enables semantic retrieval of relevant memory at inference time), Graph RAG (retrieval-augmented generation that navigates the entity relationship graph rather than performing flat vector similarity search — enabling reasoning about how concepts relate rather than just finding similar text), and Context Assembly (dynamically assembling the most relevant memories, facts, and context into the LLM prompt for each query rather than naive retrieval of recent messages). The graph-based memory representation enables queries like 'what has the user said about their budget preferences?' to retrieve relevant facts from multiple prior conversations rather than returning only the most recent message about budget.

Competitive Landscape 2025–2026

In 2025, Zep AI competes in the AI agent memory, LLM context management, and RAG infrastructure market with LangChain Memory (open-source memory abstraction in the LangChain framework), Mem0 (personalized AI memory layer, $8M raised), and MemGPT (Berkeley research project commercialized into agent memory) for AI agent developer mindshare and enterprise production adoption. The agent memory problem is one of the most actively researched challenges in production AI deployment — users of LLM applications consistently cite the lack of persistent memory as the primary limitation of chatbots and AI assistants. Zep's $1M revenue at 5 employees in the first year reflects strong product-led growth from the developer community. Y Combinator W24 backing connects Zep with the AI application developer ecosystem at the right adoption inflection point. The 2025 strategy focuses on growing enterprise adoption of Zep for production AI customer service agents (where session-to-session user memory is most critical), building the multi-agent memory sharing (shared memory graphs across specialized agents in a multi-agent system), and expanding the analytics layer for understanding what users remember and what agents learn over time.

Founded
2023
Revenue
$1M
Curated content • Fact-checked and verified

Recent Activity

View all →
8-K
8-K — 8-K

Material Event filed 2026-05-12

blog_post
Zep's 5 Context Types: How to Use and Combine Each One

Zep produces five distinct types of context from a user's graph. Each captures something different. Here's when to reach for each, and how to combine them in one prompt.

blog_post
Context You Can Trace, Filter, and Trust

Every fact in your agent's context graph came from somewhere. Zep's provenance architecture traces facts back to their source data — and lets you filter retrieval by origin.

blog_post
Stop Letting Your Agent Decide What It Needs to Know

Your agent has the tools. It just doesn't call them — and smarter models won't fix that. Here's why the unknown unknowns problem is the hardest challenge in agent context, and what to do about it.

8-K
8-K — 8-K

Material Event filed 2026-03-25

blog_post
3 Decisions That Shape Every Agent's Context Architecture

Every agent context architecture comes down to three decisions: scope, data sources, and retrieval strategy. A framework for reasoning about persistent context for AI agents.

blog_post
Evaluation and Control: Evaluation Framework, zepctl CLI, and Dashboard Overhaul

An evaluation framework for testing Zep against your data, a redesigned dashboard with analytics, and zepctl — a CLI for administering Zep projects.

blog_post
Build Better Context Graphs: Custom Instructions, Search Filters, and Webhooks

Custom extraction instructions, property-level search filters, exclusion filters, and webhooks — more control over how you build and query context graphs.

Key Differentiators

Emerging Innovator

Zep AI is an emerging player bringing innovative solutions to the Infrastructure market.

Frequently Asked Questions

Estimated Visibility Trend (Beta)

Simulated 8-week rolling score

26
↑ Trending

Based on estimated brand signals. Historical tracking coming soon.

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