Zep

Agent Memory | Graph RAG | Context Assembly . Systematically engineer relevant context from chat history & business data.

Agent Infrastructure

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Founded

2023

Location

San Francisco, CA

Employees

Funding

OSS

Zep — Memory Layer for AI Agents

**Zep** provides an AI agent memory platform that engineers relevant context from chat history and business data so agents respond with up‑to‑date, personalized information. It combines three core capabilities—**Agent Memory**, **Graph RAG** powered by a temporal knowledge graph, and **Context Assembly**—as a modern alternative to long prompt stuffing or pure vector search. Explore the [homepage](https://www.getzep.com).

Positioning: Replace brittle long-context prompts and embedding-only recall with structured, time-aware memory

Availability: Cloud and open source, with SDKs, APIs, and a [free tier](https://www.getzep.com/pricing)

Proof points: Technical blogs, research posts, and benchmarks; active GitHub organization

How It Works

Zep extracts entities, facts, and relationships from interactions and writes them to a **temporal knowledge graph**, then retrieves only what matters for each request.

Entity and relationship extraction from user-agent conversations and data sources

Time-aware graph updates to track evolving user state, preferences, and recent events

Targeted retrieval for compact prompts and higher-relevance answers

Blends static knowledge via RAG with dynamic, user-specific memory via graph semantics

Dive deeper:

Product: [Agent Memory](https://www.getzep.com/product/agent-memory)

Product: [Graph RAG](https://www.getzep.com/product/graph-rag)

Concept docs: [Concepts](https://help.getzep.com/concepts) and [Quickstart](https://help.getzep.com/quickstart)

Architecture and approach: [“Stop using RAG for agent memory”](https://blog.getzep.com/stop-using-rag-for-agent-memory) and [Temporal KG architecture](https://blog.getzep.com/zep-a-temporal-knowledge-graph-architecture-for-agent-memory)

Key Capabilities

Agent Memory: Persistent, structured memory for user-specific context across sessions

Graph RAG: Temporal knowledge graph retrieval for dynamic and time-sensitive data

Context Assembly: Automated selection of only the most relevant facts to minimize token usage

Developer Experience: Clear quickstarts, examples, and SDKs; see [Docs](https://help.getzep.com) and [GitHub](https://github.com/getzep/zep)

Enterprise Readiness: Security emphasis, production-friendly APIs, and pricing clarity on the [pricing page](https://www.getzep.com/pricing)

Use Cases

Customer support agents that recall ticket history, outcomes, preferences across sessions

Sales assistants that remember accounts, next steps, objections, and meeting context

Internal copilots that preserve task state and decisions over days or weeks

Consumer assistants with persistent profiles and evolving preferences

Agentic orchestration that needs structured state and temporal reasoning

Who It’s For

Teams shipping production AI agents that require persistent, personalized memory

Product and support teams integrating CRM/ticketing context into agent workflows

Developers who want graph-based retrieval for dynamic state vs. embedding-only search

Integrations and SDKs

SDKs: [JavaScript](https://github.com/getzep/zep-js), [Python](https://github.com/getzep/zep-python), [Go](https://github.com/getzep/zep-go)

Frameworks: [LangChain memory integration](https://js.langchain.com/docs/integrations/memory/zep_memory)

Examples and OSS: [Zep GitHub](https://github.com/getzep/zep) and [Graphiti framework](https://github.com/getzep/graphiti)

Developer Experience

Quick start: [Zep Quickstart](https://help.getzep.com/quickstart)

Concepts and guides: [Docs Hub](https://help.getzep.com)

Community and repos: [GitHub org](https://github.com/getzep)

Research and benchmarks: [State of the art in agent memory](https://blog.getzep.com/state-of-the-art-agent-memory) and a critical view of competing claims

Community discussion:

Interest around open-sourced graph memory and Graph RAG: [r/LLMDevs thread](https://www.reddit.com/r/LLMDevs/comments/1fq302p/zep_opensource_graph_memory_for_ai_apps) and [r/Rag discussion](https://www.reddit.com/r/Rag/comments/1kjgd8j/lightgraph_vs_graphitizep_or_else)

Benefits

Higher recall quality for dynamic user state via a temporal knowledge graph

Smaller, more relevant prompts with automatic context assembly

Strong DX: SDKs, quickstarts, examples, and active OSS footprint

Flexible deployment: cloud and open source, with a free tier for evaluation

Tradeoffs

Setup and configuration can be heavier than simple vector memory for small apps (see [r/Rag discussion](https://www.reddit.com/r/Rag/comments/1kjgd8j/lightgraph_vs_graphitizep_or_else))

Background processing and token usage overhead may require tuning in certain stacks (see [anecdotal discussion](https://www.reddit.com/r/ClaudeAI/comments/1m1af6a/3_years_of_daily_heavy_llm_use_the_best_claude))

Limited third-party review presence on G2/Capterra; most validation is via GitHub, blogs, and Reddit (e.g., [community thread](https://www.reddit.com/r/LLMDevs/comments/1fq302p/zep_opensource_graph_memory_for_ai_apps))

Pricing and Free Tier

Zep offers a free tier; details on the [pricing page](https://www.getzep.com/pricing)

Company Snapshot

Company: [Zep](https://www.getzep.com)

Focus: Agent Memory, Graph RAG, Context Assembly

YC: Winter 2024 (per [LinkedIn](https://www.linkedin.com/company/zep-ai))

Team size: Approximately 7 employees (per [LinkedIn](https://www.linkedin.com/company/zep-ai))

Docs: [Docs Hub](https://help.getzep.com), [Quickstart](https://help.getzep.com/quickstart), [Concepts](https://help.getzep.com/concepts)

Product pages: [Agent Memory](https://www.getzep.com/product/agent-memory), [Graph RAG](https://www.getzep.com/product/graph-rag)

Additional Resources

OSS repos: [Zep org](https://github.com/getzep), [Graphiti](https://github.com/getzep/graphiti)

Blog and research: [Stop using RAG for agent memory](https://blog.getzep.com/stop-using-rag-for-agent-memory), [Temporal KG architecture](https://blog.getzep.com/zep-a-temporal-knowledge-graph-architecture-for-agent-memory), [SOTA in agent memory](https://blog.getzep.com/state-of-the-art-agent-memory)

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