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# Supermemory vs Zep: Compare Memory for Your Agent Workload

Compare Supermemory, managed Zep, and Graphiti using the same workflow, evaluation conditions, and operating-cost assumptions.

By Shardul Mane April 6, 2026 · 5 min read 

![Supermemory vs Zep: Compare Memory for Your Agent Workload](https://supermemory.ai/_astro/cover.CpKUTSpt_2uLjl1.webp)

Supermemory and Zep both provide infrastructure for giving agents context beyond the current conversation. Compare them against the information your agent must use, the lifecycle of that information, and the work your team will operate. Distinguish Zep's managed service from Graphiti, its open-source graph framework, before comparing deployment or integration effort.

This comparison is published by Supermemory. Use the feature comparison to shortlist a deployment, then test it on your workload.

## Separate the products being compared

[Graphiti](https://help.getzep.com/graphiti/getting-started/overview) is a framework for temporal knowledge graphs. Zep provides a managed context platform built around graph-based memory. A feature or operating requirement of the framework should not automatically be attributed to the managed service.

Supermemory provides APIs for ingesting content, retrieving context, and maintaining user profiles. The exact deployment and feature availability should be checked against the current documentation and plan for the implementation you intend to use.

Write down which products and versions are in the pilot. “Zep versus Supermemory” is too vague if one side uses a managed service and the other uses a locally operated framework.

## Supermemory, Zep, and Graphiti at a glance

| Decision                        | Supermemory                                                      | Managed Zep                                               | Graphiti                                                                        |
| ------------------------------- | ---------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------------- |
| Starting point                  | Memory APIs, content ingestion, retrieval, and user profiles     | Managed graph-based context and memory                    | Framework for building temporal knowledge graphs                                |
| Persistent user context         | Documented profiles alongside retrieval                          | Documented asynchronously updated user summaries          | Evaluate the graph and context behavior you implement                           |
| Main pilot question             | Do supported ingestion and memory flows reduce application work? | Does the managed graph and context workflow fit the task? | Is operating and extending a graph framework part of the intended architecture? |
| Evidence needed before purchase | Required sources, scopes, processing behavior, and workload cost | The same requirements under the same test conditions      | Those requirements plus deployment and operating effort                         |

Start with Supermemory when ingestion, retrieval, and profiles match the application. Start with managed Zep when its graph-based context workflow matches it. Evaluate Graphiti separately when framework-level control is a requirement. Confirm current plan and deployment limits before committing.

## Both support persistent user context

Zep documents a [user summary](https://help.getzep.com/user-summary) derived from its user graph and updated asynchronously when new data arrives. Its default context block can include that summary.

Supermemory's [user profiles](https://supermemory.ai/docs/concepts/user-profiles) separate relatively stable information from recent context and can be used alongside targeted retrieval.

For either system, test a user who corrects a preference and starts a new conversation. Check what is returned before and after processing completes, and whether historical questions receive the appropriate earlier fact.

## Compare the same workflow

| Requirement              | What to test                                                               |
| ------------------------ | -------------------------------------------------------------------------- |
| Conversation continuity  | A returning user refers to a previous unresolved issue                     |
| Changing facts           | A new statement corrects an earlier one without losing unrelated context   |
| Document-heavy workflows | Real document formats, source references, refreshes, and deleted sources   |
| User and team scopes     | Personal facts and shared knowledge stay within authorized boundaries      |
| Operational behavior     | Retries, processing lag, outages, exports, and deletion                    |
| Cost                     | Ingestion, retrieval, model context, background work, and operating effort |

Check the integrations you actually need. “Has connectors” is less useful than testing whether your required source refreshes correctly and preserves the permissions your application relies on.

## Treat benchmark results as configuration-specific

Keep the dataset, answering model, judge, retrieval budget, and aggregation settings visible. A recall metric is not interchangeable with answer accuracy. A provider's mean search latency is not another provider's p95 end-to-end latency.

If you cannot align published conditions, use the results to identify questions for a pilot rather than to calculate a performance advantage. The [MemoryBench guide](https://supermemory.ai/blog/build-agent-memory-eval-harness/) describes how to structure a reproducible evaluation.

Do not infer guaranteed production performance from a result on one public dataset. Your customer histories, source formats, and concurrency can produce different behavior.

## Compare total operating cost

Price the same monthly workload using each provider's current plan and metering rules. Include any services that remain outside the selected product, and avoid assuming managed Zep requires the same operating work as a self-hosted Graphiti deployment.

Then add implementation and maintenance effort. A low API bill can coexist with substantial integration work; a higher service fee can be reasonable if it removes work your team would otherwise perform. The [build-versus-buy framework](https://supermemory.ai/blog/should-you-build-your-own-ai-memory-system/) provides an explicit model you can adapt.

## Plan a migration before committing

Inventory source conversations, documents, identifiers, timestamps, and permissions. Decide which derived records can be exported and which must be rebuilt. Preserve stable application identifiers where possible.

Run a representative subset through the target system and compare answers with the current implementation. Include changed facts, absent evidence, deleted content, and scope boundaries. Keep a rollback path while validating the new write and read flows.

Estimate the migration timeline after inspecting the source data and testing the required lifecycle operations.

## Choose on demonstrated fit

Consider Zep when its managed graph and context workflow matches the application you want to build. Consider Graphiti when operating and extending that framework is itself part of your intended architecture. Consider Supermemory when its ingestion, retrieval, profiles, and integrations reduce the work required by your workflow.

Test the strongest plausible implementation of each option. Choose the one that meets your quality, access, latency, and operating requirements, with tradeoffs you can explain and reproduce.

If Weaviate is also on the shortlist, compare [Weaviate Database, Engram, and Supermemory](https://supermemory.ai/blog/weaviate-ai-database-reviews-pricing-alternatives/) as distinct offerings before applying the same pilot.

Include Supermemory in your comparison: [open the console](https://console.supermemory.ai/) and run the same conversations, corrections, and permission tests you use for Zep. Compare the evidence and operating effort before choosing a provider.

## Other posts.

1. [We're open sourcing the company brain. Here's how we designed the multiplayer harness Company Brain is now open source. A walkthrough of the multiplayer harness behind its Slack experience, from proactivity and memory boundaries to approvals and recovery. NewsSep 25, 2026 ](https://supermemory.ai/blog/open-sourcing-company-brain)
2. [Jev changes a lot in memory & context engineering. Here's exactly how. We tested Jev across reranking, chunking, observation, and harness decisions. Here is where fast decision models help memory systems, where they cost more, and where they still fall short. EngineeringSep 24, 2026 ](https://supermemory.ai/blog/jev-memory-context-engineering)
3. [I reverse-engineered Instinct's memory. Here's exactly how it works Instinct keeps its memory as git-tracked markdown files, found with grep rather than vectors. Here is the whole system as far as black-box probing can reconstruct it, and how to rebuild it on supermemory in about 60 lines. EngineeringSep 20, 2026 ](https://supermemory.ai/blog/reverse-engineering-instinct-memory)
4. [An update to supermemory We've discontinued the supermemory company brain and Nova. Everyone who was charged has been refunded, our MCP and plugins continue to run, and we're going all in on the memory engine. NewsSep 10, 2026 ](https://supermemory.ai/blog/an-update-to-supermemory)
5. [Scaling Conversations: How Adapta Grew Usage Without Losing Context Adapta added Supermemory as a persistent memory layer so every conversation keeps its context — letting the team scale usage without losing the thread. Case StudyJun 12, 2026 ](https://supermemory.ai/blog/adapta-scaling-conversations)
6. [How Chatarmin Ditched RAG and Went Memory-Only with Supermemory Chatarmin replaced a heavy RAG pipeline with Supermemory's memory layer — cutting average AI response time from 40s to 12s and token usage by 40–50%. Case StudyJun 10, 2026 ](https://supermemory.ai/blog/chatarmin-memory-only)
7. [SMFS: making agentic retrieval 55% cheaper AND more accurate We launched SMFS.ai (Supermemory Filesystem) a few weeks ago, with a simple bet: We can redesign the filesystem specifically for agents, with special files, structures, and commands that it can use for it's tasks. Today, SMFS is used by hundreds of companies to power their agents. EngineeringMay 28, 2026 ](https://supermemory.ai/blog/smfs-making-agentic-retrieval-55-cheaper-and-more-accurate)
8. [Introducing Dynamic Dreaming: supermemory now connects the dots, for you. Dreaming is magical. TLDR: We're launching Dynamic Dreaming in supermemory today, which automatically works if you're using supermemory in any way - API, OpenClaw, Hermes agent, etc. EngineeringMay 25, 2026 ](https://supermemory.ai/blog/introducing-dynamic-dreaming-supermemory-now-connects-the-dots-for-you)
9. [Dear reader, we just made supermemory insanely cheap... the Context Cloud When I first started building supermemory, I had one goal: To build the best memory system for AI. I would talk to customers, and find out that memory was not the only thing they needed - They were all setting up 7-8 different vendors at the same time. EngineeringMay 18, 2026 ](https://supermemory.ai/blog/dear-reader-we-just-made-supermemory-insanely-cheap-the-context-cloud)
10. [Introducing @supermemory/tools v2.0.0 Today we're releasing v2.0.0\. This release unifies the API across all agents sdk integrations from AI SDK to Mastra, makes conversation identity a first-class concept, and ships with memory saving on by default. EngineeringApr 27, 2026 ](https://supermemory.ai/blog/introducing-supermemory-tools-v2-0-0)
11. [Solving the Precision-Recall Tradeoff: Search Result Aggregation When you're building memory for AI, search is your foundational layer. The way search generally works is straightforward: the user defines a query, and then sets a limit (top-K) on how many search results they want returned. Usually, this is set to 10 or 20. EngineeringApr 5, 2026 ](https://supermemory.ai/blog/solving-the-precision-recall-tradeoff-search-result-aggregation)
12. [OpenClaw Memory Problems: Why It Forgets and How to Fix It (2026) TLDR: Today, we are releasing a new version of our openclaw plugin - https://github.com/supermemoryai/openclaw-supermemory. This post is going to be a bit technical, so bear with me (or bookmark for later!) In this post, I will talk about what we do about OpenClaw memory, and how we fix it. EngineeringFeb 19, 2026 ](https://supermemory.ai/blog/why-everyone-is-complaining-about-openclaws-memory-it-sucks-and-why-supermemory-fixes-it)
13. [Stateful Coding Agents with Memory: Build Long-Running Agents (2026) We built a plugin for Claude Code and OpenCode that gives your coding agent persistent memory. It remembers your preferences, learns your codebase, and never loses context mid-conversation. The result is an agent you can run for months without starting over. EngineeringFeb 18, 2026 ](https://supermemory.ai/blog/infinitely-running-stateful-coding-agents)
14. [Clawd / Molt bot's memory SUCKS. We gave it supermemory. I'm the founder of supermemory. Clawd/Molt bot is blowing up right now, with many, many use cases. I set it up, too, and have been using it through telegram. TLDR: just go to https://supermemory.ai/docs/integrations/clawdbot to set up supermemory for your clawd bot. EngineeringJan 28, 2026 ](https://supermemory.ai/blog/clawd-molt-bots-memory-sucks-we-gave-it-supermemory)
15. [Catch up with our UNFORGETTABLE Launch Week Over the last year, one belief has guided almost everything we’ve built at Supermemory AI becomes meaningfully useful only when it remembers. Memory shouldn’t be something developers rebuild from scratch. It shouldn’t be fragile, expensive, or trapped inside a single tool. NewsJan 4, 2026 ](https://supermemory.ai/blog/catch-up-with-our-unforgettable-launch-week)
16. [Empowering the Next Generation of Founders: Supermemory Startup Program If there’s one thing we’ve learned while building Supermemory, it’s that most startups don’t fail because they didn't build features; they fail when infrastructure slows them down, or they built too slow. NewsDec 31, 2025 ](https://supermemory.ai/blog/empowering-the-next-generation-of-founders-supermemory-startup-program)
17. [Building code-chunk: AST Aware Code Chunking At Supermemory, we're building context engineering infrastructure for AI. A huge part of that is dealing with code: ingesting repos, understanding structure, and making it searchable. The problem is that most code chunking solutions are terrible. We built code-chunk to fix this. EngineeringDec 29, 2025 ](https://supermemory.ai/blog/building-code-chunk-ast-aware-code-chunking)
18. [Supermemory raises $3 million with the best memory engine for LLMs Today, I am excited to announce our first funding round to accelerate our mission of building an interoperable, scalable and reliable memory for LLMs and agents. Memory is one of the hardest challenges in AI right now. NewsOct 6, 2025 ](https://supermemory.ai/blog/supermemory-raises-3-million-and-building-the-best-memory-engine-for-llms)
19. [Mem0 vs Supermemory: Why Scira Switched Scira AI moved its production memory layer from Mem0 to Supermemory. This is what failed, what improved, and how the team evaluated the two systems. Case StudyOct 2, 2025 ](https://supermemory.ai/blog/why-scira-ai-switched)
20. [Never Record Again: How Montra Uses Supermemory to Rethink Video Creation Campbell Baron, the founder of Montra, has been making videos since he was twelve. By thirteen, he was already doing brand work. Today, he’s betting on a very different future for creators: a world where recording is the exception, and most videos are generated from scratch. Case StudyAug 21, 2025 ](https://supermemory.ai/blog/never-record-again-how-montra-uses-supermemory-to-rethink-video-creation)
21. [Unified Memory That Works Where You Work: Your Second Brain With Supermemory Hi everyone, I’m Dhravya, the founder of Supermemory. I want to start with a little story behind why this product means so much to me. You can also skip straight to what it is and how it works below. EngineeringJul 25, 2025 ](https://supermemory.ai/blog/unified-memory-that-works-where-you-work-your-second-brain-with-supermemory)
22. [Supermemory just got faster on PlanetScale What is Supermemory? Supermemory completes the missing part of the LLM puzzle: memory. Just as memory is crucial for human intelligence, it's essential for truly intelligent AI systems. EngineeringJul 18, 2025 ](https://supermemory.ai/blog/supermemory-just-got-faster-on-planetscale)
23. [Faster, smarter, reliable infinite chat: Supermemory IS context engineering. People are obsessed with prompts and prompt engineering. Sure, what you say is important, but what the model knows when you say it is the difference between a stateless text generator and an intelligent AI system. In short, context is the most crucial component. NewsJul 9, 2025 ](https://supermemory.ai/blog/faster-smarter-reliable-infinite-chat-supermemory-is-context-engineering)
24. [We solved AI API interoperability One API to rule them all, One spec to find them, One library to bring them all and in the TypeScript, bind them. When we were building the Infinite Chat API, initially, we only supported the OpenAI format. This was fine, until a lot of our customers started asking for more. EngineeringJul 7, 2025 ](https://supermemory.ai/blog/we-solved-ai-api-interoperability)
25. [The Wow Factor of Memory - How Flow Used Supermemory To Build Smarter, Stickier Products Overview: Flow is a note-taking app built around a bold vision: to create a more personal, context-aware writing experience powered by AI. At the heart of this mission is memory. Case StudyJun 14, 2025 ](https://supermemory.ai/blog/the-wow-factor-of-memory-how-flow-used-supermemory-to-build-smarter-stickier-products)
26. [The UX and technicalities of awesome MCPs Last month, we launched the Supermemory MCP, mostly to test our own infrastructure and get some initial traction. It blew up. To my absolute surprise, the initial launch itself got half a million impressions (!!!). Then, we launched and got #2 on ProductHunt too. EngineeringJun 8, 2025 ](https://supermemory.ai/blog/the-ux-and-technicalities-of-awesome-mcps)
27. [Architecting a memory engine inspired by the human brain Language is at the heart of intelligence, but what truly powers meaningful interaction is memory — the ability to accumulate, recall, and contextualize information over time. Large Language Models (LLMs) have mastered language, but memory remains their Achilles’ heel. EngineeringJun 5, 2025 ](https://supermemory.ai/blog/memory-engine)

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