MCP Memory Server Comparison: Engram vs Mem0 vs Zep

AI agents are only as useful as what they remember. Today, most agent frameworks treat every session as a blank slate. The context window fills up, the conversation resets, and weeks of accumulated knowledge vanish. Memory servers exist to fix this problem, but they take fundamentally different approaches to what gets stored, how it gets searched, and how it integrates with the tools your agent already uses.

This post compares three production-ready options: Mem0, Zep, and Engram. We build Engram, so we have an obvious bias. We'll try to be fair anyway, because the right choice depends on what you're building.

Why memory matters for AI agents

Large language models have a hard ceiling: the context window. Even models with 128k or 200k token windows hit practical limits fast when an agent runs across multiple sessions, multiple days, or multiple users. Every time a session ends, the slate is wiped. The agent forgets the debugging session from last Tuesday, the architecture decision from last month, and the user preference established three conversations ago.

The workarounds are familiar. You can stuff a system prompt with hand-written notes. You can append a “memory” section to every message. You can build a RAG pipeline from scratch and maintain it alongside everything else. None of these scale. A dedicated memory server gives agents a persistent, searchable store that survives session boundaries and grows over time.

The question is not whether you need memory. It's what shape the memory should take.

Three approaches to agent memory

The three products in this comparison represent three distinct philosophies about what “memory” means for an AI agent.

Mem0: Summarized facts

Mem0 intercepts conversations and extracts structured facts. If you tell your agent “I prefer TypeScript over Python for backend work,” Mem0 stores something like { "preference": "TypeScript over Python for backend" }. The original conversation is discarded; the extracted fact is what persists. This keeps storage compact and retrieval focused. Mem0 integrates primarily through REST APIs and OpenAI-compatible function calling, and offers both a hosted cloud and a self-hosted option.

Zep: Structured knowledge graphs

Zep takes extraction further. Instead of flat facts, it builds a knowledge graph from conversations — entities, relationships, and temporal context. If your agent discusses a project involving three team members and two services, Zep can represent those as nodes and edges with timestamps. This gives agents structured reasoning over relationships, not just keyword-matched facts. Zep offers a hosted cloud service and integrates through REST APIs and SDKs for popular frameworks like LangChain.

Engram: Verbatim transcripts

Engram stores the full, unmodified text of every conversation. Nothing is summarized, extracted, or discarded. When an agent searches memory, it gets back the original messages — the exact words, the full context, the nuance that summaries lose. Search is semantic (meaning-based), powered by vector embeddings over message chunks. Integration is MCP-native: any client that speaks the Model Context Protocol — Claude Desktop, Claude Code, Cursor, Windsurf, Zed — connects with a single config block. There is no self-hosting; Engram runs as a hosted service on Cloudflare's edge network.

Feature comparison

For a deeper technical breakdown, including latency benchmarks and integration code samples, see the full comparison in our docs.

When to pick each

Pick Mem0 if you want extracted facts and OpenAI integration

Mem0 is a strong choice if your agent pipeline is built around OpenAI function calling and you want a memory layer that fits naturally into that ecosystem. The fact-extraction model works well for use cases where you need compact, structured preferences — things like user settings, stated goals, or repeated instructions. If your agents mostly need to recall “what did the user say they prefer?” rather than “what was the full context of that decision?”, Mem0's approach keeps things lean.

The self-hosted option is also a real advantage for teams with strict data-residency requirements. You can run the open-source core on your own infrastructure and keep everything behind your firewall.

Pick Zep if you need structured knowledge graphs

Zep stands out when your agent needs to reason about relationships, not just recall individual facts. If you're building an agent that manages complex projects, tracks interactions between multiple people, or needs to answer questions like “which team members have worked on both Project A and Project B?”, the knowledge-graph model gives you something that flat fact stores and raw transcripts can't. The temporal awareness — knowing when relationships were established and whether they're still active — adds another dimension to retrieval.

Zep also integrates well with LangChain and similar orchestration frameworks, so if your stack already lives there, the friction is low.

Pick Engram if you want verbatim memory with MCP-native search

Engram is built for teams that use MCP-compatible clients and want memory to work the same way other MCP tools work — one config block, no SDK, no REST glue code. The verbatim storage model means nothing is lost during ingest: no extraction step, no LLM inference on the write path, no decisions about what counts as a “fact.” The original conversation is the memory.

The tradeoff is that Engram is hosted only. There is no self-hosted option. If you need to run memory on your own servers, Mem0 or Zep are better fits. But if you want zero operational overhead — no database to manage, no embedding pipeline to maintain, no infrastructure to scale — the hosted model means setup is a one-minute config change and you're searching past conversations immediately.

The verbatim advantage

The deepest architectural difference between these products is what happens to your data on the way in. Mem0 and Zep both run an LLM over incoming conversations to extract structured representations. Engram stores the raw text and embeds it for semantic search. This is not just a storage detail — it changes what you can get back out.

Extraction is lossy by design. When an LLM summarizes a conversation into facts or graph nodes, it makes decisions about what matters. Those decisions are usually reasonable, but they are irreversible. The hedging in “I think we should probably use Postgres, but I'm not sure about the JSONB performance for our specific query shape” might become the confident fact “user prefers Postgres.” The five-minute debugging detour that led to an important insight might not produce any extractable fact at all, so it disappears entirely.

Verbatim storage preserves everything. The uncertainty, the reasoning, the tangents that turned out to matter, the exact phrasing someone used. When an agent retrieves a verbatim memory, the model receiving it can make its own judgment about what's relevant — with the full original context, not a pre-digested summary. Six months from now, when someone asks “why did we decide against MongoDB?”, the answer is the actual conversation where the decision happened, not a one-line fact that lost the reasoning.

The cost argument against verbatim storage — that it takes more space than extracted facts — is real but increasingly irrelevant. Storage is cheap and getting cheaper. Embeddings are cheap and getting cheaper. The computational cost of running an extraction LLM on every ingest, on the other hand, adds latency, complexity, and spend on the write path. Engram skips that entirely: messages come in, get embedded, get indexed. No extraction step, no LLM inference on writes, no decisions about what to keep and what to discard.

A note on MCP-native vs. REST

The Model Context Protocol is an open standard for connecting AI agents to external tools and data sources. If your agent runs inside an MCP client — Claude Desktop, Claude Code, Cursor, Windsurf, Zed, among others — an MCP-native memory server means the agent can store and search memory using the same tool-calling interface it uses for everything else. There is no separate SDK to install, no REST client to configure, no authentication flow to wire up. The memory server is just another tool in the agent's toolbox.

Mem0 and Zep integrate through REST APIs, which means you need application code between your agent and the memory service. That's fine if you're building a custom agent framework — you were writing glue code anyway. But if you're using an MCP client out of the box, the REST integration adds a layer that MCP-native tools skip entirely.

Try Engram free

Engram has a free tier with no credit card required. Setup takes about a minute: generate an API key at getengram.app, add the config block to your MCP client, and your agent has persistent, searchable memory across every session.

If you want the full technical details on how Engram compares at the implementation level — latency numbers, integration code, storage architecture — the comparison docs go deeper than this post. The architecture page covers the Cloudflare Workers stack underneath, and the whitepaper covers the product patterns that verbatim memory enables.

Memory is the difference between an agent that starts fresh every session and one that builds on everything that came before. The right memory server depends on your stack, your integration model, and how much of the original conversation you want to preserve. We think verbatim wins in the long run, but the best way to find out is to try it.

Written by the Engram team. Published June 14, 2026. Corrections and feedback: hello@getengram.app.