Why look for a Zep alternative?

The two reasons teams cite most are cost shape and operational weight. Zep meters by Episode (each ingested message or batch), so a chatty product makes the bill move with usage rather than with value. And Zep's memory is a temporal knowledge graph you query and, on self-host, operate. CognitiveX charges a flat $20/mo, makes storing memories free, and is a hosted MCP server with no graph to run.

Is CognitiveX a drop-in replacement for Zep?

CognitiveX is exposed over the Model Context Protocol, so wherever you call a memory tool today you point that tool at CognitiveX instead. You write memories on each turn and recall them with a query. There is no graph schema to model and no episode pipeline to wire up. The LLM stays swappable.

Does CognitiveX have a temporal knowledge graph like Zep?

No, and that is deliberate. Zep's bet is that the right structure is an explicit time-versioned graph of facts. CognitiveX's bet is that the right structure is a living memory with four tiers, salience, and consolidation. If you specifically need to traverse a graph of entities and valid-time edges, Zep is the better fit. If you want a memory that learns what a user repeats, CognitiveX is.

How does CognitiveX pricing compare?

CognitiveX is flat: Free (100 recall credits/mo, 1k memories), Awakened $20/mo or $199/yr (500k credits, 1M memories), Conscious $200/mo (15M credits, unlimited), plus metered pay-as-you-go. Recall credits scale by depth (foundational 1, standard 3, deep 10) and storing memories is always free, so the cost tracks how much you read back, not how many episodes you ingest.

COGNITIVEX · ALTERNATIVE

The Zep alternative: CognitiveX

Zep gives your agent a temporal knowledge graph and meters it per Episode. CognitiveX gives it a living memory for a flat $20/mo, reached over MCP, with no graph to operate. Here is the honest comparison, and how to switch.

THE SHORT ANSWER

A flat, MCP-native memory that learns.

If you are evaluating a Zep alternative, the decision usually comes down to two things: how you pay, and how much you operate.

Zepis a good product. It builds a temporal knowledge graph (Graphiti) from your conversation history, time-stamps the facts it extracts, and lets you query them with valid-time semantics. That is genuinely useful when your app needs to answer “what was true when,” and Zep does it well. The two costs that send teams looking elsewhere are per-Episode metering, where the bill moves with every message or batch you ingest, and the fact that the memory is a graph database you query, model, and (on self-host) keep healthy.

CognitiveX takes the operational and pricing weight off the table. Storing memories is free; you pay a flat $20/mo and recall credits scale with how deep you read, not how much you ingest. There is no graph to provision. It is a hosted memory you reach over the Model Context Protocol. And unlike a fact store, it learns: it scores salience, detects repeated patterns, and consolidates overnight.

SIDE BY SIDE

Zep vs the CognitiveX LCM.

Two design bets: an explicit temporal graph you query, or a living memory that learns and consolidates.

Capability	Zep	CognitiveX LCM
Core model	Temporal knowledge graph (Graphiti) over chat history	Cognition engine: living memory that learns
Pricing shape	Metered per Episode / message ingested + storage	Flat $20/mo (Awakened); storing memories is free
What you operate	A graph database to provision, index, and keep healthy	Nothing. A hosted MCP server you point a client at
How you connect	SDK + REST API	MCP-native (one endpoint, any MCP client)
Memory types	Graph nodes, edges, facts (with valid-time)	Semantic · episodic · procedural · foundational
Learns repeated patterns	No. Extracts and time-stamps stated facts	Yes. Salience scoring + pattern detection
Episodic → semantic promotion	No (facts are versioned, not consolidated)	Yes. Overnight dream consolidation
Cross-agent recall	Via your own API / graph	MCP-native, shared across every agent
Best for	Apps that need a queryable temporal fact graph	Memory that learns a user and consolidates over time

WHAT ACTUALLY DIFFERS

Per-Episode credits vs flat. Graph vs living memory.

Pricing you can predict

Zep meters per Episode, so every ingested message moves the bill.
CognitiveX is flat $20/mo; storing memories is free.
Credits scale by recall depth, not by ingestion volume.

Nothing to operate

Zep's memory is a temporal graph DB you model and maintain.
CognitiveX is a hosted MCP server, with no schema and no indexing.
Point any MCP client at one endpoint and write memories.

Memory that learns

Four tiers: semantic, episodic, procedural, foundational.
Salience + pattern detection surface what a user repeats.
Overnight consolidation promotes episodes into preferences.

THE DIFFERENCE

The memory is the model.

Zep, at heart, is storage with retrieval: a sophisticated, time-aware version of it. You ingest episodes, it extracts and versions facts into a graph, and you read those facts back. The graph sits still between calls.

The CognitiveX Large Cognition Model (LCM) adds a loop on top of storage. Every interaction is remembered, reflected on, reasoned over, and learned from, and that learning rewrites the memory the next query reads. Concretely, the LCM ships four memory tiers (semantic, episodic, procedural, foundational), pattern detection, salience scoring, and overnight dream consolidation that promotes recurring episodes into durable semantic preferences and decays what no longer matters.

So where Zep asks “what fact was true, and when,” the LCM asks a different question: what has this user repeatedly shown me, and how should that change what I recall next time? That is the axis a fact graph leaves out: not better storage, but a memory that consolidates and evolves. And because the LLM is infrastructure in this design, swappable rather than the identity, you keep your model of choice.

MIGRATING FROM ZEP

Switching is a swap, not a rewrite.

Because CognitiveX is MCP-native, you replace the place you call Zep, not your whole memory layer.

Point a client at CognitiveX. Connect the hosted MCP server (or the HTTP API) the same way you connect any MCP tool. No graph DB to stand up.
Replace ingest with remember.Where you called Zep’s add / episode endpoint, call remember with the content and a memory type (semantic, episodic, procedural, or foundational). Storing is free.
Replace search with recall. Where you queried the graph, call recall with a natural-language query and a depth. You get back ranked memories, with no Cypher and no traversal to write.
Backfill history at your pace. Re-ingest existing transcripts as episodic memories whenever you like; consolidation will promote the recurring ones into semantic preferences on its own. There is no per-episode meter ticking while you migrate.

The full walkthrough lives in the developer docs, and your LLM stays swappable the whole way.

COMMON QUESTIONS

Quick answers.

Should I use Zep or CognitiveX?

Use Zep when you specifically need a temporal knowledge graph: entities and facts with valid-time edges you can traverse and reason over historically. Use CognitiveX when you want a flat-priced, hosted memory that learns what a user repeats and consolidates it over time, reachable by every agent over MCP.

Is there per-episode pricing in CognitiveX?

No. Storing memories is free. You pay a flat plan and spend recall credits when you read memory back, scaled by depth (foundational 1, standard 3, deep 10). The cost tracks reads, not ingestion volume.

Do I have to operate a database?

No. CognitiveX is a hosted MCP server. There is no graph to provision, index, or keep healthy. You connect a client and start writing and reading memories.

KEEP READING

Want the head-to-head with another option? See Mem0 vs Zep, browse the full list of CognitiveX alternatives, or read how the Large Cognition Model reframes memory entirely.

Start building → Try iCog →