Agent Runtime
LangChain·LlamaIndex·CrewAI·Google ADK·custom
Any agent framework — or none. The Context Lake is invoked through a single SDK.
Zep vs. Vertex AI Memory Bank
A neutral, multi-cloud alternative to Memory Bank
Vertex AI Memory Bank is Google's managed memory service for agents in Vertex AI Agent Engine. Zep is a neutral, multi-LLM, multi-cloud Context Lake that manages, governs, and serves agent memory on temporal context graphs.
Key takeaways
A hyperscaler primitive, or a neutral memory layer
- Vertex AI Memory Bank (docs) is bound to Google Cloud/Vertex; Zep is a neutral, multi-LLM, multi-cloud Context Lake.
- The decision is lock-in vs. neutrality: keep one memory strategy across Gemini, OpenAI, Anthropic, and Meta.
- Zep runs managed, BYOK, or BYOC on AWS/GCP/Azure, builds bi-temporal context graphs, and reports 94.7% LoCoMo and 90.2% LongMemEval (results).
The distinction
A Google Cloud primitive vs. a neutral layer
What Vertex AI Memory Bank is. Memory Bank is part of Google's Vertex AI Agent Engine. It extracts and stores memories for agents built on Vertex, and surfaces them back at run time, integrated with the Google Cloud agent stack. For teams standardized on Vertex/GCP, the native integration is the draw.
What Zep is. Zep is a dedicated, neutral memory layer — the Context Lake for AI agents. It builds bi-temporal context graphs from chat, business data, and documents (open-source Graphiti on Zep's Context Graph Engine), serves token-efficient context in sub-200ms p95, and deploys as managed cloud, BYOK, or BYOC on the cloud you choose. It's model- and framework-agnostic.
Ingestion
chat·JSON·documents·app events
Raw signal arrives from any source the agent touches.
Context Assembly
context blocks·templates·token-efficient
Relevant context is assembled on demand into token-efficient blocks.
Graphiti
Learn moreentity extraction·relationships·ontology·invalidation
Signal becomes a temporal context graph as new facts arrive and stale ones are invalidated.
Retrieval
sub-200ms·auto-optimized·provenance-linked·policy-filtered
Selects what's relevant and what adds the most information within the token budget.
Governance
ABAC·multi-tenant isolation·customer key encryption·retention policies·audit·provenance
Native to the substrate, not a layer bolted on. Every read and write is policy-gated for access and provenance; retention runs across the data lifecycle.
Context Graph Engine
entities·facts & edges·decision traces·episodes
Temporal context graph with provenance — sub-200ms retrieval at scale.
How they compare
Memory Bank vs. Zep, side by side
| Vertex AI Memory Bank | Zep | |
|---|---|---|
| Ecosystem | Bound to Google Cloud / Vertex | Neutral — any model, any cloud |
| Model providers | Google-centric (Gemini) | OpenAI, Anthropic, Meta, Gemini, others |
| Memory model | Memories extracted from session history (Agent Engine Sessions + Memory Bank) | Bi-temporal context graph (provenance + validity) |
| Temporal reasoning | No — extraction-based; no temporal graph | “What's true now / what was true then,” auto fact invalidation |
| Deployment | GCP / Vertex | Managed, BYOK, or BYOC (AWS/GCP/Azure) |
| Benchmarks | — | 94.7% LoCoMo (155ms), 90.2% LongMemEval (162ms) |
| Lock-in risk | Higher (ecosystem-bound) | Lower (portable across stacks) |
The strategic question
Lock-in vs. neutrality
The same logic S&P Global Market Intelligence flagged for hyperscaler primitives applies here: your agents' memory is among the most valuable and sticky data you own. A bundled memory service keeps it inside one cloud's ecosystem. A neutral layer lets you keep one consistent context strategy across Gemini, OpenAI, Anthropic, and Meta — and move between clouds without re-platforming your memory.
When to choose
Pick the tool that fits the strategy
Choose Memory Bank when
You're standardized on Google Cloud and Vertex, use Gemini as your primary model, and the bundled memory meets your needs.
- Standardized on Google Cloud and Vertex
- Gemini is your primary model
- Bundled memory meets your needs
Choose Zep when
You want neutrality across models and clouds, with governed memory at scale.
- Neutrality across models and clouds
- Temporal, provenance-tracked, governed memory (ABAC, retention, audit)
- Regulated workloads with BYOK/BYOC deployment control
- Benchmark-proven retrieval quality at enterprise scale
FAQ
Frequently asked questions
Is Vertex AI Memory Bank enough for enterprise agent memory?
If you're committed to Vertex/GCP and Gemini and need managed memory, it can be. For neutrality, temporal reasoning, provenance, and portable governance, evaluate a dedicated layer like Zep.
Can Zep run on Google Cloud?
Yes — managed, with your own keys, or inside your own VPC on GCP (or AWS/Azure).
Does Zep work with Gemini?
Zep is model-agnostic and works with Gemini as well as OpenAI, Anthropic, and others — so memory isn't tied to one model provider.