Zep Vector Store consultants

Q: What is Zep and how does it work with n8n?

Zep is a memory and vector storage platform designed for AI applications. In n8n, the Zep Vector Store node lets you store documents, search by semantic similarity, and manage conversation memory. It acts as the knowledge backbone for RAG pipelines and conversational AI agents within your automated workflows.

Q: Does Zep handle embeddings automatically?

Yes. Unlike most vector stores that require you to generate embeddings externally before inserting, Zep can process and embed documents on the server side. This means you can send raw text to Zep and it handles the chunking and vector generation internally, simplifying your n8n workflow and reducing calls to embedding APIs.

Q: Can Zep replace both a vector store and a memory node?

It can. Zep supports both document-level vector storage for RAG and session-level conversation memory for AI agents. This dual capability means you can use one Zep instance where you would otherwise need a separate vector database and a separate memory backend like Motorhead or Redis.

Q: Is Zep open source or a paid service?

Zep offers both an open-source self-hosted version and a managed cloud service. The open-source version gives you full control over your data and infrastructure. The cloud service removes operational overhead. Choose based on your team's capacity to manage infrastructure and your data residency requirements.

Q: How does Zep compare to Pinecone or Qdrant?

Pinecone and Qdrant are pure vector databases — they store and search vectors but do not handle embeddings or conversation memory. Zep bundles vector storage, automatic embedding, and memory management into one platform. If your workflow only needs vector search, a pure vector DB may be simpler. If you need the full stack, Zep saves integration effort.

Q: What volume of documents can Zep handle?

Zep is built for production workloads and can handle collections of millions of documents. Performance depends on your deployment configuration — self-hosted instances scale with the hardware you allocate, and the cloud service scales automatically. For most business knowledge bases with thousands to tens of thousands of documents, Zep performs well out of the box.

We can help you automate your business with Zep Vector Store and hundreds of other systems to improve efficiency and productivity. Get in touch if you’d like to discuss implementing Zep Vector Store.

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About Zep Vector Store

The Zep Vector Store node in n8n connects your workflows to Zep’s purpose-built memory and vector storage platform. Zep handles both long-term document storage for RAG systems and conversation memory for AI agents, making it a two-in-one solution for workflows that need both capabilities. The node manages document insertion, vector search, and memory retrieval without requiring separate infrastructure for each function.

What sets Zep apart from general-purpose vector databases is its focus on AI application needs. It automatically handles document chunking, embedding generation, and metadata indexing — tasks that typically require separate nodes in your n8n workflow. When you store a document in Zep, it processes and indexes the content on the server side, reducing the complexity of your workflow and the number of API calls to embedding providers like OpenAI.

For teams building AI agents that need both knowledge base access and conversation memory, Zep simplifies the architecture considerably. Instead of connecting separate vector store, embedding, and memory nodes, you connect one Zep node that handles all three roles. We have found this approach particularly effective for internal knowledge bots and customer support agents where the agent needs to search company documents while maintaining conversation context.

If you are building a RAG system or conversational AI agent and want to reduce infrastructure complexity, our n8n consulting team can help you evaluate whether Zep is the right fit for your workflow architecture.

Zep Vector Store FAQs

Frequently Asked Questions

Common questions about how Zep Vector Store consultants can help with integration and implementation

What is Zep and how does it work with n8n?

Does Zep handle embeddings automatically?

Can Zep replace both a vector store and a memory node?

Is Zep open source or a paid service?

How does Zep compare to Pinecone or Qdrant?

What volume of documents can Zep handle?

How it works

We work hand-in-hand with you to implement Zep Vector Store

As Zep Vector Store consultants we work with you hand in hand build more efficient and effective operations. Here’s how we will work with you to automate your business and integrate Zep Vector Store with integrate and automate 800+ tools.

Step 1

Deploy or Sign Up for Zep

Choose between self-hosting Zep using Docker or signing up for the Zep Cloud managed service. For self-hosting, pull the Zep Docker image, configure your database backend, and start the service. For cloud, create an account and note your API endpoint and key. Either way, have your connection details ready for the n8n configuration.

Step 2

Configure Zep Credentials in n8n

In n8n, create a new Zep credential with your API URL and API key. If self-hosting, the URL points to your Zep instance. If using Zep Cloud, use the endpoint provided in your dashboard. Test the connection to confirm n8n can communicate with your Zep instance.

Step 3

Create a Collection for Your Documents

Set up a collection in Zep to hold your document vectors. Think of collections like folders — group related documents together. For example, keep product documentation in one collection, support articles in another, and company policies in a third. This makes retrieval more targeted and results more relevant.

Step 4

Build the Document Ingestion Workflow

Create an n8n workflow that loads documents from your source — file storage, a CMS, email, or an API — and inserts them into Zep. If Zep is handling embeddings server-side, you can send raw text directly. If you prefer external embeddings, chain an Embeddings OpenAI node before the Zep node.

Step 5

Build the Retrieval and Query Workflow

Create a workflow that takes a user query (from a Chat Trigger, webhook, or form), searches the Zep collection for relevant documents, and passes the results to an AI model for answer generation. Configure the number of results to retrieve and any metadata filters to narrow the search scope.

Step 6

Test Retrieval Accuracy and Tune Parameters

Run test queries that represent real user questions and check whether Zep returns the most relevant documents. If results are off target, adjust your chunking strategy, add metadata filters, or experiment with the number of results returned. Good retrieval accuracy is the foundation of a useful RAG system — tune it before optimising the generation side.

Transform your business with Zep Vector Store

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