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The MemMachine Helm chart provides a production-ready deployment including the core API, PostgreSQL (with pgvector), and Neo4j. This guide covers standard installation, external database integration, and resource optimization.
Prerequisites: You will need a Kubernetes cluster (v1.19+), Helm 3 installed, and a StorageClass that supports ReadWriteMany (RWX) (e.g., NFS-client).

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High-Level Architecture

The chart exposes the MemMachine API via a NodePort (default 31001), while internal databases are kept private within the cluster using ClusterIP services.

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Quick Start

Deploy MemMachine with default settings (in-cluster databases) using a single command:
1

Set Up Your Namespace

2

Install the Chart

Provide your OpenAI API key to enable the default LLM and embedding providers.
3

Verify the Pods

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Configuration Reference

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Persistent Storage

By default, the chart requests 5Gi per volume using the nfs-client storage class.

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External Databases

If you prefer to use managed services (like RDS or Neo4j Aura), disable the in-cluster components: Bash

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Resource Limits

Our QA team has verified these baseline limits for small-to-medium workloads:

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Advanced Usage

Ollama Integration

Override model.base_url and model.provider in your values.yaml to point to a local Ollama instance.

NodePort Customization

Change the default port by setting nodePorts.http8080 to your desired range (30000-32767).

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Example: Custom Values File

For production setups, we recommend using a values-override.yaml:
Cleanup: Helm does not delete PVCs by default to prevent accidental data loss. To fully wipe a deployment, run helm uninstall memmachine followed by kubectl delete pvc -l app=memmachine.