> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memmachine.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Helm Chart (Kubernetes)

> Deploy MemMachine to a Kubernetes cluster using the official Helm chart.

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.

<Note>
  **Prerequisites**: You will need a Kubernetes cluster (v1.19+), Helm 3 installed, and a `StorageClass` that supports **ReadWriteMany (RWX)** (e.g., NFS-client).
</Note>

## 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.

***

## Quick Start

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

<Steps>
  <Step title="Set Up Your Namespace">
    ```bash theme={null}
    kubectl create namespace memmachine
    ```
  </Step>

  <Step title="Install the Chart">
    Provide your OpenAI API key to enable the default LLM and embedding providers.

    ```bash theme={null}
    helm upgrade --install memmachine . \
      --namespace memmachine \
      --set memmachine.openaiApiKey=sk-xxxx...
    ```
  </Step>

  <Step title="Verify the Pods">
    ```bash theme={null}
    kubectl get pods -n memmachine
    ```
  </Step>
</Steps>

***

## Configuration Reference

### Persistent Storage

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

| **Parameter**  | **Default**     | **Description**                            |
| -------------- | --------------- | ------------------------------------------ |
| `storageClass` | `nfs-client`    | StorageClass for all PVCs.                 |
| `accessMode`   | `ReadWriteMany` | Must match your StorageClass capabilities. |
| `pvcSize`      | `5Gi`           | Storage request for each component.        |

### External Databases

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

Bash

```
helm upgrade --install memmachine . \
  --set postgres.enabled=false --set postgres.host=pg.example.com \
  --set neo4j.enabled=false --set neo4j.host=neo4j.example.com
```

### Resource Limits

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

| **Component**  | **CPU Request** | **Memory Limit** | **Note**                                      |
| -------------- | --------------- | ---------------- | --------------------------------------------- |
| **MemMachine** | `200m`          | `2Gi`            | No CPU limit to prevent inference throttling. |
| **PostgreSQL** | `250m`          | `2Gi`            | Headroom for `pgvector` index builds.         |
| **Neo4j**      | `500m`          | `2Gi`            | Covers JVM heap + Page Cache.                 |

***

## Advanced Usage

<CardGroup cols={2}>
  <Card title="Ollama Integration" icon="microchip">
    Override `model.base_url` and `model.provider` in your `values.yaml` to point to a local Ollama instance.
  </Card>

  <Card title="NodePort Customization" icon="network-wired">
    Change the default port by setting `nodePorts.http8080` to your desired range (30000-32767).
  </Card>
</CardGroup>

### Example: Custom Values File

For production setups, we recommend using a `values-override.yaml`:

```yaml theme={null}
memmachine:
  resources:
    limits:
      memory: 4Gi
neo4j:
  heap:
    max: 4G
  resources:
    limits:
      memory: 8Gi
```

***

<Tip>
  **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`.
</Tip>