> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Valkey

> Use Valkey as an open-source vector store in Mem0 for high-performance key-value storage with vector search.

# Valkey Vector Store

[Valkey](https://valkey.io/) is an open source (BSD) high-performance key/value datastore that supports a variety of workloads and rich datastructures including vector search.

## Installation

```bash theme={null}
pip install mem0ai[vector-stores]
```

## Usage

<CodeGroup>
  ```python Python theme={null}
  config = {
      "vector_store": {
          "provider": "valkey",
          "config": {
              "collection_name": "test",
              "valkey_url": "valkey://localhost:6379",
              "embedding_model_dims": 1536,
              "index_type": "flat"
          }
      }
  }

  m = Memory.from_config(config)
  messages = [
      {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
      {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
      {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
      {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
  ]
  m.add(messages, user_id="alice", metadata={"category": "movies"})
  ```

  ```typescript TypeScript theme={null}
  import { Memory } from 'mem0ai/oss';

  const config = {
    vectorStore: {
      provider: 'valkey',
      config: {
        collectionName: 'test',
        valkeyUrl: 'valkey://localhost:6379',
        embeddingModelDims: 1536,
        indexType: 'flat',
      },
    },
  };

  const memory = new Memory(config);
  const messages = [
    { role: 'user', content: "I'm planning to watch a movie tonight. Any recommendations?" },
    { role: 'assistant', content: 'How about thriller movies? They can be quite engaging.' },
    { role: 'user', content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
    { role: 'assistant', content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
  ];
  await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
  ```
</CodeGroup>

## Parameters

<Tabs>
  <Tab title="Python">
    Here are the parameters available for configuring Valkey:

    | Parameter              | Description                                              | Default Value             |
    | ---------------------- | -------------------------------------------------------- | ------------------------- |
    | `collection_name`      | The name of the collection to store the vectors          | `mem0`                    |
    | `valkey_url`           | Connection URL for the Valkey server                     | `valkey://localhost:6379` |
    | `embedding_model_dims` | Dimensions of the embedding model                        | `1536`                    |
    | `index_type`           | Vector index algorithm (`hnsw` or `flat`)                | `hnsw`                    |
    | `hnsw_m`               | Number of bi-directional links for HNSW                  | `16`                      |
    | `hnsw_ef_construction` | Size of dynamic candidate list for HNSW                  | `200`                     |
    | `hnsw_ef_runtime`      | Size of dynamic candidate list for search                | `10`                      |
    | `cluster_mode`         | Enable cluster mode for Valkey cluster (CME) deployments | `false`                   |
    | `timezone`             | Timezone for timestamp handling                          | `UTC`                     |
  </Tab>

  <Tab title="TypeScript">
    | Parameter            | Description                                              | Default Value             |
    | -------------------- | -------------------------------------------------------- | ------------------------- |
    | `collectionName`     | The name of the collection to store the vectors          | `mem0`                    |
    | `valkeyUrl`          | Connection URL for the Valkey server                     | `valkey://localhost:6379` |
    | `embeddingModelDims` | Dimensions of the embedding model                        | `1536`                    |
    | `indexType`          | Vector index algorithm (`hnsw` or `flat`)                | `hnsw`                    |
    | `hnswM`              | Number of bi-directional links for HNSW                  | `16`                      |
    | `hnswEfConstruction` | Size of dynamic candidate list for HNSW                  | `200`                     |
    | `hnswEfRuntime`      | Size of dynamic candidate list for search                | `10`                      |
    | `clusterMode`        | Enable cluster mode for Valkey cluster (CME) deployments | `false`                   |
    | `timezone`           | Timezone for timestamp handling                          | `UTC`                     |
  </Tab>
</Tabs>

## Cluster Mode

To use Valkey with cluster mode enabled (CME), set `cluster_mode` to `true`:

```python theme={null}
config = {
    "vector_store": {
        "provider": "valkey",
        "config": {
            "collection_name": "memories",
            "valkey_url": "valkey://cluster-endpoint:6379",
            "embedding_model_dims": 1536,
            "cluster_mode": True
        }
    }
}
```

When cluster mode is enabled, the connector uses `ValkeyCluster` instead of the standalone client, which handles `MOVED`/`ASK` redirections automatically. Search queries are coordinated across all shards by the valkey-search module's built-in coordinator. See the [valkey-search documentation](https://github.com/valkey-io/valkey-search) for details on cluster mode behavior.