Valkey Vector Store

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

Installation

bash

pip install mem0ai[vector-stores]

Usage

<CodeGroup> ```python Python 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
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

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 for details on cluster mode behavior.