mem0/docs/platform/features/graph-memory.mdx

---
title: Graph Memory
description: "Enable graph-based memory retrieval for more contextually relevant results"
---

## Overview

Graph Memory enhances the memory pipeline by creating relationships between entities in your data. It builds a network of interconnected information for more contextually relevant search results.

This feature allows your AI applications to understand connections between entities, providing richer context for responses. It's ideal for applications needing relationship tracking and nuanced information retrieval across related memories.

## How Graph Memory Works

The Graph Memory feature analyzes how each entity connects and relates to each other. When enabled:

1. Mem0 automatically builds a graph representation of entities
2. Vector search returns the top semantic matches (with any reranker you configure)
3. Graph relations are returned alongside those results to provide additional context—they do not reorder the vector hits

## Using Graph Memory

To use Graph Memory, you need to enable it in your API calls by setting the `enable_graph=True` parameter.

### Adding Memories with Graph Memory

When adding new memories, enable Graph Memory to automatically build relationships with existing memories:

<CodeGroup>

```python Python
from mem0 import MemoryClient

client = MemoryClient(
    api_key="your-api-key",
    org_id="your-org-id",
    project_id="your-project-id"
)

messages = [
    {"role": "user", "content": "My name is Joseph"},
    {"role": "assistant", "content": "Hello Joseph, it's nice to meet you!"},
    {"role": "user", "content": "I'm from Seattle and I work as a software engineer"}
]

# Enable graph memory when adding
client.add(
    messages,
    user_id="joseph",
    enable_graph=True
)
```

```javascript JavaScript
import { MemoryClient } from "mem0";

const client = new MemoryClient({
  apiKey: "your-api-key",
  org_id: "your-org-id",
  project_id: "your-project-id"
});

const messages = [
  { role: "user", content: "My name is Joseph" },
  { role: "assistant", content: "Hello Joseph, it's nice to meet you!" },
  { role: "user", content: "I'm from Seattle and I work as a software engineer" }
];

// Enable graph memory when adding
await client.add({
  messages,
  user_id: "joseph",
  enable_graph: true
});
```

```json Output
{
  "results": [
    {
      "memory": "Name is Joseph",
      "event": "ADD",
      "id": "4a5a417a-fa10-43b5-8c53-a77c45e80438"
    },
    {
      "memory": "Is from Seattle",
      "event": "ADD",
      "id": "8d268d0f-5452-4714-b27d-ae46f676a49d"
    },
    {
      "memory": "Is a software engineer",
      "event": "ADD",
      "id": "5f0a184e-ddea-4fe6-9b92-692d6a901df8"
    }
  ]
}
```
</CodeGroup>

The graph memory would look like this:

<Frame>
  <img src="/images/graph-platform.png" alt="Graph Memory Visualization showing relationships between entities" />
</Frame>

<Caption>Graph Memory creates a network of relationships between entities, enabling more contextual retrieval</Caption>


<Note>
Response for the graph memory's `add` operation will not be available directly in the response. As adding graph memories is an asynchronous operation due to heavy processing, you can use the `get_all()` endpoint to retrieve the memory with the graph metadata.
</Note>


### Searching with Graph Memory

When searching memories, Graph Memory helps retrieve entities that are contextually important even if they're not direct semantic matches.

<CodeGroup>

```python Python
# Search with graph memory enabled
results = client.search(
    "what is my name?",
    user_id="joseph",
    enable_graph=True
)

print(results)
```

```javascript JavaScript
// Search with graph memory enabled
const results = await client.search({
  query: "what is my name?",
  user_id: "joseph",
  enable_graph: true
});

console.log(results);
```

```json Output
{
  "results": [
    {
      "id": "4a5a417a-fa10-43b5-8c53-a77c45e80438",
      "memory": "Name is Joseph",
      "user_id": "joseph",
      "metadata": null,
      "categories": ["personal_details"],
      "immutable": false,
      "created_at": "2025-03-19T09:09:00.146390-07:00",
      "updated_at": "2025-03-19T09:09:00.146404-07:00",
      "score": 0.3621795393335552
    },
    {
      "id": "8d268d0f-5452-4714-b27d-ae46f676a49d",
      "memory": "Is from Seattle",
      "user_id": "joseph",
      "metadata": null,
      "categories": ["personal_details"],
      "immutable": false,
      "created_at": "2025-03-19T09:09:00.170680-07:00",
      "updated_at": "2025-03-19T09:09:00.170692-07:00",
      "score": 0.31212713194651254
    }
  ],
  "relations": [
    {
      "source": "joseph",
      "source_type": "person",
      "relationship": "name",
      "target": "joseph",
      "target_type": "person",
      "score": 0.39
    }
  ]
}
```

</CodeGroup>

<Note>
`results` always reflects the vector search order (optionally reranked). Graph Memory augments that response by adding related entities in the `relations` array; it does not re-rank the vector results automatically.
</Note>

### Retrieving All Memories with Graph Memory

When retrieving all memories, Graph Memory provides additional relationship context:

<Callout type="warning" title="Filters Required">
`get_all()` now requires filters to be specified.
</Callout>

<CodeGroup>

```python Python
# Get all memories with graph context
memories = client.get_all(
    filters={"AND": [{"user_id": "joseph"}]},
    enable_graph=True
)

print(memories)
```

```javascript JavaScript
// Get all memories with graph context
const memories = await client.getAll({
  filters: {"AND": [{"user_id": "joseph"}]},
  enable_graph: true
});

console.log(memories);
```

```json Output
{
  "results": [
    {
      "id": "5f0a184e-ddea-4fe6-9b92-692d6a901df8",
      "memory": "Is a software engineer",
      "user_id": "joseph",
      "metadata": null,
      "categories": ["professional_details"],
      "immutable": false,
      "created_at": "2025-03-19T09:09:00.194116-07:00",
      "updated_at": "2025-03-19T09:09:00.194128-07:00",
    },
    {
      "id": "8d268d0f-5452-4714-b27d-ae46f676a49d",
      "memory": "Is from Seattle",
      "user_id": "joseph",
      "metadata": null,
      "categories": ["personal_details"],
      "immutable": false,
      "created_at": "2025-03-19T09:09:00.170680-07:00",
      "updated_at": "2025-03-19T09:09:00.170692-07:00",
    },
    {
      "id": "4a5a417a-fa10-43b5-8c53-a77c45e80438",
      "memory": "Name is Joseph",
      "user_id": "joseph",
      "metadata": null,
      "categories": ["personal_details"],
      "immutable": false,
      "created_at": "2025-03-19T09:09:00.146390-07:00",
      "updated_at": "2025-03-19T09:09:00.146404-07:00",
    }
  ],
  "relations": [
    {
      "source": "joseph",
      "source_type": "person",
      "relationship": "name",
      "target": "joseph",
      "target_type": "person"
    },
    {
      "source": "joseph",
      "source_type": "person",
      "relationship": "city",
      "target": "seattle",
      "target_type": "city"
    },
    {
      "source": "joseph",
      "source_type": "person",
      "relationship": "job",
      "target": "software engineer",
      "target_type": "job"
    }
  ]
}
```

</CodeGroup>

### Setting Graph Memory at Project Level

Instead of passing `enable_graph=True` to every add call, you can enable it once at the project level:

<CodeGroup>

```python Python
from mem0 import MemoryClient

client = MemoryClient(
    api_key="your-api-key",
    org_id="your-org-id",
    project_id="your-project-id"
)

# Enable graph memory for all operations in this project
client.project.update(enable_graph=True)

# Now all add operations will use graph memory by default
messages = [
    {"role": "user", "content": "My name is Joseph"},
    {"role": "assistant", "content": "Hello Joseph, it's nice to meet you!"},
    {"role": "user", "content": "I'm from Seattle and I work as a software engineer"}
]

client.add(
    messages,
    user_id="joseph"
)
```

```javascript JavaScript
import { MemoryClient } from "mem0";

const client = new MemoryClient({
  apiKey: "your-api-key",
  org_id: "your-org-id",
  project_id: "your-project-id"
});

// Enable graph memory for all operations in this project
await client.project.update({ enable_graph: true });

// Now all add operations will use graph memory by default
const messages = [
  { role: "user", content: "My name is Joseph" },
  { role: "assistant", content: "Hello Joseph, it's nice to meet you!" },
  { role: "user", content: "I'm from Seattle and I work as a software engineer" }
];

await client.add({
  messages,
  user_id: "joseph"
});
```

</CodeGroup>


## Best Practices

- Enable Graph Memory for applications where understanding context and relationships between memories is important.
- Graph Memory works best with a rich history of related conversations.
- Consider Graph Memory for long-running assistants that need to track evolving information.

## Performance Considerations

Graph Memory requires additional processing and may increase response times slightly for very large memory stores. However, for most use cases, the improved retrieval quality outweighs the minimal performance impact.

If you have any questions, please feel free to reach out to us using one of the following methods:

<Snippet file="get-help.mdx" />