knowledge-base/docs/open-source-graph-memory-overview.md

Mem0 Open Source - Graph Memory Overview

Introduction

Mem0 now supports Graph Memory capabilities that enable users to create and utilize complex relationships between pieces of information, allowing for more nuanced and context-aware responses. This integration combines the strengths of both vector-based and graph-based approaches, resulting in more accurate and comprehensive information retrieval and generation.

NodeSDK now supports Graph Memory 🎉

Installation

To use Mem0 with Graph Memory support, install it using pip:

Python

pip install "mem0ai[graph]"

TypeScript

The NodeSDK includes graph memory support in the standard installation.

This command installs Mem0 along with the necessary dependencies for graph functionality.

Try Graph Memory on Google Colab:

Demo Video: Dynamic Graph Memory by Mem0 - YouTube

Initialize Graph Memory

To initialize Graph Memory, you'll need to set up your configuration with graph store providers. Currently, we support Neo4j, Memgraph, and Neptune Analytics as graph store providers.

Initialize Neo4j

You can setup Neo4j locally or use the hosted Neo4j AuraDB.

Important: If you are using Neo4j locally, you need to install APOC plugins.

LLM Configuration Options

Users can customize the LLM for Graph Memory from the Supported LLM list with three levels of configuration:

1. Main Configuration: If llm is set in the main config, it will be used for all graph operations.
2. Graph Store Configuration: If llm is set in the graph_store config, it will override the main config llm and be used specifically for graph operations.
3. Default Configuration: If no custom LLM is set, the default LLM (gpt-4o-2024-08-06) will be used for all graph operations.

Python Configuration

from mem0 import Memory

config = {
    "graph_store": {
        "provider": "neo4j",
        "config": {
            "url": "neo4j+s://xxx",
            "username": "neo4j",
            "password": "xxx"
        }
    }
}

m = Memory.from_config(config_dict=config)

TypeScript Configuration

If you are using NodeSDK, you need to pass enableGraph as true in the config object.

import { Memory } from "mem0ai/oss";

const config = {
    enableGraph: true,
    graphStore: {
        provider: "neo4j",
        config: {
            url: "neo4j+s://xxx",
            username: "neo4j",
            password: "xxx",
        }
    }
}

const memory = new Memory(config);

Initialize Memgraph

Run Memgraph with Docker:

docker run -p 7687:7687 memgraph/memgraph-mage:latest --schema-info-enabled=True

The --schema-info-enabled flag is set to True for more performant schema generation.

Additional Information: Memgraph documentation

Python Configuration

from mem0 import Memory

config = {
    "graph_store": {
        "provider": "memgraph",
        "config": {
            "url": "bolt://localhost:7687",
            "username": "memgraph",
            "password": "xxx",
        },
    },
}

m = Memory.from_config(config_dict=config)

Initialize Neptune Analytics

Mem0 now supports Amazon Neptune Analytics as a graph store provider. This integration allows you to use Neptune Analytics for storing and querying graph-based memories.

Instance Setup

1. Create an Amazon Neptune Analytics instance in your AWS account following the AWS documentation.

2. Important Considerations:

   • Public connectivity is not enabled by default, and if accessing from outside a VPC, it needs to be enabled.
   • Once the Amazon Neptune Analytics instance is available, you will need the graph-identifier to connect.
   • The Neptune Analytics instance must be created using the same vector dimensions as the embedding model creates. See: Vector Index Documentation

Attach Credentials

Configure your AWS credentials with access to your Amazon Neptune Analytics resources by following the Configuration and credentials precedence.

Environment Variables Example:

export AWS_ACCESS_KEY_ID=your-access-key
export AWS_SECRET_ACCESS_KEY=your-secret-key
export AWS_SESSION_TOKEN=your-session-token
export AWS_DEFAULT_REGION=your-region

Required IAM Permissions: The IAM user or role making the request must have a policy attached that allows one of the following IAM actions in that neptune-graph:

• neptune-graph:ReadDataViaQuery
• neptune-graph:WriteDataViaQuery
• neptune-graph:DeleteDataViaQuery

Usage

from mem0 import Memory

# This example must connect to a neptune-graph instance with 1536 vector dimensions specified.
config = {
    "embedder": {
        "provider": "openai",
        "config": {"model": "text-embedding-3-large", "embedding_dims": 1536},
    },
    "graph_store": {
        "provider": "neptune",
        "config": {
            "endpoint": "neptune-graph://<GRAPH_ID>",
        },
    },
}

m = Memory.from_config(config_dict=config)

Troubleshooting

• Connection Issues: Refer to the Connecting to a graph guide
• Authentication Issues: Refer to the boto3 client configuration options
• Detailed Examples: See the Neptune Analytics example notebook

Graph Operations

Mem0's graph supports the following core operations:

Add Memories

Mem0 with Graph Memory supports both "user_id" and "agent_id" parameters. You can use either or both to organize your memories.

Python

# Using only user_id
m.add("I like pizza", user_id="alice")

# Using both user_id and agent_id
m.add("I like pizza", user_id="alice", agent_id="food-assistant")

TypeScript

// Using only userId
await memory.add("I like pizza", { userId: "alice" });

// Using both userId and agentId
await memory.add("I like pizza", { userId: "alice", agentId: "food-assistant" });

Get All Memories

Python

# Get all memories for a user
m.get_all(user_id="alice")

# Get all memories for a specific agent belonging to a user
m.get_all(user_id="alice", agent_id="food-assistant")

TypeScript

// Get all memories for a user
await memory.getAll({ userId: "alice" });

// Get all memories for a specific agent belonging to a user
await memory.getAll({ userId: "alice", agentId: "food-assistant" });

Search Memories

Python

# Search memories for a user
m.search("tell me my name.", user_id="alice")

# Search memories for a specific agent belonging to a user
m.search("tell me my name.", user_id="alice", agent_id="food-assistant")

TypeScript

// Search memories for a user
await memory.search("tell me my name.", { userId: "alice" });

// Search memories for a specific agent belonging to a user
await memory.search("tell me my name.", { userId: "alice", agentId: "food-assistant" });

Delete All Memories

Python

# Delete all memories for a user
m.delete_all(user_id="alice")

# Delete all memories for a specific agent belonging to a user
m.delete_all(user_id="alice", agent_id="food-assistant")

TypeScript

// Delete all memories for a user
await memory.deleteAll({ userId: "alice" });

// Delete all memories for a specific agent belonging to a user
await memory.deleteAll({ userId: "alice", agentId: "food-assistant" });

Example Usage

Here's a comprehensive example of how to use Mem0's graph operations:

1. First, we'll add some memories for a user named Alice.
2. Then, we'll visualize how the graph evolves as we add more memories.
3. You'll see how entities and relationships are automatically extracted and connected in the graph.

Step-by-Step Memory Addition

1. Add memory 'I like going to hikes'

m.add("I like going to hikes", user_id="alice123")

Result: Creates initial user node and hiking preference relationship.

2. Add memory 'I love to play badminton'

m.add("I love to play badminton", user_id="alice123")

Result: Adds badminton activity and positive relationship.

3. Add memory 'I hate playing badminton'

m.add("I hate playing badminton", user_id="alice123")

Result: Updates existing badminton relationship, showing preference conflict resolution.

4. Add memory 'My friend name is john and john has a dog named tommy'

m.add("My friend name is john and john has a dog named tommy", user_id="alice123")

Result: Creates complex relationship network: Alice -> friends with -> John -> owns -> Tommy (dog).

5. Add memory 'My name is Alice'

m.add("My name is Alice", user_id="alice123")

Result: Adds identity information to the user node.

6. Add memory 'John loves to hike and Harry loves to hike as well'

m.add("John loves to hike and Harry loves to hike as well", user_id="alice123")

Result: Creates connections between John, Harry, and hiking activities, potentially connecting with Alice's hiking preference.

7. Add memory 'My friend peter is the spiderman'

m.add("My friend peter is the spiderman", user_id="alice123")

Result: Adds another friend relationship with identity/role information.

Search Examples

Search for Identity

result = m.search("What is my name?", user_id="alice123")

Expected Response: Returns Alice's name and related identity information from the graph.

Search for Relationships

result = m.search("Who is spiderman?", user_id="alice123")

Expected Response: Returns Peter and his spiderman identity, along with his friendship relationship to Alice.

Using Multiple Agents with Graph Memory

When working with multiple agents, you can use the "agent_id" parameter to organize memories by both user and agent. This allows you to:

1. Create agent-specific knowledge graphs
2. Share common knowledge between agents
3. Isolate sensitive or specialized information to specific agents

Example: Multi-Agent Setup

# Add memories for different agents
m.add("I prefer Italian cuisine", user_id="bob", agent_id="food-assistant")
m.add("I'm allergic to peanuts", user_id="bob", agent_id="health-assistant")
m.add("I live in Seattle", user_id="bob")  # Shared across all agents

# Search within specific agent context
food_preferences = m.search("What food do I like?", user_id="bob", agent_id="food-assistant")
health_info = m.search("What are my allergies?", user_id="bob", agent_id="health-assistant")
location = m.search("Where do I live?", user_id="bob")  # Searches across all agents

Agent Isolation Benefits

1. Privacy: Sensitive health information stays with health-related agents
2. Specialization: Each agent builds domain-specific knowledge
3. Shared Context: Common information (like location) remains accessible to all agents
4. Scalability: Easy to add new agents without disrupting existing knowledge

Key Features

Automatic Entity Extraction

• Smart Recognition: Automatically identifies people, places, objects, and concepts
• Relationship Mapping: Creates meaningful connections between entities
• Context Preservation: Maintains semantic relationships in the graph

Dynamic Graph Evolution

• Real-time Updates: Graph structure evolves as new memories are added
• Conflict Resolution: Handles contradictory information intelligently
• Relationship Strengthening: Reinforces connections through repeated mentions

Intelligent Querying

• Contextual Search: Searches consider relationship context, not just semantic similarity
• Graph Traversal: Finds information through relationship paths
• Multi-hop Reasoning: Can answer questions requiring connection of multiple entities

Hybrid Architecture

• Vector + Graph: Combines semantic search with relationship reasoning
• Dual Storage: Information stored in both vector and graph formats
• Unified Interface: Single API for both vector and graph operations

Important Notes

Note

: The Graph Memory implementation is not standalone. You will be adding/retrieving memories to the vector store and the graph store simultaneously.

This hybrid approach ensures:

• Semantic Search: Vector storage enables similarity-based retrieval
• Relationship Reasoning: Graph storage enables connection-based queries
• Comprehensive Results: Queries leverage both approaches for better accuracy

Getting Help

If you want to use a managed version of Mem0, please check out Mem0 Platform. If you have any questions, please feel free to reach out to us using one of the following methods:

• Discord: Join our community
• GitHub: Ask questions on GitHub
• Support: Talk to founders

Conclusion

Graph Memory in Mem0 represents a significant advancement in AI memory capabilities, enabling more sophisticated reasoning and context-aware responses. By combining the semantic understanding of vector databases with the relationship intelligence of graph databases, Mem0 provides a comprehensive solution for building truly intelligent AI systems.

The ability to automatically extract entities, create relationships, and reason across connections makes Graph Memory particularly powerful for:

• Personal AI Assistants that understand complex personal relationships
• Customer Support Systems that maintain comprehensive user context
• Knowledge Management Platforms that connect related information
• Multi-Agent Systems that share and specialize knowledge appropriately

With support for multiple graph database backends and both Python and TypeScript SDKs, Graph Memory provides the flexibility and scalability needed for production AI applications.