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📢 Announcing our research paper: Zentry achieves 26% higher accuracy than OpenAI Memory, 91% lower latency, and 90% token savings! Read the paper to learn how we're revolutionizing AI agent memory.
Zentry now supports Graph Memory. With Graph Memory, users can now create and utilize complex relationships between pieces of information, allowing for more nuanced and context-aware responses. This integration enables users to leverage 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. 🎉

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Installation

To use Zentry with Graph Memory support, install it using pip: 
pip install "zentry[graph]"
This command installs Zentry along with the necessary dependencies for graph functionality. Try Graph Memory on Google Colab. Open In Colab

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Initialize Graph Memory

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

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Initialize Neo4j

You can setup Neo4j locally or use the hosted Neo4j AuraDB.
If you are using Neo4j locally, then you need to install APOC plugins.
User can also 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.
Here’s how you can do it: 
from zentry import Memory

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

m = Memory.from_config(config_dict=config)
If you are using NodeSDK, you need to pass enableGraph as true in the config object.

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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 can be found on Memgraph documentation. User can also 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.
Here’s how you can do it: 
from zentry import Memory

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

m = Memory.from_config(config_dict=config)

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Graph Operations

The Zentry’s graph supports the following operations:

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Add Memories

If you are using Zentry with Graph Memory, it is recommended to pass user_id. Use userId in NodeSDK.
m.add("I like pizza", user_id="alice")

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Get all memories

m.get_all(user_id="alice")

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Search Memories

m.search("tell me my name.", user_id="alice")

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Delete all Memories

m.delete_all(user_id="alice")

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Example Usage

Here’s an example of how to use Zentry’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.

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Add Memories

Below are the steps to add memories and visualize the graph:
1

Add memory 'I like going to hikes'

m.add("I like going to hikes", user_id="alice123")
Graph Memory Visualization
2

Add memory 'I love to play badminton'

m.add("I love to play badminton", user_id="alice123")
Graph Memory Visualization
3

Add memory 'I hate playing badminton'

m.add("I hate playing badminton", user_id="alice123")
Graph Memory Visualization
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")
Graph Memory Visualization
5

Add memory 'My name is Alice'

m.add("My name is Alice", user_id="alice123")
Graph Memory Visualization
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")
Graph Memory Visualization
7

Add memory 'My friend peter is the spiderman'

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

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Search Memories

m.search("What is my name?", user_id="alice123")
Below graph visualization shows what nodes and relationships are fetched from the graph for the provided query. Graph Memory Visualization 
m.search("Who is spiderman?", user_id="alice123")
Graph Memory Visualization
Note: The Graph Memory implementation is not standalone. You will be adding/retrieving memories to the vector store and the graph store simultaneously.
If you want to use a managed version of Zentry, please check out Zentry. If you have any questions, please feel free to reach out to us using one of the following methods:

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