📢 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.
Overview
Zentry provides a suite of tools for storing, searching, and retrieving memories, enabling agents to maintain context and learn from past interactions. The tools are built as Langchain tools, making them easily integrable with any AI agent implementation.
Installation
Install the required dependencies:
pip install langchain_core
pip install Zentryai
Authentication
Import the necessary dependencies and initialize the client:
from langchain_core.tools import StructuredTool
from Zentry import MemoryClient
from pydantic import BaseModel, Field
from typing import List, Dict, Any, Optional
import os
os.environ["Zentry_API_KEY"] = "your-api-key"
client = MemoryClient(
org_id=your_org_id,
project_id=your_project_id
)
Zentry provides three main tools for memory management:
The ADD tool allows you to store new memories with associated metadata. It’s particularly useful for saving conversation history and user preferences.
Schema
class Message(BaseModel):
role: str = Field(description="Role of the message sender (user or assistant)")
content: str = Field(description="Content of the message")
class AddMemoryInput(BaseModel):
messages: List[Message] = Field(description="List of messages to add to memory")
user_id: str = Field(description="ID of the user associated with these messages")
output_format: str = Field(description="Version format for the output")
metadata: Optional[Dict[str, Any]] = Field(description="Additional metadata for the messages", default=None)
class Config:
json_schema_extra = {
"examples": [{
"messages": [
{"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
{"role": "assistant", "content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy."}
],
"user_id": "alex",
"output_format": "v1.1",
"metadata": {"food": "vegan"}
}]
}
Implementation
def add_memory(messages: List[Message], user_id: str, output_format: str, metadata: Optional[Dict[str, Any]] = None) -> Any:
"""Add messages to memory with associated user ID and metadata."""
message_dicts = [msg.dict() for msg in messages]
return client.add(message_dicts, user_id=user_id, output_format=output_format, metadata=metadata)
add_tool = StructuredTool(
name="add_memory",
description="Add new messages to memory with associated metadata",
func=add_memory,
args_schema=AddMemoryInput
)
Example Usage
The SEARCH tool enables querying stored memories using natural language queries and advanced filtering options.
Schema
class SearchMemoryInput(BaseModel):
query: str = Field(description="The search query string")
filters: Dict[str, Any] = Field(description="Filters to apply to the search")
version: str = Field(description="Version of the memory to search")
class Config:
json_schema_extra = {
"examples": [{
"query": "tell me about my allergies?",
"filters": {
"AND": [
{"user_id": "alex"},
{"created_at": {"gte": "2024-01-01", "lte": "2024-12-31"}}
]
},
"version": "v2"
}]
}
Implementation
def search_memory(query: str, filters: Dict[str, Any], version: str) -> Any:
"""Search memory with the given query and filters."""
return client.search(query=query, version=version, filters=filters)
search_tool = StructuredTool(
name="search_memory",
description="Search through memories with a query and filters",
func=search_memory,
args_schema=SearchMemoryInput
)
Example Usage
The GET_ALL tool retrieves all memories matching specified criteria, with support for pagination.
Schema
class GetAllMemoryInput(BaseModel):
version: str = Field(description="Version of the memory to retrieve")
filters: Dict[str, Any] = Field(description="Filters to apply to the retrieval")
page: Optional[int] = Field(description="Page number for pagination", default=1)
page_size: Optional[int] = Field(description="Number of items per page", default=50)
class Config:
json_schema_extra = {
"examples": [{
"version": "v2",
"filters": {
"AND": [
{"user_id": "alex"},
{"created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}},
{"categories": {"contains": "food_preferences"}}
]
},
"page": 1,
"page_size": 50
}]
}
Implementation
def get_all_memory(version: str, filters: Dict[str, Any], page: int = 1, page_size: int = 50) -> Any:
"""Retrieve all memories matching the specified criteria."""
return client.get_all(version=version, filters=filters, page=page, page_size=page_size)
get_all_tool = StructuredTool(
name="get_all_memory",
description="Retrieve all memories matching specified filters",
func=get_all_memory,
args_schema=GetAllMemoryInput
)
Example Usage
Integration with AI Agents
All tools are implemented as Langchain StructuredTool instances, making them compatible with any AI agent that supports the Langchain tools interface. To use these tools with your agent:
- Initialize the tools as shown above
- Add the tools to your agent’s toolset
- The agent can now use these tools to manage memories through natural language interactions
Each tool provides structured input validation through Pydantic models and returns consistent responses that can be processed by your agent.
Help
In case of any questions, please feel free to reach out to us using one of the following methods: