What is Zentry

Zentry is a local and secure memory management solution designed for AI agents, enhancing their capabilities with intelligent memory that allows for personalized interactions and improved performance.

Use cases

Use cases include AI assistants for consistent conversations, customer support systems that recall user history, healthcare applications that track patient preferences, and adaptive environments in gaming and productivity tools.

How to use

Users can choose between a hosted platform for quick setup or a self-hosted open-source package. To use Zentry, sign up on the Zentry Platform or install the SDK via pip or npm.

Key features

Key features include multi-level memory that retains user, session, and agent states, an intuitive API, cross-platform SDKs, and a fully managed service option, offering significant improvements in accuracy, speed, and token usage.

Where to use

Zentry can be utilized in various fields such as customer support, healthcare, AI assistants, and productivity applications, where personalized and context-rich interactions are essential.

📄 Building Production-Ready AI Agents with Scalable Long-Term Memory →

⚡ +26% Accuracy vs. OpenAI Memory • 🚀 91% Faster • 💰 90% Fewer Tokens

🔥 Research Highlights

• +26% Accuracy over OpenAI Memory on the LOCOMO benchmark
• 91% Faster Responses than full-context, ensuring low-latency at scale
• 90% Lower Token Usage than full-context, cutting costs without compromise

Introduction

Zentry enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. It remembers user preferences, adapts to individual needs, and continuously learns over time—ideal for customer support chatbots, AI assistants, and autonomous systems.

Key Features & Use Cases

Core Capabilities:

• Multi-Level Memory: Seamlessly retains User, Session, and Agent state with adaptive personalization
• Developer-Friendly: Intuitive API, cross-platform SDKs, and a fully managed service option

Applications:

• AI Assistants: Consistent, context-rich conversations
• Customer Support: Recall past tickets and user history for tailored help
• Healthcare: Track patient preferences and history for personalized care
• Productivity & Gaming: Adaptive workflows and environments based on user behavior

🚀 Quickstart Guide

Choose between our hosted platform or self-hosted package:

Hosted Platform

Get up and running in minutes with automatic updates, analytics, and enterprise security.

1. Sign up on Zentry Platform
2. Embed the memory layer via SDK or API keys

Self-Hosted (Open Source)

Install the sdk via pip:

pip install Zentryai

Install sdk via npm:

npm install Zentryai

Basic Usage

Zentry requires an LLM to function, with gpt-4o-mini from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our Supported LLMs documentation.

First step is to instantiate the memory:

from openai import OpenAI
from Zentry import Memory

openai_client = OpenAI()
memory = Memory()

def chat_with_memories(message: str, user_id: str = "default_user") -> str:
    # Retrieve relevant memories
    relevant_memories = memory.search(query=message, user_id=user_id, limit=3)
    memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories["results"])

    # Generate Assistant response
    system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
    messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
    response = openai_client.chat.completions.create(model="gpt-4o-mini", messages=messages)
    assistant_response = response.choices[0].message.content

    # Create new memories from the conversation
    messages.append({"role": "assistant", "content": assistant_response})
    memory.add(messages, user_id=user_id)

    return assistant_response

def main():
    print("Chat with AI (type 'exit' to quit)")
    while True:
        user_input = input("You: ").strip()
        if user_input.lower() == 'exit':
            print("Goodbye!")
            break
        print(f"AI: {chat_with_memories(user_input)}")

if __name__ == "__main__":
    main()

For detailed integration steps, see the Quickstart and API Reference.

🔗 Integrations & Demos

• ChatGPT with Memory: Personalized chat powered by Zentry (Live Demo)
• Browser Extension: Store memories across ChatGPT, Perplexity, and Claude (Chrome Extension)
• Langgraph Support: Build a customer bot with Langgraph + Zentry (Guide)
• CrewAI Integration: Tailor CrewAI outputs with Zentry (Example)

📚 Documentation & Support

• Full docs: https://docs.zentry.gg
• Community: Telegram · Twitter
• Contact: [email protected]