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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Deeplake is an AI data runtime and GPU database built for teams developing agents, RAG systems, multimodal applications, robotics workflows, and generative media products. It is designed to solve the gap between GPU-powered AI models and CPU-bound data systems by keeping data closer to where AI workloads execute. The platform supports serverless Postgres, vector search, multimodal data storage, analytical workloads, and AI-optimized data lake functionality. Deeplake helps agents remember, retrieve, and act in fast cycles, making it useful for systems that need repeated context retrieval across long-running tasks. It can manage complex data such as video, images, point clouds, sensors, PDFs, audio, embeddings, model weights, and structured records. Developers can use familiar database concepts while gaining support for GPU-speed retrieval and scalable AI data operations. The platform is positioned for production-grade AI use cases where agents may generate databases, query thousands of times, and require faster memory access. Deeplake also supports private deployment patterns, including VPC environments, so organizations can keep sensitive data within their own infrastructure. With open-source adoption, enterprise security credentials, and a focus on agentic workloads, Deeplake helps AI teams build faster and more efficient data systems.

Description

Engram is an advanced, fully managed service designed to enhance memory and context for AI agents, enabling them to retain, learn, and evolve effectively over time. Rather than accumulating an unmanageable collection of unstructured conversations and events, it meticulously transforms chaotic interaction data into well-organized, lasting, and adaptive memories. Applications can seamlessly transmit raw text, entire dialogues, or pre-processed facts via a REST API or Python SDK with no need for prior formatting. Engram then operates asynchronous processes that extract pertinent information, streamline it by removing duplicates and aligning it with existing knowledge, resulting in a refined memory state that does not interfere with the main operations of the application. It addresses inconsistencies, adjusts to evolving preferences and changing information over time, ensuring that the context remains both relevant and efficient. Additionally, agents have the capability to access prioritized memories instantly through vector similarity, BM25 keyword searches, or a combination of retrieval methods, thereby minimizing the necessity to resend complete conversation logs. This approach significantly enhances the efficiency and effectiveness of interactions, making AI agents more responsive and capable of understanding user needs.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Activeloop
Amazon SageMaker
Amazon Web Services (AWS)
ChatGPT
Google Cloud Platform
Jupyter Notebook
LangChain
OpenAI
PyTorch
Python
TensorFlow
Weaviate

Integrations

Activeloop
Amazon SageMaker
Amazon Web Services (AWS)
ChatGPT
Google Cloud Platform
Jupyter Notebook
LangChain
OpenAI
PyTorch
Python
TensorFlow
Weaviate

Pricing Details

$0
Free Trial
Free Version

Pricing Details

$45 per month
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Activeloop

Founded

2018

Country

United States

Website

deeplake.ai/

Vendor Details

Company Name

Weaviate

Founded

2019

Country

The Netherlands

Website

weaviate.io/product/engram

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Product Features

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