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What is OpenViking?

OpenViking serves as an innovative open-source context database specifically designed for AI agents, employing a file-system-based architecture to optimize the organization of memories, resources, and skills. Instead of treating context as scattered elements within a fragmented vector store, OpenViking integrates agent context into a cohesive virtual file system via the viking protocol, which empowers agents to efficiently store, explore, retrieve, and observe essential information. This framework significantly reduces the challenges associated with manual context management for developers, providing a simplified interaction model reminiscent of traditional file operations. Additionally, OpenViking supports hierarchical context loading, enabling semantic and recursive data retrieval, effective session management, comprehensive metrics tracking, and enhanced observability. As a result, AI agents can efficiently access relevant information without being inundated by excessive prompts. Ultimately, by implementing this advanced system, developers can substantially improve the overall performance and capability of their AI solutions.

What is Engram?

Engram is a sophisticated, fully managed system created to improve memory and contextual awareness for AI agents, allowing them to retain information, learn, and progress effectively as time passes. Instead of letting a vast array of unstructured conversations and events pile up, it skillfully converts disorganized interaction data into structured, enduring, and adaptable memories. Users can easily send raw text, full conversations, or pre-processed information through a REST API or Python SDK without any need for prior formatting. Engram then employs asynchronous processes to identify relevant information, streamline it by eliminating duplicates, and synchronize it with existing knowledge, leading to an improved memory state that operates independently of the application's core functions. It effectively manages inconsistencies and adapts to new preferences and changes in information over time, ensuring that the context remains pertinent and efficient. Furthermore, agents can quickly access prioritized memories using methods like vector similarity, BM25 keyword searches, or a blend of retrieval techniques, thus reducing the need for resending entire conversation histories. This innovative approach greatly boosts the interaction's efficiency and effectiveness, making AI agents more agile and better equipped to comprehend user requirements. Ultimately, Engram not only enhances the operational capabilities of AI agents but also fosters a more user-centric experience.

Media

Media

Integrations Supported

Aider
Antigravity CLI
Claude
Claude Code
Claude Desktop
Codex CLI
Cursor
Factory Droid
Gemini CLI
GitHub Copilot CLI
Google Antigravity
Hermes Agent
Kiro
Model Context Protocol (MCP)
OpenAI Codex
OpenClaw
OpenCode
Pi Agent
TRAE

Integrations Supported

Python

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

$45 per month
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

OpenViking

Date Founded

2026

Company Location

United States

Company Website

openviking.ai/

Company Facts

Organization Name

Weaviate

Date Founded

2019

Company Location

The Netherlands

Company Website

weaviate.io/product/engram

Categories and Features

Agentic AI

Not specified

AI Memory Layers

Not specified

Categories and Features

AI Memory Layers

Not specified

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