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What is Weaviate?

Weaviate is an open-source, AI-native database that helps organizations build and ship AI applications on a single, scalable foundation. It stores data objects alongside the vector embeddings produced by your chosen machine learning models and scales smoothly to billions of records. Teams can supply their own vectors or use Weaviate's built-in vectorization, then query their data through vector, keyword, and hybrid search to surface the most relevant results, even with complex filters. By integrating with leading large language models, Weaviate makes it straightforward to build retrieval-augmented generation, grounded question answering, and intelligent search over proprietary data. Beyond core retrieval, Weaviate offers a growing platform: the Query Agent converts natural-language questions into precise, cited queries; Engram provides managed memory that lets AI agents retain context over time; and Weaviate Embeddings handles vectorization as a managed service. Organizations can self-host under an open-source license or adopt fully managed Weaviate Cloud on AWS, GCP, or Azure, with SOC 2 Type II compliance, multi-tenancy, replication, and role-based access control. From semantic search and recommendations to agentic automation, Weaviate turns business data into AI-powered products.

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.

Media

Media

Integrations Supported

Claude Code
Cursor
Gemini CLI
Model Context Protocol (MCP)
Agno
Airbyte
Amazon Bedrock
Amazon SageMaker
Cleanlab
Context Data
DSPy
Google Cloud Platform
Hugging Face
Jina AI
Mem0
Nomic Atlas
Parallel

Integrations Supported

Claude Code
Cursor
Gemini CLI
Model Context Protocol (MCP)
Antigravity CLI
Kiro
OpenAI Codex

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Open source (free); free Weaviate Cloud tier; paid Cloud plans from $45/mo.
Free Version

Pricing Information

Free
Free Version

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

Company Facts

Organization Name

Weaviate

Date Founded

2019

Company Location

The Netherlands

Company Website

weaviate.io

Company Facts

Organization Name

OpenViking

Date Founded

2026

Company Location

United States

Company Website

openviking.ai/

Categories and Features

AI Memory Layers

Not specified

Context Engineering

Not specified

Vector Databases

Not specified

Categories and Features

Agentic AI

Not specified

AI Memory Layers

Not specified

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