Databases & backend
Activeloop
Continual learning infrastructure for AI agents
Activeloop provides infrastructure that enables AI agents to learn continuously from each execution cycle, making subsequent runs smarter and cheaper through a
Overview

A look at activeloop.ai
Activeloop provides continual learning infrastructure for AI applications through three core products. Deeplake is a GPU-native database that keeps AI agents grounded, versioned, and queryable with vector and tensor storage in one place. Hivemind captures agent execution traces and turns them into shared organizational memory and skills. Refinery is a software factory that converts feedback into production improvements through continuous learning cycles.
The company's approach makes every operation a continuous loop: teams observe production work, convert those observations into shared memory, improve the next cycle's skills and workflows, and verify improvements before shipping. This explicit loop across data, memory, and software enables organizations to compound intelligence over time.
Feature notes
- GPU streaming to fine-tuning
- Serverless Postgres interface
- Trajectory capture for agent traces
- Cross-team memory synchronization
- Skill distribution across organization
- Database optimization
- Kernel generation
- Physical AI policy optimization
- Production verification and regression checking
Pricing
Pricing model: Contact for pricing
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FAQ
- What types of organizations use Activeloop?
- Activeloop is deployed in production by startups and Fortune 500 innovators across industries including biomedical, multimedia, robotics, and MedTech.
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