Give OpenClaw a Filesystem It Can Actually Use
Persistent, queryable storage purpose-built for autonomous agents.
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Your OpenClaw agent is only as fast as its database
OpenClaw is slow
OpenClaw is expensive

No persistent memory

10x Faster Agent Queries
GPU-native architecture means your OpenClaw agent retrieves data in milliseconds, not seconds. Stop waiting on your database — queries run on GPU, not CPU.
Persistent Agent Memory
Deeplake gives your OpenClaw agent long-term memory that scales. Queryable, versioned, and persistent across sessions — not a markdown file that breaks at scale.
Filesystem + CLI Built In
Your agent already knows bash. Deeplake's CLI and filesystem interface let your OpenClaw agent store, retrieve, and query data using tools it was trained on.
Built for OpenClaw Agents
Postgres
as simple as your favorite database
DuckDB
as fast as your best query engine
Deeplake
data lake optimized for AI
GPU
infinitely scalable
Your agents will produce results 3x faster
Agents are going to run an order of magnitude more queries. Deeplake enables agents to run those queries faster, at just a fraction of the cost.
Based on SF-100 data for analytical DBMS.
Total Time (seconds)
Total Cost (USD)
Database Cost per Workload
Deeplake offers significantly lower costs compared to traditional solutions.
Based on ClickBench data for analytical DBMS.
* comparison based on reported results
Loved by AI teams worldwide.
"It's next-level. We've enabled a new human-machine interface that is natural to use and yields high-accuracy results for end-users. I am confident that adopting Activeloop was a value-add through improved innovation that will compound as our AI operations grow"
"They started out with a vector store integration, so it's flown under the radar, but... @activeloopai's Deeplake is an intriguing fully-fledged serverless data lake that supports attribute based filtering, multiple distance functions, MMR search."
"As the datasets enlarge and become multi-modal, next-gen solutions built specifically to address those use cases, like Deeplake, will help AI teams deliver models to production faster, and more efficiently."
"We've 10x-ed the time to production, increased model accuracy by 19.5% and reduced training costs by 32%, setting new standards in the last-mile delivery space."
"Deeplake made working on more complex data no more complicated from a data management point of view. Whether I'm working on 10 million images with 10 different modalities or a thousand images with just one modality, it's all the same from the perspective of the user of the system."
Your OpenClaw agents will produce results 3x faster
Give your agents persistent memory, GPU-fast queries, and a filesystem they were trained on.