Coralbricks

Coralbricks offers a GPU‑native persistent memory layer that stores embeddings, KV caches, and graph data directly on the GPU, eliminating CPU round‑trips and enabling agents to resume sessions without recomputation. The platform provides a hierarchical memory architecture and a drop‑in query API compatible with CrewAI, LangChain, and OpenClaw, boosting inference throughput and reducing costs for long‑running, collaborative AI agents.

Redmond, United States210+ followers

Updated 2 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

AI agents that operate over multiple steps or sessions must repeatedly recompute context and retrieve embeddings, KV caches, and knowledge‑graph data, causing CPU‑GPU bottlenecks and high inference costs. Existing infrastructure treats each request as stateless, preventing agents from sharing information and leading to idle GPU resources.

Solution

Coralbricks provides a GPU‑native persistent memory layer that stores embeddings, KV caches, and graph data directly on the GPU, eliminating costly CPU round‑trips. The runtime dynamically allocates this context across hot, warm, and cold memory tiers, ensuring the most relevant data is always at the fastest access level. By keeping context resident, agents can resume sessions without re‑computing prior steps and can share retrieved knowledge across a swarm, improving throughput and reducing per‑inference cost. The platform offers a simple query API compatible with existing agent frameworks such as CrewAI, LangChain, and OpenClaw, allowing seamless integration into current pipelines.

Target Audience

Primary customers are developers and enterprises building long‑running, collaborative AI agents or agent swarms that require high‑throughput inference and shared context, particularly those using frameworks like CrewAI, LangChain, or OpenClaw.

Features

Persistent GPU‑resident storage for embeddings, KV caches, and knowledge‑graph data
Hierarchical memory architecture with hot, warm, and cold tiers for predictable latency
Zero‑CPU‑round‑trip context preparation, enabling 2–5× faster inference economics
Shared, resumable context across multiple agents in a swarm, eliminating re‑computation
Integrated GPU‑accelerated vector search and graph traversal, up to 15× faster than CPU‑based libraries
Drop‑in query API compatible with popular agent stacks (CrewAI, LangChain, OpenClaw)