ai-agents developer-tools memory-management serverless-architecture

Sub-Millisecond RAG on Apple Silicon. No Server. No API. One File

GitHub - christopherkarani/Wax: 🍯 Memory layer for on-device AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer.

github.com

February 17, 2026

6 min read

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Summary

Wax is a memory layer designed for on-device AI agents, replacing complex retrieval-augmented generation (RAG) pipelines with a single-file, serverless solution. It allows users to create a memory file, store information, and recall it efficiently without the need for additional infrastructure.

Key Takeaways

Wax is a serverless, single-file memory layer for on-device AI agents that simplifies the retrieval-augmented generation (RAG) process by replacing multiple services with a single file format.
The memory layer achieves fast vector search latency of 0.84ms at 10,000 documents using Metal GPU, and it operates entirely on-device with no network calls.
Wax supports deterministic recall, ensuring the same query yields the same context every time, and is designed to be portable and durable, with features that protect against power loss and data corruption.

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Coverage & discussions

Hacker News · github.com

Sub-Millisecond RAG on Apple Silicon. No Server. No API. One File ↗

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Analysis ready

Community Sentiment

Positive

Positives

The architecture leverages Metal-accelerated vector search, enabling sub-millisecond response times that enhance interactive search experiences for users.
Creating a local RAG solution without cloud dependencies democratizes access to advanced AI capabilities, making it more accessible for developers and researchers.
The ability to query embeddings directly from unified memory eliminates CPU-GPU overhead, significantly improving performance for AI applications.

Concerns

Some users feel this technology could have been integrated into macOS as a more robust feature, indicating a missed opportunity for Apple.
There are concerns about the necessity of this new approach when existing solutions like SQLite-vec and Qdrant already provide similar functionalities.