The high-speed neural cache powering real-time AI across the Praxis stack.
Hermes is the dynamic short-term memory system inside Praxis OS — a dedicated NVMe-backed cache layer built to enable **real-time AI orchestration**. It handles fast model loads, low-latency inference execution, and direct coordination with long-term archive nodes like Alexandria.
When other systems are bottlenecked by remote storage or bloated datacenter pipelines, Hermes keeps your agents responsive and your models hot — even at the edge, offline, or under 100ms constraints.
Hermes is modular and deploys in one of two distinct roles:
This flexibility lets you scale from a single edge node to a multi-tier, multi-node AI mesh — without rewriting your logic.
Delivers active model data and embeddings at SSD/NVMe speeds. Cuts response time for agents dramatically.
Anticipates what data you'll need based on context from Pantheon chains and agent routing.
In Cache Mode, Hermes replicates completed chains, logs, and agent states to Alexandria or other archive systems.
Run on a Pi 5, an edge server, or full rack node. Just pick "Hermes" in your Praxis install and go.
Hermes is the AI equivalent of RAM + L3 cache in your operating system — except distributed, sovereign, and AI-native. It routes memory, bridges nodes, and empowers your agents to function autonomously even under heavy load.
The chain of command looks like this:
User → Pantheon → Hermes → Agent Chain → Archive/Output
(Visual diagram coming soon)
Hermes is ideal for edge environments, real-time applications, and distributed mesh systems. You can deploy Hermes with any device capable of NVMe or SSD-level speeds — from Pi clusters to industrial nodes to full enterprise-grade servers.
This isn’t cloud caching. This is command-grade memory for sovereign AI.
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