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Hermes Agent for Private AI: Build Secure, Self-Hosted AI Assistants with Local Models, Persistent Memory, Private RAG, MCP Tools, and Privacy-First A
Paperback
Series: Hermes Agent Engineering
General ComputersComputer Security
Publisher: Independently Published
Published: Aug 21 2026
Pages: 394
Weight: 2.01
Height: 0.81 Width: 8.50 Depth: 11.00
Language: English
Build powerful AI assistants without giving up control of your private data.
Running a model locally is only the first step. Once an AI agent can remember information, search private documents, call tools, browse websites, connect to MCP servers, or run scheduled workflows, privacy becomes an end-to-end engineering problem.
Hermes Agent for Private AI shows you how to build a secure, self-hosted AI environment with local models, persistent memory, private RAG, controlled tools, MCP integrations, browser automation, and recovery-ready infrastructure.
Inside, you will learn how to:
Install and secure Hermes Agent
Connect Ollama, llama.cpp, vLLM, and other local model servers
Prevent unintended cloud fallback
Build safe, consent-based persistent memory
Create private RAG with local embeddings, retrieval, reranking, and citations
Secure local tools and private MCP servers
Defend against prompt injection and memory poisoning
Control browser and scheduled automation
Run Hermes offline or in restricted environments
Add authentication, user isolation, monitoring, encrypted backups, and disaster recovery
You will also build three connected projects:
Private Document Intelligence Assistant - search, summarize, compare, and extract information from confidential documents.
Private Research and Workflow Assistant - combine controlled web research with private local knowledge.
Complete Hermes Private AI Workspace - integrate models, memory, RAG, tools, MCP, automation, security, monitoring, and recovery into one self-hosted platform.
Whether you are a developer, local-AI enthusiast, security-conscious practitioner, consultant, or technical team, this book gives you a practical path from local AI experimentation to a secure, privacy-first agent system you control.
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