Hermes Agent Hosting BD: Complete Technical Architecture Explained

Most users of Hermes Agent Hosting BD want to get started quickly and never think about the infrastructure. But for developers and technically curious users, understanding what is running underneath can inform how to use it most effectively. This post covers the complete technical architecture.

The Three-Layer Hermes Stack

Hermes Agent Hosting BD is built on three primary components, each handling a different aspect of the AI agent system:

1. Language Model Layer — Google DeepMind Gemma (via SNBD API)
2. API Routing Layer — LiteLLM
3. Memory Layer — Qdrant vector database

These three layers are wrapped in SNBD HOST's managed infrastructure and connected to app integration adapters for Telegram, Discord, and Slack.

Layer 1: Google Gemma Models

The language intelligence in Hermes comes from Google DeepMind's Gemma model family, served through the SNBD API system. Gemma is the open-weight model series built on the same research as Google's Gemini, optimized for deployment on consumer and cloud hardware.

Why Gemma instead of other open-source models?

• Quality: Gemma consistently outperforms models of similar size on multilingual benchmarks, including Bangla performance
• Efficiency: Optimized for inference speed — lower latency per token than equivalently-sized alternatives
• Updateability: Google releases improved Gemma versions regularly; Hermes Agent Hosting BD users automatically get newer versions during maintenance
• Licensing: Gemma's usage terms permit commercial deployment, which allows SNBD HOST to offer it as a managed service

Layer 2: LiteLLM API Router

LiteLLM sits between your application and the Gemma model, providing:

• OpenAI-compatible API surface: The same /v1/chat/completions endpoint format as OpenAI, so any OpenAI SDK works with Hermes without code changes
• Model aliasing: You call the model as hermes or gemma; LiteLLM resolves which specific model version to use
• Token accounting: LiteLLM tracks token usage per request and enforces the 10M token allowance per Hermes instance
• Fallback routing: If the primary model endpoint is under load, LiteLLM can automatically route to a backup instance
• Request logging: Usage data is stored for the token usage dashboard in your Hermes account

Layer 3: Qdrant Vector Database

Qdrant is the memory system for Hermes Agent Hosting BD. It stores content as vector embeddings — mathematical representations of meaning — which enables semantic retrieval. When your agent needs to answer a question, it:

1. Embeds the incoming question as a vector
2. Searches Qdrant for semantically similar content from your knowledge base
3. Retrieves the top-k most relevant chunks
4. Passes those chunks as context to Gemma
5. Generates a response grounded in your actual data

This retrieval-augmented generation (RAG) pattern is what gives Hermes accurate, factual answers about your specific business — rather than generic AI responses that may be wrong or outdated.

Your Qdrant instance stores:

• Knowledge base content: Documents, FAQs, product catalogs you upload
• Conversation memory: Selected past interactions, enabling cross-session context
• User profiles (optional): Customer preference data for personalised responses

Infrastructure: SNBD HOST Bangladesh

All three layers run on SNBD HOST's infrastructure, which provides:

• BDIX peering: Direct peering with the Bangladesh Data Exchange ensures low-latency responses for local users — typically under 10ms within Bangladesh
• Dhaka-based servers: Your data stays in Bangladesh, relevant for data sovereignty and compliance
• PM2 process management: Each Hermes component is managed by PM2 with auto-restart on crash
• Regular snapshots: Qdrant data is snapshotted periodically to protect against data loss

App Integration Layer

The integration layer connects your AI agent to messaging platforms:

• Telegram: Long-polling or webhook connection via the Telegram Bot API. Messages arrive → embedded → Qdrant search → Gemma generation → reply sent
• Discord: WebSocket connection via Discord.js. Commands and mentions route to Hermes
• Slack: Events API webhook. Mentions and DMs route to Hermes
• Custom API: Your application calls the OpenAI-compatible endpoint directly

What This Means for Users

Understanding the architecture helps you use Hermes more effectively:

• Better knowledge base quality → better Qdrant retrieval → better Gemma responses. Invest in your knowledge base content.
• The system prompt is sent as context for every query — keep it concise and specific. Overly long prompts consume tokens per request.
• Qdrant retrieval has a depth limit — if you have a very large knowledge base, consider breaking documents into smaller, focused chunks for better retrieval precision.

Apply for the Hermes Agent Hosting BD beta and get access to this full stack for free.