QuData

Hermes

A self-improving agent that grows with youAn open-source agent from Nous Research with a closed learning loop: it builds skills from successful runs, remembers your preferences, and stitches messengers, CRMs and infrastructure into one autonomous flow.
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Hermes: AI agent for autonomous workflows

Hermes is one of the few AI agents built not just for text generation, but for end-to-end automation of engineering and business workflows. It is designed to work with infrastructure, preserve context across tasks, and reduce manual operational overhead.

In practice, Hermes connects Telegram, WhatsApp, CRM systems, monitoring tools, issue trackers, and internal services into one AI workflow. It can process voice messages, transcribe them, execute commands, run runbooks, and deliver reports automatically.

From an infrastructure standpoint, Hermes fits almost any environment: local, Docker, Kubernetes, Modal, or Vercel Sandbox. You can keep the control process on VPS and move inference to QuData GPU instances like RTX 4090 or A100 80GB to scale securely without cloud API lock-in.

Run Hermes Agent on a QuData GPU

A self-improving agent on a rented GPU. No cloud limits, no vendor lock-in.
Open Source MITRuns on your GPUMemory and cron included
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
Rent a GPU for Hermes
Open on GitHub

Capabilities

What ships out of the box — no add-ons, no paid modules.
Closed learning loopEvery solved task becomes a skill: the agent curates reusable playbooks and improves them as it works.
User-level memoryHoncho dialectic modelling and FTS5 search across the entire session history keep context and preferences alive.
Cron automationsBuilt-in scheduler delivers reports, backups and audits to any channel — described in natural language.
Parallel sub-agentsSpawns isolated agents for parallel work — long chains collapse into a single zero-context-cost turn.
Seven runtime backendsLocal, Docker, Kubernetes, Singularity, Modal, Daytona and Vercel Sandbox — choose where it runs without rewriting the agent.
Any LLMOpenAI, Anthropic, local Hugging Face models and your own inference — switch without vendor lock-in.

Use cases

Examples already running on QuData infrastructure.
Voice & text assistant 24/7Takes voice notes from Telegram and WhatsApp, transcribes, replies and executes commands on the server itself.
Autonomous DevOps on-callParses alerts, runs runbooks, files a report in the tracker — no human pager rotation required.
Scheduled analytics builderCollects data nightly, builds summaries and delivers them to the right channel.
Personal researcherReads sources, accumulates knowledge about your projects and is available from CLI or messenger any time.

How to run it locally

Step-by-step install with no external service dependencies.
  1. 1
    Run the one-line installer
    curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
  2. 2
    Reload your shell to refresh PATH
    source ~/.bashrc
  3. 3
    Run the initial setup wizard
    hermes setup
  4. 4
    Connect messengers and pick a model
  5. 5
    Launch the agent — it is ready to work
    hermes

Run Hermes on a rented GPU

QuData GPUs are available hourly or monthly. No overcommit, no hidden limits, no vendor lock-in.
Rent a GPU for Hermes
FAQ

Frequently asked about Hermes

What does "an agent that grows with you" mean?

Hermes turns successful runs into skills, remembers your history and gradually specialises around your workflows and infrastructure.

What GPU is needed for production?

A single user is fine on an RTX 4090, a team needs an A100 80GB. Both are available hourly and monthly on QuData.

Can I keep the agent on a small VPS?

The Hermes process itself is light — it lives on a VPS while heavy inference runs on the QuData-rented GPU.

How do I add my own integrations?

Through MCP tools and the agentskills.io standard — Hermes will pick up the new skill without a restart.