What Is Hermes Agent? A Complete Guide to Building a Private, Local AI Agent

Hermes Agent is an open-source AI agent framework developed by Nous Research. Instead of limiting an LLM to answering messages, Hermes gives it an execution environment where it can use tools, work with files, execute terminal commands, browse websites, interact with desktop applications, remember information across sessions, learn reusable skills, schedule tasks, and connect to external systems.

At a glance: Hermes Agent is not an LLM; it is an agent runtime and orchestration layer that connects a language model to tools, files, terminals, browsers, memory, skills, MCP, and automation.

The most important distinction is that Hermes Agent is not an LLM. The language model acts as the reasoning engine, while Hermes supplies the agent loop, tools, memory, orchestration, integrations, and execution controls.

A simplified architecture looks like this:

User
 ↓
Hermes Agent
 ↓
LLM
 ↓
Decision / Tool Call
 ↓
Files, Terminal, Browser, MCP, Memory, APIs
 ↓
Observation
 ↓
LLM
 ↓
Final Action or Answer

Hermes can use cloud models such as Claude, GPT, Gemini, DeepSeek and many other providers, or it can connect to local models through Ollama, LM Studio, llama.cpp, vLLM, SGLang, and other OpenAI-compatible endpoints.

Can Hermes be completely private?

Yes, depending on your configuration. If the model runs locally and the tools you use are local, model inference can remain on your machine. Hermes also stores its built-in conversations, memories, and skills locally.

However, local and fully offline are not the same thing. If you enable a cloud LLM, cloud browser, web search provider, image-generation service, hosted memory provider, or another external integration, the data required for that operation is sent to that provider.

Hermes vs Ollama

Ollama is primarily a runtime for running local models. Hermes is an agent runtime that uses a model to decide what actions to take.

Ollama = runs the model
Hermes = turns the model into an agent

A common local architecture is:

Hermes Agent
 ↓
Ollama
 ↓
Qwen / Gemma / Llama / another model
 ↓
Local GPU

What can Hermes do?

1. Terminal and file operations

Hermes can execute shell commands, manage processes, inspect files, read source code and patch files. This makes it useful for coding, DevOps, server administration and automation.

A request such as:

Inspect this repository, identify why the build fails, fix the problem and run the tests.

can become a multi-step agent workflow rather than a single text response.

2. Browser automation

Hermes can navigate websites, inspect page content, click elements, fill forms and extract information. It supports local Chromium-family browsers as well as multiple cloud browser backends.

This allows workflows such as checking dashboards, gathering information from web applications or automating repetitive browser tasks.

3. Computer Use

Computer Use extends automation beyond the browser. Hermes can interact with graphical desktop applications on supported Windows, macOS and Linux systems.

This is useful when an application does not provide a usable API. Permissions vary by operating system; macOS requires Accessibility and Screen Recording permissions, while Linux requires an accessible graphical display environment.

4. Persistent memory

Hermes has built-in cross-session memory. Its main internal memory files are MEMORY.md, which stores selected environment and project knowledge, and USER.md, which stores relevant user preferences and profile information.

Past sessions are also stored locally and can be searched later. This means an agent can recover information from previous work instead of treating every session as completely new.

5. Skills and self-improvement

Skills are reusable procedural knowledge. If Hermes discovers a reliable way to perform a multi-step task, that workflow can be stored as a Skill and loaded again when relevant.

For example:

1. Pull repository
2. Run tests
3. Build image
4. Restart service
5. Verify health endpoint

can become a reusable deployment Skill.

Hermes can create and update skills itself. This is an important part of its self-improvement model. In normal usage, however, this does not mean that Hermes continuously fine-tunes the weights of the underlying LLM. The learning loop is primarily based on memory, saved procedures, context and reusable skills.

6. MCP integration

Hermes supports Model Context Protocol. MCP allows the agent to discover and use tools hosted outside Hermes without implementing every integration directly in its core.

MCP servers can expose systems such as GitHub, databases, filesystems, internal company APIs and many other applications. Both local stdio servers and remote HTTP MCP servers are supported.

7. Plugins

Hermes has a plugin architecture for extending its behavior. A plugin can add new tools, lifecycle hooks, commands or custom integrations.

A useful mental model is:

Skill = reusable instructions and procedures
MCP = standardized connection to external tools
Plugin = native Hermes extension

8. Subagents and delegation

Hermes can delegate work to subagents with isolated context. A larger task can therefore be broken into research, coding, validation or other independent subtasks.

Multi-agent architecture is useful for genuinely complex work, but it should not be used automatically for simple tasks because it increases latency, cost and debugging complexity.

9. Scheduled automation

Hermes includes a cron system for one-shot and recurring jobs. Tasks can run after a delay, at fixed intervals, through standard cron expressions or at exact timestamps.

Jobs can be created, edited, paused, resumed, triggered and removed. They can also deliver results to configured destinations.

10. Messaging Gateway

Hermes can run as an always-on assistant reachable through messaging platforms. Its gateway supports platforms such as Telegram, Discord, Slack, WhatsApp, Signal, Email, Microsoft Teams, Matrix, Home Assistant and others.

A typical setup starts with:

hermes gateway setup

It is best to make the normal CLI agent reliable before adding an always-on gateway.

11. Vision, voice and media

Hermes supports vision-capable models, image analysis, image generation, text-to-speech and speech-to-text integrations. Whether processing happens locally or in the cloud depends on the provider you configure.

12. OpenAI-compatible API server

Hermes can expose itself as an OpenAI-compatible HTTP API. Applications such as Open WebUI, LobeChat, LibreChat or your own frontend can use Hermes as their AI backend.

Application
 ↓
OpenAI-compatible API
 ↓
Hermes Agent
 ↓
Model + Tools + Memory

This is particularly useful when building a product or internal company interface on top of Hermes.

13. IDE integration

Hermes supports Agent Client Protocol integration for compatible editors. This allows agent conversations, tool activity, file changes and terminal operations to appear inside the development environment.

14. Personality and SOUL.md

The agent’s identity can be customized through SOUL.md and personality presets. You can therefore configure separate agents for coding, research, security operations, CRM, support or other specialist roles.

Installing Hermes

Windows

Hermes Desktop is the easiest path for most users. The native CLI can also be installed from PowerShell:

iex (irm https://hermes-agent.nousresearch.com/install.ps1)

Linux, WSL2 and macOS

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

The installer manages most required runtime dependencies automatically. On Linux, Git, curl and xz-utils should be available, and Desktop builds may additionally require standard build tools.

Initial setup

Run:

hermes setup

Then select a model provider:

hermes model

Configure tools with:

hermes tools

Start the classic interface:

hermes

or the modern terminal UI:

hermes --tui

For diagnostics:

hermes doctor

Running Hermes with Ollama

Ollama is one of the easiest ways to combine Hermes with a fully local LLM.

Start your model in Ollama, run:

hermes model

and select a Custom Endpoint. A typical Ollama OpenAI-compatible base URL is:

http://localhost:11434/v1

Enter the exact model name and configure a sufficient context size.

Hermes currently expects the main model to support approximately 64K tokens of context for its multi-step tool workflows. This is an important hardware consideration because a large context window increases RAM and VRAM usage.

Hermes-managed local models

Hermes Desktop can also manage local models through llama.cpp. It can select an appropriate inference runtime and manage much of the local model configuration for the user.

Once a model is downloaded, a fully local configuration can operate without a cloud LLM, provided you do not enable cloud-dependent tools.

Hardware requirements

There are two very different deployment modes.

Cloud-model setup

When the LLM runs through a cloud provider, your computer is not performing the heavy model inference. A modern CPU and roughly 8GB or more system RAM is a practical baseline for Hermes itself, although this is a practical recommendation rather than an official minimum. A dedicated GPU is not required.

Local-model setup

Local inference changes the hardware requirements significantly.

Hermes documentation states that GPUs with at least 8GB of memory handle the smaller local catalog models comfortably, while 16GB or more can run 27–35B models at higher quality. Real requirements still depend on quantization, model architecture, context size and concurrency.

Supported platforms

Primary supported platforms include Windows 10/11, Linux, WSL2 and Apple Silicon macOS. Docker is also supported, and Android/Termux has a separate installation path.

Windows is convenient for a personal desktop agent. Linux is usually the cleaner choice for an always-on server, gateway, automation environment or multi-user deployment.

Security

An agent with terminal, browser and filesystem access is powerful software and should be treated accordingly.

Hermes includes dangerous-command approval checks and file-write protections, but those controls should not be treated as a substitute for operating-system isolation.

For sensitive environments:

• Run the agent as a restricted OS user.
• Limit filesystem access to specific workspaces.
• Keep credentials outside normal project files.
• Consider Docker isolation for terminal execution.
• Enable only the MCP servers, plugins and tools actually required.
• Test unattended automation in an isolated environment first.
• Keep human approval for destructive or financially sensitive actions.

Using private documents

Giving documents to Hermes does not automatically fine-tune the underlying LLM. Documents can instead be provided through files, project context, skills, MCP-connected knowledge services or an external retrieval system.

If you want the actual model weights to learn from a dataset, fine-tuning, LoRA or QLoRA is a separate process. The resulting model can then be served locally and connected to Hermes.

A more advanced private architecture can look like this:

Private Documents
 ↓
RAG / Knowledge Layer
 ↓
Local LLM
 ↑
Hermes Agent
 ├── Memory
 ├── Skills
 ├── MCP
 ├── Browser
 ├── Terminal
 └── Automation

A zero-to-production learning path

Start with a normal Hermes chat and one model provider. Next, learn file and terminal tools, then persistent memory and session search. Create one custom Skill, enable browser automation, connect one MCP server and add a simple scheduled task. Once that setup is stable, connect the messaging gateway and build a dedicated agent identity using SOUL.md, restricted tools, skills and memory.

For application development, expose Hermes through its API server. For production use, add sandboxing, restricted credentials, monitoring, backups and explicit approval policies.

Example projects

Hermes can be used to build a private coding assistant, server-management agent, research agent, SOC assistant, log-analysis agent, CRM agent, sales assistant, personal knowledge agent, GitHub automation agent, Telegram assistant, Home Assistant controller, document-processing agent or content-management agent.

Conclusion

Hermes Agent is best understood as an execution and orchestration layer around an LLM rather than another chatbot or language model. The LLM provides reasoning; Hermes provides tools, memory, reusable skills, browser and desktop interaction, MCP, scheduling, messaging and APIs.

You can build a fully local setup, a cloud-powered setup or a hybrid system. The safest way to start is with one model and a small set of tools, then gradually add memory, skills, MCP, browser automation, cron, gateways and multi-agent capabilities only when the use case actually requires them.