# Hermes Agent Integration

This guide shows the fastest way to connect Hermes Agent to Crynux Bridge as a custom LLM provider.

Hermes Agent is an open-source, self-improving AI agent from Nous Research. It is designed as an autonomous assistant for chat, automation, tools, and coding workflows, and it supports OpenAI-compatible endpoints, so Crynux Bridge can be used directly as a custom provider backend. Learn more on the official website: <https://hermes-agent.nousresearch.com/>.

The core setup is simple:

* set Hermes provider to `custom`
* set Crynux Bridge `base_url`
* set your Crynux API token

## Before You Start

Prepare these three items before configuration:

### 1) Base URL

The standard Crynux Bridge endpoint is:

* `https://bridge.crynux.io/v1/llm`

You can also set the VRAM limit directly in the path:

* `https://bridge.crynux.io/v1/llm/24` means VRAM limit is set to `24`

`vram_limit` is a Crynux-specific routing parameter. It defines the minimum GPU VRAM requirement (in GB) for your request, so Crynux can dispatch the task to nodes with enough GPU memory. If you choose a value that is too low for your model, the task may fail or timeout.

If you do not specify a VRAM limit, the default value is `24`.

### 2) Access Token

The public demo token has strict rate limits and is not suitable for normal use.

To get a free token with better quota:

1. Join the Crynux Discord: <https://discord.gg/y8YKxb7uZk>
2. Go to the **applications** channel
3. Request a Crynux Bridge API token from an admin

### 3) Model

Crynux generally supports open-source models that are compatible with the Hugging Face `transformers` library. In practice, the main limitation is available VRAM on network nodes, so larger models require higher VRAM settings.

Hermes workflows require tool use/function calling support, so choose a model that supports tool calling. Instruction-tuned models are usually safer choices (for example, `Qwen/Qwen2.5-7B-Instruct`). For details, refer to:

* [Tool Use/Function Calling](/application-development/how-to-run-llm-using-crynux-network/tool-use.md)
* [Supported Models](/application-development/how-to-run-llm-using-crynux-network/supported-models.md)

{% tabs %}
{% tab title="Interactive Setup (hermes model)" %}
Run:

```bash
hermes model
```

Then in the menu:

1. Choose **Custom endpoint (self-hosted / VLLM / etc.)**
2. API base URL: `https://bridge.crynux.io/v1/llm`
3. API key: paste your Crynux token
4. Model name: for example `Qwen/Qwen2.5-7B-Instruct`
5. Context length: keep auto-detect, or enter a value manually if prompted

Start Hermes:

```bash
hermes
```

{% endtab %}

{% tab title="Config File Setup (Local and Docker)" %}
Use this method for both local runtime and Docker runtime.

* Local runtime files: `~/.hermes/config.yaml` and `~/.hermes/.env`
* Docker runtime files (with `-v ~/.hermes:/opt/data`): same host files

Update these exact config items in `~/.hermes/config.yaml`:

* `model.provider`: `custom`
* `model.default`: your selected model (example: `Qwen/Qwen2.5-7B-Instruct`)
* `model.base_url`: `https://bridge.crynux.io/v1/llm`
* `model.api_key`: `${CRYNUX_API_KEY}`

Optional:

* `model.context_length`: set this only if auto-detection is incorrect

#### `~/.hermes/config.yaml`

```yaml
model:
  provider: custom
  default: Qwen/Qwen2.5-7B-Instruct
  base_url: https://bridge.crynux.io/v1/llm
  api_key: ${CRYNUX_API_KEY}
```

#### `~/.hermes/.env`

```bash
CRYNUX_API_KEY=your_real_crynux_token_here
```

After saving, start Hermes:

```bash
hermes
```

Or with Docker:

```bash
docker run -it --rm \
  -v ~/.hermes:/opt/data \
  nousresearch/hermes-agent
```

{% endtab %}
{% endtabs %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.crynux.io/application-development/how-to-run-llm-using-crynux-network/hermes-agent-integration.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.