LowRouterBeta

Hermes Agent

Hermes Agent is Nous Research’s open-source autonomous agent. It keeps memory and skills across sessions and can run unattended. Any OpenAI-compatible endpoint can serve as its model through a custom provider.

Verified against Hermes Agent v0.21.5 (release v2026.9.24). The config below is re-run against that release every week, including a tool call round trip.

Install

Hermes ships through its own installer. It does not build as a regular Python package:

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

Configure

Point the model section of ~/.hermes/config.yaml at LowRouter:

YAML
model:
  provider: custom
  base_url: https://api.lowrouter.ai/v1
  key_env: LOWROUTER_API_KEY
  default: auto/mistralai/mistral-large-2512
  context_length: 256000

key_env names the environment variable holding the key, so the key itself stays out of the file. Export it before starting Hermes:

Bash
export LOWROUTER_API_KEY=sk-lr-...

hermes model offers the same thing interactively under “Custom endpoint”. Then run a task:

Bash
hermes -z "Summarise what this repository does"

-z runs one prompt, prints only the final answer and approves every tool call without asking. hermes alone opens the interactive session.

Three settings people get wrong:

  • base_url ends in /v1. Hermes appends only /chat/completions; the bare origin gives a 404, which Hermes reports as the provider being “temporarily unavailable” after three retries.
  • Set context_length to the model’s real context window, which the model browser shows. Without it Hermes guesses, and it plans context compression around that guess. It refuses to start with a model under 64k, because its system prompt and tool schemas need that room.
  • Model IDs contain slashes. To switch models, change default: in the config (and context_length with it) rather than typing the ID into /model inside a session.

Picking a model

Hermes does everything through tool calls, so pick a model with the function-calling tag. The model browser filtered to them lists every one, and the model needs at least 64k of context.

Each request carries Hermes’ system prompt and about 25 tool schemas. In our runs the first request of a task was already around 25,000 input tokens, before any file contents. Input price matters more than output price here. The example uses auto/mistralai/mistral-large-2512, served from the EU (256k context). Models we have run through Hermes’ tool loop:

  • auto/qwen/qwen3-coder-30b-a3b-instruct, a coding model served from the EU (128k context).
  • auto/anthropic/claude-haiku-4.5, a cheaper Anthropic model (200k context).
  • Use a dedicated key. Hermes runs unattended from its scheduler and messaging gateways, so set the turn cap (below): until per-key spend limits ship, the cap and revoking the key are what end a runaway loop.
  • Cap the tool loop. agent.max_turns in the config is unlimited by default; set a number for unattended runs.
  • Narrow the tools with -t (--toolsets) when a task only needs a few; fewer tool schemas also means fewer input tokens per request.

Troubleshooting

  • “custom didn’t answer after 3 attempts” with HTTP 404: base_url is missing /v1.
  • 401 on the first request: LOWROUTER_API_KEY is not exported in the shell that runs Hermes, or key_env names a different variable.
  • Startup refuses the model for its context size: the model has under 64k of context, or context_length is set below that.