hermes-agent AGENTS.md

A set of instructions for people and coding agents working on the Hermes Agent codebase. Hermes is an AI agent that works through command-line, messaging, desktop, and terminal interfaces, and can be extended with plugins and skills.

In plain words
What is it for?
Use it when developing, reviewing, or contributing to Hermes Agent. It provides guidance on what changes fit the project and which approaches should be rejected.
Why use it?
It gives contributors the project's rules and design priorities before they change code. This helps avoid changes that increase cost or put too much functionality in the core system.

Instructions file for CodexOpenCode

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/nousresearch/hermes-agent/agents-md
Clone the repo
git clone --depth 1 https://github.com/NousResearch/hermes-agent

Made for: Codex, OpenCode.

Per session 23,135 This file is loaded in full into every session.
When invoked 23,135 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.23135 $0.23135
Opus 5 $0.11568 $0.11568
Sonnet 5 $0.04627 $0.04627
Haiku 4.5 $0.02313 $0.02313

Measured yesterday against content hash 93a12ef7f78a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

hermes-agent AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

AGENTS.md · 1,785 lines

How it starts

The opening of the file, as written. The whole thing — 1,785 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Hermes Agent - Development Guide

Instructions for AI coding assistants and developers working on the hermes-agent codebase.

Never give up on the right solution.

What Hermes Is

Hermes is a personal AI agent that runs the same agent core across a CLI, a messaging gateway (Telegram, Discord, Slack, and ~20 other platforms), a TUI, and an Electron desktop app. It learns across sessions (memory + skills), delegates to subagents, runs scheduled jobs, and drives a real terminal and browser. It is extended primarily through plugins and skills, not by growing the core.

Two properties shape almost every design decision and are the lens for reviewing any change:

  • Per-conversation prompt caching is sacred. A long-lived conversation reuses a cached prefix every turn. Anything that mutates past context, swaps toolsets, or rebuilds the system prompt mid-conversation invalidates that cache and multiplies the user's cost. We do not do it (the one exception is context compression).
  • The core is a narrow waist; capability lives at the edges. Every model tool we add is sent on every API call, so the bar for a new core tool is high. Most new capability should arrive as a CLI command + skill, a service-gated tool, or a plugin — not as core surface.

Contribution Rubric — What We Want / What We Don't

This is the project's intent layer. Use it two ways:

  1. For humans and for your own work — what gets merged and what gets rejected, so a contribution aims at the target.
  2. For automated review (the triage sweeper) — guidance on when a PR is safe to close on the three allowed reasons (implemented_on_main, cannot_reproduce, incoherent) and, just as important, when NOT to close one. Taste-based "we don't want this / out of scope" closes are NOT an automated decision — those stay with a human maintainer. The sweeper's job here is to recognize design intent and avoid wrongly closing a legitimate contribution, not to make the won't-implement call itself.

Read the full file on GitHub · 1,785 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 1,785 lines · 23,135 tokens per session scan A 93a12ef7f78a

Subscribe to this mod's changes

hermes-agent AGENTS.md is an instructions file published in the GitHub repository NousResearch/hermes-agent (238,457 stars, last pushed yesterday), licensed MIT. It adds 23,135 tokens to every session, about $0.1157 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.