Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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.
npx skills add NousResearch/hermes-agent --skill honchogit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nousresearch/hermes-agent/honcho)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/honcho"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/honcho/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/honcho"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/honcho.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 269 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00012 | $0.04673 |
| Opus 5.5 | $0.00005 | $0.01869 |
| Sonnet 5.5 | $0.00002 | $0.00935 |
| Haiku 4.5 | $0.00001 | $0.00467 |
Grade A, and why
honcho 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 13d ago.
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.
Copies of this mod
8 near-identical copies found in the catalogue:
- honcho — 100% identical, 6 lines differ
- honcho — 100% identical, 6 lines differ
- honcho — 100% identical, 6 lines differ
- honcho — 98% identical, 13 lines differ
- honcho — 98% identical, 13 lines differ
- honcho — 97% identical, 8 lines differ
- honcho — 97% identical, 8 lines differ
- honcho — 97% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 438 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Honcho Memory for Hermes
Honcho provides AI-native cross-session user modeling. It learns who the user is across conversations and gives every Hermes profile its own peer identity while sharing a unified view of the user.
When to Use
- Setting up Honcho (cloud or self-hosted)
- Troubleshooting memory not working / peers not syncing
- Creating multi-profile setups where each agent has its own Honcho peer
- Tuning observation, recall, dialectic depth, or write frequency settings
- Understanding what the 5 Honcho tools do and when to use them
- Configuring context budgets and session summary injection
Setup
Honcho is a plugin-catalog memory provider maintained by Plastic Labs. Install it once per machine (homes upgraded from a release that bundled Honcho get it automatically):
hermes plugins install honcho
Cloud (app.honcho.dev)
hermes memory setup honcho
# select "cloud", paste API key from https://app.honcho.dev
Self-hosted
hermes memory setup honcho
# select "local", enter base URL (e.g. http://localhost:8000)
See: https://docs.honcho.dev/v3/guides/integrations/hermes#running-honcho-locally-with-hermes
Verify
hermes honcho status # shows resolved config, connection test, peer info
Architecture
Base Context Injection
When Honcho injects context into the system prompt (in hybrid or context recall modes), it assembles the base context block in this order:
- Session summary -- a short digest of the current session so far (placed first so the model has immediate conversational continuity)
- User representation -- Honcho's accumulated model of the user (preferences, facts, patterns)
- AI peer card -- the identity card for this Hermes profile's AI peer
The session summary is generated automatically by Honcho at the start of each turn (when a prior session exists). It gives the model a warm start without replaying full history.
Cold / Warm Prompt Selection
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.
- 13d ago Changed · +6 lines 1264edc36686
- 1mo ago First seen · 432 lines · 12 tokens per session scan A 7623d663cb5e
honcho is a skill published in the GitHub repository NousResearch/hermes-agent (254,120 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 4,673 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other skills, from other repositories
unified-memory
Share durable, inspectable context and handoffs between Claude, Codex, Hermes, Cursor, OpenCode, and other agents through the local ECC Memory Vault. Use when an agent must save work state, transfer context, resume another agent's task, or search shared project knowledge.
memory-triage
Persistent long-term memory protocol powered by mem0. Evaluate conversations for durable facts worth storing via memoryadd. Handles identity, preferences, decisions, configurations, rules, projects, and relationships. Loaded by the openclaw-mem0 plugin when skills mode is active.
mem0-cli
Mem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search, mem0 list, mem0 get, mem0 init, mem0…
mem0-vercel-ai-sdk
Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also…
mem0-tour
Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.
mem0-scope
Views or changes the default memory scope (project, session, or global) used when saving and searching memories. Use when the user wants to control whether memories are scoped to this repo, this run, or shared across all their projects.