What counts as a use case here
These examples come from the Hermes Agent documentation, its tutorials, and its README. They describe what the software can do and what each setup needs. They are not results from Kings Landing client work. For a plain introduction to the software, read what Hermes Agent is first.
Hermes Agent use cases at a glance
| Use case | Where you use it | Main requirement | Risk if it goes wrong |
|---|---|---|---|
| Repository help | Terminal or editor | Read access to one project folder | Low for reading, higher once it edits |
| Scheduled reports | Chat or files | A stable gateway and a clear data source | Low to medium |
| Script watchdogs | Chat | A script and a delivery channel | Low |
| Team assistant in Telegram or Discord | Chat | A bot, an allowlist, and a server | Medium |
| Team assistant in Slack | Slack | A Slack app, scopes, and approval rules | Medium |
| Event-triggered runs | GitHub, GitLab, alerts | A webhook route and a signing secret | Medium to high |
| Specialist bots | Desktop app | Separate profiles per role | Depends on each bot's tools |
| Tools from MCP servers | Any | An MCP server and scoped credentials | Depends on the server |
| A private local assistant | Terminal | A local model with 64K context | Low |
1. Repository help
Start Hermes in a project folder and ask it to summarize the code, find the entry point, or explain a failing test. The quickstart uses prompts like these as first tests. Hermes can also run inside an editor that supports the Agent Client Protocol (ACP), where it shows file diffs, terminal commands, and approval prompts in the editor, according to the ACP docs.
What it needs: a model with at least 64,000 tokens of context and a folder the agent may read. Before it edits code, decide whether it works on a branch and who reviews the change.
2. Scheduled reports and reminders
Hermes has a built-in scheduler. The cron docs show three ways to create a job:
/cron add "every 2h" "Check server status"
hermes cron create "every 1h" "Summarize new feed items" --skill blogwatcher
Or in plain language, for example: "Every morning at 9am, check Hacker News for AI news and send me a summary on Telegram." Results go back to the chat that created the job, to a file, or to another configured platform. You can attach skills to a job and pin it to a cheaper model than your main chat model.
Two safety defaults matter here. Scheduled runs cannot create further scheduled jobs, which stops runaway loops. And risky commands are denied by default in scheduled runs, because nobody is there to approve them.
What it needs: a gateway that stays running, a clear data source, and a named person who receives and checks the output. The quickstart advises adding cron only after the basic setup is stable.
3. Script watchdogs with no model at all
Not every scheduled job needs AI. In no-agent mode, the scheduler runs a script on a timer and delivers its output word for word, with no model call:
hermes cron create "every 5m" --no-agent --script memory-watchdog.sh --deliver telegram --name "memory-watchdog"
This suits disk alerts, heartbeats, and CI pings. It costs nothing in model usage and behaves the same every time.
4. A team assistant in Telegram or Discord
The docs include a full team Telegram assistant tutorial. It builds a bot that approved team members can message for code reviews, research, and debugging. Each person gets their own conversation, only allowlisted users can talk to it, and scheduled jobs post daily standups and health checks to a team channel. The tutorial says a $5 a month server is enough to run the gateway, because the model calls happen at the provider.
Discord works the same way. In server channels, Hermes answers only when someone mentions it, unless you mark a channel as free-response, and it keeps each person's history separate inside a shared channel, according to the Discord docs.
What it needs: a bot token, a list of allowed user IDs, and a server that stays on. Run its commands in Docker rather than directly on the server.
5. A team assistant in Slack
Slack is the most common choice for company teams. The setup uses Socket Mode, so the server needs no public web address, and in channels the agent answers only when mentioned. Approval requests appear as buttons in Slack. Our Hermes Agent Slack setup guide covers the full steps, scopes, and tests.
What it needs: a Slack app with the right scopes and events, an allowlist of member IDs, and approval rules for anything that changes shared data. If you want help scoping this for your team, see our Slack AI agents service.
6. Runs triggered by events
The webhooks docs describe an HTTP endpoint that receives events from GitHub, GitLab, Jira, Stripe, and similar services, checks their signatures, and starts an agent run. Examples include reviewing a pull request when it opens, or posting a summary to chat when a monitoring alert fires. A webhook can also start a scheduled job immediately instead of waiting for its next run.
What it needs: a reachable endpoint, a signing secret for each service, and a tight limit on what the triggered run may do. Treat event content as untrusted input, because anyone who can open a pull request can write text the agent will read.
7. Specialist bots in the desktop app
Bot Mode in the desktop app turns Hermes profiles into a roster of named bots, each with its own role, model, memory, skills, and credentials, according to the Bot Mode docs. Bots can run routines on a schedule, message each other, and share group chats. You might keep a research bot, a writing bot, and an operations bot apart, so each one only has the tools its job needs.
What it needs: the desktop app and a clear split of tools and credentials between bots.
8. Tools from MCP servers
Hermes connects to Model Context Protocol servers, local or remote, and registers their tools at startup. The MCP docs mention GitHub, databases, file systems, and internal APIs as examples. If you already configured MCP servers in Claude Code, the importer can move them across.
What it needs: a scoped token for each server. Give the agent read-only credentials where you can, and add write access only for the tasks that need it.
9. A private assistant on a local model
With Ollama, LM Studio, or vLLM, Hermes can run without sending prompts to a hosted provider. This suits note work, drafting, and file tasks on sensitive material. The local Ollama guide notes that the model must support tool calling and that you must raise Ollama's default context to at least 64,000 tokens. The installation guide covers the steps.
Reusable skills across all of these
A skill is a written procedure that the agent loads when a task matches it. Hermes can write a skill after a complex task and improve it later, and you can install more from the Skills Hub. Each installed skill becomes a slash command. Review the skills your team depends on, because the agent can change them.
Choosing a first use case
Pick one task with a clear input, a result you can check, and low risk if the answer is wrong. A repository summary, a daily digest posted to a private channel, or a script watchdog fits that description. A message to a customer or a change to a shared record does not, until approval rules are in place. Our guide to human-controlled AI workflows explains how to set those rules.
If you are comparing agents before you choose, see Hermes Agent vs OpenClaw.
