Top 10 Things You Can Do With Hermes Agent in 2026
Hermes Agent is more than a chat interface — it is a self-improving AI runtime that automates workflows, remembers what it learns, and runs on your own hardware. These are the 10 most impactful things you can do with it, with practical examples and skill references for each.
Hermes Agent is not a chatbot. It is a self-improving AI runtime that runs on your own hardware, connects to your messaging channels, schedules its own cron jobs, and gets measurably better at recurring tasks over time through its Curator learning loop.
With 188k GitHub stars and 271 billion tokens processed monthly on OpenRouter alone, it is the most widely-used open-source agent framework in 2026. Here are the 10 things worth actually doing with it.
What it looks like: You ask Hermes once — "research trending AI tools every Monday at 9am and send me a summary on Telegram." It does it, schedules the cron job itself, and generates a skill file from the first run so subsequent runs are faster and more focused.
The learning loop is the key: after the first research run, Hermes writes a Markdown skill encoding what sources were useful, which search queries returned signal, and what output format you responded well to. By week three, the Monday brief is noticeably sharper than week one.
snippet
you: research trending open-source AI tools this week and schedule this every Monday at 9am
hermes: [runs research, writes skill, schedules cron, sends summary]
# Next Monday: runs automatically, applies learned skill, faster + more focused
Skill: Install research-curator from agentskills.io or let Hermes generate its own.
2. Full deployment pipelines from natural language
What it looks like: "Deploy the latest commit on main to staging, run the health check, and message me the result." Hermes pulls the commit, runs tests, deploys, checks endpoints, and reports back — all from a Telegram message.
Hermes's exec tool lets it run arbitrary shell commands with configurable confirmation requirements. The deployment skill teaches it the sequence: git pull → test → deploy → health check → report. Once written, any team member can trigger it from the Telegram bot without SSH access.
bash
# hermes executes (with confirmation):
git pull origin main
pnpm test
flyctl deploy
curl https://staging.yourapp.com/health
Skill: The community devops-deploy skill on agentskills.io covers most Node/Python/Docker deploy patterns. Customise it for your stack.
What it looks like: "Every weekday at 9am, summarise my email inbox and flag anything that needs a reply today." Hermes reads your inbox via the Gmail or Outlook skill, applies influence scoring, and sends a prioritised list with one-line summaries.
The self-improvement angle: each time you reply to a flagged email, Hermes updates its signal heuristics. Senders you consistently respond to get higher priority. Threads you mark as noise get deprioritised. The skill file evolves across runs.
snippet
hermes digest (09:00 cron)
→ 3 need replies today: [PR review request] [partnership email] [invoice]
→ 12 low priority: newsletters, notifications
→ 0 unread from blocked senders (remembered from last week)
Skill:gmail-triage or inbox-curator from agentskills.io.
4. UGC ad brief generation from a product URL
What it looks like: "Generate a set of UGC ad briefs for this product: [URL]." Hermes scrapes the landing page, fetches competitor analysis via web search, identifies top-performing hook patterns, and outputs a structured brief with angles, hooks, and suggested formats.
This is one of the documented high-ROI use cases: what previously took a human strategist 90 minutes produces a first draft in under five minutes. The skill refines its output format based on which briefs you actually use.
snippet
Input: product URL
Output: 5 creative briefs with hooks, emotional angles, CTA suggestions,
estimated CPM category, and reference ad examples
5. Autonomous code review with GitHub integration
What it looks like: "Review all open PRs on my repo and comment on any that have been waiting more than 48 hours without a review." Hermes lists open PRs via the gh CLI, checks timestamps, reads diffs, and posts structured review comments for anything stale.
Hermes does not replace human review for security-critical code. It handles the triage layer: identifying what needs attention, adding initial feedback on obvious issues (missing tests, type errors, pattern violations), and flagging PRs that need human eyes.
bash
hermes: checking open PRs on yourrepo/app...
→ PR #234: 3 days without review, 400 line diff
[posts review comment with high-level summary and 2 questions]
→ PR #238: 1 hour old, skip
Skill:github + code-review from ClawHub (compatible with Hermes via the claw migrator).
6. Research → content pipeline on a schedule
What it looks like: Every Sunday at 9pm, Hermes researches a configured topic (AI tools, competitor moves, market trends), extracts key insights, formats them as a newsletter draft or tweet thread, and drops it in a configured folder or Notion page.
The compounding effect: after three weeks of Sunday runs, Hermes has a skill file encoding what research sources are reliable for your niche, which insight formats your audience responds to (from your feedback), and what level of technical depth to aim for.
Skill:content-pipeline on agentskills.io — customisable research sources, output formats, and Notion/email destinations.
7. Self-hosted Telegram assistant with persistent memory
What it looks like: A Telegram bot that knows your context — your timezone, your ongoing projects, your preferences — without you having to re-explain every conversation. Message it from your phone like a colleague who was already onboarded.
Hermes stores your preferences in USER.md and environmental facts in MEMORY.md. Both load automatically at session start. The gateway keeps sessions alive between messages so mid-conversation context is not lost when you send the next message an hour later.
snippet
you: what's on my calendar tomorrow
hermes: [reads calendar — already knows you're UTC+5:30 and uses Google Calendar]
→ 9am standup, 2pm design review, 4pm 1:1
you: anything I should prep for the design review?
hermes: [searches your Notion workspace for design review notes]
→ Found 3 relevant pages...
What it looks like: "Summarise everything in my ~/research/ folder about transformer architectures and tell me what I haven't read yet." Hermes reads local PDFs and markdown files, builds a semantic summary, cross-references with your reading history in memory, and flags unread items.
This is entirely offline — no documents leave your machine. The PDF reader and file tools run locally. The model call is the only thing that leaves (to your configured provider).
snippet
hermes: reading ~/research/ (47 PDFs, 23 markdown files)...
→ Topics: attention mechanisms (12 papers), scaling laws (8), RLHF (6)
→ Unread (based on your memory): 9 papers
→ Summary of unread: [2-paragraph brief with key claims]
Skill:pdf-intelligence on agentskills.io.
9. Multi-agent parallel research
What it looks like: "Research three competing products simultaneously and give me a comparison table." Hermes spawns three subagents — one per product — each running a web research workflow in parallel, then synthesises the results into a structured comparison.
Hermes's subagent system lets the main agent spawn specialised workers for parallel tasks and collect their outputs. Each subagent can have its own model configuration — you might run the orchestrator on Claude 4 and the three research workers on a cheaper model to control cost.
snippet
Hermes (orchestrator, Claude 4):
→ Subagent A (DeepSeek): research Product X
→ Subagent B (DeepSeek): research Product Y
→ Subagent C (DeepSeek): research Product Z
→ synthesise into comparison table
10. Browser automation with self-written playbooks
What it looks like: "Scrape the pricing page of these five tools every week and alert me if any prices change." Hermes uses its browser tool to fetch pages, parses the pricing table, stores values in memory, and diffs against the previous week. If changes are detected, it sends a Telegram alert.
The playbook is written as a skill on the first run and refined on subsequent runs. By week three, the price monitoring is fast and handles edge cases (pagination, dynamically loaded prices) that the naive first implementation missed.
bash
# weekly cron
hermes: checking 5 pricing pages...
→ ToolX: unchanged ($49/mo)
→ ToolY: CHANGED — was $29/mo, now $39/mo (24% increase)
→ [sends Telegram alert with diff]
Skill:price-monitor on agentskills.io or auto-generated by Capability Evolver after the first manual run.
Getting the most out of these workflows
Three things separate shallow Hermes usage from deep usage:
Let the Curator run. Every 15 tool calls and after complex tasks, Hermes reflects and writes skills. Don't cancel these pauses — they are where the compounding happens.
Give feedback on outputs. When Hermes surfaces research or writes a brief, tell it what you liked and what missed. Those corrections get encoded in skill files. The agent that ignores your feedback is stateless; the one that encodes it compounds.
Run it persistently. The cron and gateway features only deliver value if Hermes stays running. A $5 VPS or a spare Raspberry Pi 5 is sufficient for most single-user setups.
