{"slug": "the-ai-agent-that-learns-while-it-works-a-complete-guide-to-hermes-agent", "title": "The AI Agent That Learns While It Works — A Complete Guide to Hermes Agent", "summary": "Here is a factual summary of the article:\n\nThe article introduces Hermes Agent by Nous Research, an autonomous AI agent designed to solve the problem of \"stateless\" AI systems that have no memory of past interactions. Unlike standard agents that start from scratch each session, Hermes features a closed learning loop that allows it to remember users and improve over time. It can be installed with a single command on multiple platforms, supports persistent sessions, and offers integration with messaging platforms, skills, MCP servers, and voice mode.", "body_md": "*This is a submission for the Hermes Agent Challenge*\n\n## Most AI Agents Are Goldfish. Hermes Is Different.\n\nLet me describe the standard agentic experience of 2024–2025.\n\nYou open a terminal, give an agent a task, watch it spin through a dozen steps, and feel genuinely impressed — right up until you close the session. Tomorrow, you start completely from scratch. The agent has no memory of what it learned, no idea who you are, and zero awareness that it made the same mistake three sessions ago.\n\nYou're its first user. Every single time.\n\n\"We've been shipping amnesia as a feature and calling it 'stateless architecture.' The emperor has no clothes, and the emperor can't remember what clothes were.\"\n\nThis is the problem that **Hermes Agent**, built by [Nous Research](https://nousresearch.com), is genuinely trying to solve. Not with a wrapper around an existing API. Not with a clever prompt. With a fundamentally different architecture: **a closed learning loop** baked into the agent itself.\n\nThe longer Hermes runs, the more capable it becomes — at working *with you, specifically*.\n\n## What Is Hermes Agent, Really?\n\nBefore we get into setup, the architecture, or how it compares to other frameworks — it's worth being clear about what Hermes actually *is*, because it doesn't fit neatly into existing categories.\n\nIt's **not** a coding copilot tethered to an IDE.\n\nIt's **not** a chatbot wrapper around a single API.\n\nIt's **not** a rigid workflow automation engine.\n\nIt's an autonomous agent that lives wherever you put it — a $5 VPS, a GPU cluster, or serverless infrastructure that costs nearly nothing when idle. You can talk to it from Telegram while it works on a cloud VM you never SSH into yourself. It runs on your infrastructure. You own the runtime and the data.\n\nHere's a quick snapshot of what ships in the box:\n\n| Stat | Number |\n|---|---|\n| Built-in tools | 70+ |\n| Messaging platform integrations | 20+ |\n| Terminal execution backends | 6 |\n| License | MIT |\n\n## Part 1: Getting Started — From Zero to Running in Under 5 Minutes\n\n### Step 1 — Install\n\nOne command. Works on Linux, macOS, WSL2, and Android via Termux.\n\n```\n# Linux / macOS / WSL2 / Android (Termux)\ncurl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash\n\n# Reload your shell after install:\nsource ~/.bashrc   # or source ~/.zshrc\n\n# Windows (PowerShell, early beta):\nirm https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.ps1 | iex\n```\n\nThat's the whole install step. No dependency hunting. No `pip install`\n\nhell. The script handles everything and places the agent at `~/.hermes/hermes-agent`\n\n.\n\n### Step 2 — Choose Your AI Provider\n\nThis is the most important setup step. Run `hermes model`\n\nfor an interactive selection menu. Here are the main options worth knowing:\n\n| Provider | Best For | Setup Method |\n|---|---|---|\nNous Portal |\nZero-config start, subscription-based | OAuth via `hermes model`\n|\nOpenRouter |\nMulti-model experimentation | API key |\nAnthropic |\nClaude models | OAuth (Max plan) or API key |\nGitHub Copilot |\nUsing your existing subscription | OAuth via `hermes model`\n|\nCustom Endpoint |\nLocal models via Ollama / VLLM / llama.cpp | Base URL + API key |\n\n⚠️\n\nCritical:Hermes requires a model withat least 64,000 tokens of context. Most hosted models (Claude, GPT, Gemini, Qwen, DeepSeek) meet this easily. If you're running locally, set your context size to at least 64K (e.g.,`--ctx-size 65536`\n\nin llama.cpp).\n\nYou can switch providers at any time with `hermes model`\n\n— no lock-in.\n\n### Step 3 — Your First Session\n\n```\nhermes          # classic CLI\nhermes --tui    # modern TUI with overlays and mouse support (recommended)\n```\n\nYou'll see a welcome banner showing your provider, model, available tools, and loaded skills. Start with something easy to verify:\n\n```\nSummarize this repo in 5 bullets and tell me the main entrypoint.\nCheck my current directory and tell me what looks like the main project file.\n```\n\n**What success looks like:**\n\n- Banner shows your chosen model and provider\n- Hermes replies without error\n- It uses a tool when needed (terminal, file read, web search)\n- The conversation continues normally for more than one turn\n\n### Step 4 — Verify Sessions Work\n\nThis matters more than most tutorials mention:\n\n```\nhermes --continue   # Resume the most recent session\nhermes -c           # Short form\n```\n\nIf this works, you have persistent sessions. That's the foundation everything else is built on.\n\n### Step 5 — Add the Next Layer\n\n*Only after the base chat works. Don't skip ahead.*\n\n**Messaging platforms** (Telegram, Discord, Slack, WhatsApp, Signal, Email, and 15+ more):\n\n```\nhermes gateway setup\n```\n\n**Skills** — structured knowledge documents the agent loads on demand:\n\n```\nhermes skills search kubernetes\nhermes skills install openai/skills/k8s\n```\n\n**MCP servers** — add to `~/.hermes/config.yaml`\n\n:\n\n```\nmcp_servers:\n  github:\n    command: npx\n    args: [\"-y\", \"@modelcontextprotocol/server-github\"]\n    env:\n      GITHUB_PERSONAL_ACCESS_TOKEN: \"ghp_xxx\"\n```\n\n**Docker sandbox** — for safety on production work:\n\n```\nhermes config set terminal.backend docker\n```\n\n**Voice mode:**\n\n```\ncd ~/.hermes/hermes-agent\nuv pip install -e \".[voice]\"\n# Then inside a session: /voice on  (Ctrl+B to record)\n```\n\n💡\n\nPro tip:Run`hermes doctor`\n\nany time something feels off. It diagnoses config problems and tells you exactly what to fix. Don't add features until`hermes doctor`\n\nis clean.\n\n## Part 2: How the Learning Loop Actually Works\n\nThis is the part that separates Hermes from the rest of the field. Let me walk through what actually happens under the hood when you use Hermes over time.\n\n### The Five-Stage Learning Loop\n\n**Stage 1 — Context Loading**\n\nBefore the agent responds to anything, it loads `MEMORY.md`\n\n(persistent facts about you and your projects) and `USER.md`\n\n(a behavioral model of who you are). It also discovers and loads any context files in your project directory — `.hermes.md`\n\n, `AGENTS.md`\n\n, `CLAUDE.md`\n\n, `SOUL.md`\n\n. The agent starts every session knowing your history.\n\n**Stage 2 — Tool Selection and Multi-Step Planning**\n\nFrom 70+ built-in tools, Hermes selects what the task needs. It can spawn subagents via `delegate_task`\n\n— up to 3 concurrent child agents by default, each with isolated context, restricted toolsets, and their own terminal sessions. This is how it parallelizes complex work without the threads stepping on each other.\n\n**Stage 3 — Skill Creation for Novel Tasks**\n\nWhen Hermes successfully completes a task it hasn't done before, it can write a reusable **Skill document**. Next time it faces a similar problem — even in a different session — it loads the Skill and executes efficiently without rediscovering the approach from scratch.\n\n**Stage 4 — Memory Consolidation**\n\nAfter sessions, Hermes uses FTS5 full-text search with LLM summarization to curate what's worth keeping. It doesn't dump raw logs into memory — it actively decides what to remember. This keeps memory bounded and useful even after hundreds of sessions. It also uses [Honcho](https://github.com/plastic-labs/honcho)'s dialectic user modeling to build a deepening picture of who you are across time.\n\n**Stage 5 — Self-Improvement**\n\nSkills created in previous sessions are eligible for improvement. Hermes can notice when an old Skill isn't working optimally and update it mid-use. The agent gets measurably better at your specific workflows over weeks and months.\n\nThis is a closed loop. Most agents have none of it.\n\n### The `execute_code`\n\nPower Move\n\nThis is the feature that surprised me most. The `execute_code`\n\ntool lets Hermes write Python scripts that call its own tools programmatically, via sandboxed RPC execution:\n\n```\n# Instead of: search → wait → read → wait → summarize → wait → write\n# Hermes collapses this into a single LLM turn:\n\nasync def research_pipeline(topic):\n    results = await tools.web_search(query=topic, n=10)\n    pages = [await tools.read_url(r.url) for r in results[:3]]\n    summary = await tools.summarize(pages, style=\"technical\")\n    await tools.write_file(\"research.md\", summary)\n    return summary\n```\n\nThis dramatically reduces the token cost of multi-step pipelines. Instead of burning inference tokens on \"I will now do step 3 of 7,\" Hermes writes the whole pipeline as code and runs it. The LLM is only involved at the decision point, not at every mechanical step.\n\n### Terminal Backends: It's Not Tied to Your Laptop\n\nSix backends let you separate *where you talk to Hermes* from *where it actually runs*:\n\n-\n**Local**— direct execution, fast, simple -\n**Docker**— sandboxed isolation, the right choice for production work -\n**SSH**— talk locally, execute on a remote server -\n**Daytona / Modal**— serverless; the environment hibernates when idle and costs nearly nothing\n\nThe Modal and Daytona backends are worth understanding. You can talk to your Hermes agent from your phone via Telegram while the agent's actual work runs on a cloud VM. The environment sleeps when you're not using it. For a personal always-on assistant this changes the economics completely.\n\n### The Skills System and agentskills.io\n\nSkills are structured knowledge documents — procedures the agent loads on demand. They follow **progressive disclosure**: Hermes loads just the skill index first, then drills into full skill content only if the task requires it. Token usage stays low even in long sessions.\n\nSkills are compatible with the open [agentskills.io](https://agentskills.io) standard — meaning skills you write for Hermes are portable and shareable with the community, and community skills work with your setup without any conversion.\n\n## Part 3: How Hermes Stacks Up Against Other Agentic Frameworks\n\nThere are serious alternatives in the open-source agent ecosystem. Here's an honest look at where Hermes fits.\n\n| Feature | Hermes Agent | AutoGen | CrewAI | OpenDevin |\n|---|---|---|---|---|\n| Persistent cross-session memory | ✅ Native | ❌ | ❌ | ⚠️ Limited |\n| Autonomous skill creation | ✅ Built-in | ❌ | ❌ | ❌ |\n| Multi-step tool use | ✅ | ✅ | ✅ | ✅ |\n| Messaging platform gateway | ✅ 20+ platforms | ❌ | ❌ | ❌ |\n| Runs on $5 VPS / serverless | ✅ Yes | ⚠️ Possible | ⚠️ Possible | ❌ GPU needed |\n| Multi-agent delegation | ✅ Subagents | ✅ Core feature | ✅ Crews / Flows | ⚠️ |\n| Local / self-hosted LLM | ✅ Any endpoint | ✅ | ✅ | ✅ |\n| Voice mode | ✅ CLI + messaging | ❌ | ❌ | ❌ |\n| MCP server support | ✅ | ⚠️ Via plugins | ⚠️ Via plugins | ❌ |\n| Primary focus | Personal autonomous agent | Multi-agent orchestration | Role-based agent teams | Software engineering |\n\n### Plain-English Breakdown\n\n**AutoGen** is brilliant if you want fine-grained control over agent-to-agent communication patterns. But it's a framework you orchestrate — not a ready-to-run agent. You write the coordination logic yourself.\n\n**CrewAI** makes multi-agent teamwork feel intuitive — define crews with roles, let them coordinate. Great for structured pipelines. Less suited for the kind of open-ended \"figure it out\" sessions where Hermes excels.\n\n**OpenDevin** is purpose-built for software engineering tasks: browsing, code execution, file editing. Excellent in its lane. That lane is narrower than Hermes's.\n\n**Hermes** is the agent you'd deploy as your actual daily assistant — one that knows your name, remembers your projects, and gets measurably better at helping you over weeks and months. That's a genuinely different product from the others.\n\nWhen NOT to use Hermes:If you need a complex multi-agent pipeline with dozens of specialized roles that coordinate on a strict workflow, AutoGen or CrewAI give you more structured control. If your only use case is automated software engineering on repos, Claude Code or OpenDevin are sharper tools. Hermes shines brightest as apersonal, persistent, do-everything agent— not a single-purpose workflow engine.\n\n## Part 4: What Open Agentic Systems at This Capability Level Actually Mean\n\nI want to say something that might be obvious, but I haven't seen it written plainly.\n\nWhen your agent genuinely *knows* you — your preferences, your projects, your quirks, your bad habits — you need to be the one in control of that knowledge. Hermes runs on your infrastructure. You own what it learns about you.\n\nThat changes the trust calculus entirely.\n\nMost AI products in this space are cloud-hosted services with \"open-source\" labels slapped on for marketing. You're renting access to an agent that lives on their servers, stores its state in their database, and disappears if they change their pricing. Hermes runs on a $5 VPS you control, hibernates when idle via Modal or Daytona, and costs nearly nothing when you're not actively using it. The economics and the ownership model are completely different.\n\nThe second thing worth saying: agents that compound matter more than agents that are capable on day one.\n\nThe tools with the most features in their initial release rarely win. The tools that get measurably better at working *with you* over time, that reduce the friction of repeated patterns, that remember what you learned together last Tuesday — those are the ones that stick.\n\nHermes is one of the very few systems being built with that end state explicitly in mind. Not as a future roadmap item. As a first-class architectural property, shipping today, MIT license, running on your hardware.\n\nThat's worth paying attention to.\n\n## Quick-Start Checklist\n\nBefore you go — here are the six things to actually do today:\n\n- [ ] Run the one-line installer\n- [ ] Run\n`hermes model`\n\nand pick a provider - [ ] Launch\n`hermes --tui`\n\nand complete your first conversation - [ ] Test session resume with\n`hermes --continue`\n\n- [ ] Install a skill with\n`hermes skills search ...`\n\n- [ ] Share what you built on DEV.to for the challenge 🎉\n\n## Resources\n\n- 🏠\n[Hermes Agent Home](https://hermes-agent.nousresearch.com/) - 📖\n[Documentation](https://hermes-agent.nousresearch.com/docs/) - 🎬\n[Quickstart Video](https://www.youtube.com/watch?v=R3YOGfTBcQg) - 💻\n[GitHub Repository](https://github.com/NousResearch/hermes-agent) - 🧩\n[Skills Hub (agentskills.io)](https://agentskills.io) - 💬\n[Discord Community](https://discord.gg/NousResearch) - 🏆\n[The Challenge Page](https://dev.to/challenges/hermes-agent-2026-05-15)\n\n*Built during the Hermes Agent Challenge, May 2026. If you found this useful, I'd love to see what you build — drop it in the comments.*", "url": "https://wpnews.pro/news/the-ai-agent-that-learns-while-it-works-a-complete-guide-to-hermes-agent", "canonical_source": "https://dev.to/aditya_007/the-ai-agent-that-learns-while-it-works-a-complete-guide-to-hermes-agent-n2n", "published_at": "2026-05-23 12:55:26+00:00", "updated_at": "2026-05-23 13:02:19.907770+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "open-source", "developer-tools"], "entities": ["Hermes Agent", "Nous Research"], "alternates": {"html": "https://wpnews.pro/news/the-ai-agent-that-learns-while-it-works-a-complete-guide-to-hermes-agent", "markdown": "https://wpnews.pro/news/the-ai-agent-that-learns-while-it-works-a-complete-guide-to-hermes-agent.md", "text": "https://wpnews.pro/news/the-ai-agent-that-learns-while-it-works-a-complete-guide-to-hermes-agent.txt", "jsonld": "https://wpnews.pro/news/the-ai-agent-that-learns-while-it-works-a-complete-guide-to-hermes-agent.jsonld"}}