The Self-Improving Agent With a Learning Loop: A First Look at Hermes Agent

Hermes Agent is an open-source, self-improving autonomous agent built by Nous Research and released under the MIT license. It launched February 25, 2026, has a built-in learning loop that creates skills from experience, refines them during use, persists memory across sessions, and runs on 20+ platforms — from a $5 VPS to a GPU cluster to serverless infrastructure. The current release is v0.18.2, tagged July 7, 2026, and the GitHub repo carries roughly 217,000 stars.

Key Takeaways

• Hermes Agent is a model-agnostic autonomous agent from Nous Research, MIT licensed and free to self-host on Linux, macOS, WSL2, native Windows, or Android via Termux. It launched February 25, 2026.
• Its defining feature is a closed learning loop: agent-curated MEMORY.md, autonomous skill creation, and self-improvement of those skills during use.
• It runs as CLI, desktop app, OpenAI-compatible API server, and gateway across 20+ messaging platforms including Telegram, Discord, Slack, WhatsApp, and Microsoft Teams.
• Nous Portal is the optional paid backbone: 200+ models plus the Tool Gateway (web search, image generation, TTS, browser automation) starting at $20/month for the Plus tier.
• Nous Portal Cloud launched July 8, 2026, offering one-click hosted Hermes instances billed hourly from credits.

What Is Hermes Agent?

Hermes Agent is an autonomous AI agent from Nous Research designed to get more capable the longer it runs. It is not a coding copilot tethered to an IDE and not a wrapper around a single model API. It is a long-lived process that manages its own memory, writes its own skills, runs tool calls in parallel through a ThreadPoolExecutor with up to 8 workers, and streams responses token-by-token.

The agent core lives in a single AIAgent class inside run_agent.py, and every surface — CLI, TUI, gateway, API server, cron scheduler — routes through it. That means the behavior you see in Telegram is the same behavior you get in the terminal or through the OpenAI-compatible endpoint at /v1/chat/completions.

Hermes Agent is publicly released and actively maintained. The first Hermes Agent release entry on the Nous Research releases page is dated February 25, 2026, and the project has since shipped through v0.18.2 on July 7, 2026, with roughly 1,720 commits between v0.17.0 and v0.18.0 alone.

Hermes Agent at a Glance

Field	Value
Developer	Nous Research
Type	Autonomous AI agent (multi-platform, multi-provider)
Current version	v0.18.2 (July 7, 2026)
License	MIT
Repository	github.com/NousResearch/hermes-agent (~217k stars)
Modality	Text, voice, vision, tool use, computer use
Model support	200+ via Nous Portal; OpenRouter; direct provider keys; local (llama.cpp, MLX, vLLM)
Minimum context	64K tokens
Runtime targets	Local, Docker, SSH, Daytona, Singularity, Modal
Platforms	CLI, TUI, desktop (macOS/Windows), 20+ gateways
API	OpenAI-compatible /v1/chat/completions and Responses
Pricing (agent)	Free — MIT license
Pricing (Nous Portal)	Free, Plus $20/mo, Super $100/mo, Ultra $200/mo
Cloud hosting	Nous Portal Cloud (launched July 8, 2026), from ~$0.32/day
Release date	February 25, 2026

How Hermes Agent Works

Hermes Agent is built around four subsystems that run on the same agent core.

The Learning Loop is the mechanic that gives the project its identity. The agent writes durable facts to MEMORY.md with periodic nudges to persist knowledge, generates reusable skills after completing complex tasks, and revises those skills the next time they run. Skill YAML lives under ~/.hermes/skills/, and Skill Bundles group multiple skills under one slash command so a "write code" bundle can load code review, testing, and PR workflow skills together.

The Three-Tier Memory system separates roles: USER.md and MEMORY.md are short and agent-editable, while SOUL.md stays immutable and defines the agent's default voice. According to a three-month community writeup on r/hermesagent, the Markdown-based setup is what most heavy users end up trusting after trying pluggable memory providers.

The Gateway is one process that fronts every platform. One YAML config sends the same agent to Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Mattermost, Email, SMS, Microsoft Teams, Google Chat, LINE, and a dozen more. Unified streaming ships tokens as they generate on Telegram, Discord, Slack, and the CLI; platforms without a message-edit API fall back automatically.

The Tool Gateway covers hosted web search, image generation, TTS, and browser automation, and it bills against the same Nous Portal credits as models. MCP integration is first-class: connect a server, filter its tools, and Hermes can call them like any built-in.

Anchor claim, verbatim from the docs: "It's not a coding copilot tethered to an IDE or a chatbot wrapper around a single API. It's an autonomous agent that gets more capable the longer it runs."

What You Can Do With Hermes Agent

Concrete uses posted publicly in the last few weeks give a good picture of the surface area.

Ivan Fioravanti has demonstrated Hermes Agent driving a local AI lab — liteLLM, Postgres, Prometheus, and Grafana configured through conversation — and running Qwen3.6-35B-A3B on DGX Spark for real work. He also shipped an official Unreal Engine skill: "Hermes Agent has now an official skill for Unreal Engine! This is the unlocker for 3D magical things!"

Alvaro Cintas built a personal knowledge graph on top of Hermes using Gbrain, an open-source project from Y Combinator's Garry Tan. The agent installs Gbrain, adds it as an MCP server, and then queries it for entity-aware recall — "31 points more precise than plain vector search" by the project's benchmark.

Shubham Saboo wired Hermes Agent to Robinhood to run a fully agentic trading setup with research signals, orchestrated by GPT-5.6 Sol. Ashutosh Shrivastava rewrites his custom skills through Hermes using GPT-5.6 Sol after originally authoring them on Fable 5 and Opus 4.8 — a workflow that would be tedious as manual edits and is trivial as a delegated task.

The Kimi K3 pairing is particularly common in community posts. If you want the model context, our Kimi K3 overview covers the flagship that many Hermes users route agentic work through.

How Hermes Agent Compares

The most-discussed comparison in the community is Hermes Agent versus OpenClaw. A Reddit thread from r/openclaw with dozens of side-by-side reports summarizes the sentiment: users describe Hermes as more stable and more reliable at multi-step task execution using the same underlying model, and specifically call out the self-writing Skills as the reason it holds up on complex tasks where OpenClaw becomes fragile.

One community benchmark cited on X measured Hermes Agent against OpenClaw on a real security audit using Grok 4.5, with Hermes completing the run at $4.63 for 1.42M tokens in 5 minutes 4 seconds. That is a single data point from one auditor, not a controlled benchmark, but it matches the pattern in longer writeups.

Dimension	Hermes Agent	Typical coding-copilot agent
Scope	Autonomous, long-running, multi-platform	IDE-tethered, session-scoped
Memory	Persistent MEMORY.md + skills + FTS5 recall	Session-only or basic vector store
Runtime targets	Local, Docker, SSH, Daytona, Modal	Local process only
Gateway platforms	20+	Usually 1 (IDE)
License	MIT	Varies, often closed

Availability: How to Access Hermes Agent

Hermes Agent is available today through three paths.

Self-hosted, free forever. The one-line installer works on Linux, macOS, WSL2, and Android via Termux:

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

Windows users can run the equivalent PowerShell installer. Once installed, hermes setup --portal completes the OAuth flow that connects the agent to models and the Tool Gateway in one step. The official docs cover the full setup path.

Desktop apps. Hermes Desktop is available for macOS and Windows and bundles the CLI plus a GUI.

Nous Portal Cloud. Launched July 8, 2026, portal.nousresearch.com hosts Hermes on a dedicated cloud instance with sizes billed hourly from credits: Small at roughly $0.32/day running (1GB RAM, 2 vCPUs), Medium at $0.59/day (2GB, 4 vCPUs), Large at $1.12/day (4GB, 8 vCPUs). Stopped instances bill only for storage at about $0.06/day. Deploy takes about 60 seconds, and idle instances scale down when unused.

Nous Portal subscription tiers (which cover model usage, hosted tools, and cloud):

Tier	Monthly	Credits included
Free	$0	Pay-as-you-go, free models only
Plus	$20	$22
Super	$100	$110
Ultra	$200	$220

Third-party frontends like Open WebUI connect via the OpenAI-compatible API server, and DeepSeek's docs list Hermes as a supported agent integration for using its models as the backing provider.

If you want to build with the same model families Hermes users route agent work through, on kie.ai you can call Kimi K3 directly through the API.

What We Don't Know Yet

• Long-run reliability of Nous Portal Cloud under sustained multi-user load. It launched in preview July 8, 2026 and community reports on billing edge cases are still surfacing.
• How much the learning loop actually compounds capability over months of production use, versus how much of the community enthusiasm comes from the initial "self-writing skills" novelty. The three-month writeup on r/hermesagent is a rare longitudinal data point.
• Whether independent, controlled benchmarks will confirm the community's "more stable on long tasks" claim against OpenClaw and other agents.
• Whether upcoming enterprise features (team agents, access controls) will materially change the self-host vs. cloud tradeoff.

Frequently Asked Questions

Is Hermes Agent open source?

Yes. Hermes Agent is released under the MIT license by Nous Research and hosted on GitHub at NousResearch/hermes-agent, which carries roughly 217,000 stars. Self-hosting is free forever; the optional Nous Portal subscription only covers hosted models, tools, and cloud runtime.

How much does Hermes Agent cost?

Hermes Agent itself is free to install and self-host. If you use Nous Portal for models and tools, paid tiers are Plus ($20/month for $22 in credits), Super ($100 for $110), and Ultra ($200 for $220). Hosted cloud instances launched July 8, 2026 and are billed hourly from credits, starting around $0.32/day running for the Small size.

Who makes Hermes Agent?

Hermes Agent is built by Nous Research, the lab behind the Hermes model family and the Psyche distributed training network. It first shipped February 25, 2026 and is now at v0.18.2 (July 7, 2026).

What models does Hermes Agent support?

Hermes Agent is model-agnostic. Through Nous Portal it can call 200+ models including Claude, GPT-5.6, Gemini, DeepSeek V4, Grok 4.5, and Kimi K3. It also works with OpenRouter, direct provider API keys, and local models via llama.cpp, MLX, or vLLM.

How is Hermes Agent different from Claude Code or OpenClaw?

Hermes Agent is not tied to an IDE or a single provider. It has a closed learning loop that creates and improves its own skills, a persistent three-tier memory system, and runs on 20+ messaging platforms in addition to the CLI and desktop app. Community threads describe it as more stable across long-running, multi-step tasks.

What platforms does Hermes Agent run on?

Hermes Agent runs on macOS, Windows, Linux, WSL2, and Android via Termux. It exposes gateways for Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email, SMS, Microsoft Teams, Google Chat, and more, plus an OpenAI-compatible API server for frontends like Open WebUI.

What is the Hermes Agent learning loop?

The learning loop is the mechanism that lets Hermes Agent improve itself. It writes durable facts to MEMORY.md, generates reusable skills from repeated task patterns, refines those skills during use, and models user preferences through pluggable providers like Honcho.

What to Watch Next

Three signals are worth tracking. First, the v0.19.0 release notes, which Nous has publicly committed to using as the curated writeup for everything shipped since v0.18.0 — that will consolidate the post-Judgment Release feature set. Second, how Nous Portal Cloud's pricing shifts once it exits preview and whether the idle scale-to-zero behavior holds up under real subagent workloads. Third, whether third-party benchmarks confirm the community's "more stable on long tasks" claim under controlled conditions.

Building similar agentic workflows? On kie.ai you can try Kimi K3, Claude Sonnet 5, and GPT-5.6.