Hermes Agent
by Nous Research
Open-source autonomous AI agent by Nous Research with a self-improving learning loop. Runs on your own server, remembers what it learns, and supports 20+ messaging platforms.
Hermes Agent is an open-source autonomous AI agent built by Nous Research — the lab behind the Hermes and Nomos open-source LLM families — and released under the MIT licence in 2026. It is not a coding copilot or a chatbot wrapper. It is a persistent agent that lives on your server, builds a growing knowledge of your projects and preferences across sessions, and gets more capable the longer it runs. The core differentiator is a closed learning loop: after completing complex tasks, the agent autonomously creates SKILL.md files that codify what it learned, improves those skills during future use, and uses full-text search with LLM summarisation to recall relevant context from past sessions. A Honcho dialectic user model builds a structured understanding of your working style and preferences over time. Setup takes minutes using a single curl command on Linux, macOS, or WSL2. The agent runs from a terminal CLI or through a unified messaging gateway that connects it to Telegram, Discord, Slack, WhatsApp, Signal, Email, and 15-plus other platforms simultaneously — start a task from your laptop terminal and check progress on Telegram. Seven terminal backends — local, Docker, SSH, Singularity, Modal, Daytona, and Vercel Sandbox — allow deployment anywhere from a $5 VPS to a GPU cluster, with serverless options that cost nearly nothing when idle. Built-in cron scheduling runs recurring tasks in natural language with delivery to any connected platform: daily briefings, nightly backups, weekly audits. Isolated subagents with their own terminals and Python RPC scripts enable parallel workstreams without sharing context windows. MCP integration connects the agent to any MCP-compatible server for extended tool capabilities. Hermes works with any LLM provider — Anthropic, OpenAI, Google, OpenRouter (200-plus models), Nous Portal, HuggingFace, or a locally hosted model via Ollama. The ecosystem includes 647 community skills across four registries following the agentskills.io open standard. As of May 2026, the GitHub repo has 143,983 stars and 22,487 forks, making it one of the most widely adopted open-source AI agent projects in existence. Autonomous rate is approximately 70-80%: scheduled tasks, skill creation, memory management, and routine workflows run fully without human initiation; complex novel tasks and security-sensitive operations use a command-approval flow. There is no subscription, no telemetry, and no tracking — all data stays on your machine.
Pricing
free · Free
Segment
b2b
Setup
moderate
Verified
May 11, 2026
Capabilities
Pros & Limitations
Editorial assessmentPros
- ✓Self-improving learning loop with no manual upkeep: after each complex task the agent automatically creates and refines SKILL.md files so it never forgets how to solve recurring problems, and the 647-skill community ecosystem means most common workflows have a starting point without any user configuration.
- ✓Runs on infrastructure you control with zero telemetry, zero tracking, and zero data leaving your machine — a meaningful security and privacy advantage over SaaS agents for teams handling sensitive data, proprietary research, or regulated information.
- ✓MCP compatible with full cross-session memory across 20-plus platforms: start a task in the terminal, follow progress on Telegram, and pick up the same conversation on Discord — the agent maintains a single continuous context thread regardless of which interface you use.
Limitations
- ⚠CLI-first setup with moderate technical requirements: deployment needs a server or VPS, familiarity with a terminal, and an LLM API key — there is no hosted SaaS version or graphical setup wizard, which limits accessibility for non-technical users who cannot configure a Linux environment.
- ⚠No built-in cost controls on LLM API usage: the agent runs autonomously and will continue making API calls during scheduled tasks and multi-step workflows, which can generate unexpected token costs without careful monitoring of usage and setting provider spending limits.
- ⚠Memory system uses two small character-limited files injected as a frozen snapshot at session start rather than a vector database — which keeps the system lightweight and predictable but means very large or rapidly growing memory contexts require manual curation to stay within limits.
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