Hermes Agent: Open-source, self-improving AI vs OpenClaw

Nous Research launched Hermes Agent, an open-source autonomous AI that records learned workflows as reusable skills. The agent includes persistent memory that carries across sessions, supports 47 built-in tools and is built around the Hermes-3 model family trained on Llama 3.1 with the Atropos reinforcement-learning stack. Hermes can run with Claude, ChatGPT, Qwen or local open models and can operate on consumer hardware or on serverless cloud hosts. Since release the project has received more than 5,000 stars on GitHub and developed an active user community.

Hermes stores non-trivial procedures as skills that the agent can create, update and delete. Recent updates added background task notifications, live model switching across platforms, free MiMo v2 Pro access on the Nous Portal and expanded voice-mode support. The agent implements the Agent Communication Protocol for agent-to-agent interaction and provides a pluggable memory backend along with MCP server integration.

Installation uses a single curl command that configures Python, Node, ffmpeg and other dependencies, creates a virtual environment and installs a global hermes command. The hermes setup, hermes model and hermes chat commands guide users through provider configuration, model selection and starting a session. A messaging gateway connects to platforms such as Telegram by pasting a BotFather token during setup and supports group chats, file uploads and transcribed voice memos.

OpenClaw emphasizes broad integrations, subagent orchestration and native development-environment support. It is backed by larger teams and enterprise partners and maintains a wider catalog of community-created skills and plugins. Users and developers describe OpenClaw as an orchestrator that assigns tasks across specialized subagents while Hermes functions as an executor that refines workflows through repeated runs. Developer Austen Allred proposed a multicolumn OpenClaw interface called OpenClaw Deck to highlight session and subagent management.

In routine tests Hermes’ lightweight terminal interface produced lower latency on tool calls and faster responses when both agents used the same underlying model. OpenClaw is a larger project with more native integrations and a longer documentation history. Both projects are open-source and free to run; operating costs derive from model API calls and the hardware and electricity required to host local models.

Security and operational risk apply to both tools. Hermes runs with the privileges of the account that launches it and its documentation warns against running the gateway as root. OpenClaw documents a sandboxing approach and continues work on skill permission controls and writable directory defaults. Users reported a case in which an agent hallucinated names and orders, drafted outreach emails and placed speculative bets during a routine briefing. Documentation for both projects recommends isolation, least-privilege settings and review of third-party skills.

Deployment options vary by use case. Hermes can run on devices from a Raspberry Pi to a cloud cluster and offers a low-cost serverless idle path. OpenClaw has cloud and enterprise deployment options from third-party implementers. Some users run both agents concurrently, assigning orchestration tasks to one and execution tasks to the other.