Hermes Agent — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Hermes Agent. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Jun 20, 2026 • Sentiment updated: Jun 21, 2026

GitHub Statistics

• GitHub Stars: 197,655
• Forks: 34,990
• Open Issues: 22,343
• Primary Language: Python
• License: MIT
• Weekly Star Velocity: +5681 stars/week

Community Sentiment

Community Buzz: Hermes basically rules my personal life at this point - it is a _very_ useful personal assistant, as stated on HackerNews. Additionally, a user on Dev.to mentioned 'Congrats to the Gemma 4 Challenge Winners!', indicating a sense of community engagement.

Pros & Cons

What People Love

Hermes Agent's usefulness as a personal assistant, as praised by HackerNews users, The Hermes Agent Challenge winners, as announced on Dev.to

Common Complaints

Lack of attribution, Bugs and issues with the agent

Biggest Positive: Very useful assistant

Biggest Negative: Lack of attribution

Why Hermes Agent Stands Out

Hermes Agent stands out from other AI agent frameworks due to its self-improving capabilities, which enable it to learn from experience and adapt to user behavior. Its closed learning loop, autonomous skill creation, and user modeling features make it a unique and valuable tool for building personalized chatbots and research assistants. Additionally, its support for various LLM providers and serverless infrastructure options makes it a flexible and cost-effective solution.

Built With

Build a self-improving AI agent that learns from experience — Hermes Agent's closed learning loop enables autonomous skill creation and improvement, Build a research assistant that searches and summarizes large volumes of text — Hermes Agent's integration with various LLM providers allows for efficient text analysis, Build a personalized chatbot that adapts to user behavior — Hermes Agent's user modeling and personality features enable customized interactions, Build a task automation system that runs on a serverless infrastructure — Hermes Agent's support for Daytona and Modal allows for cost-effective automation, Build a multi-agent system that delegates and parallelizes tasks — Hermes Agent's ability to spawn isolated subagents enables efficient workflow management

Getting Started

1. Run `curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash` to install Hermes Agent
2. Run `source ~/.bashrc` to reload the shell and make the Hermes Agent command available
3. Run `hermes` to start the interactive CLI and begin a conversation
4. Run `hermes model` to choose an LLM provider and model
5. Try `hermes tools` to configure which tools are enabled and verify that the agent is working as expected

About

The agent that grows with you

Official site: https://hermes-agent.nousresearch.com

Category & Tags

Category: research

Tags: ai, ai-agent, ai-agents, anthropic, chatgpt, claude, claude-code, clawdbot, codex, hermes, hermes-agent, llm, moltbot, nous-research, openai, openclaw

Market Context

Competing with other AI assistant projects, such as EvoMap's Evolver