Hermes Agent Surges 108K Stars in a Month, Tops GitHub Trending Repositories Chart

Core Metrics

Metric	Value	Ranking
Monthly New Stars	+108,100	GitHub Monthly Growth #1
Total Stars	127,352+	Top 200 Site-Wide
Monthly Forks	Continuously climbing	#1 in Agent Category
Daily Average	~3,600 stars/day	One of the highest growth rates in open-source AI history

In early May 2026’s GitHub monthly trending chart, NousResearch/hermes-agent topped the growth chart with over 108K new stars, surpassing Andrej Karpathy’s Claude tips file (+100.7K) and hundreds of other projects to claim the #1 spot.

What Is It

Hermes Agent’s core philosophy in one sentence: Agent that grows with you.

It’s not another prompt wrapper or API glue layer. Hermes Agent’s design philosophy revolves around three pillars:

1. Self-evolving skills system: The agent automatically optimizes its own skills based on execution results, rather than relying on manual writing and debugging
2. Memory system: Cross-session contextual memory, making the agent understand your work patterns better with each use
3. Curator mechanism (v0.12+): Introduces a skill curator role that automatically filters, evaluates, and version-manages agent capabilities

Why 108K Stars Matters

Getting 10K stars for an AI project on GitHub is not uncommon, but 100K+ monthly growth is a different story. Horizontal comparison:

• The Agency (msitarzewski/agency-agents): 50K stars in 2 weeks, 93K total
• ruflo (ruvnet/ruflo): 42K stars, ~2,400/day growth
• DeepSeek-TUI: 6,308 stars, ~2,389/day growth

Hermes Agent’s daily growth rate (~3,600) far exceeds comparable projects. Such growth curves typically appear in only two scenarios:

1. The product genuinely solves an industry pain point
2. Community operations + KOL propagation create network effects

Hermes Agent has both.

Differentiation from Similar Frameworks

Feature	Hermes Agent	LangChain	CrewAI	OpenHands
Self-Evolution	✅ Curator + skill self-optimization	❌ Manual orchestration	❌ Fixed roles	❌ Manual configuration
Cross-Session Memory	✅ Built-in memory system	⚠️ Requires external	⚠️ Requires external	❌ None
Multi-Model Support	✅ Multi-Provider	✅	✅	✅
Skill Version Management	✅ Curator auto-management	❌	❌	❌
Positioning	Self-evolving personal agent	Development framework	Multi-agent collaboration	Autonomous coding

Hermes Agent’s unique value proposition: it’s not just a tool that runs tasks for you, but a partner that gets stronger the more you use it.

Who Should Use It

Get Started Now

• Individual developers: Use Hermes Agent as your AI colleague—it learns your preferences and context with each interaction
• Research teams: Use the Curator mechanism to manage experiment workflows, automatically recording and optimizing agent execution strategies

Evaluate Mid-Term

• SMBs: As agent capabilities accumulate, they can form enterprise-level automation workflows
• Platform teams: Build internal AI toolchains based on Hermes Agent, lowering the AI usage barrier for non-technical staff

Getting Started

# Clone the repo
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent

# Install dependencies
pip install -e .

# Configure your LLM Provider (supports OpenAI/Claude/Gemini/local models)
# Start the agent
hermes-agent start

Key configuration items:

• memory_backend: Choose SQLite (lightweight) or PostgreSQL (production)
• skill_registry: Enable Curator auto-management
• provider: Configure your preferred LLM

Industry Signal

Hermes Agent’s explosive growth reflects a fundamental shift in the 2026 AI Agent market:

From “one-off prompt engineering” to “continuously learning agent infrastructure”.

2025’s agent projects were mostly “write a set of prompts → call an API → get results.” The new paradigm represented by Hermes Agent is: agents accumulate knowledge during execution, optimize skills, adapt to context—closer to the essential definition of “intelligent agent.”

If the 108K star growth trend continues, Hermes Agent is on track to become one of the de facto standards in the AI Agent space by mid-2026.