“If OpenClaw was the phenomenon that ignited the AI Agent space in early 2026, then Hermes Agent has taken the competition to an entirely new dimension — evolving from a ‘tool that can do work’ into a ‘partner that grows with you.’”
That’s not my take. That’s how a columnist from Tencent Cloud Developer Community described Hermes Agent. As someone who’s been through the Autopilot Prompts era, banged my head against LangChain, watched OpenClaw rise, and now digs into Hermes — I can tell you that assessment isn’t an exaggeration.
What Is Hermes Agent?
Hermes Agent is an **open-source autonomous AI agent framework** developed by Nous Research, released under the MIT license with 17,000+ stars on GitHub. Its core differentiator? **It gets better on its own.**
Most AI agent frameworks on the market today still operate on a “you write instructions, I execute” model. Hermes is different. It comes with a built-in closed-loop learning system — it retains memory across sessions, autonomously extracts reusable skills, and optimizes its own behavior based on your usage patterns. In plain English: the more you use it, the better it understands you.
The Killer Features
1. Persistent Memory Across Sessions
Anyone who’s used ChatGPT knows the pain of closing a conversation window — the AI forgets everything, and you have to re-explain context every time. Hermes Agent has a multi-tier memory system baked in:
– **Short-term Memory (ConversationMemory):** Current session context, configurable turn limits
– **Long-term Memory (VectorMemory):** Vector-based retrieval that remembers important info across sessions
– **Shared Memory (SharedMemory):** A data exchange hub for multi-agent collaboration
Tell it on Monday “I’m researching Agentic RAG,” and ask on Friday “what’s new on that topic I was researching” — it actually remembers. Once you’ve experienced this, there’s no going back.
2. Autonomous Skill Extraction
This is the feature that makes Hermes genuinely addictive. You teach it one complex workflow — say, “search competitor data → organize data → generate analysis report” — and Hermes automatically abstracts that workflow into a **reusable skill**. Next time you need something similar, just call the skill. No need to re-describe.
From a developer’s perspective, this is **the automation of automation**.
3. Multi-Platform, Multi-Model Support
Hermes supports all major models — GPT-4o, Claude, DeepSeek, open-source models — with **a one-line `base_url` change**. It runs across web, CLI, and API, serving as a unified entry point for your AI operations.
4. Multi-Agent Collaboration
Hermes natively supports multi-agent collaboration: a Router dynamically assigns tasks to sub-agents based on current state, with both sequential and dynamic orchestration modes. This is incredibly useful for complex business workflows.
5-Minute Quick Start
Let’s go beyond theory and get our hands dirty. Here’s a minimal working example:
“`python
from hermes_agent import Agent, tool
from hermes_agent.llm import OpenAIChat
# Configure LLM
llm = OpenAIChat(
model=”gpt-4o”,
api_key=”your-api-key”,
base_url=”https://api.your-provider.com/v1″
)
# Register a tool
@tool
def get_weather(city: str) -> str:
“””Get current weather for a specified city”””
weather_data = {
“Beijing”: “Sunny, 28°C, humidity 45%”,
“Shanghai”: “Cloudy, 26°C, humidity 72%”,
“Shenzhen”: “Thunderstorms, 31°C, humidity 85%”,
}
return weather_data.get(city, f”No data available for {city}”)
# Create the Agent
agent = Agent(
name=”Weather Assistant”,
llm=llm,
tools=[get_weather],
system_prompt=”You are a weather assistant. Keep responses concise and friendly.”
)
# Run it
response = agent.run(“What’s the weather in Shenzhen? Is it good for outdoor running?”)
print(response)
“`
Notice what happens here: the Agent doesn’t just pass through weather data — it reasons about the user’s intent (“suitable for running?”). Thunderstorms + high humidity = recommend indoor workout. That’s the fundamental difference between an Agent and a regular API call.
Multi-Agent Collaboration: Build an Automated Competitor Analysis System
Here’s a more real-world scenario — input a product name, and let a team of agents handle competitor search, data analysis, and report generation automatically.
**Architecture:**
“`
User Input: Product Name
↓
[Router] → Evaluates current state
↓
[Researcher Agent] → Searches competitor info
↓
[Analyst Agent] → SWOT analysis
↓
[Writer Agent] → Generates report
↓
Final Output
“`
Hermes’ dynamic Router is the star of the show. The routing strategy is **written in natural language**:
“`python
dynamic_router = Router.dynamic(
llm=llm,
strategy=”””
Decide next step based on task state:
– No competitor data yet → hand to researcher
– Data exists but not analyzed → hand to analyst
– Analysis complete → hand to writer
– Data insufficient → route back to researcher
“””
)
“`
This is fundamentally different from CrewAI’s static orchestration where you predefine “who goes first.” When the Writer Agent finds that some competitor data isn’t detailed enough, it can automatically route the task back to the Researcher. Implementing this kind of “backtracking” in other frameworks is painful; in Hermes, it’s built-in.
Hermes vs Other Major Frameworks
| Dimension | AutoGPT | CrewAI | Hermes Agent |
|---|---|---|---|
| Learning Curve | Steep, tons of config | Moderate, many concepts | Low, core API < 10 calls |
| Tool Registration | Plugin template required | Decorator-based | Decorator + auto schema inference |
| Multi-Agent Collaboration | Not natively supported | Static orchestration | Dynamic orchestration + message bus |
| Memory System | Built-in but hard to extend | Basic short-term | Multi-tier, pluggable |
| Token Efficiency | High | Medium | Low (smart pruning + prompt compression) |
| Custom LLM | Config file changes | Supported, poor docs | One-line base_url change |
Lessons from the Trenches
Pitfall 1: Tool Return Values Too Large
The researcher agent returned raw HTML, bloating the context window. **Solution:** Clean data inside the tool function — don’t rely on the LLM to handle noise.
“`python
@tool(max_output_tokens=800)
def search_web(query: str) -> str:
raw = call_search_api(query)
cleaned = extract_key_info(raw)
return cleaned[:2000] # Double insurance
“`
Pitfall 2: Dynamic Router Gets Stuck in Loops
Researcher says “enough data,” analyst says “not enough.”
**Solution:** Add hard termination conditions.
“`python
strategy=”””
– If researcher has been called > 3 times → force analysis phase
– If total rounds > 6 → send directly to writer with existing data
“””
“`
Pitfall 3: Overly Long System Prompts Hurt Performance
A 500-word system prompt actually made agent behavior less stable.
**Rule of thumb:** Keep system_prompt under 200 words, and no more than 5 core instructions.
Who Should Use Hermes Agent?
– **Solo developers & tech bloggers**: prototype up and running in a weekend, excellent debugging experience
– **Small teams**: rapidly build internal automation tools with minimal glue code
– **AI application founders**: multi-agent collaboration is a natural fit for complex business processes
If you’re already using OpenClaw, Hermes Agent is a complementary choice — OpenClaw excels at personal AI assistant scenarios and instant messaging, while Hermes shines at multi-agent collaboration and skill extraction. Combined, they cover almost everything you could imagine doing with AI agents.
> **References:**
– [Hermes Agent GitHub Repository](https://github.com/NousResearch/Hermes-Agent)
– [From OpenClaw to Hermes: The AI Agent Evolution Revolution – Tencent Cloud](https://cloud.tencent.com/developer/article/2654157)
– [Hermes Agent Getting Started Guide – GrapeCity](https://grapecity.csdn.net/69e83f940a2f6a37c5a176c4.html)
– [Hermes Agent Comprehensive Review – Zhihu](https://zhuanlan.zhihu.com/p/2022015752258027715)
📖 Recommended Reading
These posts pair well with what you just read:
“OpenClaw: The Revolutionary Open-Source Platform for Personal AI Assistants in 2026”