Hermes Agent: Why Users Are Quietly Migrating to the Next Generation of AI Agents

Core Question This Article Answers: In the rapidly iterating landscape of AI Agent technology, why has Hermes Agent triggered a massive community migration in just six weeks, and what is the fundamental difference between it and traditional Agent frameworks like Lobster (OpenClaw)?

The hottest topic in the tech community recently is undoubtedly the phenomenal rise of Hermes Agent. In just six weeks, it has garnered over 30,000 Stars on GitHub, attracted 242 contributors, and undergone 8 major version iterations. Behind these numbers lies a pressing user demand for a new paradigm of Agent interaction. From Reddit to X (formerly Twitter), and YouTube, discussions about “migrating from Lobster to Hermes” are everywhere.

This phenomenon is not merely a traffic stunt. The official integration of a one-click migration command signifies that the development team is confident in their technology and has accurately identified the pain points of existing users. Many veteran users who have maintained Lobster projects for months are now seriously considering—and executing—the “move.”

This is not just a change of tools; it is a paradigm shift in Agent design philosophy.

Architecture Comparison: The Choice Between a Static Gateway and an Evolutionary Engine

Core Question of This Section: What fundamental differences exist in the underlying architecture design between Hermes Agent and Lobster, and how do these differences define their application boundaries?

Many people initially mistake Hermes for a “budget alternative” or a simple feature upgrade of Lobster. This is a misunderstanding. Their kernels are fundamentally different, a difference that directly dictates user experience and maintenance costs.

Lobster: The Classic Paradigm of Gateway Control

The core of Lobster’s architecture is a “Central Controller.” Its operating model resembles a sophisticated switch: all messages flow in from various chat platforms (WeChat, Feishu, DingTalk, etc.), and this controller uniformly routes, distributes, and executes them.

Its Skill system is a typical static model:

• Manual Definition: Users need to write Markdown files to clearly define every operation flow.
• Mechanical Execution: The Agent strictly follows preset steps and lacks autonomous flexibility.
• Passive Growth: Without manual intervention, the Agent’s capability on day one is no different from day thirty.

It is like raising a pet that only learns what you feed it. If you don’t write Skills, it’s just a chatbot connected to many channels; if you write Skills, it works according to your will. The advantage of this model lies in extreme controllability. For enterprise-level applications, this certainty is crucial.

Image Source: Unsplash

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Author’s Reflection:
In my past engineering practices, the “trap of determinism” is very obvious. The better the Skills you write, the better the system works, but this shifts immense pressure onto the maintainer. You need to continuously tune instructions like training a new employee. Once you stop investing, the system’s intelligence level stagnates. This creates a scenario where the tool becomes a burden rather than a solution for many.

Hermes: The Closed-Loop Mechanism of Self-Evolution

Hermes’s architecture completely subverts this logic. Its core is no longer a mere message gateway, but a Closed-Loop Learning Engine.

Its workflow follows the cycle of “Observe → Plan → Execute → Learn.” The real magic happens in the final step—”Learning.”

When Hermes completes a complex task (usually involving more than 5 tool calls), it automatically executes three key actions:

1. Structural Extraction: It automatically distills the entire problem-solving process into a structured Skill file. This file contains not only the operation steps but also records common pitfalls encountered and verification methods.
2. Persistent Storage: It stores the generated Skill in a persistent memory bank. When a similar task is encountered next time, it retrieves it directly without re-reasoning.
3. Dynamic Optimization: During usage, if a better solution is found, it automatically updates the Skill document.

This is like raising a creature that can forage and grow by itself. You use it to complete a task, and it incidentally learns how to complete that task better.

Case Study:
A Reddit user provided representative feedback. After using Hermes for only two hours, the system automatically created 3 Skill files. Subsequently, when handling the same type of research tasks, the speed improved by 40% directly. This experience of “getting smarter the more you use it” is something traditional static Agents cannot provide.

Jeffrey Quesnelle, CEO of Nous Research, demonstrated an extreme case: letting Hermes autonomously complete a 79,000-word novel. This requires spanning multiple sessions and maintaining context coherence during continuous iteration. This is almost impossible to achieve under the Lobster architecture because Lobster lacks the mechanism to accumulate experience across sessions.

The Memory System: The True Watershed of Intelligence

Core Question of This Section: Why is the difference in memory systems the key indicator distinguishing Agent 1.0 from Agent 2.0?

If architecture is the skeleton, then the memory system is the soul of the Agent. This is also Hermes’s most subversive advantage compared to Lobster.

Lobster’s Shallow Memory Dilemma

Lobster’s memory system is often criticized by users for being “forgetful.” At the end of each session, most context information is instantly lost. Although it can be supplemented through manual memory files like CLAUDE.md, this essentially throws the maintenance work back to the user.

This leads to an awkward situation: things you taught it yesterday, it might forget today; preferences you set last week, it will revert to default settings next week if you don’t reiterate. For users hoping an Agent becomes a long-term partner, this “goldfish memory” is an insurmountable experience barrier.

Hermes’s Layered Persistent Memory

Hermes has built a four-layer memory architecture, truly realizing the ability to “know you”:

Memory Layer	Functional Description	Technical Implementation
Working Memory	Processes context information for the current session, ensuring immediate dialogue coherence.	Context Window Management
Session Memory	Cross-session information retrieval, quickly finding key details from past conversations.	SQLite Full-Text Search + LLM Summarization
User Modeling	Long-term learning of user preferences, work habits, and project backgrounds; understands you better over time.	Honcho Dialectical User Modeling
Skill Memory	Stores auto-generated Skill files, supporting reuse, sharing, and cross-instance migration.	Open Standard Document Storage

An early user described this shift in experience: “After using it for a week, I started feeling like it really knows me. Not that shallow recognition of ‘you said you like concise style last time,’ but that it defaults to my habits when executing tasks without me needing to teach it like a new employee every time.”

The root of the community saying “it gets addictive the more you use it” lies in the “sense of companionship” brought by this memory system.

Image Source: Pexels

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Unique Insight:
Memory is not just data storage; it is the dynamic construction of a user portrait. The memory in the Lobster era was static notes, while Hermes’s memory is dynamic model fine-tuning. This shift from “storage” to “modeling” is a key step for AI Agents moving towards true intelligence.

In-Depth Analysis of Real-World Scenarios

Core Question of This Section: In different actual workflows, what specific efficiency gains can Hermes’s self-evolution characteristics bring?

To understand Hermes’s value more intuitively, we analyze several typical application scenarios based on the information provided.

Scenario 1: Daily Information Curation

Workflow:
The user sets a natural language scheduled task: “Every morning at 8 AM, search for the latest open-source AI news on Reddit and X, organize it into a structured report, and send it to my Telegram.”

Hermes’s Performance:
It doesn’t just mechanically grab keywords. Based on past interaction history, it learns your preferences:

• If you frequently click on content about “LLM inference optimization,” it automatically raises the priority of such information.
• If you often ignore “funding news,” it automatically lowers the weight of such information or even stops pushing it.

This automatic tuning based on feedback is difficult for static Skills to achieve.

Scenario 2: Persistent Coding Partner

Workflow:
Developers connect Hermes to their codebase for daily code writing, bug fixing, and refactoring.

Hermes’s Performance:
It can long-term remember your codebase structure, variable naming habits, and deployment processes. More powerfully, it supports cross-session collaboration:

• You close your computer and go to sleep.
• Hermes continues executing the tasks you left behind (like running tests, generating documentation) on a cloud VM.
• The next morning, you open Telegram, and it has already sent you the progress report.

This “asynchronous working” capability greatly extends the time boundaries for developers.

Scenario 3: Style-Consistent Writing Assistant

Workflow:
Writing technical blogs or proposal documents.

Hermes’s Performance:
The biggest issue with traditional AI writing assistants is that you need to reset the style every time (“I want a concise style, don’t use the word ‘boundary'”).
Hermes, through user modeling, locks onto your writing style after a few interactions. It automatically avoids words you dislike and defaults to the sentence structures you prefer. This “invisible” tacit understanding significantly lowers communication costs.

Scenario 4: Methodology Sedimentation in Research Automation

Workflow:
Conducting competitive analysis or industry research.

Hermes’s Performance:
The first time you do a competitive analysis, you need to guide it step-by-step.
After the task is completed, Hermes automatically generates a Skill file named “Competitive_Analysis,” which solidifies the best path for research (e.g., check financial reports first, then public opinion, finally comparison tables).
Next time you say “Help me analyze Company XX,” it directly calls this Skill, doubling efficiency and ensuring stable quality.

Technical Foundation and Iteration Speed

Core Question of This Section: How does the team behind Hermes Agent support its rapid version iteration and technical evolution?

Hermes Agent is not a “wild” personal open-source project; it is backed by Nous Research—a professional AI lab that secured $65 million in financing. This team is already famous in the open-source community as the developers of the renowned Hermes series of models (Nous Hermes 2, Nous Hermes 3) and the Nomos and Psyche models.

Deep Fusion of Model and Agent

This brings a unique advantage: The people building the Agent are the same people training the models.

This means Hermes Agent isn’t just putting a shell around a model; it considers Agent needs starting from the model training layer. They use DSPy and GEPA technologies to automatically optimize Skills and Prompts and have established a separate self-evolution repository (hermes-agent-self-evolution). This is a research pipeline actually running, not a marketing slogan.

Deep Fusion of Model and Agent

This brings a unique advantage: The people building the Agent are the same ones training the models.

This means Hermes Agent isn’t just a shell around a model; it considers Agent needs from the model training layer itself.

Technical Evolution in v0.7.0

Taking the v0.7.0 version released on April 3, 2026, as an example, this version merged 168 PRs and introduced key features:

• Pluggable Memory Providers: Allows users to switch different memory storage backends according to needs, greatly increasing flexibility.
• Credential Rotation: Enhances security, adapting to enterprise-level application scenarios.
• Anti-Detection Browser Backend: Improves the success rate of automated browsing tasks.
• MiniMax Partnership: MiniMax’s M2.7 model became one of the highest-utilized models in Hermes Agent, showing its compatibility with a multi-model ecosystem.

This dense technical iteration demonstrates the team’s powerful engineering capabilities and clear vision for the product’s future.

Migration Decision and Operation Guide

Core Question of This Section: How to decide whether to migrate and how to complete the migration at the lowest cost?

Facing the rise of Hermes, existing Lobster users face a decision. This is not a zero-sum game, but a matter of scenario matching.

Decision Matrix: Who Should Switch?

To help readers judge quickly, we have organized the following decision table:

User Persona	Recommendation	Core Reason
Individual Power Users	Switch Recommended	Need a long-term companion assistant; Hermes’s memory and evolution mechanism significantly reduce maintenance burden.
Repetitive Task Users	Strongly Recommended	Repetitive tasks like daily research or report writing; Hermes automatically sediments methodology, significantly boosting efficiency.
Enterprise/Team Users	Wait or Combine	If heavily reliant on Lobster’s 50+ channel integrations (e.g., WeChat, DingTalk enterprise portals) or needing strict auditability, Lobster currently has a more mature ecosystem.
Multi-Agent Builders	Combine Recommended	Lobster has mature solutions for multi-agent collaboration orchestration; let Hermes handle high-level decisions while Lobster handles low-level execution.

Low-Cost Migration Operation

If you decide to try Hermes, the migration cost is kept extremely low. The official one-click migration command is designed to seamlessly take over historical assets.

Detailed Migration Steps:

1. Preview Migration Content:
   To ensure safety, it is recommended to execute the preview command first to see what data will be migrated.

   hermes claw migrate --dry-run
   

   This step will list your memories, skills, persona settings, and API keys, but will not actually execute the write.

2. Execute Migration:
   After confirming everything is correct, execute the formal migration command.

   hermes claw migrate
   

   The system will automatically import your historical configuration into the Hermes environment.

3. Verify and Run:
   Start Hermes and try a few familiar tasks to verify if its performance meets expectations. You will usually find that it not only inherits old capabilities but also starts showing new “sparkles” of intelligence.

Conclusion and Author’s Insights

Lobster undoubtedly opened the door to AI Agents, letting the public intuitively feel the possibility of “AI working for you.” This craze of raising Lobsters was essentially a mass enlightenment of AI Agents. Its historical status cannot be shaken.

However, the limitations of its architecture are becoming increasingly apparent: It overly relies on human feeding. For most ordinary users, after the novelty wears off, “raising an Agent” becomes a burden. You not only have to use it but also teach it, and even help it “remember things.”

The appearance of Hermes solves exactly this pain point. It automates the “feeding” process and makes “memory” persistent. If Lobster represents Agent 1.0—human-driven Agent, then Hermes previews Agent 2.0—Agent driving itself.

These two paradigms are destined to coexist for a long time, each with its applicable scenarios. But for individual developers and tech geeks pursuing efficiency and intelligent experience, the future shown by Hermes is undoubtedly more attractive. Since migration only takes one line of command, give it a try—perhaps you won’t go back.

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Practical Summary / Checklist

To facilitate quick implementation, here is an action checklist based on this article:

1. Self-Assessment: Check if you fall into the “repetitive task” or “long-term companion need” user category. If so, Hermes can significantly improve your efficiency.
2. Data Backup: Although the migration command is relatively safe, it is always a good habit to back up your Lobster configuration files before any major change.
3. Try Migration: Use hermes claw migrate --dry-run for a dry run to confirm if key skills and memories are on the migration list.
4. Scenario Testing: After migration, select a typical task you perform frequently (like daily reporting or competitive analysis) and compare the execution efficiency and result quality before and after.
5. Observe Evolution: After using it for a week, check the Skill files automatically generated by Hermes to observe how it understands and optimizes your workflow.

One-Page Summary

• Core Difference: Lobster is “Gateway + Static Execution,” requiring manual maintenance of Skills; Hermes is “Engine + Closed-Loop Learning,” capable of automatically generating and optimizing Skills.
• Memory Architecture: Hermes possesses four layers of memory (Working, Session, User Modeling, Skill), solving Lobster’s “forgetfulness” problem.
• Target Audience: Individual users and repetitive task scenarios prefer Hermes; enterprise-level complex integration scenarios may continue using or combining Lobster.
• Migration Cost: Extremely low; one command completes the full migration of memory, skills, and keys.
• Technical Backing: Developed by Nous Research, possessing strong underlying model optimization capabilities and rapid version iteration speed.

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Frequently Asked Questions (FAQ)

1. Are Hermes and Lobster in competition? Do I have to choose one?
It’s not strictly an either/or choice. Many community users choose to combine them: using Hermes for high-level decision-making and long-term memory storage, while utilizing Lobster’s mature tool ecosystem to execute specific channel integration tasks.

2. Can I use Hermes without knowing code?
Yes. Although Hermes’s underlying technology is complex, its design goal is to lower the maintenance threshold. As long as you can configure the basic running environment, its natural language interaction and automatic learning features make it even more suitable for non-technical users for long-term use compared to Lobster.

3. Will the migration process lose my previous settings?
The official migration command is designed very comprehensively, supporting the automatic import of memories, skills, persona settings, and API keys. It is recommended to use the --dry-run parameter to preview first to ensure nothing is missed.

4. Will Hermes’s “self-evolution” cause it to mess up my configurations?
Hermes’s evolution is mainly reflected in the generation and optimization of Skills. It will store newly learned content as new files or optimize existing processes, usually without destroying your core configurations. This process is observable; you can view the Skill files it generates at any time.

5. Why is Hermes’s memory system said to be better than Lobster’s?
Lobster’s memory is mostly based on the current session or simple file references, where context is easily lost. Hermes has a layered memory architecture, especially session memory and user modeling functions, which can retrieve information across sessions and learn your preferences long-term, truly realizing “remembering you.”

6. Which models does Hermes support?
Hermes has strong model compatibility. Besides the Hermes series models trained by the official team, version v0.7.0 announced a partnership with MiniMax, with the M2.7 model currently being one of its highest-utilized models.

7. If I want Hermes to help me write code, can it remember my project structure?
Yes. This is one of Hermes’s strengths as a coding partner. It can not only remember the project structure but also your naming conventions and deployment processes, maintaining this memory across sessions.

8. Is Hermes completely free?
According to the input file, Hermes Agent is an open-source project (viewable on GitHub) and follows open-source protocols. However, the large model APIs called during use (like GPT-4 or MiniMax) may require the user to bear the relevant API costs.