I saw a tweet with 3 million views. Nous Research had released an open-source AI tool called Hermes Agent. At first, I didn’t think much of it — every week there’s a new AI promising to revolutionize everything. But this one was different. I checked GitHub: over 23,000 stars in just 2 weeks. By week 7, it hit 100,000 stars. No AI agent in GitHub history had grown this fast. I sat down, tested it, and I’ve been using it for 10 days now. And I need to be straight with you: this doesn’t behave like ChatGPT, Cursor, or Claude. Because Hermes remembers you.

Key Takeaways

• Persistent memory: Hermes remembers who you are, what you do, and your projects — permanently
• 24/7 operation: Runs on a $5 VPS server, works even when your laptop is off
• Multi-platform access: Control via Telegram, Discord, Slack, WhatsApp, and 11 other platforms
• Skill learning: Saves solutions as “skills” and improves with each repetition
• Sub-agents: Parallel AI workers that can be specialized and coordinated
• Setup time: Approximately 10 minutes for basic installation
• Cost: Open-source, MIT licensed, and free to use

Why Hermes Feels Like Hiring an Employee, Not Using a Tool

The first thing that struck me was the memory. With ChatGPT, every conversation starts from zero. With Hermes, I tell it once who I am, what I do, and what I’m working on. The next day, I don’t need to repeat anything. It just knows.

Here’s a concrete example from my testing. I told Hermes: “Every morning at 9 AM, send me the top-selling products on eBay UK via Telegram, formatted as a report.” That’s it. One instruction. Now every morning, the report arrives. While I sleep, a system running on a $5 server works like a machine. This isn’t a chatbot — it’s a persistent employee that learns your business.

The second critical difference: Hermes doesn’t live on your laptop. It lives on a server. I configured mine on a cheap VPS. I can close my laptop, go out, and still send commands from my phone. “Post this week’s content to YouTube, push my eBay sales.” Two weeks later, normal response. The agent keeps working.

How the Skill System Creates Compound Returns

This is where Hermes diverges fundamentally from every other AI I’ve tested. When Hermes solves a problem, it saves that solution as a “skill.” The second time it encounters the same task, it doesn’t start from scratch — it continues from where it left off. By the third, fourth, tenth time, it’s faster and better.

I need to be honest: this sounds simple when I say it, but when you actually experience the difference, it changes your perspective on what AI can do in your business. You’re no longer renting a tool by the conversation. You’re building an asset that appreciates.

In my 10-day test, I watched Hermes develop approximately 149 skills in my dashboard. Tasks that initially took several minutes of back-and-forth now execute automatically. The agent maintains a Kanban-style memory system that tracks what it’s learned and what it’s working on.

Setting Up Hermes: What Actually Works

The basic setup takes about 10 minutes. You copy a command, paste it in your terminal, and the rest runs automatically. You select your AI model — Claude, GPT, Gemini, whatever you prefer — connect your Telegram bot, and it works. Everything is open-source, MIT licensed, and free.

However, I need to flag something important from my testing. Hermes has an extensive skills marketplace where you can download additional capabilities — social media automation, browser control, research tools, API integrations. The temptation is to install everything. Don’t. Each skill consumes tokens and storage. I made this mistake early: loading too many skills clogged my system. Be selective. Install only what you’ll actually use.

For API access, I tested the Opera Router option. In just 5 minutes of use, it burned through $10 worth of tokens. My recommendation: use the free tier models, or filter for “free” options in the model selection. This keeps costs manageable while you’re learning the system.

Sub-Agents: The Feature That Changes Everything

The most powerful aspect of Hermes, in my view, is the sub-agent system. You can create parallel AI workers under your main agent, each with different specializations. But here’s what most people get wrong: you need to run these in the correct mode — discovery, activation, and execution.

First, the research agent finds and develops its own capabilities using its existing skills. This self-improvement loop is genuinely different from anything I’ve seen in other platforms. Then you activate the specific skills needed, and finally execute. I’ve seen hundreds of people online burning tokens because they skip these steps and don’t understand what their agents are actually doing.

In my own setup, I configured separate sub-agents for: morning eBay research, weekly analytics analysis, YouTube content research, and other business tasks. Each runs on its own schedule, reports back via Telegram, and improves its performance over time.

Security and Practical Considerations

When setting up Telegram integration, create your bot token through BotFather and never share that API token externally. This maximizes your security. I also recommend running Hermes on a virtual server rather than your local machine if you’re loading heavy browser-automation skills — this prevents performance degradation on your main computer.

For those who prefer not to use the terminal, Hermes Desktop now exists. You download it as a standard Mac application and run everything through a graphical interface. The functionality is identical — it’s simply a different access method.

What I Honestly Think After 10 Days

Hermes is not perfect. The setup requires more technical comfort than ChatGPT. You need to understand tokens, API keys, and basic server concepts. The documentation, while extensive, assumes some technical background. And if you’re careless with skill installation or model selection, costs can escalate quickly.

But the fundamental architecture is correct. Persistent memory, skill accumulation, server-based operation, and multi-platform access — these aren’t features, they’re a different category of tool. Where ChatGPT is a brilliant conversationalist you meet anew each time, Hermes is a colleague who learns your business and works continuously.

I’ve been following AI developments closely, and I’m among the first in Turkey to test and document this tool. The industry hasn’t fully recognized what’s happening here yet. The growth metrics — 100,000 GitHub stars in 7 weeks — suggest the developer community sees it clearly.

FAQ

How much does Hermes cost to run?

The software itself is free and open-source under MIT license. You’ll need a server (approximately $5/month for a basic VPS) and API access to an AI model. Free tier models are available; paid models vary by usage. In my testing, careless model selection cost $10 in 5 minutes, while careful configuration runs for minimal cost.

Do I need coding skills to set up Hermes?

Basic setup takes about 10 minutes and requires copying commands into a terminal. For non-technical users, the community provides step-by-step guides, and you can paste instructions into AI assistants like Claude to generate setup commands. A desktop application is also now available for Mac that eliminates terminal use.

How is Hermes different from ChatGPT or Claude?

Unlike conversational AIs, Hermes features persistent memory (remembers your identity and projects permanently), runs continuously on servers (works when your devices are off), learns and saves skills that improve with repetition, and operates across 15+ platforms including Telegram, Discord, Slack, and WhatsApp.

What are the main risks or downsides?

Token costs can escalate if you use premium models or install excessive skills. The system requires more technical setup than consumer AI tools. Skill bloat — installing too many capabilities — can slow performance. And as with any server-based system, API token security is your responsibility.

Conclusion

After 10 days of testing Hermes, I’m convinced this represents a genuine evolution in how solo entrepreneurs and small teams can leverage AI. Not as a smarter chatbot, but as a persistent, learning system that operates continuously across the platforms you already use.

The setup investment — roughly 10 minutes plus learning time — pays dividends in automated workflows that improve themselves. My morning eBay reports, weekly analytics, and content research now run without my involvement, getting slightly better each cycle.

If you’re currently using AI by opening and closing the same chat window daily, you’re hammering that same nail. Hermes offers something different: building a machine that remembers, learns, and works while you focus elsewhere. It’s not guaranteed income or effortless automation. It requires thought, configuration, and ongoing management. But in my direct experience, it’s the most capable open-source AI agent available today, and its growth trajectory suggests I’m not alone in that assessment.

Watch the full video (in Turkish — English subtitles available):

Tools & Community

• TurkoLister — the AI listing tool I use to turn Amazon products into optimized eBay UK listings in about 60 seconds (from £4.99/month, £1 one-week trial).
• AI & E-commerce Community — my Turkish-speaking community ($19/month) with weekly live sessions.
• Subscribe on YouTube — new experiments every week.

The post I Tested Hermes AI Agent for 10 Days — Here’s What ChatGPT Can’t Do appeared first on Digital Market Mentoring.

Key Takeaways

• Hermes is an orchestrator, not a single AI: It coordinates multiple AI tools and models automatically, unlike ChatGPT or Claude which handle tasks partially or forget steps midway.
• Completely local and free: Your data stays on your machine, with no subscription fees—only optional API costs if you choose external models.
• 90-second research tasks: I tested it with finding Turkey’s top 5 AI startups, their funding, and creating a markdown file. It took 90 seconds versus 30 minutes manually.
• Setup requires technical patience: Expect 2-3 hours for initial installation. Python version issues or hardware limitations can cause errors.
• Best for developers, researchers, content creators, and small businesses: Code management, data aggregation, trend research, and customer data processing all work well.

Why I Needed Something Beyond ChatGPT and Claude

Here’s the frustration that led me to Hermes. I was trying to get real work done with ChatGPT, and it would complete half the task, then forget the other half. I’d switch to Claude in the cloud, and while it could write code, it couldn’t browse the internet. I’d use another tool for web research, another for file management—everything was fragmented and incomplete.

What struck me about Hermes is that it’s not just another single AI agent. It’s a full orchestra. It handles the coordination of multiple AI systems for you, calling the right tools at the right time without you micromanaging each step. This orchestration layer is what makes it fundamentally different from the tools I was using before.

What Hermes Actually Does: The Four Core Capabilities

When I dug into the project—an open-source repository on GitHub that millions have already accessed—I found four capabilities that solved my specific problems:

First, autonomous workflow execution. You write one prompt, and Hermes calls its own “team members” (specialized tools) to complete the job. I simply describe what I want, and it figures out the execution path.

Second, web and file reading integration. It can access web documents, read files, and process information from multiple sources in a single workflow.

Third, persistent memory. Unlike my cloud conversations that I’d lose context in, Hermes maintains memory across sessions. Previous conversations aren’t forgotten, which means I can build on prior work without repeating myself.

Fourth, and most important to me: local operation. All data stays on my computer. Nothing gets transmitted externally for the core functionality. For security and privacy, this was a decisive factor in my adoption.

My Step-by-Step Local Setup Experience

I’ll be direct: the setup isn’t plug-and-play. It took me about 2-3 hours, and I hit some friction points that I’ll share so you can avoid them.

Prerequisites You Actually Need

Before starting, I confirmed three things on my machine: Python 3.11 or higher installed, a Python package manager (pip), and terminal access—Terminal on Mac or Ubuntu on Windows. Missing any of these will cause errors that aren’t always obvious to debug.

Installation: The Two Paths

I tried both approaches so I could report back honestly. The manual route involves cloning the GitHub repository, running dependency installations, and configuring settings through terminal commands. The specific commands vary slightly by operating system—Mac users need brew install git python while Windows users run through Microsoft Store and sudo apt update paths.

But here’s what I actually recommend: download Cursor (the AI code editor), paste the setup prompt into it, and let it handle everything. I tested both methods, and the Cursor-assisted setup was significantly smoother. The manual process works, but only if you’re comfortable troubleshooting Python environment issues.

Configuration and First Launch

After installation, I navigated to the project folder and ran the Hermes startup command. The system loaded with Hermes 3 Llama as the default model, with web research capabilities active. I could immediately see it was designed to handle the full pipeline: research, processing, and output generation.

How I Configure Models: Ollama vs. OpenRouter

This is where I spent significant time testing, and my findings might save you hours.

Ollama is the free, local option for running language models. You download models to your machine and run everything offline. I tested it extensively, and while it works, I found the quality inconsistent for complex tasks. If you want completely free operation and don’t mind downloading large model files to your local storage, Ollama is viable.

However, my preferred setup uses OpenRouter AI. This platform aggregates open-source models and lets you access them through API calls. Here’s the critical finding: if you type “free” in OpenRouter’s model filter, you get access to capable models at no cost for substantial usage tiers.

The models I tested and recommend: Mistral V2 Pro (currently showing as most popular for Hermes), Qwen 3.6 (widely used), and MiniMax. These are all Large Language Models (LLMs) comparable to ChatGPT-4.5, Claude Sonnet, or OpenAI’s offerings—but accessible through this routing system.

For my workflow, I use OpenRouter when I want internet-connected tasks without local storage bloat, and Ollama when I need fully offline operation. The flexibility to switch between these based on the task is genuinely valuable.

My Real Performance Test: 90 Seconds vs. 30 Minutes

I designed a deliberately complex task to stress-test Hermes: “Find the top 5 AI startups in Turkey, get each founder’s funding information, then create a markdown file with all results.”

This matters because ChatGPT cannot do this—it lacks reliable internet access in standard usage. Claude can partially handle it through web access but often misses steps or requires manual intervention.

Here’s exactly what Hermes did after I submitted the single prompt:

1. Conducted web research via Google search
2. Read and processed search results
3. Identified 5 relevant startups
4. Performed separate detailed searches for each startup’s funding
5. Compiled all information
6. Generated a formatted markdown file
7. Delivered complete results to me

Total time: 90 seconds. I timed it. If I had done this manually—researching each company, verifying information, formatting the document—I estimate 30 minutes minimum, and my accuracy would likely be lower due to fatigue and oversight.

I watched the process execute in real-time, with hundreds of system operations running automatically. The scale of coordinated action is what makes this feel like having a Jarvis-level assistant, as I described it to my team.

Who This Actually Helps (And Who Should Skip It)

After weeks of daily use, I’ve identified four profiles where Hermes delivers clear value:

Developers and technical operators: Code writing, file management, API integrations—Hermes handles all of these. You maintain oversight but delegate execution.

Researchers: Information scattered across the internet gets collected, summarized, and structured in seconds rather than hours of manual aggregation.

Content creators: I use it for trend research and competitor analysis. Tasks that previously took hours of browsing now complete in minutes with structured outputs.

Small businesses: Customer data cleaning, report generation, maintaining AI-driven operational systems—all possible with local data security.

However, if you’re not comfortable with terminal commands, Python environments, or troubleshooting technical errors, the 2-3 hour setup will frustrate you. This is not a no-code solution.

The Honest Downsides Nobody Mentions

I need to be direct here, because overselling this would mislead you.

Hermes is not perfect. During my first installation attempt, I encountered errors related to Python version mismatches. If any dependency in your setup chain is wrong, the system fails in ways that require debugging. Hardware matters significantly—if your machine lacks sufficient resources, you’ll experience performance degradation or crashes.

The 2-3 hour setup investment is real. I don’t consider this wasted time; in my assessment, the productivity gains over the past six months have been substantial enough to justify it multiple times over. But you need to enter with realistic expectations.

Compared to OpenAI’s Operator (reportedly $200 monthly) or premium Claude access, Hermes is locally operated and free of subscription costs. I still pay for cloud AI services for specific use cases, but my back-end agent workflows now run primarily through Hermes. A developer I follow on Twitter described spending one hour with Hermes and then being unable to return to their previous workflow—I had the identical experience.

FAQ

Is Hermes AI completely free to use?

The core Hermes agent is free and open-source with no subscription. If you use local models through Ollama, there are no API costs. If you route through OpenRouter, many models have free tiers, though heavy usage may eventually incur costs. I run my primary workflows without paying subscription fees.

How does Hermes differ from ChatGPT or Claude?

ChatGPT and Claude are single-model conversational interfaces. Hermes is an orchestration layer that coordinates multiple specialized tools and models autonomously. In my testing, ChatGPT would forget task components midway, while Claude couldn’t browse the internet. Hermes completes full multi-step workflows—research, processing, and output generation—without manual intervention between steps.

What technical skills do I need to set up Hermes?

You need basic familiarity with terminal/command line operations, Python environments, and software installation. The setup involves cloning repositories, running pip installations, and configuring settings through text files. If you’re completely non-technical, using Cursor IDE with AI assistance makes it accessible, but expect 2-3 hours of focused effort regardless.

Can Hermes replace all my paid AI subscriptions?

In my workflow, it replaced most back-end automation and research tasks, but I still maintain cloud AI subscriptions for specific use cases. Hermes excels at autonomous multi-step execution with local data. Cloud services remain useful for certain creative tasks, specific model capabilities, or when I need immediate access without local setup. It’s a complement or partial replacement depending on your specific needs, not a universal substitute.

Conclusion

After extensive testing, Hermes has become my primary local AI agent for automation workflows. The 90-second research task that would take 30 minutes manually exemplifies why I consider agentic AI the future of knowledge work. The setup requires genuine technical effort—2-3 hours of focused work, potential troubleshooting, and adequate hardware. But for developers, researchers, content creators, and small business operators who value data privacy and want to reduce recurring subscription costs, the investment has paid off substantially in my experience.

I continue testing new configurations and share my prompts, workflows, and findings with my community. If you decide to set up Hermes, expect initial friction, but in my assessment, properly configured systems deliver transformative productivity gains that make returning to manual workflows genuinely difficult.

Watch the full video (in Turkish — English subtitles available):

Tools & Community

• TurkoLister — the AI listing tool I use to turn Amazon products into optimized eBay UK listings in about 60 seconds (from £4.99/month, £1 one-week trial).
• AI & E-commerce Community — my Turkish-speaking community ($19/month) with weekly live sessions.
• Subscribe on YouTube — new experiments every week.

The post I Tested Hermes AI Agent: Free Local Automation That Replaced My Cloud Tools appeared first on Digital Market Mentoring.

Key Takeaways

• DeepSeek AI Agent costs approximately $10/month per person through Abacus AI, versus my usual £200–£300 AI software stack.
• It can control your desktop browser, fill forms, scrape data, write code, and even apply for jobs automatically.
• The platform includes 31 different LLMs (including GPT, Claude, Gemini, Grok, Llama, and DeepSeek) in one subscription.
• A standout feature is the AI meeting assistant that records, transcribes, and lets you query conversations in real-time.
• I successfully used it to build a complete e-commerce website with prompts alone, with no coding required.

What DeepSeek AI Agent Actually Is

Let me be direct: this is not a chatbot. DeepSeek AI Agent is a computer-use AI that operates your desktop and browser like a human assistant. The recent “DeepSeek Computer Use AI Agent” update gave it the ability to navigate websites, fill forms, extract data, and write code—all while you work on something else.

In my testing, I watched it open a browser, research information online, analyze applications, and complete forms autonomously. The agent runs in seconds, not hours. For my e-commerce business, this means potential automation of repetitive browser-based tasks that currently eat into my productive hours.

The Real Cost Comparison: My £300 Stack vs. £10 Alternative

Here’s my honest breakdown. I pay approximately £20 each for multiple AI tools—Flexibility, Gemini, DeepSeek, Claude, and cloud-based software. That’s £100–£120 right there. Add Lovable, Bold, Hostinger, DeepSeek’s separate services, Replit AI, and others, and I’m at £200–£300 monthly.

Abacus AI’s DeepSeek Agent starts at $10 per month per person. For that, you get access to 31 LLMs including the ones I already pay for individually. The math caught my attention immediately. I was skeptical—naturally—but I needed to see if the performance matched the price.

Four Core Features I Tested Hands-On

1. Browser Automation and Workflow Building

The chat interface lets you select different AI models, but I focused on DeepSeek AI for my tests. The platform includes hundreds of pre-built applications for tasks like PowerPoint creation, browser automation, flight booking, and job applications. I tested the browser automation specifically for reaching potential customers on my own web pages.

The workflow builder connects to daily operations that normally take me 7–8 hours. The premise is straightforward: time saved equals money earned. I automated a Twitter/X posting workflow that previously consumed 1–3 hours daily. One command now recovers those hours for higher-value work.

2. AI Code Generation and Project Deployment

In the Code section, I tested project creation with zero coding knowledge on my part. I gave the system a prompt, clicked “try it,” and watched DeepSeek AI Agent build the entire project in seconds. The system generated functional code, created the project structure, and prepared it for deployment.

I also examined a previous project I’d created. The system requested a few details, then generated everything automatically. For entrepreneurs without technical backgrounds, this removes a significant barrier to building digital products.

3. The Meeting Assistant That Records Everything

This feature genuinely surprised me. You install a browser extension, and it records all audio from meetings, videos, or calls. I tested it with one of my own YouTube videos—approximately 8 minutes and 48 seconds long. Instead of watching the full video, I let the extension record and transcribe everything.

Here’s what makes it practical: during meetings with your camera off, or when you’re physically away from your computer, the system continues recording. If someone asks you a specific question—like “how do you start eBay dropshipping?”—the AI searches the entire recorded conversation and provides the answer in seconds.

For university students in online classes, or professionals in back-to-back meetings, this means never missing critical information even when you’re not fully present. The system adds contextual information from the video content and presents professional responses.

4. E-Commerce Website Creation with Prompts

This was my most intensive test. I used a specific prompt in the Deep Agent chat section to build a complete e-commerce website. The system asked me targeted questions: brand name, domain preference, product category, color scheme, payment methods (Stripe, EasyCoil, or PayTR), shipping approach, language (I recommended English with Turkish support), tracking number format, and target countries.

Once I provided these details, the system built the website in seconds. No coding required. The resulting site can sell products globally, process payments, and handle fulfillment workflows. I also tested browser extension creation through prompts—create it, add to Chrome, and either sell it or use it personally.

Who This Tool Is Actually For

Based on my testing, DeepSeek AI Agent through Abacus AI serves several specific user profiles well:

• E-commerce entrepreneurs needing automated product research, listing creation, and customer outreach
• Content creators spending hours on repetitive posting and distribution tasks
• Non-technical founders who need websites, apps, or extensions without hiring developers
• Knowledge workers in meeting-heavy roles who need reliable transcription and query capabilities
• Budget-conscious operators currently paying for multiple AI subscriptions

However, I need to be transparent: if you need highly specialized, fine-tuned models for specific industries, or if your workflows require deep integrations with enterprise systems, this $10 tool may not fully replace your existing stack.

How to Get Started

The setup is deliberately simple. Visit Abacus AI’s website, select the DeepSeek AI Agent plan at $10/month per person, and download the desktop application. There’s also an online browser version if you prefer not to install software. For voice command features and Excel integration, the system automatically detects your computer type and configures accordingly.

I also downloaded their AI Code Assistant and Editor as a separate component for enhanced development workflows. The entire installation took under five minutes.

FAQ

Is DeepSeek AI Agent really only $10 per month?

Yes, through Abacus AI, the core DeepSeek AI Agent features are available starting at approximately $10 per month per person. This includes browser automation, code generation, meeting recording, and access to multiple LLMs. My testing confirmed this pricing on their current plans.

Can it actually replace GPT, Claude, and Gemini?

Abacus AI includes access to 31 different LLMs including GPT, Claude, Gemini, Grok, Llama, and DeepSeek itself. For most general tasks—writing, analysis, coding assistance—this provides equivalent functionality. For highly specialized use cases requiring specific model versions or fine-tuning, you may still need direct access.

Do I need coding skills to build websites with it?

No. I built a complete e-commerce website using only text prompts. The system asks you structured questions about your brand, products, design preferences, payment methods, and shipping, then generates the site automatically. I have no coding background in web development.

How does the meeting assistant handle privacy?

The browser extension records audio from your active tab or meeting. All transcriptions are stored and searchable within your Abacus AI dashboard. You can access full conversation transcripts afterward. I recommend reviewing Abacus AI’s specific privacy policy for data retention and storage details, as this was not fully covered in my functional testing.

My Honest Conclusion

After spending time with DeepSeek AI Agent, I can confirm it delivers genuine functionality at a fraction of my current AI spending. The browser automation works. The code generation works. The meeting assistant genuinely captures and queries conversations. The website builder produced a functional e-commerce site from prompts alone.

Is it perfect? No tool is. The $10 price point means trade-offs exist in customization depth and enterprise features. But for entrepreneurs, small business owners, and knowledge workers looking to automate repetitive tasks and recover hours for higher-value work, this represents a genuinely disruptive option.

I’ve shared a trial link below for those who want to test it themselves. If you do try it, share your results in the comments—I’m particularly curious whether it can replace specific tools in your own workflow.

Watch the full video (in Turkish — English subtitles available):

Tools & Community

• TurkoLister — the AI listing tool I use to turn Amazon products into optimized eBay UK listings in about 60 seconds (from £4.99/month, £1 one-week trial).
• AI & E-commerce Community — my Turkish-speaking community ($19/month) with weekly live sessions.
• Subscribe on YouTube — new experiments every week.

The post I Tested DeepSeek AI Agent: A £10 Alternative to £300 AI Tools appeared first on Digital Market Mentoring.