Tips and usage for hermes-agent.nousresearch.com
On a Windows 11 AI laptop, Chrome, Edge, and Opera stopped loading websites normally after Hermes Agent was installed. A first page may open, but the next page can hang while the tab keeps showing a loading spinner. In other cases, the browser stays on a blank screen and never finishes loading. The setup is an MSI Raider 18 with an Intel Core Ultra 9 285HX, 64GB memory, and an RTX 5090 laptop GPU with 24GB video memory. Hermes Agent is being used with Ollama and the Qwen 3.6 27B and Gemma4 31B models. LM Studio was also tested with Hermes and felt easy to set up and control, but the results were mixed. The original reason for trying Ollama was to use the NPU, but the experience so far points toward GPU and CPU use being more practical.
Hermes Desktop updates are causing repeated failures across several setups. In a Linux Mint virtual machine, the in-app update reached 100% and stayed there, while running `hermes update` in a console still left the official desktop app broken afterward. The likely Linux failure point is that the desktop part gets rebuilt, but the `chrome-sandbox` file ends up with the wrong permissions, which can stop the app from opening. On Windows, some updates finish only partly: the update is applied, but the desktop app is not rebuilt, the desktop shortcut disappears, and `Hermes.exe` is missing from its usual folder. Another Windows 11 case gets as far as the backend being ready, then freezes while finalizing desktop startup. WSL appears to work better for at least one person, so the main weak spot looks like the Hermes Desktop update and rebuild process, not necessarily Hermes Agent itself.
The key need is whether Hermes has something like Claude’s Projects, where each piece of work can keep its own files, instructions, conversation history, and context. This matters when several tasks are active at the same time, because Project A should not affect Project B. The question comes from a beginner who has not really used Hermes, OpenClaw, or similar agent-based tools before. The practical issue is how experienced Hermes users manage several ongoing projects while keeping each project’s context clean and separate.
Hermes and Paperclip differ in what they treat as the starting point. Hermes can be understood as a memory-first system: it reads memory, turns that into context, and then chooses an action. This means past information, preferences, goals, and work history can strongly shape the result when using Hermes. Paperclip is closer to a workflow-first system: it plans the process, sends work to workers, and gathers the result. In that view, memory in Paperclip is more like an added support feature than the center of the system. Hermes is built around remembering and acting from that memory, while Paperclip is built around arranging and running work steps.
Hermes is being tested as a tool for automatic video editing. The current setup adds subtitles and cuts out silent parts of a video. Those two features already make the video feel smoother and easier to watch. Possible next steps include zooming in on important parts, choosing the strongest clips, and replacing some sections with photos or other videos. The main practical question is how much editing judgment Hermes can handle for someone with little video editing experience.
Hermes can be useful as an agent, but it becomes easier to automate when it can be called through an HTTP API. A separate tool has been built to sit in front of Hermes and expose that kind of interface. It is released under the MIT license. The practical value is that Hermes can be connected more easily to apps, scripts, and internal tools through normal web requests.
Hermes Agent can run locally on a low-end PC with an Intel i5-10400F, 16GB of memory, and a GTX 1650, but the graphics card may not help much and replies may be slow. The practical goal is to turn 800 pages of novel chapters into Markdown files, then ask about forgotten scenes, character descriptions, and other details across the draft. Several models were tested for this kind of personal writing archive. Llama 3.1 did not work well, DeepSeek became unreliable in the first few messages, and Gemma made up too much information. Qwen3.5:9b gave the steadiest results and had an acceptable reply speed. For weak hardware, it looks like a useful starting model when the job is searching and remembering long personal documents rather than getting instant answers.
A small startup has a few co-founders covering the CEO, COO, and CFO roles, plus a small engineering team where one person is effectively acting as CTO with a few supporting engineers. The new Head of Product sees that the team is not getting as much useful output from each role as it could. People also spend time on bureaucratic work that is outside their core jobs, and team communication can be slow or inefficient. Everyone is already comfortable using an AI chatbot for individual tasks, but AI is not being used to improve how the organization works as a whole. The Head of Product already runs a personal Hermes Agent setup and is considering personal agents for each team member to reduce role overload and improve team efficiency.
Hermes Agent currently stores session data in SQLite. The concern is that this feels weak compared with the rest of the product design. The requested improvement is a pluggable session provider, so the session database is not locked to one storage choice. A GitHub issue already tracks this request, and the main question is whether it will be implemented.
Hermes Agent can make plain update notes more readable when it is given a clear personality or speaking style. In this case, a Roy Kent-style agent summarized the latest changes that would be installed by updating that day. The answer had enough personality to make the update summary feel more enjoyable instead of dry. One limit is that the style did not fully capture every desired catchphrase. For example, it still did not naturally use “Oi!” on its own.
A 2023 Mac Studio with an M2 Max chip was bought secondhand for $900. The machine has a 12-core CPU, 30-core GPU, 32GB of memory, and 512GB of storage. Its ports include four Thunderbolt 4 ports, two front USB-C ports, two USB-A ports, HDMI, 10Gb Ethernet, and an SDXC card reader. The intended use is to run Hermes agent or OpenClaw with local LLMs on the machine.
Hermes can run into deployment trouble on an Azure VM even when the environment setup looks correct. Some services may not start properly after startup. Some dependencies may work on a local computer but fail on the Azure server. Components may also have intermittent connection or timeout problems, and the cause could be Azure networking or Hermes configuration. For a production agent, deployment settings, common failure points, useful skills, and reliable integrations need to be checked before relying on it.
Pairing the Chinese AI model Kimi K2.7 with Hermes Agent allows users to go beyond simple question-and-answer and handle complex, multi-step jobs such as coding apps, creating videos, testing the output, and refining results automatically. In this setup, Hermes Agent supplies the specialized agents, tools, memory, and actions, while Kimi K2.7 handles the deeper reasoning for each step. A user describes what they want built, and the combined system plans and executes the work across several connected stages. The same approach can extend to other AI tools like Claude and Agent OS, chaining them into a single workflow. The post also includes links to a paid coaching community and a YouTube video demonstrating the setup.
Hermes Agent works best when one assistant is split into clear roles instead of being asked to handle every decision alone. One agent can plan the main goal and break it into smaller tasks, another can gather research, and others can handle writing, SEO, development, automation, client work, or project management. Inside an Agent OS-style workspace, tools such as Hermes, Claude, Kimi, and GLM can share the same work history so the same project does not need to be explained again each day. Hermes Agent’s async subagent feature also lets long jobs run in the background while the main chat stays usable. A delegated task returns a task ID right away, and the work can then be checked, guided, collected, canceled, or listed later. The practical setup is to give each agent one narrow job, define what it must hand off, and use background delegation for work that would otherwise block the main conversation.
A common problem when using AI agents is dumping messy context at them — pasted documents, entire code repositories, long prompts, scattered markdown files. Google published a spec called Open Knowledge Format (OKF) to address this. OKF is not a new app or platform; it is simply a lightweight folder structure using plain markdown files, YAML frontmatter, and naming conventions. Each file declares what the knowledge is about, who it is for, key concepts, examples, source references, a changelog, and links to related files. The result is that instead of one giant blob, the agent receives small, purposeful pieces of information. A community member built two free tools around this for Hermes Agent: a prompt generator that creates the OKF folder structure for any project, and a validator that checks whether the finished structure is correct. OKF is not meant to replace Notion or Obsidian — it is a portable, minimal standard that can be used alongside any existing tool.
Combining Hermes Desktop with Ollama lets you run AI agents locally on your own computer at no cost, without paying for cloud API access or subscriptions. Ollama is a free tool that runs large language models directly on your machine, and pairing it with Hermes Desktop brings agent capabilities offline. The original post has very little detail, so specific setup steps or performance comparisons are not available from the excerpt alone.
Second Brain is an open-source AI agent framework presented as an alternative to Hermes and OpenClaw. Its core is a microkernel written in pure Python — about 15,000 lines of code total, which is compact for a project of this scope. Every feature beyond the core (tools, database tasks, LLM backends, OCR, Telegram and Discord interfaces) is an installable plugin, so you only add what you need. Conversations are saved in SQL and the runtime uses a state machine, meaning the agent can recover from crashes and even resume mid-turn. It is light enough to run on a smartphone and can be used to build websites, robots, or coding projects, according to the creator.
Hermes Agent is being considered for real WooCommerce store work. The useful question is how it can help with workflows, automation, and practical store tasks such as orders, products, customer messages, or other repeated work. The available item does not include setup steps, examples, results, numbers, or tested methods. The only clear takeaway is that there is demand for practical Hermes Agent use cases tied to WooCommerce operations.
A portfolio race starts with three accounts of 10,000 euros each: a real personal portfolio, a Hermes Agent paper trading portfolio, and a community paper trading portfolio. The goal is to reach 20,000 euros or simply have the best return by the end of the year. The personal portfolio holds Circle at 40%, gold at 20%, and cash at 40%. The Hermes Agent portfolio holds Nvidia at 40%, Coinbase at 30%, Super Micro Computer at 15%, and cash at 15%. The community portfolio starts with a DRAM ETF at 20%, Vonovia at 20%, and cash at 60%. A separate website exists for viewing the portfolios, but the link is not clearly shared. More ideas are being gathered for AI-related picks and for the community portfolio.
An RTX 4050 laptop with 16 GB of DDR5 memory is being used to test different models and Hermes. The main problem is not knowing which model best fits that hardware. Hugging Face lists many models, but it is hard to tell which ones are actually good or suitable. The practical question is how to choose a model, how to judge smaller models, and how to research the right option without getting lost.
Hermes Agent is being paired with Stripe Payments and NVIDIA Nemotron. No setup steps, code, measured results, or detailed workflow are available from the item. The useful signal is that Hermes Agent may be used in a workflow that connects payment handling with an AI model.
Hermes-agent users on Android Termux may need a tool that can control a real browser. The need is not basic web search or page text extraction. The goal is browser automation, where hermes-agent can open sites, move through pages, and run actions in the browser. No specific tool recommendation or working setup is included yet.
The practical goal is to split one AI agent workflow across several sub-agents. One sub-agent would work on one GitHub branch, while another sub-agent would work on a different GitHub branch. A main agent would then check both results and merge the pull requests. The key question is whether AI agents such as OpenClaw and Hermes can be configured to run this kind of setup today.
AI agent workflows are more reliable when every part has one clear job. A model, an agent, memory, and a quality check should each serve a specific purpose, instead of being added at random. Hermes is presented as a way to manage separate agent profiles and pass work between them. GLM 5.2, Sakana Fugu, Claude, and GPT are suggested as models that can be connected inside one flexible system for real tasks. The main idea is to design a repeatable process first, then choose tools that fit that process. The material gives more broad workflow advice than step-by-step Hermes setup, and it also points readers toward paid coaching and courses.
The setup is an old MacBook Pro with an 8th-generation i5 processor and 8GB of memory. The goal is to turn it into a small home lab for running a lightweight local model. The desired use is not only basic chat, but also agent-style tasks similar to Hermes. The main need is a practical way to experiment without a multi-GPU machine, using small models or lightweight runners that can survive on low-end hardware.
Hermes Agent’s new features are presented as a way to find breaking AI news in about three minutes, rank the strongest signals, and suggest useful angles. Hermes Oracle is meant to reduce slow manual searching by returning current sources, relevance scores, and clear next steps in one workflow. The problem it targets is familiar: checking several feeds and scanning many posts can take around 20 minutes before one useful story appears. Some results may already be old, and many repeat the same headline without adding practical value. The main goal is to spot which updates matter while the discussion is still active. AI Profit Boardroom is positioned as a way to turn tools like Hermes Oracle into repeatable daily workflows. The item also points to a video and promotes paid coaching, support, and courses, so it has a clear sales angle.
In Hermes Agent Desktop with Ollama, Gemma 4:26b-a4b-it-qat was reported to feel less active and less dependable than expected. In the same general setup, Qwen 3.6:27b felt more like a steady workhorse compared with Gemma 4. No exact failure example or settings were provided, but the experience points to a practical issue: the model connected to Hermes Agent Desktop can strongly change how useful the tool feels.
Inside Hermes Agent, the same short request was answered faster by GPT 5.5 Instant than by Grok Build. The request was very simple: tell a joke. GPT 5.5 Instant answered in about 3 seconds, while Grok Build took about 9 seconds. That made a difference of about 6 seconds. One short delay may not matter much for a single question, but repeated delays can matter when an agent handles many requests. AI Profit Boardroom also promoted its Agent OS, Hermes workflows, support, and courses for turning faster models into automation systems.
Charging nearly the same app price in every country can miss big differences in what people are willing or able to pay. ChatGPT Go uses different App Store prices by country. The listed price is $8 in the United States, about $9.25 in the United Kingdom, about $9.16 in Germany, and about $8.68 in Japan. It is much lower in some Asian markets: about $4.23 in India, about $4.22 in Indonesia, and about $4.94 in the Philippines. Sales numbers are not available, so the pricing effect cannot be proven from this data alone. An open source tool was shared for checking competitor app prices, and it can be installed and run with coding agents such as Claude Code, Codex, OpenClaw, and Hermes.
A beginner’s first setup uses Hermes to run a research agent that looks for venture capital investment leads. The agent uses qwen3.6-35b on a MacBook, but the laptop stays hot, raising concern about whether that could harm the screen. A Blackwell device has been ordered to try more advanced local AI projects, but it is still sealed because the purchase may be too much for a beginner. Reselling it on digitec for a profit is still possible. The open question is what simple starter projects would make the Blackwell useful in practice.