Tips and usage for hermes-agent.nousresearch.com
Running several Hermes agents at the same time can help split work into separate tasks. When each agent performs well, it can handle more than one person could comfortably manage alone. Pairing the agents with Obsidian makes it easier to save, organize, and reuse the ideas or outputs they produce. The practical point is not simply to add more agents, but to give each one a clear role and keep its results organized.
r/hermesagent has a Friday check-in thread every week at 6 AM ET for sharing Hermes Agent work. People can share finished projects, work in progress, experiments, cron jobs, skills, automations, or anything useful Hermes did. The format is loose: say what was done, and optionally add the model or setup, a link, or a screenshot. Unfinished work is welcome too.
Hermes agent is being used as part of an AI image and video generation pipeline. It is already connected to HiggsField CLI, and wavespeed is also being tested. The main problem is that the outputs vary a lot. The practical need is better guidance on how to create agent files and skill files so the generated images or videos become more consistent.
A NVIDIA NIM model stopped working inside Hermes Agent. The available information only confirms the symptom. It does not include an error message, a cause, or a tested fix. The item is mainly a short request to see whether others have the same problem or know a solution.
Hermes can be installed inside a container on a VPS so it keeps working even when the personal computer is not in use. The main setup problem is how to organize and view files. Deliverables are being kept in Obsidian and synced to a laptop every day, but it is unclear how best to inspect and manage Hermes’s own file structure. There is also uncertainty about whether files like agents.md should live in Obsidian or stay in the native filesystem on the server. Another problem is visibility: the web dashboard does not give a good enough view of what Hermes is currently doing. Using the desktop app to control a Hermes instance on a VPS may be possible, but the setup appears messy. The broader work-structure question is how to arrange everyday life work, large projects, subprojects, and tasks across folders, profiles, and kanbans. It is also unclear whether projects should share profiles, share kanbans, or start the main chat inside or outside a project folder.
A Hermes workspace setup on a VPS has reached a partial working state. The Hermes agent is running, but an unwanted state or screen remains and cannot be cleared. The issue appears to be connected to sessions. No exact error message, screenshot, or command output is included, so the cause cannot be identified from the available information. The practical point is that running the Hermes agent is not always the same as having a clean, usable workspace.
A first part of a website for the Swiss market was built with Hermes Agent. The early feedback was good, but the working result broke during later fixes. The product is framed as something Swiss law could make useful for about 225,000 people. With a 1% to 10% conversion rate, that would mean about 2,250 to 22,500 customers. The planned price is 19.90 CHF per month. The offer is a 50/50 partnership: the developer builds, while the other partner handles partnerships, marketing, sales, and real early costs. There is also a contract promise of at least 500 customers in the first 12 months after launch, or the developer’s expenses would be repaid. Past hiring went badly because it was hard to judge code quality without being able to read code, so proof of real development skill is required.
The planned PC has a Ryzen 7 5700X processor, 64GB of DDR4 memory, an Nvidia RTX 4070 with 12GB of graphics memory, and a 2TB SSD. It can currently run models with up to 12 billion parameters in LM Studio using default settings. The goal is to adjust the settings so larger models can still produce long outputs, but no tested configuration or benchmark is available. The required work includes reasoning, image recognition, speech-to-text, difficult problem solving, deep web research, organizing research data, and occasional local coding. The quality target is roughly 80–85% of Sonnet. A specific model and suitable LM Studio settings for this hardware have not yet been identified.
hermes doctor can show warnings for items that may not be real problems for every setup. Examples include no GITHUB_TOKEN, not logged in to Nous Portal auth, not logged in to xAI OAuth, and no OpenRouter API setup. These warnings can feel noisy when those services are not part of the user’s workflow. The main pain point is that there does not appear to be an obvious way to silence warnings for services that are optional or irrelevant.
Hermes Agent and OpenClaw are presented as popular frameworks for building AI agents. They support useful agent features such as long-term semantic memory, skill acquisition, autonomy, and agency. Their main weakness, in this view, is that they feel more like capable tools than lasting companions. They can complete a task and then fade away, instead of feeling like a continuing presence. PaulusAI is an open-source framework built to explore that missing companion feeling. It keeps the common agent features but adds a mood system, so the agent’s tone and replies can shift in a way that suggests continuity. Contributors can experiment with areas such as improving memory retrieval.
Hermes Agent and GBrain may need an extra connection layer between them. No clear failure symptom, fix, required setting, or code example is available from the provided text. There is not enough detail to tell whether this is a real product gap, a setup issue, or a misunderstanding of how the tools should work together.
Even Realities G2 smart glasses are being considered as a hands-free way to use Hermes agent in engineering work, mainly because of their terminal mode. Public examples still look thin, with many paid collaboration videos showing the same small set of features instead of real work situations. The practical question is whether G2 can make Hermes agent easier to check, control, or use for short commands. Building a small app with the SDK before buying could show how complex G2 apps can get and whether the glasses are flexible enough for this kind of setup.
An old laptop from around 2016 can work as a small home server. ZimaOS makes the first setup easier because apps can be installed with little friction. The server is mainly being used to store personal data, while Hermes agent is also being tried on it. For safer long-term storage, a future move to a 3-2-1 backup setup would reduce the risk of losing data.
Vibe Thinker 3B is being considered for agent-style work such as running Hermes Agent. A firsthand test did not produce the expected output, even though the model has been getting strong interest. The main question is whether something was missed in setup or whether a 3B model is simply too small for this kind of task. The practical limit being considered is another language model at 12B or smaller.
A computer science student is starting with Hermes Agent for the first time. The setup uses a laptop RTX 4080 mobile GPU with 12GB of memory. The model in use is a fully unlocked qwen 3.5 4b. The main use case will be computer science work. No concrete workflow, settings, or troubleshooting steps are included yet.
Running Hermes in the cloud may be the cheapest and simplest choice if there is no spare computer ready to use at home or in an office. The tradeoff is that some device-tied features may be lost, such as iMessage support. Browser use is another practical concern. If Hermes needs to open websites while working, the cloud setup may need a headless browser, and the difficulty of setting that up is the main open question.
Hermes Agent is being considered for a separate Windows mini PC instead of a main Mac mini M4 with 24GB of memory. The mini PC has an Intel 12th Gen Alder Lake-N97 CPU, 16GB of LPDDR5 memory, and 512GB of SSD storage. It uses about 15 watts of power. The practical question is whether this kind of small, low-power machine is good enough for a beginner to test Hermes Agent without putting it on their main computer.
The goal is to build a local AI setup with a budget of €3,000 to €3,500. The setup would be used to build small personal tools and apps, run agents for market analysis, research, and competitive intelligence, and learn how to build and operate data pipelines and work automations. Hermes and OpenClaw are named as agent systems to learn and test. Claude suggested either a used RTX 4090 or 5080, or ready-made AI workstations such as the Corsair AI Workstation 300 and GMKtec EVO-X2. Those machines appear to fit the budget and may run smaller models with decent performance, but it is unclear whether a better custom build is possible for the same money. Running 70B models would be ideal, but the budget and hardware may not be a good fit for that goal.
Hermes Agent can be paired with Apify as a stronger setup for content creation work. The main idea is that Hermes Agent is not only used as a standalone writing or planning tool; it can become more useful when connected to Apify, which can collect web data for a workflow. The available item does not include concrete setup steps, prompts, cost details, performance numbers, or example outputs. The only confirmed practical signal is the Hermes Agent and Apify pairing for content creation.
The available content only claims that hidden features can greatly improve a Hermes Agent setup. It does not include the feature names, where to find them, which settings to change, or what practical result to expect. The only usable takeaway is that lesser-known Hermes Agent options may be worth checking before assuming the default setup is enough.
The same model and the same agent task were used across three platforms for two weeks. The task was a daily morning briefing agent: check Gmail for important overnight messages, pull today’s calendar events, summarize 5 relevant news stories, and send everything to Telegram at 8 a.m. It also handled ad-hoc chats during the day. The task was simple enough for all three platforms, but complex enough to reveal differences in daily use, setup, and security. OpenClaw took about 4 hours to get working. The first hour went into Docker, environment variables, a gateway token, and port binding. Security needed separate attention because the default gateway bound to 0.0.0.0, which could let the wider internet reach the agent. The setup had to be limited to loopback, with proper authentication and SSL. Gmail OAuth also required a Google Cloud project. The supplied content does not show the specific Hermes results or verdict.
A beginner wants to run Hermes Nous and Claude Code together inside Docker. The needed files are already downloaded on the desktop. The main blocker is how to create a container and then run Hermes Nous and Claude Code inside it. No working setup steps or commands are included.
A single keyword can start a connected workflow for research, script writing, avatar video production, editing, music, and quality control. Kimi K2.7 handles reasoning, while Hermes Agent coordinates the specialist steps that turn the keyword into finished content. The useful idea is not basic speed automation. A judge agent checks the work and sends weak output back for revision until it reaches the required standard. The example is aimed at making video content for Google search ranking.
The target use is basic AI chat for genealogy work, file sorting and manipulation, extracting data from documents, research, and similar non-coding tasks. The available devices are a MacBook Air M1 with 16GB of memory and an HP EliteBook with 32GB of memory. The HP has an AMD Ryzen 5 PRO 2500U and Radeon Vega Mobile Gfx, but it will likely run most local models on the CPU, with limited shared VRAM. That means both machines are better suited to smaller local LLMs. The MacBook Air M1 may be able to run larger models, but tokens per second could become very slow. Claude, Codex, Goose, and Hermes are all being tested, while Ollama is the main tool for running local models.
The key issue is the cost difference between using the DeepSeek API directly and using the OpenRouter API as a middle layer for Hermes Agent and Opencode. The pricing pages are hard to compare at a glance, so the cheaper option is not immediately clear. The comparison is specifically about calling DeepSeek models through Hermes Agent and Opencode. The practical question is whether direct DeepSeek access saves money compared with routing the same use through OpenRouter.
Frona v2026.6.0 is a self-hosted personal AI assistant. It lets people create agents that browse the web, run code, build apps, make phone calls, talk through messaging channels, hand tasks to each other, and remember information across conversations. Access to files, the network, and credentials is controlled with a separate sandbox for each principal. The main change in this version is a pause and resume system for agent actions that need human input. When an agent wants to deploy an app, ask a multiple-choice question, or use a credential it does not have, it stops and asks instead of continuing on its own. After the person answers, the chat continues from the same point in the tool flow. Telegram, Discord, Slack, and WhatsApp Cloud show these prompts as real buttons.
Coding normally happens in OpenCode when a laptop is available. When only a phone is available, the workflow shifts to several agents already set up in Hermes for many different tasks. The main question is whether a coding-focused agent should live inside Hermes itself. The alternative is to keep using a separate coding setup like OpenCode and connect to it from the phone through SSH or another phone-friendly method. The real issue is which setup is easier for mobile coding: a Hermes agent or remote access to a development environment.
Hermes Agent is being used to help manage a YouTube channel. The main confirmed point is that the tool is connected to a real channel workflow, not just used for chat. The available details do not show which tasks it handles, such as uploading videos, writing titles, or managing comments.
Hermes Agent pet and sprite images are being collected in one place so people can look at examples and get ideas. The useful part is seeing what others have made before choosing what kind of character or image to create or take inspiration from. No concrete setup steps, settings, or quality tips are included in the shared text.
A user running a Framework Strix Halo machine with 128 GB of memory shares a problem: their locally hosted Hermes and Claude Code setup for task automation is frustratingly slow. They run the Qwen3 35B model at Q8 precision inside LM Studio, accessible remotely via a phone terminal or Discord gateway. GPU acceleration through ROCm keeps failing, so they fall back to Vulkan instead. They have tried adjusting inference parameters like eval_batch_size and physical_batch_size, and have also experimented with breaking prompts into smaller steps, but neither approach has yielded a clear speed improvement. The core question is whether there is a reliable method or resource for measuring how these parameters actually affect agentic workflow performance.