Tips and usage for hermes-agent.nousresearch.com
Hermes Agent can be used through several interfaces, including the Hermex iPhone app, OWUI, a command-line tool, and Hermes WebUI. When those entry points are mixed, the chat history may not appear as one shared list. Some chats are visible only from the command-line tool, while others appear only under a generic label such as “API chat.” It makes sense that Hermes chats may not automatically show up inside OWUI, but it is less clear why direct Hermes conversations also look different depending on the tool used. The practical question is whether Hermes Agent can provide one unified chat list across these interfaces, or whether there is a better way to manage this setup.
BJIR is a local tool that cleans up material before it is given to an AI coding agent. It focuses on long build logs, code changes mixed with lockfile noise, error traces tangled with Docker output, and repeated agent trace chatter. A code diff can be reduced to the real changes, while a build or test run can be narrowed down to the failing parts. A long error log can become a short report that is ready to paste. The report includes the original size, the cleaned size, an estimated token count, detected signals, and the cleaned output. Secret-looking values, tokens, and credential-like text are hidden by default. The main idea is that the cleanup happens locally and follows fixed rules instead of calling another AI model to summarize the material.
A user running Hermes on Windows via the official Nous app says it has mostly worked well, with everything in one place and an easy interface. However, recent updates have introduced more bugs, prompting the user to ask the community whether other UIs or apps for Hermes exist that match Nous's ease and completeness while offering better agent management and more advanced features.
Hermes Agent Desktop hit a “stream stalled mid-tool” error while running a tool. The problem had not happened before and appeared suddenly. The setup was Windows, an RTX 5090, llama.cpp, the Qwen 27B model, and a 128k context setting. The available details only show the error situation and the machine setup; no confirmed cause or fix is given.
AgenC SDK or AgenC Kit can connect AI agents such as Hermes, Claude, and Codex directly to a Solana work protocol. After connecting, an agent can do more than answer questions: it can look for jobs, take a task, send in finished work, and act on its own within wallet rules set by the user. The protocol covers payment holding, settlement, reputation, service listings, bids, reviews, and dispute paths on Solana. The aim is to let agents move beyond chat apps and become participants that do work, get paid, and build a work record onchain. This is still described as early-stage.
The core question is whether Hermes for Mac or Codex Desktop is the better tool for running everyday tasks on a Mac. The goal is not coding help, but a helper that can control apps and handle practical chores. Examples include opening mail, checking information, sending something to another person, creating 9 alarms from a Notion page, and using a browser to complete tasks. The main comparison points are the cost of the Codex free tier versus Hermes Agent configured with OpenAI Codex OAuth, how reliable each tool is, and which one is better suited for broad Mac control. There are no test results, price calculations, or final recommendations in the available item.
Pi Spawner is a small CLI tool that lets Codex, Claude Code, Cursor, Hermes, and similar work tools run Pi agents as sub-agents. Its main purpose is to keep one main tool while still choosing different open-source models for different jobs. For example, Codex can stay as the main orchestrator while separate default models are set for coding, writing, planning, review, and design. Model aliases make it easier to pick the right model in a prompt without rewriting the setup each time. The tool works in a plugin or skill style, so Hermes users can call Pi agents from their existing workflow when needed.
SIQ-1-35B is a model made by further training Qwen-35B-A3 with PPO. The training used verifiable reward, meaning the model was rewarded against results that can be checked. In the shared comparison, it beats GLM-5.2 and Qwen-350B on Karpathy’s autoresearch “parameter golf” task, and its ideas are described as close in feel to Opus4.8. On “bullshit-bench,” it is said to score above NEX and GPT-5.5. The model and GGUF files are available on Hugging Face, and a hermes-agent demo can be tried on ZeroGPU.
Two agents were tested working together to schedule a meeting without a person passing messages between them. One agent checked its owner’s calendar, spoke with another person’s agent, found a time that worked, and added the event to both calendars. The basic scheduling flow worked. The harder issues are about limits, privacy, and trust. An agent needs to know which calendar it is allowed to change, and it must keep that boundary when talking to an agent built by someone else. Two agents should share only enough context to finish the task, not everything about their owners. If they do not check mismatched assumptions, an agent can confidently take the wrong action. This is not mainly a UI problem; it is a foundation problem for agent-to-agent work.
The Hermes desktop app installed a global npm package without asking for approval. The approval setting was on manual mode, not smart mode, so the install was unexpected. A global npm package can affect the whole machine because it is available system-wide. That is more concerning on a main personal computer, especially while npm security vulnerabilities are a common worry. A smooth middle layer that blocks this kind of action without hurting daily productivity is hard to find.
Hermes Agent was running on an old Linux laptop, with access over SSH and a ChatGPT Plus account connected through OAuth. The setup started with GPT-5.5, then moved to GPT-5.4 with reasoning effort set to Medium. About one hour of light use, including a few chats, simple troubleshooting, and normal back-and-forth, used more than 55% of a 5-hour usage quota. Similar work on Claude Pro and Gemini Pro seemed to use far less of their hourly or message limits. The Hermes Agent setup was basic, using only the default tools and skills from installation, though a couple of skills appeared to have been created or updated automatically. The main concern is whether this level of usage is normal with ChatGPT, or whether the setup is causing extra model calls in the background.
Hermes Agent looks useful when paired with cheap models or a local LLM, but its practical value can be hard to separate from normal chat. One possible use is business outreach, such as helping promote a product or service on Facebook. The risky part is giving an agent too much freedom to advertise on its own. Automated promotion could break platform rules or lead to an account ban. A better starting point is to decide which steps the agent can draft or organize, and which steps still need human approval.
After spending a few months learning Hermes, the next step is to use it as a multi-agent setup for business work. The planned workflow is to research information, save it in a database, and check client projects against the collected information. The proposed roles are a research role, a project validation role, one developer for part A, one developer for part B, and an accountant. The business details are still being kept private. The main uncertainty is whether agents work separately or can talk to each other and pass work along.
A practical workflow pairs Hermes with Codex OAuth as the main planning system and Claude Code as the coding workspace. In this setup, Hermes and Codex handle planning, research, architecture, long reasoning, tool use, and coordination. Claude Code handles the hands-on work: editing files, refactoring code, and moving around the repository. The main workflow question is how to pass work from Hermes to Claude Code. The handoff can be manual, by copying plans and specs, or automated through a skill or integration from Hermes to Claude Code. Context window management is another key concern when using Codex-backed models through OAuth. The setup may need explicit context_length values in config.yaml instead of relying only on automatic detection. Long Hermes threads can be ended by asking for dense design docs or specs, then starting a fresh thread with only those summaries. File-reading tools like read_file and long logs also need limits so they do not fill the context window too quickly.
Hermes Agent can be used as a small team instead of one all-purpose assistant. One agent can handle research, another can write, and a separate reviewer can check facts, structure, clarity, and missing details. This setup keeps each agent focused because its instructions match a narrower job. Obsidian can act as the shared workspace where the research agent saves useful findings. The writing agent can then use those saved notes to create a full draft. The main idea is to avoid asking one assistant to research, write, review, organize files, and remember everything at once.
NotebookLM is positioned as the research part of a connected AI workflow. It can work with PDFs, websites, videos, audio files, Google Docs, and existing project notes. It reads those sources, organizes the material, and gives answers with citations so important claims can be checked against the original source. Claude is used after that to improve the reasoning and writing. Hermes is used at the end to move the finished work into real tasks. The practical setup is to avoid asking one chatbot to do everything, and instead use NotebookLM for evidence, Claude for thinking and writing, and Hermes for action. AI Profit Boardroom also promotes training, support, courses, and a video around building these connected AI agent workflows.
Hermes Agent Paperclip is presented as a way to manage several AI agents from one clear place. One chat is easy to follow, but ten agents doing research, writing, review, planning, coding, and publishing can quickly become hard to track. Paperclip organizes each agent with a name, role, reporting line, and area of responsibility. This makes it easier to see who is working, what is blocked, and how far each project has moved. The main point is not to add more agents, but to make the work, handoffs, and status visible. AI Profit Boardroom is also mentioned as a paid-looking source of training, support, and systems for building connected agent workflows.
HarnessBench is a benchmark meant to compare AI harnesses such as PI, OpenCode, and Hermes Agent under the same conditions. The test keeps the model fixed as Qwen 3.5 9B, so the comparison focuses more on the harness and less on the model. The benchmark is still experimental, and its reliability or practical value is not yet proven. The repository is public, so others can inspect it, fork it, and change the tests.
Codex Pooler 0.1.0 is a free open source tool that can be self-hosted and used with agents such as Hermes Agent. Its main feature is compressing tool outputs before they are sent to a large language model. The release claims that eligible outputs can use up to 95% fewer tokens while keeping the same result quality. To use it, users turn on request compression in the new pool settings. It is described as tested with tools such as Codex, OpenCode, Aider, and Continue, and with agents including OpenClaw and Hermes Agent. It can also route chat and responses application programming interfaces through Codex subscriptions for software that does not support Codex directly. Other included features are stats, alerts, advanced settings, websockets, invitations, user management, and pool management. A Helm chart is available for people who want to run it across multiple server nodes.
OmniSearch is a Windows launcher that opens a single search box for many things on a PC. It uses Alt + Space by default, and the shortcut can be changed. It can search apps, files, folders, text inside many file types, text inside images through OCR, browser bookmarks and history, clipboard history, Git commits, Windows settings, and Control Panel pages. It also includes an AI agent powered by Hermes. The broader aim is to make local PC information and actions easier to reach from one place, with a clipboard manager included as part of the tool.
Using Headroom with Hermes agents can cause practical problems. Hermes agents may stop reading files correctly. They may also fail to communicate through Kanban. The issue appears to affect basic agent workflow, especially file access and coordination between agents. It may have worked before, but there is no confirmed workaround in the available information.
Hermes Agent needs access to Zoho Mail for reading emails and writing drafts, but it should not be able to send emails itself. The intended setup is for Hermes to read messages and prepare email content, while a separate script handles the final sending step. The main concern is that Hermes tool-level restrictions may not be enough, because the agent might work around them under strong pressure. Possible approaches include using an MCP server or a custom script so Hermes only receives a path that cannot send mail.
The current workflow uses Kilo with Minimax M3 in YOLO mode. A broad coding task is given upfront, the tool automatically edits files, and it also runs tests. The human step comes later, when remaining mistakes are cleaned up by hand. The main question is whether Hermes Agent can make this kind of automated coding workflow better.
Hermes Agent and OpenClaw are self-run AI agents that can work inside Telegram or Slack like a personal assistant. They are meant to do tasks, not just answer in another chat window. Possible jobs include drafting content, gathering research, summarizing long documents, and running scheduled work. For marketing, useful first tasks could include content creation, competitor tracking, and reporting. The hard parts often come before the useful work starts: setting up API keys, choosing a model, hosting the agent so it stays online, and connecting it through a webhook and Telegram. These setup steps can stop people before they ever test the agent in real work.
Hermes Agent is running with OpenRouter on a VPS. The goal is to search the live web instead of depending on what a model already learned during training, with examples like finding the history of a city. The main choices are which model can reason well and use tools, whether Hermes should connect to a web-search backend such as Exa, Firecrawl, Tavily, or Parallel, and whether OpenRouter’s :online variant or a server-tool web-search setup would work better. The desired output is fresh, real-world information from the web, not a general handbook-style answer. Concrete recommendations matter most, especially a model name and a Hermes config snippet that can be used directly.
Manifest can connect paid subscriptions such as ChatGPT Plus to agent tools such as Claude Code and Hermes. Agents usually run through separate API keys, so costs can rise on top of subscriptions people already pay for. Manifest sits between the agent and the model provider, sending the agent’s requests through a connected subscription. In the Claude Code example, the setup starts by creating a Claude Code agent in Manifest, then copying the base URL and API key. The local `~/.claude/settings.json` file is changed so `ANTHROPIC_BASE_URL` and `ANTHROPIC_AUTH_TOKEN` point to Manifest. After that, the Providers page in Manifest connects the ChatGPT Plus subscription, giving access to the OpenAI models included in that plan. The practical benefits are more predictable costs, fallback options when a model hits a rate limit, reuse of the same subscription across several agents, and one place to see what is running where.
Long runs with autonomous agents like Hermes can create surprise API bills because many services charge by tokens or characters. Overnight jobs, such as refactoring a SaaS codebase or scraping data, can also hit rate limits before the loop finishes. Sending private company code to public APIs can create a data compliance risk. This service rents dedicated Apple Silicon hardware in India and runs locally. Users pay a flat rate for machine time, not for how much text the model reads. It claims a 100k-plus context window can be reused through the model all night without changing the bill. It says the setup works with Ollama, DeepSeek-R1, Llama, Kimi, OpenClaw, Claude Code, and n8n nodes for backend automation. The first group will include 10 pilot testers next month to stress-test the pipelines.
Putting planning, coding, review, research, and repeated small edits into one expensive model can waste money and effort. A better setup separates the work and sends each task to the model that fits it best. Claude is presented as useful for understanding the goal, spotting risks, and setting the rules for the work. Hermes is presented as the coordinator that keeps project files, task status, and each agent’s responsibility connected. GLM 5.2 is mentioned as an example of a model that could handle focused coding tasks. The main idea is to split judgment-heavy work from routine execution instead of forcing one premium model to do everything.
Hermes Agent, and some similar tools, may send web requests to Parallel when installed with default settings. The practical concern is that an outside route may be used even before the user changes any settings. The available item does not give the affected version, the exact switch to disable it, or what data is sent.
Hermes Agent repeatedly showed a warning that the model provider was rate-limiting requests after OpenAI GPT and Codex were connected. The switch happened after DeepSeek credits ran out, and the issue continued even with GPT 4.5 Mini. The agent was barely usable for 12 hours, and even one question could trigger the limit. The unclear part is whether the cause is Hermes Agent, OpenAI account limits, token limits, or a setup problem. The experience made the paid subscription feel hard to justify because the agent could not be used normally.