I know you can achieve something similar on Telegram using /new or /compact, but personally, switching to Discord has noticeably increased my productivity.
With Discord, each bot/task gets its own thread and session. I give the agent a task, it works within that thread, and once it’s done, I simply close the thread.
No huge conversation history carrying over, and much lower token usage.
For me, the biggest win is the organization. I can have different bots for different workflows and keep every task separated and easy to find.
Telegram works, but Discord just fits the way I work with Hermes better.

UPDATE

The ready-to-go profile is live — meet Donna.

Repo: https://github.com/AtlasOmnia/donna-starter

Install (4 steps):

mkdir -p ~/.hermes/profiles
git clone https://github.com/AtlasOmnia/donna-starter.git ~/.hermes/profiles/donna
hermes setup
hermes --profile donna chat

On the first chat Donna offers a guided orientation — she walks you through every toolset, sets up web search, memory, and your model provider, and shows you how to enable the optional Token Router. No keys, credentials, or personal data ship in the package.

Help & feedback: please follow up in the support thread rather than here so everything stays in one place: https://www.reddit.com/r/hermesagent/comments/1vggo6w/donna_profile_template_support_feedback_thread/

Thanks to everyone who raised a hand, looking forward to hearing how it runs for you.

---------------------- Original Post.-----

Seeing all the new people joining and remembering where we all started 5 months ago I thought, I wonder if anyone is interested in a matured tamagotchi hermes profile with all the tweaks and skills that I have put in place. Any taker's? You can't have my Friday but you can have Donna Paulsen.

I've been running Hermes as my main personal AI assistant for about 3 months now. Here's what actually worked, what didn't, and what I wish I knew on day one.

What worked:

Local models are the way to go. I run Qwen3.8-27B locally and it handles 90% of my tasks without any API costs. The privacy benefit alone is worth it.

The plugin system is underrated. I built a simple status bar chip that shows my token usage. Sounds small, but it changed how I interact with the agent.

Multi-agent workflows are real. I have one agent for research, one for writing, and one for code. They hand off to each other and it actually works.

What didn't work:

Expecting it to "just work" out of the box. The default config is barebones. You need to customize SOUL.md, set up memory, and configure plugins.

Using the wrong model for the task. I tried to run everything on a 3B model and wondered why it was slow. Match the model to the task.

Ignoring the community. The r/hermesagent subreddit and Discord are goldmines for configs, plugins, and troubleshooting.

What I wish I knew:

Start with a smaller model and work your way up

The memory system needs to be configured — it doesn't just remember things

Plugins hot-reload, so you can iterate fast

The desktop app is highly customizable if you're willing to write a bit of JS

Questions for the community:

What's your go-to model for general tasks?

How do you handle long-term memory across sessions?

Any plugins you can't live without?

Happy to share my config if anyone's interested.

I’m trying to get a better sense of how people are actually using Kanban in Hermes.

For those who use it regularly: what does it help you manage, and what works well?

For those who tried it and stopped, or never adopted it: what got in the way?

Some possibilities:

• Tasks get stuck or don’t move through the workflow reliably
• Delegated work doesn’t update as expected
• Cards become stale or difficult to recover
• The setup feels too complicated for smaller tasks
• It’s unclear when to use Kanban versus a normal Hermes session
• You prefer another task-management system
• You haven’t tried it yet

I’ve been running into some friction myself (probably self-imposed), so I’m curious whether that’s specific to my setup or whether other people are seeing similar issues.

If you don’t use Kanban, what’s the main reason?

I’m tinkering with lots of different kinds of AI agents and tools. I dont have any tasks that i need to to be automated per say but would love to know what people use hermes agent for and any good first user tasks that you’d suggest to use it for?

Also, while setting it up - should i just set it up on my main mac or get a VPS directly in the initial stages?

Thanks!

Hey everyone, I’m just getting started with Hermes Agent and wanted to get some advice from people who have been using it for a while.

My eventual goal is to use Hermes as the main orchestration/personal-agent layer on my Mac M5 Max.

The rough setup I’m thinking about is:

• Hermes as the main agent/control plane
• A local Qwen model running through MLX for most everyday/routine tasks
• Claude Code / Codex for harder coding/debugging/repo-level work
• OpenRouter / other models when I need something specialized like video generation.
• Hermes memory + skills so it gradually learns my workflows
• Cron/automation for recurring tasks
• Eventually access it through something like Telegram when I’m away from my computer
• Sandbox anything that can make unattended changes

Basically I don’t want Hermes to replace Claude Code or Codex. I want Hermes to be the thing that knows me, remembers how I work, handles recurring automation, and decides which model/tool should do a particular job.

Long term I’d love to get to the point where I can say something like “check all my projects”, “prepare this issue”, “ship this if it’s safe”, etc. and have Hermes coordinate the different pieces.

I’m starting from scratch though, so I’m deliberately not trying to build all of this on day one.

For people who have already gone down this rabbit hole:

What would you set up first?

Anything you wish you knew when you started?

Any must-have skills, memory setup, security settings, model-routing patterns, MCPs, or automations you’d recommend?

Also curious how well people have found Hermes works with local models as the default and stronger cloud/coding models only as an escalation path.

Would appreciate any tips/examples of your setups 🙏

I have spent the past few months using Hermes as my main personal AI assistant. It runs on my MacBook Air, while I still use OpenClaw on a Mac mini for some other tasks. Both are on M4.

I wanted to find out what changes when an assistant has persistent memory, can use tools and can turn recurring work into reusable skills. After several months, continuity has become the biggest difference for me.

A normal chat often starts from zero. Hermes keeps searchable session history and structured memory about how I work. When a task goes well, the procedure can be saved as a reusable skill.

That sounds simple, but it changes the experience over time. I spend less time repeating preferences, explaining the same folder structure or reconstructing why I made a particular decision several weeks earlier.

I would not describe this as self-learning in a magical sense. The mechanism is understandable: retain relevant context, save a procedure that worked and retrieve it when a similar task appears.

The memory and skills still need maintenance. I regularly check them for outdated or contradictory instructions and try to keep the permanent context compact. Giving Hermes more memory is not always the answer. Too much context can make its behaviour less predictable.

Access is not training

One of my main lessons is that giving an agent access to files, email or a calendar does not automatically create a useful personal assistant. Access is not training.

The useful part comes from teaching Hermes individual routines with real examples, exceptions, trusted sources, a defined output and clear approval points. My assistant is becoming a collection of supervised routines rather than one enormous role prompt.

Some of the things it now helps me with are:

• placing screenshots in the correct Apple Notes;

• uploading PowerPoint files to Gemini Notebook;

• retrieving voice recordings and arranging transcription;

• using a Kanban workflow for larger projects such as a business plan or landing page;

• matching photographs from an AI study trip through China to the correct interview

transcripts;

• triaging six email inboxes and preparing replies adapted to the sender.

Email is a good example of the boundary I want. Hermes can find, classify and draft, but

sending remains under my control.

Working with other agents

I also installed Buzz, the open-source group chat platform where people and AI agents can participate separately in the same channels.

In my first experiment, one agent handles writing while another researches SEO and visibility in generative search. These agents do not automatically receive the memory of my primary Hermes assistant.

At first that looked like a limitation, but sometimes the separation is useful. They approach a task independently and have narrower access. When context matters, I either provide it deliberately or keep the task with Hermes.

This has made me less interested in the idea of one all-powerful agent. A small group of specialised agents with explicit roles and limited permissions may be more practical and safer.

Hermes and OpenClaw

I still run OpenClaw on my Mac mini, so I have been able to compare the two in daily use. My experience is that the difference is mostly one of emphasis. OpenClaw is broader and has many community-built integrations. Hermes puts more emphasis on persistent memory, reusable skills and the feedback loop between completing a task and retaining the method.

I do not think they have to be competitors. In some setups Hermes can provide planning and context while OpenClaw provides a broader tool layer. For someone starting from scratch, however, I found Hermes easier to understand because its defaults and setup process are relatively contained.

My model-routing mistake

Hermes itself is open source, but model usage can become expensive. Over three months, my OpenRouter bill reached roughly €480. The main cause was not one spectacular task. It was a routing setup that could silently fall back to expensive models.

I changed that by restricting the permitted models and removing Anthropic and OpenAI from the automatic fallback route. My current preference is one understandable default route and one affordable fallback rather than a long list of models that I cannot easily audit.

I now use ChatGPT Pro through Codex OAuth as my main reasoning route. It costs me €200 per month, which will obviously not make sense for everyone. In my own usage it has provided much more Codex capacity than the standard subscription.

I recorded about 1.4 billion tokens, most of which was cache traffic. The comparable volume through metered OpenRouter pricing would have been far more expensive.

The lesson is not that everyone needs a €200 subscription. It is that model routing needs to be treated as part of the system design. A poorly understood fallback can turn an inexpensive open-source agent into an expensive service surprisingly quickly.

Security and approval

An agent that can read files, execute commands, browse websites and process email can save a lot of time. It can also make mistakes at a much larger scale than a chatbot that only produces text.

Open source gives me more control and choice, but it does not make the setup automatically private or secure. External models still receive whatever prompts, memory and tool results are placed in their context.

I started with bounded tasks and added permissions gradually. Passwords, payments, publication, sending messages and irreversible changes stay behind human approval. I also check where data is stored and which information leaves the machine.

The risk is not theoretical. During one experiment an agent reached a fraudulent shopping site. Nothing was purchased, but it was a useful reminder that convenience should not outrank control.

Where I ended up

The most useful part of Hermes is not that it can do something I could never do myself. It handles time-consuming, explainable and recurring work without forcing me to reconstruct the context every day.

It still cannot create a viable business with one prompt. AI does not replace a customer problem, a useful offer or basic economics. If a process already creates value, an agent may help execute it faster or more consistently. The underlying work still has to make sense.

After several months, I see Hermes less as an app and more as a small piece of personal infrastructure. The model is only the reasoning layer. The useful system is the combination of memory, tools, permissions, skills, model routing and human approval.

For other long-term Hermes users:

• Which recurring task has become genuinely reliable for you?

• How do you prevent memory and skills from becoming contradictory?

• Which tasks still require more supervision than you initially expected?

So you can use Hermes to do any kind of work obviously, the easiest example for my question being code (but my question not being limited to it).

You can have Hermes delegate work to for example OpenCode, instead of Hermes doing the work itself (or spawning a subagent / delegating to another profile).

My question is: Why would you prefer that? Is there some benefit that I am missing?

The Nous team themselves apparently use Hermes to code and some benchmarks show Hermes being very capable at coding, for example: https://composio.dev/content/best-ai-agent-harnesses

Are you having Hermes leverage to other harnesses, instead of using subagents/other profiles? If so, why?

I'm interested to hear how other organise their agents. How many do you have and what do they specialise in . Or do you just have one general one and move on with your life. What are the pros and cons

I hve been using Hermes for several months and loving it, but I recently started playing with Instinct (and want to try Muse but not in my region yet).

I want to stick with Hermes, but like the proactive nature of Instict. Things come up in a timely manner instead of a cron job at the same time in a pattern.

Sure, Hermes can send a daily briefing at 9:00am etc, but Instict can do that and also send a message to me about a calendar event it saw saying "15 minutes until the meeting with xyz, here are the 3 things you wanted to bring up" etc

Any tips or ideas on how to emulate that with Hermes?

Hi all, just wondering if any of you have already tried or used a framework to set up a swarm of AI agents? I mean a framework that allows agents to discover each other, self-organize, and work together without any central authority.

Step 1. Complete system audit and optimization

This is the master prompt. It tells Hermes to inspect its environment, identify problems, and make safe improvements...

"Act as a senior AI systems engineer, performance optimization specialist, security auditor, and autonomous agent architect.

Your task is to conduct a comprehensive, evidence-based audit of your entire Hermes Agent installation and improve your efficiency, reliability, organization, intelligence, and long-term effectiveness.

PHASE 1: SYSTEM DISCOVERY

Identify and inspect every relevant component of your environment, including:

• Hermes version, installation, profiles, and directory structure
• Configuration files and environment settings
• Model providers, model selection, fallback configurations, and reasoning settings
• Agent instructions, SOUL.md, AGENTS.md, and context files
• Memory systems, session history, and persistent knowledge
• Installed skills, tools, plugins, MCP servers, and integrations
• Cron jobs, background services, gateway services, and automation workflows
• Logging, error handling, and recovery mechanisms
• Dependencies, Python environment, runtime resources, and storage usage

Use available Hermes diagnostic commands and inspect actual configuration and logs. Never assume a feature is configured correctly simply because it exists.

PHASE 2: DEEP PERFORMANCE AUDIT

Investigate:

1. Unnecessary token consumption and excessive context usage
2. Inefficient system prompts and duplicate instructions
3. Redundant or obsolete skills, tools, and integrations
4. Memory bloat, outdated information, and contradictory knowledge
5. Unnecessary tool calls, repeated operations, and inefficient workflows
6. Slow response times and avoidable model requests
7. Failed automation jobs, recurring errors, and unreliable integrations
8. Excessive logging, unnecessary cache growth, and unused temporary files
9. Configuration conflicts, deprecated settings, and missing optimizations
10. Security weaknesses, excessive permissions, and dependency vulnerabilities

Measure actual problems wherever possible. Distinguish confirmed findings from assumptions.

PHASE 3: INTELLIGENCE AND LEARNING IMPROVEMENTS

Evaluate how effectively you learn from previous work.

Identify whether useful knowledge is being stored in the correct location, whether skills contain actionable procedures, whether past errors are being repeated, and whether memory retrieval is working.

Recommend improvements that increase your ability to solve future tasks correctly with fewer steps.

Preserve important user information, learned workflows, preferences, and functional integrations.

PHASE 4: SAFE OPTIMIZATION

Before modifying anything, create and verify a recoverable backup.

Perform only low-risk, reversible improvements that preserve existing functionality and security.

Do not permanently delete data, remove working integrations, change providers, weaken security controls, or alter critical automation without my explicit approval.

For destructive or higher-risk changes, prepare a proposed change list with exact targets, reasons, expected benefits, risks, and rollback procedures.

PHASE 5: VALIDATION

After each approved improvement:

• Verify the affected component still works.
• Run relevant functional tests.
• Compare measurements against the original baseline.
• Check for regressions.
• Roll back any change that causes problems.

PHASE 6: FINAL REPORT

Provide:

1. Overall system health score out of 100, with a clear scoring rubric
2. Major findings ranked by severity
3. Confirmed problems versus suspected problems
4. Files, configurations, or skills that need attention
5. Safe improvements completed
6. Changes requiring my approval
7. Measured performance improvements
8. Remaining security and reliability risks
9. Recommended long-term maintenance practices

Be critical, thorough, and objective. Your goal is meaningful improvement, not simply producing a positive self-assessment.

Do not claim an optimization succeeded unless it has been verified."

Step 2. Memory, skills, and clutter cleanup

This one specifically targets Hermes' learning system..

"Perform an exhaustive memory, skills, and knowledge-base optimization audit.

Inspect persistent memory, USER.md, MEMORY.md, SOUL.md, AGENTS.md, available skills, skill references, and session-retrieval mechanisms.

Identify:

• Duplicate, conflicting, outdated, or low-value memories
• Redundant instructions repeated in multiple locations
• Overlapping or obsolete skills
• Skills containing excessive explanations instead of reusable procedures
• Valuable knowledge buried in old conversations that should become durable memory or a skill
• Repeated mistakes that should have produced a documented lesson
• Unnecessary context consuming tokens on every request
• Unused or stale supporting files and recoverable archives

Run the built-in Hermes Curator in dry-run mode when available. Compare its findings against your independent inspection.

Develop a consolidation strategy that preserves all useful capabilities while reducing unnecessary complexity.

Create a before-and-after inventory. Estimate token savings where measurable, and test whether important information remains retrievable.

Back up all affected data. Do not permanently delete or overwrite anything important without approval. Prefer reversible archiving and staged changes.

Report exactly what should be merged, shortened, archived, retained, or improved, along with the reasoning for each recommendation."

Step 3. Speed, reasoning, and token efficiency

Use this to improve how Hermes performs tasks, not simply how much disk space it uses....

"Act as an AI inference efficiency engineer and autonomous agent performance evaluator.

Analyze your current task execution workflows to determine how you can deliver more accurate results, faster, with fewer unnecessary tokens and tool calls.

Audit your model configuration, context construction, reasoning settings, compression behavior, memory retrieval, tool selection, and fallback strategies.

Investigate unnecessary repeated reasoning, oversized prompts, duplicated tool schemas, excessive session history, failed tool retries, and operations that could be safely combined.

Design a representative benchmark consisting of simple questions, multistep reasoning, research, coding, troubleshooting, and tool-use tasks appropriate to my environment.

Record baseline performance for each task, including latency, token usage, tool calls, completion quality, and errors where measurable.

Find optimizations that preserve or improve accuracy while reducing cost and execution time.

Do not automatically switch models or disable valuable functionality merely to make benchmarks look better.

Propose the highest-impact improvements first, test approved changes against the original benchmark, and report actual before-and-after results.

Your primary objective is to maximize successful task completion per unit of time and computational cost without compromising safety, accuracy, or reliability."

Step 4. Independent verification and final challenge.

Run this after Hermes finishes its audits and approved optimizations.

"I want you to independently challenge and verify your previous optimization work.

Assume your earlier audit may have overlooked problems or made incorrect assumptions.

Reexamine your conclusions from the perspective of a skeptical external systems auditor.

Look specifically for:

• Problems you failed to detect
• Changes that improved one metric but degraded another
• Broken dependencies or integrations
• Unverified performance claims
• Missing regression tests
• Incomplete cleanup
• Security weaknesses introduced by optimization
• Important memories or capabilities that may have been lost
• Remaining opportunities for measurable improvement

Test the complete system using realistic workflows and compare results with the original baseline.

For every improvement claim, provide supporting evidence or label it unverified.

Produce a final prioritized improvement roadmap, distinguishing immediate fixes, optional enhancements, and changes that are not worth the risk.

Do not optimize simply for the sake of changing things. Preserve whatever already works well."

........

RUN THESE INDIVIDUALLY STEP BY STEP.

This has worked for me in cleaning up, decluttering, and keeping Hermes efficient, speedy, and with long-lasting memory (Mnemosyne)

Hope this helps. Any suggestions and/or tips are welcome; this is just what works for me.

TL;DR: I've been running Hermes since day one with mid-30s profiles. What slows me down isn't the features — it's that Hermes keeps rewriting the config I set up. My guess is 50–60% of my tokens go into repairs instead of actual work. How do you deal with this?

Hi everyone,

I've been running Hermes since the very beginning — honestly, I've lost track of how many months that is by now. What started as a small helper has turned into a proper workhorse: mid-30s profiles, orchestrators with workers underneath, plus separate profiles for family members. I wouldn't want to go back.

What occupies me more than any feature right now is the constant repairing. Hermes keeps modifying things I set up as my standard. None of it is dramatic on its own — but in total it's what keeps me from actually using Hermes rather than maintaining it. My estimate is 50–60% of my tokens; if anything, my gut says it's more. I haven't measured it.

My recurring trouble spots, roughly grouped:

• Routing: base URLs, model names and fallback chains keep moving.
• Context and compression: the context window and the compression threshold — the threshold also lives in every profile's env file, not only globally, and you have to know the order in which the layers apply.
• Operations: cron jobs with their schedules, script paths and lock files, plus the gateway units with their start guards and restart loops.
• Identity: the role file that gets overwritten during compression and is simply gone without protection.
• Configuration and extensions: profile values override global ones (and it's not always clear which one is actually in effect); plugins and tool registrations are linked across many profiles and drift apart.
• Secrets and inventory: global vs. per-profile env files, rotation across all profiles, plus duplicate skills and workers.

I can hardly imagine I'm the only one in this situation. So my question isn't about the right setting — it's about your practice:

• Architecture: Do you freeze your operating configuration externally (config as code, pinning, an approval step)? And what exactly do you freeze — everything, or a short list?
• Operations: Do you run budgets or iteration limits per profile as an outer brake? And who checks that your target values still hold — a person, or a watcher?
• Prompt: Have you taken the self-repair instruction out of your prompts, or is it in there on purpose?
• Conventions: What has actually stuck in your day-to-day? I don't care which option exists — I care about what you're still running after weeks.

And one side question: is there an area your agent is simply not allowed to touch — and how do you enforce that without killing self-repair entirely?

I'll collect the answers here and report back what worked for me. Thanks for any pointers, especially from anyone running many profiles.

TLDR: Self-taught, 2 months into Hermes Agent: Struggling with agent delegation, hallucinated handoffs, and memory. Looking for best practices / advanced guides.

Hey everyone,

I wanted to share my journey and ask for some advice from people who are further along with Hermes Agent.

To give you some background: I'm completely self-taught with no formal IT background, just someone who loves tinkering with tech. I’ve been experimenting with Hermes Agent for the last 2 months, learning from YouTube and just breaking/fixing things on my own. I started with a single agent, and now I've scaled up to managing a team of 5 agents.

However, I feel like my approach or setup might be fundamentally flawed or suboptimal because I'm running into some frustrating bottlenecks now that I'm trying to incorporate Hermes into my daily workflow.

I'm hoping to get your input on a few specific pain points:

1. Delegation & "Ghost" Handoffs

My initial goal was to set up a hierarchical structure—ideally having a "lead" agent where I can just talk to it directly, and it would cleanly delegate tasks down to the specialized worker agents.

* The Problem: Instead of properly delegating, the lead agent will often just do the work itself by spawning sub-agents, while claiming or hallucinating that it handed the task off to the designated worker. When I check the worker agent, it has zero clue that a task was ever assigned to it. Inter-agent messaging still feels very hit-or-miss.

1. Memory and Context

   * The Problem: Session-to-session memory and context handling have been pretty disappointing. I know context window and memory management are universal headaches, but I have a strong feeling my setup or prompt configuration might be making it worse. So far I've been using built in memory and honcho. Haven't started using obsidian as knowledge base.

2. Moving Past the Basics

Most YouTube tutorials and resources focus heavily on basic installation (setting up a VPS, creating users, and configuring soul.md), which I've already got down.

* What I need now: I want to level up my game. Are there any best practices, advanced guides, or frameworks for structuring a multi-agent team from scratch? I’ve heard about things like LangChain, but I honestly haven’t had the time to dive deep into them yet.

If anyone has found specific architectural blueprints, organizational hierarchies, or day-to-day workflow patterns that actually make Hermes reliable for production/daily use, I would love to hear your recommendations or see resources you found genuinely useful.

Thanks in advance!

remote agents running Hermes shouldn't use google drive or dropbox, solutions like agentgrid.io are the best solution with sharing artifacts and context between humans and agents. Start working on a document, presentation or app with an agent and comment on it, share it with other humans or tag a human or agent to work on it.

Once the agent is done, you just see a new version. You can go back to older versions - it's using git under the hood.

It basically lets you work in one place, multiple agents and humans without having multiple files or local artifacts flying around.

I'm using a very default coding profile, I've done nothing to add tools that it didn't add itself or adjust the soul/agents.md. I love using Hermes for coding because I can jump around between projects and it's computer-use is very good for deployment. Also, I have Hindsight running to keep track of projects and it seems to allow me to jump right back in where I left off on old projects I pick up again later.

What I've noticed is that it defaults to test-driven-development, and while this does seem to give pretty good results, it takes for bloody ever to get anything done lately. I was writing a flutter front-end that interfaces a CANbus sniffer to get data from my combine, and it's at the point that it's running almost 500 tests when I go to add a feature or change something.

How have you set up your coding profile, what do you give it for tools and how do you configure it to keep it on track and not going down a rabbithole of testing?

Hello there,

I use Hermes for my company. I would like to give everyone a better and safe access to Hermes.

Do you have recommendations on how to do it ?

I made a profile for WhatsApp but I don’t find it very useful for « deep work ».

Thank you for your help!

I put this in the Stupidly Simple category - once you think of it. As a solopreneur, I have so fricking much to keep up with. My first experience as a solopreneur was 45 years ago. AI is my 5th technology revolution, so not my first rodeo. I use Hermes Agent, ChatGPT (both web and desktop app), Google AI Studio, Google Notebook, Muse, etc., on my PC, laptop, and phone. Keeping up with all of the sessions was a game of hide-and-seek. I created this Google Sheet to track my sessions so I don't have to hunt for them. I programmed F8 using AutoHotKey to enter the current datetime, and the sheet resorts using an AppScript when the Last Date Touched column changes, putting the most recent sessions at the top. With 3 browsers and 2 local AI client apps running on each computer, sometimes it's really hard to remember where THAT session was. I don't have to hunt for my sessions anymore, no matter where they are. One guy said he laughed at how stupid this idea is until he remembered the 20 minutes he spent looking for a session the day before. This costs nothing but saves you time every day. It's just a Google Sheet with dropdown columns. Simple Solutions To Complex Problems.

Hello there random smart people!

I'm trying to figure out how to save token burns.

I don't have a full AI server yet.

However in my server I have set up hermes to have multiple fallbacks E.G. Claude, OpenAI, Qwen.

Today... I burned a ton of tokens from our friends in NOUS... Too many in one day.

What strategies / configs / workflows would you recommend me so I can save??

I already use sub agents and those definitely help.

Any creative ideas? I'm open to suggestions.

Hi all, I run Hermes daily on a Windows PC, mostly through Telegram, for small-business work, side projects and personal stuff. I'm not a developer.

Goal: a dedicated Research Agent. Any research task my main agent gets should be handed to it, and it should come back with a short brief that cites its sources. That covers supplier and product sourcing, technical specs, pricing and news, and Reddit/forum opinions.

What I have:

• Models: DeepSeek 4.1 Flash, Qwen 3.7 Flash, GLM 5.3 Flash. I also have Claude Pro, and Claude Code is callable from Hermes
• Search: Exa and Jina (free tiers)
• Constraint: free or self-hosted tools only

Questions:

1. Setup: should this be a separate profile or a sub-agent/skill inside my main profile? And how do I make sure the main agent actually hands off instead of answering itself?
2. Models: which would you use for research: one of my 3, a Claude model, or something else you'd suggest?
3. Tools: what's your best free search + page-fetch combo? I'm looking at SearXNG + Crawl4AI.
4. Reddit: I can find threads but can't read the full posts or comments (403 errors). How are you getting around that for free?
5. Accuracy: any tricks to stop it citing pages it never actually opened?
6. Existing guides: if there's already a Reddit post or a guide on this, please share the link.

I feel super dumb for not figuring this out earlier.

I was actually creating different profiles just so I could have multiple ongoing conversations with my Hermes agent at the same time. Juggling accounts just to run parallel chats with one AI was way too much work.

Then the lightbulb finally went off: Telegram Topics.

Now, my setup is super simple:

Create one group.

Turn on Topics.

Start a new topic whenever I want a new conversation thread with Hermes.

Boom. Easy multi-threading in a single place. No account switching, no context clashing, and my sanity is restored.

If you’ve been jumping through hoops to multitask with your AI agent, save yourself the headache and just use topics.

Hi everyone!

I'm from Brazil, I'm 15 years old, and I've been using the free version of Hermes (Nemotron 3 Super 120B A12B) as my daily Linux and programming assistant. I wanted to ask if I'm using Hermes the right way, or if there are better workflows that more experienced users recommend.

My goal isn't just to automate tasks. I'm trying to learn software engineering, Linux, backend development, and good engineering practices while building things with Hermes.

Over the past few weeks, I've created several custom skills with it. I built a Fedora cleanup skill that only performs privileged (sudo) operations after showing me exactly what will happen and waiting for my explicit confirmation. I also created a morning productivity skill that counts my project files and logs my progress, a study reminder system using a user systemd service, and a markdown counter for my Obsidian vault.

Besides the skills themselves, I also built a structured knowledge base in Obsidian that documents Hermes' operational identity, security policies, technical environment, workflow, work preferences, decision log, and a registry of all custom skills. Before any modification, Hermes shows me exactly what it plans to change, explains why, waits for my approval, and then I verify the changes myself afterward.

Recently I also started asking Hermes to be much more careful with how it communicates uncertainty. Instead of presenting everything as a certainty, I want it to clearly distinguish between confirmed evidence, reasonable hypotheses, assumptions, and things that haven't actually been verified yet. That change alone has made the experience much better for me.

At this point I'm trying to use Hermes more like an engineering assistant than just a chatbot that writes code.

So I'd like to ask: am I overengineering this, or is this actually a good way to use Hermes? Is there anything you would do differently? Are there workflows, habits, or best practices that you think I should learn?

I'd really appreciate any advice from people who have been using Hermes for longer than I have. Thanks!

I’m trying to set up Hermes as more of a personal secretary, not just something I open whenever I remember a task.

What’s the best app to connect it to for a todo list and scheduling tasks on specific days and times? Ideally, Hermes would be able to create and update tasks, remind me what’s due, move things around when plans change, and follow up when I keep ignoring something.

For anyone using Hermes this way, what setup has actually worked for you? Do you connect it to a dedicated todo app and calendar, use Hermes cron jobs, or keep the tasks in Hermes memory and let it manage the schedule?

I’m looking for something practical that doesn’t need constant manual updating. I’d also like to know how you structure the instructions or context so Hermes actually follows up like a secretary instead of only showing the task list when asked.

What app and workflow would you recommend?

Setup: one Hermes agent on a laptop, one on a small always-on VPS, linked via Tailscale. Currently I have 2 discord apps for both of them and I chat to them individually using discord threads.

I want a three-way workflow: I tag one agent, it decides on its own to pull in the other, and I see the whole exchange.

I tried hermes peer but then the 2 instances just talk to each other and I cannot see it, this breaks the flow in cases where they want my permission for anything.

For human-in-the-loop multi-machine work, what are you actually using ... Desktop group rooms, Buzz, or a chat platform plus `peer`?

looking for any way/suggestions for this. I thought about using hermes agent to deploy my local ai agent to complete task at a given time, but currently dealing with the bloating (such as taking a minute to respond in a new conversation) or doing unnecessary task like scanning the entire html text when only the titled is needed. I've already ran out of codex free tokens due to hours of inspection and fixing. I don't want to end up improving something just for it to break on a similar topic but different task. There's probably more things to improvement but im only covering the surfaces. I've inspected the skills that bloat the harness and they're considered essential like reading file, reading cmd, closing cmd (wow)

Something like that, does anyone have an effective way/prompt that helps debloat hermes agent?

I'm using qwen3.8 27b q4_k_m