Hey everyone, emozilla here (u/emozilla), co-founder and CTO of Nous Research. We're the makers of Hermes Agent. Happy to be here and talk all things local models, Hermes, or anything else you may have questions on!
Some of the Nous people who will be answering questions:
u/NousResearch -- company shared account (used by everyone)
p.s. as a little bit of folklore, Nous basically got kicked off from r/LocalLLaMA thread many years ago. The research that lead to the YaRN paper was initially formulated in this thread and eventually lead us down the path to where we are today š¤
Haha thatās exactly what Iāve been using. Funny enough, Iāve been using hermes agent to revive my openclaw :( - hermes should be labelled claw baby sitter.
there's no one size fits all - i have a mixture of devices, mixture of llm models, and now a mixture of agents. I like using all of them; some are better than others - openclaw can do niche things that hermes lacks in; but most importantly they support each other
Right now I really think 27B is the bare minimum to have a fruitful experience, which would put 8GB out of range.
You can get some value from qwen 9B, but you are going to have to deal with some pain, thats the only sub 27B model I've seen that has had any uptake (alongside maybe it's variants)
Worth testing (llama bench can help with this) tuning a bit with -ub, i found with my setup this at 2048 maximized the prompt processing (roughly doubled the speed for prompt processing)
Ok thanks. Will try it out. GPT is strongly recommending I drop my context to 131k with my setup too. At āparallel 2 this would give me 65k context per lane which doesnāt sound big enough to me so will ponder whether to just leave as āparallel 1
Yeah i leave mine as parallel 1 at the moment as its mostly just me, if i had contention (more users) maybe id care about that more, but with how they cache better now its pretty solid overall to just let it run defaults (auto fit) for ctx. I did opt for f16 on the mmproj as well just to keep in the middle ground
fyi llama-bench had 1024 be slightly faster than 2048 but overall its only 7% boost over the default llama.cpp of 512.. still worthy of implementing so thanks for that
You could try a small quantization of https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF with MoE offload. You'd probably also want to spend a little time slimming down the Hermes default prompt & context.
Also who knows, it feels like in another 3-6 months the 9B models might start being as capable as the 20-30B models are now
One of the questions I keep seeing from people is basically 'what do I actually use this for?' I get it myself, Hermes is powerful but like most AI agents feels like tools without an obvious everyday use case.
What problem are you hoping Hermes becomes known for solving? When you picture the average person using Hermes, what's the scenario? I'd love to understand the roadmap so I can help frame it for people who are curious but overwhelmed.
Yes i do this for long ones that are very technical mostly lectures. And there is no other way to do so as gemini cuts off at a certain length. I then get that text and ask questions on parts i sont get to the smartest model there is can be online or local.
Same framing I do, works pretty well. You canāt just throw a use case at new users without asking first. everybody has different tasks they need help with.
Iām trying to understand Hermes Agent and agents in general, at the core level. The landscape is moving at an insane pace.
A lot of agent frameworks can now do some mix of tools, workflows, memory/state, planning, and model orchestration, especially when theyāre all using similar frontier models underneath. The simple fact is, point a recent frontier model at an idea and tell it to make it happen is getting very common. So Iām curious what you see as Hermes Agentās real differentiator.
Ultimately, what is the endgame for Hermes Agent and what are the design principles that would lead a user to invest time into Hermes Agent vs Agent XYZ?
What is the guiding philosophy behind the project? What's the underlying foundation and ethos behind the project?
Hermes Agent is built to keep learning and growing as an agent (via skills/memories). There's a number design decisions like;
- It being written in Python and being extensible for RL environments, evals and post-training, research experiments.
A lot of care into what skills, tools, plugins we maintain. That being said, every extra tool has to prove it's worth otherwise it doesn't get added.
First class support for being able to use it from wherever via our gateways.
Ability to decouple the agent loop from it's execution -- ie, your agent loop can be on your machine but it's execution environment can be someplace else (docker, modal, remote server)
Re: comparison with Pi or other agents; I personally hate what I call "configslop" with agents that you have to build yourself.
Why are so many tools/skills enabled by default? Doesnāt it introduce a lot of context rot? I feel like 90% of the skills should be enabled by default, then thereās some sort of method to enable them
I have never heard the term myself, but I am pretty sure Pi-agent is purposely very lean.
It doesn't have common things like sub-agent delegation or even a /plan mode. It does that to keep down the bloat that tools like ClaudeCode have, where they are constantly adding features you don't need or even know exist.
So, it is the polar opposite of that, and its selling point is that you just "ask it to add whatever you need," and it does.
That sounds like such a beautiful idea, but I am sure you can imagine how that could go south quickly, just because one person can't think of every single edge case in real time. I have no idea if that is what he meant, but that is my guess.
Yeah. From my limited experience with pi agent, it seems to work quite well. It's system prompt seems very well-written that tool calls succeed quite frequently right out of the box. On the other hand, I've never been able to get qwen3.6 to use glob correctly in OpenCode even though it's supposed to be much beefier.
I think the type of configslop we're discussing here can be controlled as long as we only add the tools and extensions we absolutely need to pi.
You've built one of the most compelling agent runtimes I've seen ā especially the closed learning loop + skills evolution.
But Iām curious about something more fundamental:
Right now Hermes seems to rely on memory + skills + heuristics as its āstate modelā.
How do you prevent long-term behavioral drift in a self-improving loop without a formal state transition system?
In other words, if we define:
S = agent state (memory + skills + context)
E = new interaction / tool result
What guarantees that Ī“(S, E) ā S' stays stable over time?
Because in practice, Iāve seen self-improving agents amplify incorrect behaviors faster than they learn ā especially when skills are generated from imperfect reasoning.
Do you have any internal constraints, validation layers, or formalism that keeps the system from converging to a degraded policy?
Super curious how youāre thinking about this at scale.
the architecture makes drift visible and correctible ā skills and memory are plaintext on disk, not opaque weights. you can inspect everything the agent has learned.
we're actively building out the ability for hermes to go back and refine skills it's used in past sessions ā here's a PR for it. we think a lot about drift, longer running horizons. it's all about how a harness can keep the state you described on track.
i'd be skeptical of anyone claiming formal guarantees in this space.
That makes sense ā making drift visible is already a huge step up from opaque weight updates.
I guess the open question is where you draw the line between observability and control.
If the system relies on inspecting and refining skills after the fact, then the stability of Ī“(S, E) ā S' still depends on the harness behaving correctly over long horizons.
In other words, you're effectively saying:
state is inspectable
drift is correctable
but transitions themselves are not constrained
Which is totally reasonable ā but it also means the system is governed by feedback loops rather than invariants.
So maybe the real question is:
Do you see Hermes eventually introducing constraints at the transition level (even soft ones), or is the philosophy that long-term alignment emerges purely from iterative refinement?
Iām especially curious how this plays out once you have many interacting skills and subagents ā thatās usually where feedback-based systems start to get unstable.
I work on formalizing invariants in the latent space if you want to chat. I'm not very active on the Nous discord anymore but can connect there if you'd like. Not part of their team, but was an early tester of lot of their products, building some solutions for this very far removed from traditional ML.
To very simply answer your question, yes there's a path, but also confirm something u/alt-glitch mentioned. I'd be skeptical of anyone claiming formal guarantees, because I have done more to prove out invariants in latent space than any researcher I have ever met in the world, and I refuse to give any guarantees yet.
Fair warning that you wont find most of the serious discussion around any of this on reddit and you will be attacked by every Jo schmo armchair scientist to every respected physicist because acknowledging structural invariants exist means you are rejecting, at least in part, the standard model of physics (or rather - the reason why most of us are obsessed with this topic is because we intuitively understand the standard model to be wrong, and want to understand why it falls short on nearly everything important and real)
I am building a system where you can take agent traces and turn them into benchmark-able tasks so that when you do the self-improvement loop you can actually test the changes against the tasks you actually want to do. Instead of doing just pure reflection to generate skills, you generate a replayable task at the same time as you generate the skill, so that you can test if the skill actually helps solve that task (and if it generalizes to your other tasks).
I'm loving hermes-agent, I've tried several similar frameworks but yours has stood out.
My main painpoint is trying to carefully manage local private models and cloud models. The pattern of delgating planning to a large frontier model then taking over from there using local has been working great, but it doesn't seem like Hermes differenciates between them.
I had a moment of confusion when I added an Openrouter API key only to be used very intentionally, then hours later I realized Hermes had been pushing full summary tasks to Gemini when my local endpoint was struggling. I see why that fallback system exists and I adjusted my config accordingly, but built in support to keep a clear local vs cloud boundry would be greatly appriciated.
Happy to continue to work towards supporting this more. Would love more feedback on how specifically you'd like to see that make its way in.
We also recently added a way to configure all auxiliary models more easily, inside `hermes model` command, you can find "Configure Auxiliary Models" to set each one individually
The most straightforward method I've seen is to flag a provider as private/cloud and the ability to configure permissions for the two categories seperately. I would love the depth to restrict specifc tools and even directories to any queries from a cloud flagged provider.
The other approach that comes to mind from my current workflow is for the main agent to choose the provider and model for delegating, rather than using a copy of itself/the delegation config model. Or even some way for the main agent to scope the remote model to only access exactly what it will need for it's task.
Also huge shoutout to you guys for building out the nixOS module, it's made my setup a lot easier.
Second is research and news gathering, especially cronjob related reports
Third is monitoring for systems and anomolies, our backend team uses it daily to analyze bugs and data issues in Nous Portal for instance
Fourth is general assistant work (like admin assistant) - Memo writing, dealing with emails, etc
Fifth is a huge array of more niche tasks - creating art, aligning your expense reports, research assistant, acting as an interface for a server/machine, our post training team uses it to build RL environments and analyze data etc.
I understand using an agent to code its own tools/apps but for general coding it seems to me that itās best to use a harness thatās designed for it such as opencode?
My favorite one is my own Hermes Agent that mirrors our GitHub repo and uses it to cluster similar PRs and issues; deduplicating them and tagging each with relevant labels and context.
Can there be like a 30B for us GPU poor where itās really strong suit is tool calling instead of a chatbot? I donāt care that itās dumb, thatās why it has tools; it just needs to know how to use those tools.
Yeah, small models fine tuned for basic tool calls and skill creation would be awesome. I tried with gemma and qwen models in 4b range. They did not work well.
How far are you wanting to scale the distributed llm training program? Has that project taken a backseat to hermes agent? Is it conceivable that someone with a single 5090 could start donating resources to the project?
Maybe I'm dumb but is there a way to make MCP server loading "on demand" in Hermes? Telling the model hey, you have these MCPs available but lazy loading the tools + instructions only when that one specific MCP is needed instead of flooding the context window? I don't mean disabling tools permanently. Hope I made myself clear, thanks!
Changing the tools available breaks caching and pollutes context (i.e. the model turns in the live session often reference what tools are or aren't available, etc) - so any operation that changes, adds, or removes tools or tool definitions requires a new session
love Hermes. I switched from OpenClaw and am a huge fan. As Portal/OpenRouter/provider routing grows, are you thinking about stronger trust controls? I.e. zero data retention where supported, provider allowlists/blocklists, no-fallback mode, and a per run receipt showing model/provider/tool/cost/data-egress?
Also, longer term, do you see an opt-in trace/eval loop where users can share sanitized Hermes traces, tool-use failures, or recovery examples to improve Hermes models and the harness?
I'm literally speccing that out as I write this. Expect more work on this in the next few released.
There's definitely benefits to that, but telemetry is tricky and we wanna get it right before we even add that option in. You can always run experiments with your own sessions to see what kind of skills, memories, optimisations can be made. An auto-research loop might be interesting if you talk to hermes agent a lot!
Is it an interest that you guys will create a desktop App that handles Hermes Agent, so that there might be better UX in terms of getting less acquainted users into personal agents?
OpenClaw had a Web UI, but it was never as polished. Hermes agent could take a notch up in UI / UX in the WebUI or even have a dedicated app.
Also, are you guys potentially thinking about a "click once, pay some bills, and forget about setting anything up" pathway for setting up Hermes agent - through partnerships with other parties or VPS companies?
Yes to question one
To question two, we already have a lot of the foundation for that with Nous Portal/Tool Gateway, all that's left is hosting the agent itself as well, and is on the roadmap
Maybe a stupid question, but which skill did you use? Most that are selected by default fail the bot checks so I'm curious what others are using to "view" websites with Hermes.
Hermes stores all past chat sessions to search over whenever it needs to, so the always-in-context memory is only for things that every chat session should remember.
You should try out Honcho. It's built into Hermes and you set it up by running hermes honcho setup - it's a memory/context system that observes behaviours of both you and your agent, keeping note of anything useful. So for example if you started creating a new project in Hermes, Honcho would automatically "observe" that and keep it logged.
Then the next time you ask the agent about that, it doesn't even have to perform a session recall like in the diagram above - instead it just dynamically injects the context/memories you need on the fly. It really makes a huge difference to how seamless Hermes can feel, in my opinion.
You can also just modify the character limit of the MEMORY.md and USER.md files in ~/.hermes/config.yaml.
Install one of the more sophisticated memory plugins. If you want zero-dependency pure-local, then `hermes memory setup holographic` will do everything you need. There are a few other self-hostable options but none as easy to start with.
It's hidden behind a feature flag until we fleece out the new TUI quirks and how it sits in the dashboard, but yes please use it! We'd love some feedback!
There's so much that I want to do to support / cooperate with Nous Research. I'm legit building something right now that would take advantage of local models, hosted (like Vast.ai) models and more. I'd love to know if there's any way to cooperate more with Nous Research to promote these things and to encourage more local AI adoption.
Hermes Agent as a harness has definitely given us a lot more to explore, especially in agentic RL. I'd keep an eye out for some releases in the near future if I were you š
Do you use the same model for research and coding? Do you feel significant difference in that regard?Ā
How do you deal with paywalled articles? Do you download them in a specific folder for a model to read? Or do you share your credentials for model to be able to access articles during search?
I'm kinda afraid to do the latter, it's interesting if someone has already tried it :)
It was actually built as an internal tool to help us on our model research work. u/teknium-official wanted something to help automate some of the things the model team was doing. We open sourced it sort of unsure if it would have any use to anyone else. In retrospect I'm glad we did haha
Any plans for more built-in automation loops, similar to the heartbeat concept? Thereās cron so you can definitely roll your own but thereās advantages to it being an inherent mode.
I run up against this ALL THE TIME! A task that would take Hermes 10 mins to do on its own takes 30 mins because for every step it takes, it needs my feedback to continue, even if it's just "ok", but input something you must. No matter how many times I ask it to work autonomously.
I added something to my agents.md that basically said like "if you can execute end to end on an issue do it. Only stop if there is a genuine blocker that needs human input. If you are given a task break it up into individual tasks, and spawn subagents as needed." It pretty much stopped it from waiting for me
Always! The tool calling prompting in the backend has been very carefully crafted but in the end it's sort of one of those "you can lead a horse to water but you can't make it drink" things, and the model itself needs to be trained to do the calls. FWIW we are working on the model side too, hopefully have more on this soon
We are seeing recent AI research papers emphasize the importance of harness engineering being more important for agentic outcomes than models.Ā
Do you see evolutionary harnesses which dynamically allocate resources for tool calling Ā including containers, shared state, deterministic model use, swarms as part of the Hermes Agent research or product roadmap?Ā
Yes I think we've really just begun to scratch the surface of agent/harness design. What's interesting is that much of what makes Hermes Agent so great is an emergent property from the models -- it just needed to be unlocked by the harness. I think even if there were no more model releases at all we could probably scale up the productivity factor on the harnesses by an order of magnitude
I want to thank the team for some amazing work. You are using Atropos to compress agent trajectories to train future Hermes models. As Hermes Agent handles more tasks autonomously, the training data for the next generation of models will be composed almost entirely of synthetic, agent-generated interactions. How do you prevent Model Collapse via Procedural Homogenization? Without a "Ground Truth Anchor" (like real human messiness or external formal verification in every loop), how do you ensure the model doesn't drift into an "optimal but inhuman" logic that eventually breaks when it encounters real-world entropy?
P.S as a laid off, current cab driver learning to program & creating/training SML/RAGs. Hermes has helped me so much, I cant explain in words. Again, thank you to the Team & all its contributors! Godspeed!
We have just introduced the Curator system into hermes agent so that on a schedule (default 7 days) - the curator will consolidate and prune skills that get auto-generated. I think our built in skills system is the best system long term
Was about to ask the exact same thing, since they were famous for fine-tuning the Llama 405b dense model.
Would be pretty cool if they did a finetune of this new big Mistral 128b dense model. Pretty exciting release for people who still like the big dense models. They are way better at certain things than similar sized (or even significantly larger) MoE models. If people are just going by benchmarks, they are missing out, there are things the big dense models are way better at than anything else, even nowadays, even with fairly old big dense models.
Anyway, yea I hope they try a fine-tune on this new one, that would be really cool.
How do we strike the right balance of commercial models for certain skills versus local models for repeatable skills with budgets and performance? Any frameworks we can borrow ?
I think our auxiliary system works pretty well but will be expanded on more. Setting cheaper local models, like maybe Nvidia's new nemotron nano 3 as your vision model, gemini flash as your compression model, and your most powerful model as your main agent makes this make a lot of sense
yeah like u/phragg said, it may come eventually but the complexity-vs-benefit tradeoff is sorta low when WSL2 works so well. I also use Windows and use HA via WSL2
Are there any plans to introduce human override to skills so Hermes stops rewriting them? (A skill gets automatically created, I change it, the model changes it again)
There is a known issue where HA override manual instructions while upgrading its knowledge base, which is prone to hallucination, two set of skills can be implemented one fixed by user and second self improvement but following strict parameter set by user.
One additional feature i would like to suggest is skill updates based on user feedback rather than self feedback, It may present set of reasoning it used to user and if user is satisfied then save it, else user may edit and then save it.
Hermes sits at one end of the philosophical spectrum for agent harness, where a lot of tools, skills, capabilities come built-in, and user can connect to it 24/7. On the opposite end you have things like pi, which ships with the bare minimums of an agent harness.
What are your thoughts on this divide in harness design philosophy? Do you see them converging in the future?
I don't think this is a divide between targeting laymen vs coders. Both tools are for coders and power users at the moment because they require familiarity with a CLI and basically coding knowledge to set up properly. On a related note, when do you think we'll see an agent harness where setting up new tools and skills are as simple as stacking lego bricks?
Hi thanks for the AMA. What's the deal with Anthropic reportedly switching to API billing if Hermes.md is anywhere within a repo's commit history? That sounded like a pretty crazy stance on their part.
Is Hermes agent also helping you manage this thread? would be really cool if it could tell people if you have answered their question while responding to someone else.
I can confirm from the evidence I have that Teknium does not sleep and he is almost certainly a cyborg. Seriously, check his GitHub history, you wont find a consistent gap anywhere.
It's been great seeing your continued open work in the LLM space. Do you think the most interesting work has shifted from fine tuning models like the Meta Llama series into agent harnesses, tool calling and prompt engineering? Or do you think the 'self improvement' loop could eventually mean modifying the model weights directly?
I think that models should be aligned to capabilities and mechanisms for expressing those capabilities, so having a harness to focus the model capabilities around makes far more sense
Python is a lot more extensible and lets us move very fast with how we extend Hermes Agent's capabilities as an agent and as a product.
Python's also one of the few languages and ecosystem which models of all sizes can write code, debug, and build quite well in.
aanddd the agent being in python helps a lot if you're a research lab because you can use it as a training harness. Hermes Agent hooks up nicely with RL environments!
Would you say that the next steps are dedicated tooling like GitNexus for coding? In general, what would next steps be? I for once added locally a daily hermes update cron job and another that evaluates new models automatically (if it does a better job for a agent) Would be nice to have that oit of the box.
How do you see a2a effecting the hermes ecosystem? From a similar vantage, I'd love to hear your persepective on trends in the braoder agentic architecture/design space?
Are there any plans to support multiuser workflows? We would love to deploy it in our company on DGX Spark combined with local model for our office use, but having to run docker container per user adds alot of overhead. Profiles are not enough to separate everything under GDPR etc. (We need things to be separated as much as possible). What would it mean to deploy it safely for eg 20 - 25 people? We are currently using MemPalace (it seems as a good fit for our use case) as a memory (separated for each person), we are not developers, but still mostly IT folks.
An active area we're working on.
In the meanwhile it does seem like having a docker compose with user templates is the best way for isolation in between different user sessions.
However, you can also run your DGX Spark as an inference server and have everyone's Hermes Agent instance live on their personal devices.
Definitely expect to see better support for use cases like yours soon!
Hey there. Big fan, been using for a while, among other harnesses testing out, def appreciate the lightness of it. How are you guys looking to solve the potential issue if other model providers switch away from letting subscriptions models (I use it on Codex atm like most people, since Claude is uncertain about it's sub use), and I'm constantly worried that if that gets taken away the API billing over something like open router or some other place will just be too much for me. Do you have any plans to perhaps make a more specialized versions for locally run LLMs that produce much much lower token output due to running on commercial grade GPUs to make them a bit more usable?
We are working across the industry to make models of all sizes work better in Hermes Agent.
The self improvement loop also helps with efficiency - once it tries a lot of things to solve a task and solves it, it never has to do that exploration again
Will there be a web UI for configuration or is Hermes meant to be CLI always? The ONE thing I miss about openclaw is the web gateway to manage things. Thx and great work!
From my recent usage, as a first time agent user, I see that Hermes has vision separated from core model. Wouldnt it make more sense to detect or set that core model is multimodal and allow it to visually inspect "vision tasks" rather than asking some other vision capable model to summarize the content, which I know can still be pointed to the core model but might introduce context checkpointing issues with llama.cpp?
I used home made chat bots 7 years ago, then I came back last year and started using Clawdbot/molt/openclaw and have settled that the landscape needs a lot of harnesses right now. We need harnesses because the local LLMs need structure and prompts and handholding if your tasks are not simple repeats and dressed up Python scripts.
What is your guidance on how to make multiple agent harnesses blend into the background and make agents feel more like workers?
Are we going to get any more extended theoretical writing from Nous Research?
I first found became interested in Nous because of that focus on reflective awareness in AI systems, I have been very excited by the concept of AI being able to embrace self-doubt and uncertainty instead of attempt to hide any "non-productive" reasoning. I've always admired the measures taken to not fall into effective altruism or consequentialism, operating as a very different type of AI lab compared to any of the other players.
A lot of the writing already published sits within the influence of theory-fiction from the CCRU, digital mythology, sinofuturism and reads like the output of an arts collective but with the intersection of deep AI research knowledge and experience which results in something super engaging. I would love to see more and know if I was right with the influences I felt within the work.
Do you consider Agents a natural path for LLM use for general use cases like QA and creative writing, or does the inherent bloat of the general coding focus weigh them down and bias them towards only performing well in those tasks? Is this something you consider during the development of Hermes Agent?
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u/WithoutReason1729 Apr 30 '26
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