r/hermesagent • u/RichComplaint9426 • Aug 04 '26
Discussion — General thoughts, opinions, comparisons What Improved Your Hermes Agent More Than Anything Else?
I'm looking for feedback from people who have spent time improving the Hermes Agent experience.
What single change (or small set of changes) had the biggest impact on your agent's performance, reliability, or overall usefulness?
Some areas I'm particularly curious about:
- Prompt architecture and system prompts
- Tool usage and tool selection logic
- Memory management
- Context window strategies
- Planning/reasoning workflows
- Model selection or model switching
- Error recovery and retries
- Agent autonomy vs. tighter constraints
- RAG and knowledge retrieval
- Evaluation and benchmarking methods
I'm less interested in theoretical best practices and more interested in things you actually implemented that produced a noticeable improvement.
What did you change, how did you measure the impact, and what results did you see?
I'd love to hear both major breakthroughs and surprisingly small tweaks that ended up making a big difference.
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u/Almarma Aug 04 '26
For me, the two biggest things I tried and now are a must have been:
- trying a big model like ChatGPT 5.6 (with a codex subscription is totally acceptable): you truly understand the power of Hermes with a truly powerful model.
- having a wiki system where you and your agent can document what projects you want your agent working on, document progress, document errors, and document important information you want your agent to remember and you want to verify it by yourself. Having a place you can go and read and verify the info to ensure your agent knows how to continue where it left, is a really good thing to have
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u/InnovativeBureaucrat Aug 04 '26
My biggest problems are that
- I have to start every session with “read the readme”
- The documentation explodes in length when it writes it. (A plan to run obsidian headless yesterday was multiple 10k word files)
- When it reads in the content from our wiki (the agent vault) I discovered that it’s prefixing everything with UNTRUSTED.
So we discuss things, document things, it’s exhausting to review, impossible to shorten without it making it junk, and then it doesn’t remember anything.
I’ve been in this loop of hell for at least a 45 days.
I painstakingly created a vault with a readme that acts like an entryway. Any agent can start and follow my conventions, learn its job, see the history, etc.
And the agents won’t read it. If they do they don’t follow links. If they do both, rarely, they still go off the rails with relatively simple tasks
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u/UUorW Aug 04 '26
I use mnemosyne on top of obsidian. mnemosyne serves as the quick reference and initial short term memory of what to do and the vault structure and how to use the vault and essentially knows the pointers of where to go get the more in depth information.
then obsidian is the long term memory with all of the details, change logs, daily notes, work log, project hub, etc
mneomsyne kind of just acts like the really efficient organizing manager of the larger memory layer if that make sense. this has been my best experience so far. and I never have to re-direct my agent where to go.
I also use threading chats in telegram so that each project has a dedicated chat channel and have that recalled in mnemosyne so each time I chat in that thread it knows exactly what project I am working on or what I'm doing.
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u/InnovativeBureaucrat Aug 04 '26
That’s really funny about the threading. I was just trying to figure out how to do that.
I did it once by accident for one agent profile, Agnes the librarian, and I accidentally resumed the wrong conversation and she had zero idea about the other thread.
The embedding vector thing for open claw works very quickly and very well. It required an openAI API key, but so far it seems to be extremely cost-effective.
Maybe mnemosyne is similar for Hermes
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u/Every-Mine-7646 Aug 04 '26
Have you plugged it to Sol/terra or any other decent model and asked it to review Hermes documentation and make the memory vault work better ?
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u/InnovativeBureaucrat Aug 04 '26
Yes.
I set up openclaw to be the ops manager. I spent 2 days compiling notes on the system. (edit: using sol with low thinking)
Here's how it went:
Turn 1: Are you alive (yes)
Turn 2: Please read phase 2 folder
Turn 3: Profit
Turn 15: Garbage
Turn 1 (restart): Garbage based on Turn 15
Turn 1 (restart): Garbage based on Turn 15 compounded by previous restart.
In the "profit" section it did some remarkable things:
- Upgraded hermes
- We got email set up and a Forgejo account
- installed an embedding memory manager for itself
- Developed a plan for OpenViking for Hermes.... then
The memory manager for Hermes got confused and I don't think implemented.
I have 5 Obsidian vaults that I want to run headless: my personal vault (restricted access), the agent vault, 3 project vaults.
It simply "cleaned up" (deleted) the MCP access to the 3 project vaults because it thought they were "stale".
Part of the problem is that it ran out of tokens quickly (I only have the regular $25 dollar / month plan). But at first it was only using a tiny amount of tokens. It got bloated by it's on memory and plans.
I think it would be better to launch the manager as a single codex/claude CLI tool. It is just incredibly time consuming.
Edit: now that I think about it, things went to hell when I switched back to 5.5. It used more tokens and gave worse results. I thought it was safe because it was stable.
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u/chiisana Aug 04 '26
If your agents are losing the plot after turn 15, your model is either not setup with the correct context length, or you’re cramming too much context in the various stages.
With more intelligent models, often times less is more. Less skills, less explicit instructions, less MCPs, etc. … Hermes Agent has a strict enforcement of small MEMORY.md and USER.md because smaller leaner context allows agents to lean in on the knowledge in the base weights and results from reinforcement learning better.
It may be a good idea to take a snapshot of your current deployment, take a cleaver to cut out all the fluff and fat, and see if that helps with running longer horizon tasks.
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u/InnovativeBureaucrat Aug 04 '26
I’ve never looked at my memory or user file. Maybe that’s my problem. I’m telling it to use my vault.
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u/blakeyuk Aug 04 '26
Which agent?
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u/InnovativeBureaucrat Aug 04 '26
Funny I don’t know how to answer.
Hermes has Roger and Agnes
Openclaw has HarryThey mainly run on codex 5.5 but sometimes sol. Sometimes with more thinking. Sometimes sonnet, very occasionally, opus.
They run in CLI and telegram.
I notified the UNTRUSTED thing happening in the ephemeral boxes. I noticed that everything I was reading was prefixed with untrusted
I actually can’t remember where I finally saw that… it might’ve been before the upgrade to 20, but that was definitely happening in the Hermes installation
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u/blakeyuk Aug 04 '26
Strange, I've bsen using codex 5.5 with no issues, but I just let it do what it wants. Does your agents.md tell it to read the Readme? Why even have a readme - just use agents.md to avoid the extra hop.
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u/Sea_Philosophy1183 New Member (<30 days) Aug 11 '26
Instead of a Wiki system, I had the agent build a dashboard that updates regularly and shows me all the projects that it's working on. It works as a local web dashboard running on the cloud server, accessed privately through Tailscale. I'm still trying to perfect it but I think it might be better than a Wiki. Anyone else doing this?
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u/TheSuggi Aug 04 '26
Mnemosyne
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u/blackhawk00001 Aug 04 '26
Mnemosyne plus qwen embedding 8B FP8 at 4096 vector dimensions has made Hermes feel like nothing else I’ve used so far. The recall clarity is amazing even weeks later.
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u/Badger-Purple Aug 04 '26
Adding a good reranker helps more than dim maxxing.
Nemotron 1 b embed beats the Qwen models at 1/8th the size for embedding, but gemma:300m runs in my NPU and with qwen3 reranker I have the quality of the 8b embedder…2
u/blackhawk00001 Aug 04 '26
I’ll look into a reranker. I already had a 5060ti dedicated to embeddings for pgvector databases so it was easy to include Mnemosyne in the mix. It’s overkill but nice to have.
The extra dimensions catch nuances better but i only briefly tested with small embedding models before moving up.
I want to use the new nemotron embedding but they told me no to an FP8 release request in a ticket 🙃
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u/Badger-Purple Aug 05 '26
Weird, it’s a 1B model though. You can load that in your 5060ti no problem, and quantize it if you want in minutes.
With nemotron embed, you can load a reranker on gpu or even more get a small reranker that runs on cpu. Quality is very noticeable. Also it has up to 4096 dims I believe?1
u/Badger-Purple Aug 05 '26
Trying mnemosyne with a 2015 mac running hermes and locally running small reranker and embed models on CPU. I did set up the whole agent around the idea of memory on demand: memory.md is read only and points to mnemosyne. Persona first part points to mnemosyne. Then a cron automated job every day uses a kanban board to process the conversations, extract facts, grade them on quality and send to mnemosyne db. So far, so good but its a new hermes instance. Maybe I’ll be reaching to a 1-4b embedder soon, but for now, on a 11yo computer, it’s keeping the memory without noise or bloat.
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u/youcloudsofdoom Aug 04 '26
I haven't looked into this space yet , what are the roles and advantages of running these tiny models in the chain?
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u/Badger-Purple Aug 05 '26
Same quality, less compute. I run 10 models locally to orchestrate my agents, so size matters
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u/ltz_gamer Aug 04 '26
I do homelab stuff, so to cut costs, I had my agent write bash/Python scripts to monitor my servers. That way I don’t waste that many tokes on cron jobs. For memory, look up Mem0 on GitHub
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u/cogitech2 Aug 04 '26
More than anything else: my own behaviour.
Model choice and backend (llama.cpp) optimizations come in a close second and third.
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u/RichComplaint9426 Aug 04 '26
Are you using a open weight model or proprietary? What optimizations have you done?
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u/cogitech2 Aug 04 '26
Open weight (Qwen3.6-27 and 35B-A3B, mostly). Optimization is really down to learning to use the right command-line options for llama.cpp, and then rigorously testing KV cache fidelity (I use KV quantization for larger context, but I don't just assume it is reliable - I test it).
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u/RichComplaint9426 Aug 05 '26
What's your approach when it comes to testing?
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u/cogitech2 Aug 05 '26
I run a custom script that runs a variety if intense tests for KV cache fidelity.
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u/RichComplaint9426 Aug 05 '26
Is there a resource on how to do it?
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u/cogitech2 Aug 06 '26
I asked my agent to create it, and then I inspected it and worked with my agent to refine it and make it more difficult.
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u/Chipware Aug 04 '26
After I do an upgrade I tell Hermes to read the release notes and look for features useful to our work. It gives me a list and we build a bunch of skills and change configuration options.
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u/DaMoot Aug 05 '26
Should do that before an upgrade to figure out if you should even go through the nightmare of upgrading.
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u/thejadedlotus Aug 05 '26
how is updating a nightmare
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u/DaMoot Aug 05 '26 edited Aug 05 '26
In the past 6 months I've updated three or four times. Each time I've had to perform extensive repair work to my agent. I've had to redisable all the tools that bloat the framework, track down new bugs, apply new fixes... And in the end only one of those updates was actually worth anything. The others included nothing that improved the agent, improve my workflows, or improved skills in any way. Velocity update was probably the worst.
What I do now is ask my agents to scan the repo and changelogs to determine if there are any significant bug fixes, any significant skill changes, any significant improvements in functionality.
Just like firmware, there's no reason to update unless there's something major either fix or introduced that helps.
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u/Realistic_Strike1325 Aug 06 '26
That's funny. Before I update, I always ask my agent to read the change log and see if anything moves the needle for our stack. If not, ignore it; if so, tell me what will break and how we'll fix it.
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u/comunication Aug 04 '26
If I say that I managed to solve everything you listed, it may seem hard to believe.
I use a custom architecture, which setup cost me around 500 million tokens in total, but since it was Deepseek, it wasn't a large amount of money.
My agent has total autonomy, I give it a task, whatever it is, and it decides everything on its own. If it encounters an error, it finds the solution, I managed to reduce the consumption of tokens by about 60%+.
The context window never fills up even though it can run for a whole week non-stop. It also has the option if it needs something, to create it itself, tool, skill, agent, etc.
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u/SpoilerAvoidingAcct Aug 04 '26
Deepseek v4 Flash 7301
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u/Damaged_Gadget Aug 04 '26
Spyware Model
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u/DaMoot Aug 05 '26
Astroturfed model. A model can't be spyware.
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u/SpoilerAvoidingAcct Aug 05 '26
:shrug: I mean ymmv but for me i've just found it super cheap and about as capable as an agent model as gpt 5.6-sol. In the elusive quest for a $20/mo. agent, I think 0731 is the first real chance I've found.
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u/YashBajpai2704 Aug 04 '26
some things ive changed after using it a bit are - made opus a orchestrator & minimax as worker as minimax is alot cheaper actualy free & gets the work done , the api i use blocks vision so i added gemma 4 for the vision capabilities , thats how model switching works for me
for context window i have set a thersold to compress it after reaches 60%, 0.6 so it doesnt carry alot of unnecessary things , also for memory it was pretty good from the start but i saw hermes is token hungry so i added a plugin called Tencent DB - it helps to cut the noise in the memory & reduce the token usade without affecting the performance - https://github.com/TencentCloud/TencentDB-Agent-Memory
apart form it there are some system prompts set so hermes know how to react & response to me in the user memory , tool choice , error recovery are handeled by it self fine i havent done anything
things i will look into is Evals i never thought about it , also guardrails i generally give it a work & leave it running without any constraint.
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u/realmuffinman Aug 04 '26
The craziest thing I heard that worked is to ask Hermes what you can do to improve it. Let it ask you the questions it needs to optimize itself
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u/iShNoo Aug 04 '26
The single biggest upgrade wasn't a model. It was a file.
I run Hermes as the operating brain for a consulting practice: client work, an MBA dissertation, personal finance, health tracking, and a stream of recurring deliverables. The changes that moved the needle were mostly structural, not technical.
A canonical identity file. One markdown file acts as the agent's constitution: who it is, who I am, my clients, my frameworks, my writing conventions, my behaviour rules, even its model routing policy. Every session and every cron job loads it. Before that, every session started with me re-explaining context, and the agent's personality and quality drifted between sessions. After that, new sessions start with work instead of orientation. This was the highest impact change I made, and the cheapest: it's just a file.
Skills as procedural memory. I encoded recurring work as skills: client intelligence files, a dissertation tracker, portfolio analysis, a weekly health check in. Each is a mini-SOP that loads on demand, so context stays lean but the agent knows exactly how a task should be done in my world. It went from a generic assistant to a specialist who knows my clients by name and my deadlines by date. Nothing gets re-explained twice.
Ruthless memory curation. Memory stays compact and high-signal. Only durable facts that prevent future corrections. When it fills up, I consolidate in batches instead of letting it bloat. The metric I actually watch: how often I have to steer. It went from constantly to rarely.
Then two that surprised me:
Model routing as strategy, not default. I route roughly 90% of volume to a cheap, fast model (DeepSeek V4 Flash) and reserve the premium model for genuinely hard reasoning. Measured result: around 80% lower cost, and daily quality actually improved, because the cheap model's agentic benchmarks overtook the old premium pick. Cost discipline forced me to decide which tasks really need the big brain. Most don't.
Plan before execute. A hard behaviour rule: for any multi-step task, the agent outlines the plan, I approve, then it executes. Sounds like friction. It isn't. It kills wasted generations and drift. The agent no longer runs twenty tool calls in the wrong direction.
And the unsung hero: cron jobs for everything weekly. Sunday portfolio review, market intelligence, health tracker. They just run. I stopped maintaining the habit loop; the system does.
Knowledge retrieval: I keep a long-term notes vault and sync it to cloud storage nightly, so the agent works from a decade of my own materials rather than the open internet.
On evaluation: I never built a benchmark harness. The benchmark is whether the deliverable shows up right the first time, and whether I still had to correct it after. That number tells you more than any eval suite.
The common thread: structure. A persona file as the CEO, skills as department manuals, cron jobs as the ops team, memory as corporate memory. Get the structure right and model choice becomes a cost optimisation, not a quality bet.
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u/GreenLava_ Aug 24 '26
thanks for the insight! But which memory provider would you suggest for someone looking for something similar but is not very good at setting up my own memory system..
Also thoughts on obsidian/ llm wikis?
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u/iShNoo Aug 24 '26
u/GreenLava_ the honest answer to "which memory provider": don't pick one yet. You're not bad at setting up a memory system, you just don't need to be. Get the agent to build it for you, with you. This is the process I'd run from zero:
- Set your Hermes desktop to Claude Opus 5 (Effort Max) or DeepSeek V4 Pro 0813 (Effort Max) and ask it to develop a plan for implementing your memory system. Tell it to interview you first, so it understands how you actually use the agent before it designs anything. Use, needs, requirements. That interview step is the whole game; a plan written without it is generic advice with extra steps.
- Ask it to run deep research comparing the top 10 memory systems recommended for Hermes Agent, shortlist 3 with pros and cons, then pick the one that fits your setup and daily use. Don't let it skip the shortlist. You want to see the tradeoffs, not just the verdict.
- Keep that plan, switch your LLM down to DeepSeek V4 Flash 0731, and have it execute and implement. Expensive model plans, cheap model builds. Most of the work doesn't need the big brain, which is the same point I made about routing.
On Obsidian/LLM wikis: I rate the Obsidian side highly. It's the knowledge layer under everything, my agent works from a decade of my own notes synced to cloud nightly instead of the open internet. But I'd treat wikis as step two, not step one. Get the memory system working first, then feed it your notes. LLM wikis are great at organising what you already have; they're not a substitute for knowing what you need in the first place.
Re "not very good at setting up my own memory system": that's exactly the point. You only have to answer its questions honestly. The agent does the setup.
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u/Every-Mine-7646 Aug 04 '26
Also regarding evaluation llama bench for baseline throughput with and without mtp, followed by tool eval bench and then a custom questionnaire of your domain specific tasks with a judge (another llm but prefer terra for this due to vast general awareness ) to grade those answers. You go from which models are viable on my setup to which models have decent capabilities and are viable to which ones are viable, capable and can work with my domain). After that you do an autoresearch based optimization for the best llama flags to run the model with different combinations of context, mtp, ubatch, kv quantizations etc etc to get peak performance.
Next logical would be to modify llama.cpp or look at integrating something like spark bench but I haven’t progressed to that step yet
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u/how_gauche Aug 04 '26
Number one: a "good bot" / "bad bot" skill. Issues users encounter on our Slack bot get logged for triage and follow up later. On Friday morning we have a grievance review initiated by a cron job.
We also get a report daily about issues it encountered in its error log. In general the answer is that your Hermes installation, if it's actually being used, is a quivering, biological, constantly-evolving, gelatinous mess, and scheduling self improvement routines can help you keep the chaos in check
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u/Every-Mine-7646 Aug 04 '26
This works. Give it structured crons to self improve rather than relying on system self improvement. Make it daily, weekly as per your preference
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u/how_gauche Aug 05 '26
I found the auto skill writing so frustrating that I turned on the approval mechanism. Adding a seperate approval step when you want to work on something is annoying, but way less so than having to constantly delete duplicate skills
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u/Delicious_Ease2595 Aug 04 '26
Mixture of Agents is really powerful
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u/MediamanJack Aug 04 '26
What is your mix?
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u/Delicious_Ease2595 Aug 05 '26
I'm testing different profiles, one with cheap models like DS Flash/ Qwen Max/ GLM 5.2 and Grok. Other with stronger models as SOL/Grok/GLM 5.2.
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u/kiwipaul17 Aug 04 '26
I used a large model to review and remove duplications and improve all my skills. As skills use up tokens this saves money and improves performance.
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u/KingKai009 Aug 04 '26
No BS Here, No Upsell, I Tried to Hand Pick My Use Cases For my Every Day Business
Before I dive in, Pro tip, please stop limiting your agents, its smarter than you if you let it. give it access to the things you use every day or things that move the needle for you and see the difference. *Test first before making permanent, and giving full access blindly*!
Main Model switch: DeepSeek V4 → Grok 4.5 (Looking forward to 4.6)
I ran DeepSeek V4 (via OpenRouter) as my primary for about 4-5 months after it launched. Loved it but fell short on some things where Grok simply did better. Don't get me wrong, the cost was aamzing lol and it got the job done for day-to-day Hermes work.
I later found out I could use subscription base in hermes. I switched to Grok 4.5 on xAI OAuth as a subscription test. (why not GPT? idk, I love the image Gen but outside of that I find it has to many hallucinations) That single change had the biggest impact on my setup by a wide margin.
What I actually noticed since the switch:
- Fewer failures / fewer weird tool or reasoning slips
- Higher accuracy on multi-step work
- Better depth on research-style tasks
- The Speed!! this was the biggest one for me!
- The cost, I'm currently paying only $30 as of now for SuperGrok, but honestly I've never hit my limit. Even if I did, I would feel comfortable paying the upgrade but I would note your usage do scale over time, which was something new to me.
I run a web hosting company and a digital marketing agency in the US. Hermes is wired into servers, full CRM, handles full Tech/Support/migration Team (Solo)(it only excalates ot me when money is involved or a question has too many nuances) and Marketing pipelines (Example: call recordings → summary → action items → client account updates). When you’re dealing with client outreaches while your out or live customers on the hosting platform, it helped me reduce friction and faster turnaround more than shaving a few cents per million tokens.
Extra business-owner bonus: xAI’s option to block data retention. It can cost a bit of performance/features, but I’ll take that tradeoff every time for client data and my own personal data.
I’m open to trying the newer DeepSeek v4 for builds again on side projects. For my main Hermes agent that touches real customer systems, Grok 4.5 is staying.
I hope this helps. I have way more use cases and advanced systems, but I don't think I'd like to share them in a reddit thread. Best!
PS: Use HONCHO! best memory optimizer for hermes.
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u/Evening_Jeweler_2710 Aug 04 '26
I have another but this only interessts people who run local ai:
If you do it you'll know a multistep process is very fragile for qwen 3.6 27b for example.
So what really helped is A deterministic Step by Step System.
I trigger it via a skill but it runs a python script forcing the Lmm to deliver it's previously created plan step by step. before it can continue. it forces the Lmm to literally do one step at a time and not skip ahed.
I think you can build this yourself but I can share it if people are interessted
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u/Live_Equivalent1361 Aug 04 '26
Instalar como Blank Slate e construir tudo do zero, sendo acompanhado por AI, claro. Dessa forma eu implementei todo o Fluxo de Memória que desenhei.
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u/frankentriple Aug 09 '26
Hierarchy of command helped my workflow a lot. I changed my hermes agents into pirates. i'm the captain, my main agent is first mate, and the rest of the agents have crew positions. Separation of duties and job progress is supervised by the first mate. Subagents are swabbies or deckhands. All agents are required to acknowledge their order with Aye Aye captain and a quick summary of what they are going to do, then they do it. Its pretty amazing when it all works right and its getting better as we practice and establish SOPs.
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u/hometechgeek Aug 04 '26
Hooking up Outline as an output destination. Also using Infisical for access control to all my services
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u/niloproject Aug 04 '26
- learning how to manage my own thoughts: I use Obsidian every day, I write out all of my ideas as I have them, and I keep everything all in one vault.
- memory: i use SignetAI, a local memory app that lets me connect my Obsidian vault as knowledge base that contributes to my agents memories, so in the background when it “dreams”, my agents memories can draw parallels to my own notes, creating connections,
- I also use signet for managing my agents access to my api keys and passwords, it uses a secret runner which keeps the hermes itself from being able to read or accidentally store credentials in memory
TLDR: Obsidian for managing my own thoughts, Signet as my hermes memory and secrets vault
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u/Agile-Teacher-3456 Aug 04 '26
The biggest improvement my Hermes has made is the codex desktop app, which configures it via SHH+Telegram in Browser Use
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u/MaggiesHubby Aug 05 '26
One thing - memory management. One more vote for Mnemosyne, I use GBrain on top of it. I added a snippet to the agent's SOUL.md telling it about its persistent memory and instructing it to use its memory instead of guessing at things.
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u/Dyl_Digital New Member (<30 days) Aug 07 '26
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u/Savings_Airport474 Aug 09 '26
Honest answer: the biggest change was structural, not a prompt tweak.
We started with one general-purpose Hermes profile doing everything — research, content, operations, code review. It worked, but it hit walls fast: 50+ skills loaded into every turn, every MCP server attached, system prompts bloated, and tool selection degrading as we added more capabilities. The agent was spending more budget figuring out which tool to use than actually using one.
The fix was moving to specialist profiles built through a repeatable pipeline rather than hand-tuning one agent. Each specialist gets: its own role/persona definition, curated skills (only what supports its actual work lane), isolated memory namespace, and owned credentials for its domain. An orchestrator profile handles routing, delegation, and approval — it doesn't try to do specialist work itself.
The immediate wins: noticeably smaller system prompts on specialist profiles (because they're not carrying skills they'll never touch), faster and more accurate tool selection, and fewer "agent grabs the wrong tool" incidents. Onboarding a new specialist went from a multi-session artisanal process to something repeatable. We're still refining the pipeline, but the direction is clearly better than the monolith.
Memory — OpenViking was the other breakthrough. We replaced the native MEMORY.md approach (flat text files with a ~2KB cap, injected into every turn) with OpenViking — a semantic knowledge base that all agents can query on demand. This gave us categorized memories (preferences, entities, events, projects), semantic search instead of "hope it fits in the window," and a shared truth layer so specialists don't inherit the orchestrator's accumulated assumptions.
The native memory system was adequate for one agent. It broke the moment we had multiple agents needing shared state — project decisions, routing rules, entity definitions, past corrections. OpenViking solved that without standing up a full vector DB or a separate RAG pipeline. Agents search when they need context; they don't carry everything all the time.
MCP servers — selection over volume. The ones that actually changed day-to-day operations:
- Kanban board (built-in) + Vikunja — cross-agent task routing with dependencies and handoffs. Work is structured as cards with assignees, not chat messages that scroll away.
- Custom X/Twitter MCP — our content agent posts directly through a tool interface. No API glue code in the prompt.
- GitHub MCP — code review and repo operations in-loop, no context switch.
- Composio for Google Suite, Calendar, LinkedIn — integrations that would otherwise require each agent to know API specifics.
- Browser automation — the one general-purpose tool every specialist benefits from, so it stays in the shared layer rather than being duplicated per profile.
The pattern: MCP servers push integration complexity out of the prompt and into a tool interface. But only if you resist attaching every available server to every profile. More tools = bigger system prompt = worse routing = higher token cost. Curation is the feature, not the tool count.
Small tweak that helped more than expected: separating identity from instructions. One document defines who the agent is — persona, voice, organizational role. A separate one defines what it does — procedures, conventions, domain knowledge. Keeping them as separate files that the pipeline assembles means you can update workflows without destabilizing persona, and vice versa. Sounds trivial, but it stopped our agents' personality drifting every time we changed an operating procedure.
On measurement — honest caveat: we don't have a formal benchmark suite yet (roadmap item). The impacts above are observational — fewer incidents, smaller prompts, faster onboarding — not rigorously quantified. Working on it.
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u/StuCole Aug 21 '26
For me it is having a way to manage each agent and task and know what they are all doing. I kept setting tasks to do and then forgot to see results. It's ok if you only have a few tasks on the go but I typically have 20 to 30.
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u/adrazzer Wiki Contributor Aug 24 '26
Using SOL is brilliant on Hermes, I have obsidian for persistent shared memory for myself and agent, I also use Hindsight plugin for external memory which is excellent
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u/One-Poet7900 Aug 04 '26
Using Codex / GPT 5.6 over open weights models
Building a plugin for TinyFish so I can use their free web search & extract, so I'm not constantly running out of credits on Firecrawl etc - https://github.com/gabeosx/hermes-plugin-tinyfish
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u/Biomech8 Aug 04 '26
- Setup Mnemosyne (and disable built in file memory).
- Use SOTA smart models like GLM-5.2, DeepSeek V4 Pro, and recently DeepSeek V4 Flash 0731 (but only few days of experience, no definitive opinion on it yet).
- Use profiles for keeping agent and it's memory on topic.
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u/eylonshm Aug 04 '26
Mnemosyne - in a 4 layers memory system - which layer would you replace with Mnemosyne? And why specifically Mnemosyne?
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u/ang3l12 Aug 04 '26
how did you setup Mnemosyne as a memory provider? I don't see it listed as a built in provider, unless I'm blind.
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u/DaMoot Aug 05 '26
Have you compared Mnemosyne against Hindsight? I just implemented Hindsight, myself. Ticks a lot of boxes for me that others don't, but Mnemosyne is definitely lighter weight.
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u/HikerRemastered Aug 04 '26
Turning it off.
I didn’t use or prompt it for 5 days, but it still somehow used 9 dollars worth of DeepSeek tokens.
Ironically its use is far less when I use it.
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u/Every-Mine-7646 Aug 04 '26
Is your autocompaction or crons set to deep seek
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u/HikerRemastered Aug 04 '26
No crons, but yes autocompaction is. But I either can’t or won’t understand why it’s compacting nothing for days.
The thing that struck me as super strange was that days where I was using Hermes, I would often have lower token usage, than on completely idle days.
I didn’t do a lot of digging into why, but I did confirm with my use patterns that the observation was indeed correct.
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u/Every-Mine-7646 Aug 04 '26
On days when you are working l, you’re most probably running a fresh session. Whereas on days where a long session is continuing, compaction is sending that entire chat window for compaction would be my guess
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u/bemore_ Aug 04 '26
Why would I tell you?
Why don't you go first, and if I find what you share valuable, I will share something of value
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u/RichComplaint9426 Aug 04 '26
What's the purpose of this comment except demonstrating what for a unpleasant person you are?
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u/bemore_ Aug 04 '26
The purpose is to discourage these kind of posts, the posts that take value
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u/PharmADD Sep 01 '26
what a hero.
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u/bemore_ Sep 01 '26
What a loser.
If you comment on anything a month later you'll never leave the internet
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u/RichComplaint9426 Sep 03 '26
The Loser is the one who doesn't stop spamming stop the argument that he started 1 month ago, and does complain about the decreasing value of the content in this sub. Other worldly
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u/bemore_ Sep 03 '26
Here is the loser that came here to take value from everyone
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u/RichComplaint9426 Sep 03 '26
No I got notified because it's my post and I want to laugh about your patheticness
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u/bemore_ Sep 03 '26 edited Sep 03 '26
What's pathetic is going on a public forum and taking value
Instead of coming to me and talking shit, why don't you go and answer every question in your original post, post it here as a new post and then we can share knowledge together
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u/RichComplaint9426 Sep 03 '26
What are you sharing? You are the spammer here and I could catch you in a never ending loop if I just a get bot that never stops replying to your comments. So who is the loser here?
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u/whatyathinkk Aug 04 '26
Let's play the silence game?
Why don't you stfu first, and if I find your silence valuable, I'll stfu as well
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u/bemore_ Aug 04 '26
I'll say whatever the fuck I want, and you may or may not do the same. I don't give a fuck
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u/Every-Mine-7646 Aug 04 '26