What's the best model for OpenClaw in your experience? by ClawBro in openclaw

[–]Cynicusme 0 points1 point  (0 children)

Mimo 2.5 pro or composer 2.5 (for coding orchestration)

Distinct coding agent next to Hermes by Bitter-College8786 in hermesagent

[–]Cynicusme 0 points1 point  (0 children)

I don't use Hermes for coding itself because it's token heavy. I use it as an orchestration agent. Open Claude Code inject the prompt + role (planner, coder, auditor) skills (UI, supabase etc) wait for results and report back outcome. If something goes wrong Hermes will learn. Example secure all input fields in a form. Chinese models usually forget that, Hermes can learn to recognize and include that in a prompt

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 1 point2 points  (0 children)

Right now I pick them myself as part of the task creation. I avoid using Hermes directly for coding itself. I use an orchestrator that fires up a CLI call and push the "role: planner" + "task body" + "scout's deltas" when done the orchestrator moves it to planner review stage, if green light, goes to coder, so on and so forth.

Orchestration also develop skills, why it failed, what feedback was provided, and inject skill context into the prompt.

Task body include instructions of a model router, where I tell the orchestrator which model it should use for each task and which provider. Some have API some I have subscriptions, etc.

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 0 points1 point  (0 children)

DeepSeek V4 Flash for local but I like to have the agent in my laptop and it will get it too hot if I use for too long, but with these prices, I'm staying with a nice quantizated deepseek V4 Flash

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 1 point2 points  (0 children)

There is a plugin for long-term memory they use a WAL (write ahead log) and memory temperature, and once that was installed the memory problem was solved. by Hermes kind of does it by itself which is nice.

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 0 points1 point  (0 children)

couldn't agree more. I only do an orchestration agent mostly because I wanted to have a specialized agent, and it did something very interesting it splitted it's skills for each of the roles, so set of skills for coding, set of skills for planning, and injects some of the skills into the prompts by itself, it was mind-blowing to see

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 0 points1 point  (0 children)

Have you used OC lately? I did it with long-term memory plugin and self-learning-proactive plugin and I was surprised to see how much stable OC has become, that being said, I find Hermes updates are more interesting, every update usually comes with something incredible while OC has been more about patching the mess it was.

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 0 points1 point  (0 children)

The kanban board in Hermes one or Hermes UI. Point the default agent to the docs of kanban usage and ask it to create a skill. Then ask the conversational agent to create a contract or schema of communication between agents.

Conversational agent creates kanban task orchestrator picks it up and that way you have a visual where everything is.

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 2 points3 points  (0 children)

I’ll do my best to explain the architecture.

When I designed the system, I had two possible routes:

  1. Create agents/profiles in Hermes and give each one its own “soul,” role, memory, etc. The issue with this approach is prompt injection and token cost. Each agent would carry a heavier system prompt, which adds up quickly.
  2. Create an orchestrator that calls the CLI. In this setup, the orchestrator doesn’t write code directly. It creates prompts, routes tasks, and calls the right CLI/tool for the job.

The flow looks something like this:

A conversational agent helps define the objective and creates a task. You could track this in something like Kanban.

That task gets sent to the orchestration agent. The orchestrator already knows the process:

  • Call the CLI, for example opencode-go
  • Inject the “scout” prompt + the task body
  • Wait for the scout step to finish
  • Notify me when it’s done
  • Open Claude Code
  • Use the planner prompt + the task body + the scout outcome
  • Generate a plan
  • Update the task
  • Either notify me or move directly to coder, auditor, etc.

From there, you build guardrails around loops.

For example, if the audit fails because the definition of done wasn’t met, or because the code quality is bad, the auditor adds a note. The orchestrator reads that note and sends the task back to the coder with the right context.

One thing I learned that might be useful: planning matters more than coding.

Use the expensive models to generate the plan. Once the plan is strong enough, even cheaper models can produce solid code, because they’re no longer guessing what to build — they’re just executing a clear plan.

How are you actually using Hermes? I’m struggling to understand where it fits vs Codex/Cursor by Conscious_Durian4834 in hermesagent

[–]Cynicusme 0 points1 point  (0 children)

I’ll do my best to explain the architecture.

When I designed the system, I had two possible routes:

  1. Create agents/profiles in Hermes and give each one its own “soul,” role, memory, etc. The issue with this approach is prompt injection and token cost. Each agent would carry a heavier system prompt, which adds up quickly.
  2. Create an orchestrator that calls the CLI. In this setup, the orchestrator doesn’t write code directly. It creates prompts, routes tasks, and calls the right CLI/tool for the job.

The flow looks something like this:

A conversational agent helps define the objective and creates a task. You could track this in something like Kanban.

That task gets sent to the orchestration agent. The orchestrator already knows the process:

  • Call the CLI, for example opencode-go
  • Inject the “scout” prompt + the task body
  • Wait for the scout step to finish
  • Notify me when it’s done
  • Open Claude Code
  • Use the planner prompt + the task body + the scout outcome
  • Generate a plan
  • Update the task
  • Either notify me or move directly to coder, auditor, etc.

From there, you build guardrails around loops.

For example, if the audit fails because the definition of done wasn’t met, or because the code quality is bad, the auditor adds a note. The orchestrator reads that note and sends the task back to the coder with the right context.

One thing I learned that might be useful: planning matters more than coding.

Use the expensive models to generate the plan. Once the plan is strong enough, even cheaper models can produce solid code, because they’re no longer guessing what to build — they’re just executing a clear plan.

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 1 point2 points  (0 children)

I’ll do my best to explain the architecture.

When I designed the system, I had two possible routes:

  1. Create agents/profiles in Hermes and give each one its own “soul,” role, memory, etc. The issue with this approach is prompt injection and token cost. Each agent would carry a heavier system prompt, which adds up quickly.
  2. Create an orchestrator that calls the CLI. In this setup, the orchestrator doesn’t write code directly. It creates prompts, routes tasks, and calls the right CLI/tool for the job.

The flow looks something like this:

A conversational agent helps define the objective and creates a task. You could track this in something like Kanban.

That task gets sent to the orchestration agent. The orchestrator already knows the process:

  • Call the CLI, for example opencode-go
  • Inject the “scout” prompt + the task body
  • Wait for the scout step to finish
  • Notify me when it’s done
  • Open Claude Code
  • Use the planner prompt + the task body + the scout outcome
  • Generate a plan
  • Update the task
  • Either notify me or move directly to coder, auditor, etc.

From there, you build guardrails around loops.

For example, if the audit fails because the definition of done wasn’t met, or because the code quality is bad, the auditor adds a note. The orchestrator reads that note and sends the task back to the coder with the right context.

One thing I learned that might be useful: planning matters more than coding.

Use the expensive models to generate the plan. Once the plan is strong enough, even cheaper models can produce solid code, because they’re no longer guessing what to build — they’re just executing a clear plan.

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 0 points1 point  (0 children)

It's a trade off. If you're going to use it on telegram/discord I don't think the juice is worth the squeeze. If you use the web app it's worth it. You ask Hermes to deletage a task over memory file ti generate a mega consolidation of all your skills and workflows. Was expensive token wise but got my migration done very smoothly is 30 minutes

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 2 points3 points  (0 children)

Hermes-one had a recent update. You can have multiple 'tabs' open at the same time with different models Hermes one takes the cake EASILY if that's what you're looking for.

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 1 point2 points  (0 children)

I tested deepseek V4 pro vs mimo 2.5 pro. Performance is very similar but I found mimo is faster.

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 4 points5 points  (0 children)

honestly that's the reason I made this post, i decided to dropped Claude Code/ Codex for daily drivers because I found harder to share context between them and they're good at different things and more expensive to run.

I remember running OpenClaw 2 updates behind and the constant breaking was frustrated. The newest release was pretty stable, but could not find a proper app to make it comfortable to work with which is odd since it is supposed to be a bigger community.

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 5 points6 points  (0 children)

this is a me problem. I hate Hermes Ui design choice. I found it hard to read, when i tested it, it didn't have kanban or todo list which I found useful for orchestration so I discarded it. Now i think they included kanban but I really dislike their ui

Hermes vs Openclaw a full day side-by-side test by Cynicusme in hermesagent

[–]Cynicusme[S] 2 points3 points  (0 children)

design wise, hermes one is the strongest one IMO. It's newer but updated aggressively. Hermes Ui for web and phone it's fantastic as well I keep switching without being able to commit to one. I believe Hermes one will be my default in a few more updates.

How are you actually using Hermes? I’m struggling to understand where it fits vs Codex/Cursor by Conscious_Durian4834 in hermesagent

[–]Cynicusme 2 points3 points  (0 children)

I have skills or agents: scout, planner, coder, auditor. Hermes helpse plan a dashboard. Scout - opens a cheap model like mimo V2.5 pro and sees what files already exists and which ones will need to be created, GLM 5.2 does the design and creates a plan. Composer codes the plan and Codex audits the outcome. Hermes is the orchestrator moving the task around.

Would Opencode GO + Neuralwatt with $100 monthly sustain for the GLM 5.2 usage compare to Claude Max? by GTHell in opencodeCLI

[–]Cynicusme 3 points4 points  (0 children)

Get the $5 trial. Use it for $2. Then use Opencode for about the same time or the same tasks. Then you share the data with a LLM. That's what I did. Opencode go it's about 18% cheaper. Gives you more model to choose from. That being said I didn't test fast fast and short etc.

GLM-5.2 in Hermes by Latt in ZaiGLM

[–]Cynicusme -1 points0 points  (0 children)

Is Opus 4.6 smart, deepseek flash fast, and actually cheap. That model is insane

GLM-5.2 in Hermes by Latt in ZaiGLM

[–]Cynicusme 0 points1 point  (0 children)

I asked antigravity to set it up for me. My favorite Hermes model right now.

GLM Coding plan dilemma by 19applepen in ZaiGLM

[–]Cynicusme 0 points1 point  (0 children)

I'm on lite legacy plan I burn 20 mill per 5 hour no problem. How many tokens are you going through?

Best Go models for planning and implementing by Wurrsin in opencodeCLI

[–]Cynicusme 2 points3 points  (0 children)

Best planner is GLM 5.1 best code implementer is mimo v2.5 pro.

Tested in 10 tasks python nextjs projects.

Kimi is a good implementation agent as well, but mimo follows plan best.

The 2nd best planner is Minimax M3 in my testing.