r/hermesagent

I have been going back and forth with it this morning. Anyone else ever get this kind of response?

Is there any subscription or api pricing that could beat minimax token plan? I've been using the 20$ subscription, 1,82B tokens in 15 days, and it hasn't reached its limits yet. Yesterday it hit the 5 hours limits and I had to wait 4 minutes to reset, every calculation I do with deepseek , open router, or any other models, doest come close to this. Even mimo plan, it hit its limits within 3 days of use. Btw I'm using m3, not M2.7. it's really smart, fast, reliable. Have you guys found any other option where almost 2B tokens would cost 20$ with the quality of M3?

I previously used Claude Desktop to plan my vacation and used it to maintain a detailed intineray document in Google Docs. I have to set up an MCP server to use Google Docs; I did not use the standard one.

I am now trying to switch to Hermes. I am using Google Codex with GPT-5.5 as the backend. First, I tried standard Google Drive integration, and the results were pretty dismal. It lost formatting in the document, and most edits were appended to the end. I tried to switch to this MCP https://github.com/a-bonus/google-docs-mcp for Google Docs editing (same as I've used with Claude Desktop), which worked slightly better but still.

So the question is: how to get good Google Docs editing support in Hermes? Is it the model, MCP, or harness problem? What do people use?

Am I the only one who prefers coding with V4 Flash over V4 Pro? Flash is way faster and I can get more iterations in a shorter period of time, and the result is better after a few iterations with Flash than it is with one run of Pro. Also, because of the fast feedback loop, coding feels more rewarding, like driving a sports car compared to a grocery getter.

For really complex and heavy work, I'll switch to Pro. But for general coding and website building, I don't default to V4 Pro. I build with Flash first and wait until I run into a problem where I need more intelligence and reasoning.

Does anyone else do the same, or have anything to add to that?

Post Content:

Running a persistent agent like Hermes Agent for heavy-duty tasks (long multi-turn debugging, file search, codebase exploration) can easily burn through API credits on Anthropic or OpenAI.

To solve this, I’ve built and open-sourced two reverse proxies that bridge web-app/flat-rate sessions into standard, OpenAI/Anthropic-compatible endpoints that work seamlessly in Hermes:

1. 🛠️ chatgptproxy: An OpenAI-compatible reverse proxy with built-in account pooling, session rotation, and tool-calling/function translation.
2. 🚀 CursorProxyFleet: A fleet manager designed to expose Cursor’s models as a standard endpoint, handling token limits and routing on the fly.

Both projects are 100% free and open-source.

---

How it works under the hood

* Dual Compatibility: Serves standard /v1/chat/completions (OpenAI) and /v1/messages (Anthropic) endpoints.
* Account Pooling & Rotation: Handles multiple account cookies, rotating requests automatically to bypass rate limits.
* Tool-Calling Translation: Translates Hermes’ native tool execution schema back and forth without breaking the agent loops.
* SSE Streaming: Native Server-Sent Events (SSE) support for smooth real-time response generation.

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Repositories

If you want to test it locally or contribute:
* Cursor Proxy Fleet: github.com/acruz6421-bot/CursorProxyFleet
* ChatGPT Proxy: github.com/acruz6421-bot/chatgptproxy

Disclaimer: These are self-hosted tools built strictly for personal research and cost mitigation on local agent testing.

I’d love to hear your thoughts, test cases, or help if anyone runs into configuration issues!

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Hermes Integration Guide

Integrating it with Hermes Agent is straightforward. Once you have the proxy running locally (by default, chatgptproxy runs on port 3535), you just add it as a custom provider in your ~/.hermes/config.yaml:

providers:
  custom:
    chatgptproxy:
      api_base: "http://localhost:3535/v1"
      api_key: "any-dummy-key" # Handled by the local pool config
      models:
        - gpt-4o
        - claude-3-5-sonnet

# Set it as your active model
model:
  provider: "custom:chatgptproxy"
  model: "claude-3-5-sonnet"
  temperature: 0.2
  max_tokens: 32768 # Cap for web backends

note: running on a mac mini m4 16gb ram.

I get twenty plus emails a day. Newsletters, invoices, shipping confirmations, phishing attempts, client inquiries, server alerts. Most of it is noise. Some of it is critical. A small percentage is dangerous.

I wanted my Hermes agent to read my emails and draft replies. But I did not want Hermes to have access to everything. Specifically not 2FA codes, password resets, or banking notifications. If my agent ever got compromised, those should never be within reach.

Gmail filters check the sender, not the content. They can't tell the difference between a legitimate password notification from GitHub and a fake one from a phishing domain. And forwarding everything to a cloud AI means your password resets and 2FA codes end up in someone else's chat history. That was not an option. So everything runs locally.

The Core Idea: Two macOS Accounts

Hermes runs as a standard user. The email gatekeeper runs as an admin user. They communicate through a shared directory with read-only access. Hermes can read filtered summaries and create drafts. It can never access IMAP credentials, raw emails before filtering, or the .env file with passwords.

If Hermes ever got compromised, an attacker would find filtered summaries and the ability to search. But not the login credentials. Not the raw inbox. Not the 2FA emails. The separation is enforced by the operating system. No configuration can bypass it.

How It Works

Every 60 seconds, a Python script checks all mailboxes via IMAP. It grabs the newest unread emails. But here is the important part. It reads them with BODY.PEEK, which means they stay marked as unread in your inbox. Nothing changes on the server side.

First, every email goes through a data leak filter. Regex patterns catch passwords, IBANs, credit card numbers, and 2FA codes before anything else sees them. Those get redacted immediately.

Then the cleaned version goes to a local Ollama instance running llama3.1:8b. The model decides. Is this safe to forward? Is it spam? Is it a phishing attempt? It returns a verdict in strict JSON format.

If Ollama is down, every email gets blocked. If the model returns something unexpected, every email gets blocked. If the script crashes, a lock file prevents restarts for five minutes. Fail-closed, not fail-open. A false positive is annoying. A false negative is a data leak.

What Gets Through

Invoices, newsletters, server alerts, client inquiries, tracking updates, support tickets, appointment reminders, GitHub notifications, package deliveries. The ordinary noise of running a small business.

What Gets Blocked

Phishing attempts, password reset emails, 2FA codes, bank transfer notifications containing IBANs, emails applying pressure to act fast, requests to change email forwarding settings, logins from unknown devices. Anything that could be used against you if the system were compromised.

Supported Providers

Yahoo, Gmail, iCloud. I run all three through the same pipeline. The gatekeeper checks them in sequence, applies the same filters, sends the same verdict structure to the shared directory. Hermes never knows which mailbox an email came from. Only that it survived the filter.

What I Learned

Email gatekeeping needs to be paranoid by design. Not because someone is targeting you specifically. But because automation changes the risk profile. When you give an agent access to your inbox, the blast radius of a compromise is your entire digital life. The only way to sleep well is to architect that radius as small as possible before the agent ever sees a single email.

The two-account separation was the key insight. It costs nothing. macOS already supports it. It requires no additional infrastructure. And it is provably secure. You can test that user A genuinely cannot read files owned by user B. Everything else, the LLM judge, the regex filter, the fail-closed pipeline, is just implementation detail. The architecture is what makes it safe.

note: running on a mac mini m4 16gb ram.

I've come across this thing called "agent-office" where the agents are made into pixel workers in an office, and you can see then work through the office graphics (https://github.com/harishkotra/agent-office).

It got my attention since it's fun and intuitive, and it gives you the "head of operations" vibe a bit more clearly than just multiple CLIs or delegating to subagents.

I should really just use it before asking here, but I wanted to see if this was something people in the community actually engaged in.

If anyone uses this, is it worth the computation, and does it really help your work? Or is it just for fun?

Today I saw this in StepFun’s blog for their Step 3.7 Flash model. Running the model with CC performed better results vs Hermes. Curious why?

Update:
I believe they used older versions to display the results somehow. Later, I came across a comparison of OpenCode and ClaudeCode in artificialanalysis.ai, where OpenCode performed better in certain tests.
Check here.