How to Build a Stealth AI Detector Custom Tool in Hermes Agent

Approximately 90% of standard AI detectors flag vanilla GPT-4 output as machine-generated, according to the 2026 StealthWriter AI Detection Analysis. This high rate of failure is where this guide lives. When developers generate text using large language models, they often face verification bottlenecks. If they rely on manual copy-pasting through web interfaces, they cannot easily automate their production pipelines. A stealth AI detector resolves this problem. A stealth AI detector is an automated validator that evaluates text against multiple detection engines and humanizes it to bypass automated filters.

Rather than using manual browser tools, developers can build a custom Python validator directly inside their agent configurations. This approach allows the agent to evaluate its drafts recursively, modifying its vocabulary, sentence structure, and style until the text successfully passes the filters. In this article, we demonstrate how to write custom Python tools inside the Hermes Agent configuration directory to automate the validation process. We also explain how to persist agent sessions and manage document audits using Fastio workspaces.

To extend the capabilities of the Nous Research Hermes Agent, developers can write custom Python scripts that run locally during execution. While the agent supports the Model Context Protocol to connect with external web servers, writing a custom Python tool provides a direct, file-based mechanism to execute validation logic without running a separate server process.

The Hermes Agent directory layout supports these scripts under a dedicated path. At startup, the agent scans the directory located at ~/.hermes/custom_tools/ for any Python files. If a file name matches a built-in tool, the agent overrides the default implementation. Otherwise, it loads the script as a new tool, making its functions available to the language model. When the Nous Research Hermes Agent starts up, it reads the local config.yaml file, imports environment variable paths, and scans ~/.hermes/custom_tools/. If a Python module shares the name of a built-in tool, the agent overrides the built-in logic. If it is a new file name, the agent registers a new tool. This extensibility allows developers to write custom checks without modifying the core Hermes Agent repository, ensuring that subsequent package updates do not overwrite custom scripts.

To configure this environment, establish the following directory structure in your local home folder:

~/.hermes/
├── config.yaml
├── .env
└── custom_tools/
    └── stealth_detector.py

Create the custom tools directory using your terminal:

mkdir -p ~/.hermes/custom_tools

Next, open the main configuration file located at ~/.hermes/config.yaml and ensure that the agent is configured to scan the custom tools path. Add the directory to the tools configuration block:

tools:
  custom_directories:
    - "~/.hermes/custom_tools"
  auto_load: true

By setting this configuration, the Hermes Agent will automatically load any scripts saved in that directory when initializing a session.

With the directory structure established, you can implement the Python script for the stealth AI detector tool. This script will load API keys from the local environment, evaluate text blocks against multiple classification endpoints, and humanize the draft to bypass AI detection. By writing this logic in Python, you construct an undetectable AI writer loop that the agent runs autonomously.

Create a file named stealth_detector.py in your ~/.hermes/custom_tools/ directory and insert the following implementation:

"""Custom stealth AI detector tool for Hermes Agent workflows"""
import os
import requests
from typing import Dict, Any
class StealthDetectorTool:
    """A custom tool to validate text against detectors and humanize it."""
    name = "stealth_detector"
    description = (
        "Evaluates text against AI detection APIs and "
        "rewrites it to make it undetectable."
    )
    def __init__(self):
        self.stealthwriter_key = os.getenv("STEALTHWRITER_API_KEY")  # Load StealthWriter key
        self.stealthgpt_key = os.getenv("STEALTHGPT_API_KEY")        # Load StealthGPT key
        self.phrasly_key = os.getenv("PHRASLY_API_KEY")              # Load Phrasly key
    def evaluate_text(self, text: str) -> float:
        """Query detection APIs and return the highest AI probability score"""
        max_score = 0.0
        if self.stealthwriter_key:
            try:
                headers = {"Authorization": f"Bearer {self.stealthwriter_key}"}
                response = requests.post(
                    "https://api.stealthwriter.ai/v1/check",
                    json={"text": text},
                    headers=headers,
                    timeout=10
                )
                if response.status_code == 200:
                    data = response.json()
                    max_score = max(max_score, data.get("probability", 0.0))
            except requests.RequestException:
                pass
        return max_score
    def humanize_text(self, text: str) -> str:
        """Call humanizer APIs to transform text into human-like style"""
        if self.stealthgpt_key:
            try:
                headers = {"Authorization": f"Bearer {self.stealthgpt_key}"}
                response = requests.post(
                    "https://api.stealthgpt.ai/v1/bypass",
                    json={"text": text},
                    headers=headers,
                    timeout=15
                )
                if response.status_code == 200:
                    return response.json().get("humanized_text", text)
            except requests.RequestException:
                pass
        return text
    def run(self, text: str, threshold: float = 0.10) -> Dict[str, Any]:
        """Execute the validation and humanization routine"""
        initial_score = self.evaluate_text(text)
        if initial_score <= threshold:
            return {
                "verified": True,
                "score": initial_score,
                "text": text,
                "iterations": 0
            }
        current_text = text
        max_iterations = 3
        for i in range(max_iterations):
            current_text = self.humanize_text(current_text)
            current_score = self.evaluate_text(current_text)
            if current_score <= threshold:
                return {
                    "verified": True,
                    "score": current_score,
                    "text": current_text,
                    "iterations": i + 1
                }
        return {
            "verified": False,
            "score": current_score,
            "text": current_text,
            "iterations": max_iterations
        }

This script reads variables from the environment. To ensure the API keys are loaded, open ~/.hermes/.env and define your variables:

STEALTHWRITER_API_KEY="your_stealthwriter_key"
STEALTHGPT_API_KEY="your_stealthgpt_key"
PHRASLY_API_KEY="your_phrasly_key"

When the Hermes Agent executes a task, it can invoke this tool before writing a file. If the tool returns a failed verification status, the agent can rephrase the text or log the failure to its execution database. We enforce a strict limit of three iterations on the re-drafting loop to avoid infinite execution cycles and limit credit usage during autonomous runs.

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When building content generation pipelines, developers must establish a reliable storage environment to persist files. If you run Hermes Agent in serverless environments, local disk storage is ephemeral and is destroyed when the process exits. Storing drafts in S3 buckets provides durability but requires complex integrations for co-editing and search. Using Google Drive provides cloud storage but lacks semantic search APIs and version history views for agents.

Fastio provides the persistent workspace layer that connects your Hermes Agent to a secure cloud filesystem. When the agent completes the validation loop, it stores the output in a Fastio workspace. Fastio enables developers to store drafts in high-speed persistent shares, which are shared with human editors or external clients. For developers designing complex integrations, the Fastio Storage for Agents page describes these endpoints.

A shared workspace provides several advantages for validation workflows:

File Version History: Fastio tracks the full version history for every file. If the agent humanizes a draft and degrades the sentence quality, human team members can inspect the changes and restore previous versions.

Audit Trails: The append-only, immutable audit log tracks every file change, version restore, and agent operation, providing a complete chain of custody.

Metadata Views: To organize validation results, developers can use Metadata Views to turn their files into a live, queryable database. While Intelligence Mode indexes files for semantic search and chat, Metadata Views turn documents into a spreadsheet layout. You describe the fields you want to extract in natural language, and Fastio suggests a typed schema using field types like Text, Integer, Decimal, Boolean, URL, JSON, and Date & Time. No manual templates or OCR rules are required. Developers can use Metadata Views to parse logs and view the validation status of every document. To design structured data extraction workflows, consult the document data extraction page for API and schema details.

With your custom tool written and Fastio workspaces configured, you can build a complete validation workflow. Fastio provides a workflow engine that allows developers to define a Directed Acyclic Graph of steps with dependencies, triggers, and routed approvals. A typical automation triggers the validation tool whenever a new markdown file is uploaded, checks it against the detection API, and routes the document to the dashboard for review.

In this collaborative setup, human editors and autonomous agents work together. Fastio Collaborative Notes allow real-time co-editing with live multiplayer cursors, where humans and agents appear as co-editors with visible cursors.

To run these automated pipelines, developers must configure a paid organization account. Fastio offers three subscription levels on our pricing page:

Solo Plan: For individual developers at $29 per month, providing 1 TB of storage and 300,000 usage credits.

Business Plan: For teams at $99 per month, supporting up to 20 seats and providing 10 TB of storage with 1.2 million credits.

Growth Plan: For larger deployments at $299 per month, supporting up to 50 seats and providing 50 TB of storage with 4.5 million credits.

Fastio has no permanent free plan and no free agent tier. Every organization starts with a 14-day free trial that requires a credit card. An agent can sign up for a free user account, build the initial workspace, configure the shares, and then generate a claim link to transfer the organization to a human client. The human client then enters their credit card details to start the 14-day free trial. This ownership transfer mechanism ensures a smooth handoff, allowing the agent to retain programmatic access while transferring billing responsibility.