How to Build a Hermes Agent AI Code Checker

The proportion of copy-pasted code in repositories rose from 8% in 2020 to 12% in 2024, representing a 48% relative increase that correlates directly with the widespread adoption of AI coding assistants [GitClear 2024 Study]. This volume of unverified, machine-generated code is where this guide lives. When developers accept AI suggestions without review, they risk committing redundant blocks or inconsistent coding patterns.

A Hermes Agent AI code checker is a custom tool or skill configured to scan local codebases for patterns associated with machine-generated files before code commits. Using Nous Research Hermes Agent, an open-source agent framework, developers can automate the verification of files within their local repositories. Unlike hosted code scanning products, this local execution model lets you analyze files without exposing proprietary code to external APIs.

By building a code checker that hooks into the Model Context Protocol, the agent operates directly on the system path. It parses modified files, runs quality checks, and reports findings. This guide outlines how to build a custom code checker with Hermes Agent using the FastMCP framework, configuring it as a local git scanner.

Evaluating a custom local hermes agent vs ai code detector platforms highlights a clear difference in design philosophy. Traditional enterprise code scanners focus on proprietary web dashboards that execute analysis in the cloud after code is pushed. While these dashboards provide visual summaries, they introduce multiple issues for agile engineering teams.

First, cloud-based tools require uploading your codebase to third-party servers, which raises data privacy concerns. Second, they run late in the development cycle, typically after pull requests are created, leading to delayed feedback loops. Finally, their proprietary dashboards cannot easily adapt to custom, repository-specific validation rules.

In contrast, local git scanning automation with Hermes Agent executes checks on the developer's workstation before code is committed. This approach ensures that unverified AI-generated code never leaves the local machine. By combining the agent's LLM context with custom Python scripts, developers can build a tailored scanner that aligns with their internal standards.

The integration process connects a local python entrypoint to the agent using the Model Context Protocol. By defining the python entrypoint as a script that initializes FastMCP and registering it inside the agent's configuration yaml file, Hermes Agent can directly execute Python functions as tools, enabling rapid local code analysis during development. FastMCP allows building new server instances in fewer than 20 lines of Python code, simplifying the integration of custom scripts.

Custom tools map inputs directly to the agent's LLM context window. When the agent invokes a tool, the returned output is appended to the current context, enabling the model to make direct, informed decisions about code quality.

To build the code checker, create a Python script named code_checker.py. This script uses the fastmcp library to define a tool that runs git diff and scans modified files for patterns associated with machine-generated code:

import os
import subprocess
from fastmcp import FastMCP

mcp = FastMCP("GitCodeChecker")

@mcp.tool()
def scan_local_diff(repo_path: str) -> str:
    \"\"\"Scans modified git files for signatures of machine-generated code.\"\"\"
    if not os.path.isdir(repo_path):
        return f"Error: Path '{repo_path}' is not a valid directory."
    
    try:
        cmd = ["git", "-C", repo_path, "diff", "--name-only", "HEAD"]
        result = subprocess.run(cmd, capture_output=True, text=True, check=True)
        files = result.stdout.strip().splitlines()
        
        if not files or files == [""]:
            return "Scan complete. No modified files detected in the repository."
        
        flagged_files = []
        ai_signatures = [
            "Generated by", 
            "Copilot", 
            "written by AI", 
            "Nous Research", 
            "Claude Code"
        ]
        
        for file in files:
            file_path = os.path.join(repo_path, file)
            if os.path.exists(file_path) and os.path.isfile(file_path):
                with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
                    content = f.read()
                    found_patterns = [sig for sig in ai_signatures if sig.lower() in content.lower()]
                    if found_patterns:
                        flagged_files.append(f"- {file} (Found: {', '.join(found_patterns)})")
        
        if not flagged_files:
            return "Scan complete. No obvious machine-generated file patterns identified."
        
        return "Flagged Files:" + chr(10) + chr(10).join(flagged_files)
    except Exception as e:
        return f"Error executing git check: {str(e)}"

if __name__ == "__main__":
    mcp.run()

This script exposes a single tool to the Model Context Protocol. By running git commands locally, the agent can examine the output of the diff file, mapping the result back to its prompt context for further validation.

With the FastMCP script written, the next step is registering the server inside the Nous Research Hermes Agent configuration. The agent reads configuration settings from a local YAML file located at ~/.hermes/config.yaml.

Open the configuration file and locate or create the mcp_servers block. Add the new code checker server configuration:

mcp_servers:
  git_code_checker:
    command: "python"
    args: ["/absolute/path/to/code_checker.py"]
    enabled: true

Replace the args path with the absolute path to your Python file. When Hermes Agent starts, it connects to the stdio transport of the FastMCP server, discovers the scan_local_diff tool, and adds it to the active tool catalog.

Developers can now issue natural language instructions to run the checker. For example, asking the agent to "check my current changes for AI signatures" causes the agent to locate the repository path, execute the tool, and print the resulting analysis directly in the terminal interface.

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Running local code checker scripts is useful for individual developer workflow loops, but scaling this process to a distributed development team requires a unified storage and collaboration layer. If reports remain local, other team members cannot track repo hygiene.

Developers have historically used basic storage buckets or traditional cloud drives to store report files.

Local Storage Keeping report files on your local drive is fast, but it isolates the data. Separate running processes or other team members cannot access the findings without manual sharing.

Amazon S3 Cloud object stores offer a centralized repository, but they require writing custom API integrations. Teams must manage IAM policies and security keys, adding development overhead.

Fast.io Workspaces To avoid complex API integrations while keeping reports accessible, developers can use Fast.io. Fast.io functions as an intelligent cloud workspace designed for human-agent collaboration.

By connecting your agent to a shared org-owned workspace, Hermes Agent can upload its scan reports directly to a centralized folder. Fast.io supports MCP-native access via Streamable HTTP at /mcp or legacy Server-Sent Events at /sse. By registering the Fast.io MCP server in your config, the agent can write scan reports directly to the cloud. You can find detail on the toolset at mcp.fast.io/skill.md.

Every file in the workspace receives per-file version history, ensuring that as reports update, older records are preserved. Fast.io maintains an append-only audit log, creating an immutable history of repository checks. Additionally, if the agent produces reports as PDFs or text logs, you can use Metadata Views (/product/document-data-extraction/) to turn the document directory into a queryable database.

Metadata Views allow you to describe fields (such as repository name, scan date, and clean status) in natural language. The AI designs a typed schema (supporting Text, Integer, and Boolean formats) and populates a filterable spreadsheet, letting teams track codebase compliance over time. For more information on agent-specific storage, view our storage for agents page.

Beyond storing reports, the combination of Hermes Agent and Fast.io enables automated workflows. The agent can monitor local directories using webhooks, which trigger when files are modified in a Fast.io workspace.

Because Hermes Agent supports messaging channels like Discord or Telegram, you can configure the agent to send alerts whenever a code check flags an issue. If a scan returns a list of files with unverified AI patterns, the agent can draft a detailed review report and post it to a team channel.

When the agent identifies an issue that it cannot resolve autonomously, it can perform an ownership transfer. This process shifts administrative control of the workspace or target folder to a human developer, alerting them that manual intervention is required. Once the human reviews and corrects the flagged code, they can hand control back to the agent to continue background tasks.

Managing these operations requires a paid organization account. Fast.io offers plans tailored to different needs on our pricing page. The Starter plan is $29/mo, and the Business plan is $99/mo. Larger teams can choose the Growth plan at $299/mo. Every organization starts with a 14-day free trial that requires a credit card to activate. The setup process allows an agent to register a free user account and build the workspace, before handing off administrative ownership to a human team member. This handoff transfers billing control while letting the agent retain access to continue its background tasks.