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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

HOL Guard is a security layer designed for AI agents that operates on a local-first basis, monitoring the actions of an AI assistant and preemptively preventing potentially harmful activities. It functions as an intermediary between the agent and the computer, assessing tool calls and local resources for various threats, including the risk of secret and credential leaks, harmful commands, actions driven by prompt injection, and the use of compromised or altered packages, as well as risky configurations and unsafe plugins, skills, hooks, and settings. Threats that are identified can be automatically blocked, while uncertain actions are temporarily halted to seek user consent, ensuring that individuals maintain oversight. Operating entirely on the developer’s local machine, Guard does not require an internet connection and refrains from uploading any files, prompts, or sensitive information. Local evaluations are typically completed in less than 50 milliseconds, and the implementation of Guard does not necessitate modifications to current code or workflows. It is compatible with various coding agents including Claude Code, Cursor, Codex, Gemini CLI, OpenCode, Hermes, and OpenClaw, providing custom integrations that analyze actions prior to their execution. Additionally, this enhances the overall safety and reliability of AI interactions, fostering greater trust in automated processes.

Description

Manifest, developed by Omfang, serves as an action layer for AI agents, transforming any webpage into a structured representation that delineates the capabilities available to an agent. By submitting a URL to the Manifest API, developers obtain a clean JSON output that details the state of the page, encompassing available buttons, forms, inputs, navigation links, mandatory fields, and interdependencies among actions. Manifest operates by loading the webpage in a genuine Chromium browser after executing JavaScript, examining the accessibility tree utilized by screen readers, and cross-referencing it with the underlying DOM to capture intricate details often overlooked by the accessibility layer, such as input types, placeholders, disabled states, and submission prerequisites. Once potential actions are identified, their locators are resolved with precision instead of relying on model-generated guesses; any ambiguous matches are flagged as uncertain rather than assumed. This process provides agents with a machine-readable framework that enhances their reasoning capabilities regarding interactions like clicking, filling, or submitting without depending on screenshots or fragile CSS selectors. Ultimately, Manifest elevates the efficiency and reliability of AI agents in navigating and interacting with web content.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Antigravity CLI Yes 
CSS No 
Chromium No 
Claude Code Yes 
Cursor Yes 
Gemini CLI Yes 
GitHub Yes 
GitHub Copilot CLI Yes 
Grok Yes 
Hermes Agent Yes 
JSON No 
Kimi Yes 
Model Context Protocol (MCP) Yes 
OpenAI Codex Yes 
OpenClaw Yes 
OpenCode Yes 
Pi Agent Yes 
Slack Yes 
ZCode Yes 

Integrations

Antigravity CLI No 
CSS Yes 
Chromium Yes 
Claude Code No 
Cursor No 
Gemini CLI No 
GitHub No 
GitHub Copilot CLI No 
Grok No 
Hermes Agent No 
JSON Yes 
Kimi No 
Model Context Protocol (MCP) No 
OpenAI Codex No 
OpenClaw No 
OpenCode No 
Pi Agent No 
Slack No 
ZCode No 

Pricing Details

$4.99 per month
Free Trial No 
Free Version Yes 

Pricing Details

$29 per month
Free Trial No 
Free Version Yes 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

HOL

Founded

2024

Country

United States

Website

hol.org/guard

Vendor Details

Company Name

Omfang AB

Founded

2026

Country

United States

Website

omfang.io

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Product Features

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