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Description
Actionbook is an innovative open-source platform designed as an “action playbook” that equips AI agents with current manuals and a relevant Document Object Model (DOM) structure, allowing them to interact with any website swiftly and accurately without the need to analyze complete pages or make guesses about element selectors. This efficiency significantly cuts down on execution time and reduces token costs while enhancing the robustness against dynamic or intricate web applications. By offering pre-constructed action manuals with exactly verified selectors, it ensures that agents target the correct elements consistently without occupying extensive context windows to navigate page layouts. Additionally, Actionbook is compatible with contemporary web technologies like virtual DOMs, streaming components, and single-page applications, thereby eliminating the need for outdated scraping methods or fragile automation techniques. The platform's unique methodology can lead to savings of up to 100 times in token consumption, accelerates agent performance, and is adaptable to work seamlessly with any large language model, agent framework, or browser automation tool, empowering developers to incorporate it into their current technology stacks. This versatility not only enhances efficiency but also fosters innovation in how web interactions are managed and automated.
Description
OmniParser serves as an advanced technique for converting user interface screenshots into structured components, which notably improves the accuracy of multimodal models like GPT-4 in executing actions that are properly aligned with specific areas of the interface. This method excels in detecting interactive icons within user interfaces and comprehending the meanings of different elements present in a screenshot, thereby linking intended actions to the appropriate screen locations. To facilitate this process, OmniParser assembles a dataset for interactable icon detection that includes 67,000 distinct screenshot images, each annotated with bounding boxes around interactable icons sourced from DOM trees. Furthermore, it utilizes a set of 7,000 pairs of icons and their descriptions to refine a captioning model tasked with extracting the functional semantics of the identified elements. Comparative assessments on various benchmarks, including SeeClick, Mind2Web, and AITW, reveal that OmniParser surpasses the performance of GPT-4V baselines, demonstrating its effectiveness even when relying solely on screenshot inputs without supplementary context. This advancement not only enhances the interaction capabilities of AI models but also paves the way for more intuitive user experiences across digital interfaces.
API Access
Has API
API Access
Has API
Integrations
Cua
GPT-4
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Actionbook
Founded
2025
Country
United States
Website
actionbook.dev/
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
microsoft.github.io/OmniParser/