LaVague is an open-source framework that enables the development of AI-powered web agents capable of executing complex tasks. With its two core components - the world model and the action engine - LaVague allows you to create agents that can process objectives, understand the current state of a web page, generate instructions, and translate them into executable actions.

One of the standout features of LaVague is its ability to automate job applications. The framework, combined with Hugging Face's models, can perform optical character recognition (OCR) on a resume, extract the relevant information, and automatically fill out job application forms. This streamlines the application process, allowing you to apply to multiple positions with ease.

Beyond job applications, LaVague can be used to develop a wide range of web agents. For example, you can create agents that can navigate and retrieve information from Notion workspaces, or agents that can perform data entry tasks by extracting information from invoices and automatically filling out forms.

To get started with LaVague, you can simply install it using the pip command. The framework's documentation provides detailed guidance on customizing content, working with large language models, and integrating third-party tools. With its growing capabilities and the ongoing development of a dedicated data set, LaVague is poised to become an increasingly powerful tool for automating complex web tasks.

LaVague is an open-source framework that empowers developers to create powerful AI web agents with ease. With its recent upgrades, LaVague now offers the ability to build agents that can automatically apply for jobs using the PNG of your resume.

The framework's core components include a world model that translates objectives and the current web page state into instructions, and an action engine that compiles these instructions into executable code. This allows the agents to perform complex tasks, such as optical character recognition on resumes and filling out job application forms.

Beyond job applications, LaVague can be used to develop a wide range of web agents. For example, you can create agents that can navigate and retrieve information from Notion workspaces, or agents that can automate data entry tasks by extracting information from invoices and filling out forms.

To get started with LaVague, simply install it using the provided pip command. Then, you can build your own agents by following the detailed documentation and tutorials provided by the LaVague team. With its powerful capabilities and user-friendly interface, LaVague makes it easy for developers to harness the power of AI and automate a variety of web-based tasks.

LaVague, an open-source large action model framework, has recently introduced a powerful feature that can revolutionize your job application process. With its latest upgrade, LaVague now allows you to build an AI agent that can automatically apply for jobs using the PNG of your resume.

This agent is capable of performing optical character recognition (OCR) on your resume, extracting the relevant information, and then filling out job application forms with the extracted data. This means you can now apply to a large batch of jobs with minimal effort, as the agent will handle the tedious task of form-filling for you.

The code for this feature has been updated and is now accessible on Google Colab, making it easy for you to try it out. Simply follow the instructions in the description below to get started.

LaVague's framework consists of two key components: a world model that takes an objective and the current state (web page) and generates instructions, and an action engine that compiles these instructions into action codes. This allows the agent to navigate the web, process complex tasks, and execute the necessary actions.

In addition to the job application automation, LaVague has also showcased other impressive web agents, such as the ability to retrieve information from Notion workspaces and perform data entry tasks. The framework's versatility and continuous development make it a valuable tool for streamlining your workflows and boosting your productivity.

To get started with LaVague, simply install it using the provided pip command, and then start building your own AI agents to tackle a wide range of web-based tasks. Explore the documentation, customization options, and the growing community to unlock the full potential of this powerful framework.

LaVague, an open-source large action model framework, has recently introduced several exciting upgrades that expand its capabilities in developing AI web agents. One of the standout features is the ability to build an agent that can automatically apply for jobs using the PNG of your resume.

This agent leverages LaVague's core components - the world model and the action engine - to process the resume, extract the relevant information, and fill out job application forms. The world model takes the objective (applying for a job) and the current state (the job application form) to generate instructions, while the action engine compiles these instructions into executable actions using tools like Playwright and Selenium.

Beyond the job application use case, LaVague's web agents have demonstrated their versatility in various other tasks. They can be used for knowledge retrieval, navigating through platforms like Notion and extracting relevant information. The agents can also be employed for data entry, automating the process of extracting data from invoices or other sources and populating forms.

The framework's developers are also working on expanding the dataset used to train the large action model, aiming to create a comprehensive resource for the AI community. This data collection effort will further enhance the capabilities of LaVague's web agents, enabling them to tackle an even broader range of tasks with increased efficiency and accuracy.

With these recent advancements, LaVague has solidified its position as a powerful and flexible framework for developing AI-powered web agents. Developers interested in exploring its capabilities can easily get started by following the installation and customization guides provided in the documentation.

LaVague, the open-source large action model framework, is taking a significant step forward in its development. The team is now focused on building their own data collection initiative, aiming to create a comprehensive dataset that can be utilized by the AI community and further enhance the capabilities of their web agents.

The data collection update is a crucial milestone for LaVague, as it will provide the foundation for a more robust and versatile large action model. By leveraging various data resources, the team is committed to creating a dataset that can better represent the diverse range of tasks and objectives that web agents may encounter.

This initiative will not only benefit the LaVague framework but also contribute to the broader AI ecosystem. The dataset generated through this effort can be made available to the community, allowing researchers and developers to access high-quality data for their own projects and experiments.

The team's approach to data collection is multifaceted, drawing from a wide range of online resources and leveraging their existing web agent capabilities. By automating the process of data gathering and curation, they aim to create a scalable and efficient system that can continuously expand the dataset over time.

As the development of this data collection initiative progresses, users of the LaVague framework can look forward to experiencing the enhanced capabilities of their web agents. The improved large action model, trained on the new dataset, will enable web agents to tackle even more complex tasks and objectives with greater accuracy and efficiency.

In summary, LaVague's data collection initiative represents a significant step forward in the evolution of their open-source framework. By investing in the creation of a robust and diverse dataset, the team is poised to unlock new frontiers in the world of AI-powered web applications, empowering developers to push the boundaries of what's possible.

Le is an open-source framework for developing AI web agents that can perform complex tasks on the web. The recent updates to the framework have expanded its capabilities, allowing agents to perform tasks like automatically applying for jobs using the information extracted from a resume.

The framework consists of two key components: a world model that takes an objective and the current state (web page) and generates instructions, and an action engine that compiles those instructions into executable actions using tools like Playwright and Selenium.

The framework supports a variety of use cases, from knowledge retrieval and data entry to more complex tasks like the automated job application example. Additionally, the team is working on building a dataset to improve the large language model that powers the web agents.

Overall, Le provides a powerful and flexible platform for developing AI-powered web agents, and the recent updates have demonstrated its growing capabilities. Developers interested in exploring the framework can find the necessary resources and documentation in the links provided.