LaVague

Welcome to LaVague

A Large Action Model framework for developing AI Web Agents

LaVague: Web Agent framework for builders

LaVague is an open-source framework designed for developers who want to create AI Web Agents to automate processes for their end users.

Our Web Agents can take an objective, such as "Print installation steps for Hugging Face's Diffusers library," and generate and perform the actions required to achieve the objective.

LaVague Agents are made up of:

• A World Model that takes an objective and the current state (aka the current web page) and outputs an appropriate set of instructions.
• An Action Engine which “compiles” these instructions into action code, e.g., Selenium or Playwright & executes them

LaVague QA: Dedicated tooling for QA Engineers

🌊 Built on LaVague

LaVague QA is a tool tailored for QA engineers leveraging our framework.

It allows you to automate test writing by turning Gherkin specs into easy-to-integrate tests. LaVague QA is a project leveraging the LaVague framework behind the scenes to make web testing 10x more efficient.

For detailed information and setup instructions, visit the LaVague QA documentation.

🚀 Getting Started

Demo

Here is an example of how LaVague can take multiple steps to achieve the objective of "Go on the quicktour of PEFT":

Hands-on

You can do this with the following steps:

1. 1.Download LaVague with:

pip install lavague

1. 1.Use our framework to build a Web Agent and implement the objective:

from lavague.core import  WorldModel, ActionEngine
from lavague.core.agents import WebAgent
from lavague.drivers.selenium import SeleniumDriver

selenium_driver = SeleniumDriver(headless=False)
world_model = WorldModel()
action_engine = ActionEngine(selenium_driver)
agent = WebAgent(world_model, action_engine)
agent.get("https://huggingface.co/docs")
agent.run("Go on the quicktour of PEFT")

# Launch Gradio Agent Demo
agent.demo("Go on the quicktour of PEFT")

For more information on this example and how to use LaVague, see our quick-tour.

Note, these examples use our default OpenAI API configuration and you will need to set the OPENAI_API_KEY variable in your local environment with a valid API key for these to work.

For an end-to-end example of LaVague in a Google Colab, see our quick-tour notebook

Key Features

• ✅ Built-in Contexts (aka. configurations)
• ✅ Customizable configuration
• ✅ [A test runner](https://docs.lavague.ai/en