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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:
🌊 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.
Here is an example of how LaVague can take multiple steps to achieve the objective of "Go on the quicktour of PEFT":
You can do this with the following steps:
pip install lavague
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
We support three Driver options:
Note that not all drivers support all agent features:
| Feature | Selenium | Playwright | Chrome Extension |
|---|---|---|---|
| Headless agents | ✅ | ⏳ | N/A |
| Handle iframes | ✅ | ✅ | ❌ |
| Open several tabs | ✅ | ⏳ | ✅ |
| Highlight elements | ✅ | ✅ | ✅ |
✅ supported
⏳ coming soon
❌ not supported
If you're experiencing any issues getting started with LaVague, you can:
We would love your help and support on our quest to build a robust and reliable Large Action Model for web automation.
To avoid having multiple people working on the same things & being unable to merge your work, we have outlined the following contribution process:
GitHub issueshelp-wantedgood first issuecommunity assignedPlease check out our
for more details.contributing guide
To keep up to date with our project backlog here.
LaVague uses LLMs, (by default OpenAI's
gpt4-oThe cost of these LLM calls depends on:
Please see our dedicated documentation on token counting and cost estimations to learn how you can track all tokens and estimate costs for running your agents.
We want to build a dataset that can be used by the AI community to build better Large Action Models for better Web Agents. You can see our work so far on building community datasets on our BigAction HuggingFace page.
This is why LaVague collects the following user data telemetry by default:
Be careful to NEVER includes personal information in your objectives and the extra user data. If you intend to includes personal information in your objectives/extra user data, it is HIGHLY recommended to turn off the telemetry.
If you want to turn off all telemetry, you should set the
LAVAGUE_TELEMETRY"NONE"For guidance on how to set your
LAVAGUE_TELEMTRY