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
🌊 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:
- Download LaVague with:
pip install lavague- 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
- ✅ Built-in Contexts (aka. configurations)
- ✅ Customizable configuration
- ✅ A test runner for testing and benchmarking the performance of LaVague
- ✅ A Token Counter for estimating token usage and costs
- ✅ Logging tools
- ✅ An optional, interactive Gradio interface
- ✅ Debugging tools
- ✅ A Chrome Extension
We support three Driver options:
- A Selenium Webdriver
- A Playwright webdriver
- A Chrome extension driver
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:
- Check out our troubleshooting guide where we list information and fixes for common issues.
- Opening a GitHub issue describing your issue
- Messaging us in the '#support channel' on our Discord server
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:
- 📢 We outline tasks using
GitHub issues: we recommend checking out issues with thehelp-wanted&good first issuelabels - 🙋♀️ If you are interested in working on one of these tasks, comment on the issue!
- 🤝 We will discuss with you and assign you the task with a
community assignedlabel - 💬 We will then be available to discuss this task with you
- ⬆️ You should submit your work as a PR
- ✅ We will review & merge your code or request changes/give feedback
Please check out our contributing guide for more details.
To keep up to date with our project backlog here.
LaVague uses LLMs, (by default OpenAI's gpt4-o but this is completely customizable), under the hood.
The cost of these LLM calls depends on:
- the models chosen to run a given agent
- the complexity of the objective
- the website you're interacting with.
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:
- Version of LaVague installed
- Code / List of actions generated for each web action step
- The past actions
- The "observations" (method used to check the current page)
- LLM used (i.e GPT4)
- Multi modal LLM used (i.e GPT4)
- Randomly generated anonymous user ID
- Whether you are using a CLI command (lavague-qa for example), the Gradio demo or our library directly.
- The objective used
- The chain of thoughts on the agent
- The interaction zone on the page (bounding box)
- The viewport size of your browser
- The current step
- The instruction(s) generated & the current engine used
- The token costs & usages
- The URL you performed an action on
- Whether the action failed or succeeded
- The extra used data specified
- Error message, where relevant
- The source nodes (chunks of HTML code retrieved from the web page to perform this action)
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 environment variable to "NONE".
For guidance on how to set your LAVAGUE_TELEMTRY environment variable, see our guide here.
