SpicyGen
What is LaVague?
LaVague offers an open-source framework designed for constructing and deploying AI-powered Web Agents efficiently. It allows users to automate browser interactions using state-of-the-art Large Action Models, enabling agents to navigate websites, perform data entry, orchestrate processes across different SaaS applications, and retrieve specific information based on user instructions.
The framework emphasizes customization, permitting users to tailor the tools, knowledge base, and integrations for their specific agent requirements. It supports deployment with local models like Llama 3 or on-premise/VPC models such as Azure OpenAI or Gemini, ensuring privacy and control over data. LaVague facilitates tasks ranging from automated test generation from Gherkin specifications to complex process automation within CRMs or ERPs.
The framework emphasizes customization, permitting users to tailor the tools, knowledge base, and integrations for their specific agent requirements. It supports deployment with local models like Llama 3 or on-premise/VPC models such as Azure OpenAI or Gemini, ensuring privacy and control over data. LaVague facilitates tasks ranging from automated test generation from Gherkin specifications to complex process automation within CRMs or ERPs.
Features
- Open-Source Framework: Build and deploy web agents with readily available code.
- AI Web Agent Construction: Utilizes Large Action Models for browser automation.
- Customizable: Adapt agent tools, knowledge, and integrations for specific use cases.
- Private Deployment: Supports local models (Llama 3) and on-VPC models (Azure OpenAI, Gemini).
- Performant: Leverages state-of-the-art models for efficient web interactions.
- Gherkin to Test Automation: Converts Gherkin specifications into executable tests (via LaVague QA).
Use Cases
- Automate QA test writing from Gherkin specifications.
- Automate web navigation, data entry, or data orchestration across SaaS platforms (CRM, ERP).
- Retrieve specific information from SaaS solutions by simulating user exploration and decision-making.
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