Description
Agent-E is an agent-based system designed for automating actions within a user's web browser. Built upon the AG2 agent framework, it offers a natural language interface for interacting with websites. The system focuses on enabling users to automate a wide range of web-based tasks, from simple form submissions to complex e-commerce product searches and media playback management.
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At its core, Agent-E utilizes a skill-and-agent architecture. Skills represent atomic actions, such as clicking elements, entering text, or opening URLs. These skills are orchestrated by agents, primarily a User Proxy agent that executes skills and a Browser Navigation agent responsible for web interactions. This modular design allows Agent-E to flexibly assemble skills to create sophisticated web automation workflows. The system prioritizes using configured skills over allowing LLMs to generate arbitrary code, aiming for safer and more predictable outcomes.
Key capabilities include filling out web forms, searching and sorting products on e-commerce platforms like Amazon, locating specific content on websites, navigating and controlling web media like YouTube videos, performing comprehensive web searches, and managing tasks on project management platforms. Agent-E also offers advanced features through its managed web agent and orchestrator, including enterprise enhancements, advanced logging, role-based access, and cloud-hosted scalable infrastructure.
The target audience for Agent-E includes developers, researchers, and power users looking to automate repetitive or complex web-based tasks. Its value proposition lies in its ability to translate natural language commands into actionable browser interactions, saving time and reducing manual effort. The system also supports customization of LLM parameters and offers an API for programmatic integration, making it a versatile tool for various automation needs.
Agent-E provides a quick start guide with installation scripts for macOS/Linux and Windows, followed by environment variable configuration. Users can run Agent-E via a command-line interface or launch a FastAPI server for web endpoint access, enabling programmatic task execution. The project also emphasizes community contribution and provides resources for reporting issues and engaging in discussions.
- CategoryBrowser & Web Automation
- Pricing ModelOpen source
- DeploymentCloud, Self-hosted
- PlatformsmacOS, Linux, Windows
Agent-E's Core Features
Agent-driven web automation
Natural language interface for browser interaction
Form filling capabilities
E-commerce product search and sorting
Web content location and extraction
Web media playback control
Comprehensive web search functionality
Task management on platforms like JIRA
FastAPI wrapper for programmatic access
Customizable LLM parameters
Support for open-source LLMs via LiteLLM and Ollama
DOM Distillation for efficient element identification
Skills library for sensing and action capabilities
Getting Started with Agent-E
Clone the repository
Install dependencies using provided scripts (e.g., ./install.sh or .\win_install.ps1)
Configure environment variables in .env and agents_llm_config.json
Run Agent-E using 'python -m ae.main' or './run.sh'
Launch the FastAPI server for API access using 'uvicorn ae.server.api_routes:app --reload --loop asyncio'
Execute tasks via HTTP POST requests to the API endpoint
Customize LLM parameters via environment variables or JSON configuration files
Agent-E's Use Cases
- Web Scraping
- E-commerce Automation
- Form Filling
- Content Aggregation
- Task Management
- Personalized Assistance
- API Integration
