https://github.com/TheAgenticAI/TheAgenticBrowser
Open-source AI agent for web automation and scraping.
https://github.com/TheAgenticAI/TheAgenticBrowser
Last synced: 3 months ago
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Open-source AI agent for web automation and scraping.
- Host: GitHub
- URL: https://github.com/TheAgenticAI/TheAgenticBrowser
- Owner: TheAgenticAI
- License: other
- Created: 2025-01-15T22:21:27.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-31T19:36:56.000Z (over 1 year ago)
- Last Synced: 2025-01-31T20:28:24.908Z (over 1 year ago)
- Language: Python
- Homepage: https://www.theagentic.ai/
- Size: 768 KB
- Stars: 34
- Watchers: 0
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-agent-experience - TheAgenticBrowser - Open-source AI agent for web automation and scraping. (Tools / Agent Web Interaction)
README
# Agentic Browser
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Architecture](#architecture)
- [Agents Workflow](#agents-workflow)
- [Quick Start](#quick-start)
- [License](#license)
- [Acknowledgements](#acknowledgements)
## Overview
Agentic Browser is an agent-based system designed to automate browser interactions using a natural language interface. Built upon the [PydanticAI Python agent framework](https://github.com/pydantic/pydantic-ai), Agentic Browser allows users to automate tasks such as form filling, product searches on e-commerce platforms, content retrieval, media interaction, and project management on various platforms.
## Features
### Browser Automation
- **Web Research and Analysis**: Intelligent web research across academic papers, travel sites & code repositories with natural language queries.
- **Data Extraction**: Extracts and compiles data of various types such as sports data, historical data, stock market and currencies.
- **E-commerce Information**: Scrapes information like price, specifications, availaibility of a product on various e-commerce websites.
- **Web Traversal**: Smart cross-domain navigation with context-aware website traversal & data correlation.
## Architecture
Agentic Browser uses three specialized agents working in harmony:
- **Planner Agent**: The strategist that breaks down user requests into clear, executable steps. It creates and adapts plans based on feedback and progress.
- **Browser Agent**: The executor that directly interacts with web pages. It performs actions like clicking, typing, navigating, and extracting information using browser automation tools.
- **Critique Agent**: The quality controller that analyzes actions, verifies results, and guides the workflow. It determines if tasks are complete or need refinement.
The agents work in a feedback loop to ensure that actions are taken correctly and tasks are completed effectively.
## Agents Workflow
### Step 1: Planning Phase
- The **Planner Agent** receives a user request
- Analyzes the task requirements
- Creates a step-by-step execution plan
- Determines the first action to take
### Step 2: Execution Phase
- The **Browser Agent** receives the current step
- Executes precise browser actions (navigation, clicks, text entry)
- Uses tools like DOM inspection and screenshot analysis
- Reports action results
### Step 3: Evaluation Phase
- The **Critique Agent** reviews the execution
- Analyzes screenshots and DOM changes
- Verifies if the step was successful
- Decides whether to:
- Complete the task and return results to user
- Continue to next step in plan
- Request plan modification from Planner Agent
This cycle continues until the task is successfully completed or a terminal condition is reached.
## Quick Start
### Setup
To get started with Agentic Browser, follow the steps below to install dependencies and configure your environment.
#### 1. Install `uv`
Agentic Browser uses `uv` to manage the Python virtual environment and package dependencies.
- macOS/Linux:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
- Windows:
```bash
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
```
You can install uv using pip
#### 2. Clone the repository:
git clone https://github.com/TheAgenticAI/TheAgenticBrowser
#### 3. Set up the virtual environment
Use uv to create and activate a virtual environment for the project.
uv venv --python=3.11
source .venv/bin/activate
# On Windows: .venv\Scripts\activate
#### 4. Install dependencies
uv pip install -r requirements.txt
#### 5. Install Playwright Drivers
playwright install
If you want to use your local Chrome browser over Playwright, go to chrome://version/ in Chrome, find the path to your profile, and set BROWSER_STORAGE_DIR to that path in .env
#### 6. Configure the environment
Create a .env file by copying the provided example file.
cp .env.example .env
Edit the .env file and set the following variables:
# AGENTIC_BROWSER Configuration
AGENTIC_BROWSER_TEXT_MODEL=
AGENTIC_BROWSER_TEXT_API_KEY=
AGENTIC_BROWSER_TEXT_BASE_URL=
# Screenshot Analysis Configuration
AGENTIC_BROWSER_SS_ENABLED=
AGENTIC_BROWSER_SS_MODEL=
AGENTIC_BROWSER_SS_API_KEY=
AGENTIC_BROWSER_SS_BASE_URL=
# Logging
LOGFIRE_TOKEN=
# Google Search Configuration
GOOGLE_API_KEY=
GOOGLE_CX=
# Browser Configuration
BROWSER_STORAGE_DIR=
STEEL_DEV_API_KEY=
#### 7. Running the project
You can directly run the project from the main.py file or even spin up a server to interact through an API
- Direct
```bash
python3 -m core.main
```
- API
```bash
uvicorn core.server.api_routes:app --loop asyncio
```
Details -
```
POST http://127.0.0.1:8000/execute_task
{
"command": "Give me the price of RTX 3060ti on amazon.in and give me the latest delivery date."
}
```
### Running API with Docker (for AgenticBench)
#### For Ubuntu/Windows :
```bash
docker build -t agentic_browser .
docker run -it --net=host --env-file .env agentic_browser
```
#### For macOS :
```bash
docker build -t agentic_browser .
docker run -it -p 8000:8000 --env-file .env agentic_browser
```
## Acknowledgements
- [Agent-E](https://github.com/EmergenceAI/Agent-E?tab=readme-ov-file)
- [PydanticAI Python Agent Framework](https://github.com/pydantic/pydantic-ai)