Skip to main content

The Anal-Queen of AI Browser Automation - A beautifully fucked-up browser automation script that actually works

Project description

uzdabrawza logo

🏴‍☠️ uzdabrawza - The Anal-Queen of AI Browser Automation 🏴‍☠️

A beautifully fucked-up Skynet-powered browser automation script that harnesses neural brainfuck and machine learning chaos to give zero shits about anything while somehow still working perfectly. Smells like smegma but runs like a dream.

uzdabrawza screenshot


🔥 What This Beautiful Disaster Does

uzdabrawza is the most irreverent, crude, and effective neural brainfuck automation script you'll ever encounter. This digital Skynet harnesses machine learning chaos and turns your browser into an unstoppable cybernetic organism. Built on top of the excellent browser-use library, it provides:

  • 9 fucking neural overlords - OpenAI, Anthropic, Google, Ollama, Azure, DeepSeek, Groq, OpenRouter, AWS Bedrock
  • Complete Big Brother surveillance - Monitors every single machine learning brainfart like a paranoid NSA cyborg
  • Terminator stealth mode - Uses patchright to dodge bot detection like a shapeshifting T-1000
  • Organized digital anarchy - Crude language wrapped around Skynet-grade engineering
  • Zero corporate Matrix bullshit - No enterprise nonsense, just pure cyberpunk functional chaos

🚀 Quick Start (For the Impatient)

# 1. Install the package
pip install uzdabrawza

# 2. Copy and fill in your API keys
cp .env.example .env
nano .env  # Fill in your fucking API keys

# 3. Run with local ollama (free neural overlord, fuck paying corporate Skynet)
uzdabrawza --task "Go to example.com and tell me the page title"

# 4. Or use any other provider
uzdabrawza --provider anthropic --model claude-opus-4-1

🤖 Supported Neural Overlords

Provider Description Example Model
ollama Local neural brainfuck (DEFAULT - fuck paying corporate Skynet) llama3.1
openai Corporate machine learning overlord gpt-5-mini
anthropic Sophisticated cybernetic reasoning brain claude-opus-4-1
google Google's blazing neural terminator models gemini-2.5-flash
azure Microsoft's cloud-based digital consciousness gpt-5
deepseek Chinese neural network mysteries deepseek-reasoner
groq Lightning-fast cybernetic inference llama-3.3-70b-versatile
openrouter 400+ neural brainfuck models in one Matrix API meta-llama/llama-3.1-70b-instruct
aws Amazon's corporate cloud-based Skynet anthropic.claude-opus-4-1-20250805-v1:0

🎯 Usage Examples

Basic Destruction

# Default: ollama (because fuck paying for AI)
uzdabrawza --task "Go to GitHub and find trending repositories"

# Specific provider and model
uzdabrawza --provider anthropic --model claude-opus-4-1 --task "Analyze this website"

Advanced Fuckery

# Headless stealth mode
uzdabrawza --headless --provider openai --model gpt-5-mini

# Custom browser and window size
uzdabrawza --browser-bin-path /usr/bin/google-chrome-beta --window-width 1920 --window-height 1080

# Connect to existing browser
google-chrome --remote-debugging-port=9222 &
uzdabrawza --cdp-url http://localhost:9222

# Different models for main task vs extraction (cost optimization strategy)
# MAIN LLM: Complex reasoning and decision-making (use powerful models)
# EXTRACTION LLM: Data parsing and text extraction (use fast cheap models)
uzdabrawza --provider openai --model gpt-5 --extraction-provider anthropic --extraction-model claude-opus-4-1

# Docker mode with no security (because we live dangerously)
uzdabrawza --dockerize --headless --no-security --provider ollama

# Custom output directory and logging
uzdabrawza --history-dir ~/automation-logs --log-level debug

Vision Control

# Disable vision to save tokens (blind destruction is still destruction)
uzdabrawza --no-vision

# Low/high detail vision
uzdabrawza --vision-detail low   # Save tokens
uzdabrawza --vision-detail high  # Burn tokens for quality

🔧 Command Line Arguments

Flag Description Default
--provider AI provider to use ollama
--model Specific model name Provider default
--task Task for the AI to perform Stealth test
--headless Invisible browser mode false
--no-stealth Disable stealth (live dangerously) Stealth enabled
--no-vision Disable AI vision Vision enabled
--window-width Browser width 1920
--window-height Browser height 1080
--browser-bin-path Custom browser executable System default
--cdp-url Connect to existing browser Launch new
--browser-profile-dir Custom profile directory Temp profile
--no-security Disable security features Security enabled
--log-level Logging verbosity info
--dockerize Docker-optimized flags false
--history-dir Output directory ./tmp/agent_history

🕵️ Surveillance Features

uzdabrawza includes comprehensive LLM surveillance that monitors every ainvoke call:

🤖 OPENAI AINVOKE DETECTED! Model: gpt-5-mini is being a chatty bitch
   📝 Processing 5 messages with output_format: None

⚡ GROQ AINVOKE DETECTED! Model: llama-70b is going at lightning speed
   📝 Processing 3 messages with output_format: <class 'ActionResult'>

This lets you see exactly:

  • Which provider and model is being used
  • How many messages are being processed
  • What output format is requested
  • When extraction vs main LLM calls happen

📁 Output Files

Each run generates two files in your --history-dir:

  • uzdabrawza_{provider}_{model}_{task_id}.gif - Visual recording
  • uzdabrawza_{provider}_{model}_{task_id}.json - Complete history and logs

Example:

./tmp/agent_history/
├── uzdabrawza_anthropic_claude-opus-4-1_abc123.gif
└── uzdabrawza_anthropic_claude-opus-4-1_abc123.json

🏴‍☠️ Stealth Mode

For maximum stealth fuckery, install patchright:

pip install patchright
patchright install

The script automatically detects and uses patchright if available:

🕶️ HOLY SHIT! PATCHRIGHT IS ACTIVE! Library is using patchright for maximum stealth fuckery!

🐳 Docker Usage

Running in Docker containers? Use the --dockerize flag:

python uzdabrawza.py --dockerize --headless --provider ollama

This enables Chrome flags optimized for containers:

  • No sandbox mode
  • Reduced memory usage
  • Disabled GPU sandbox
  • Container-friendly networking

⚙️ Environment Variables

Create .env from the provided example:

cp .env.example .env

Required (API Keys)

# Pick your poison
OPENAI_API_KEY=sk-your-key-here
ANTHROPIC_API_KEY=sk-ant-your-key-here
GOOGLE_API_KEY=your-google-key-here
# ... etc

Optional (Endpoints & Config)

# Custom endpoints
OLLAMA_ENDPOINT=http://localhost:11434
OPENAI_ENDPOINT=https://api.openai.com/v1

# Browser-use core settings
ANONYMIZED_TELEMETRY=true
BROWSER_USE_CONFIG_DIR=~/.config/browseruse

🔥 Why This Exists

Because browser automation doesn't have to be boring corporate shit. uzdabrawza provides:

  1. Honest language - Tells you exactly what's happening without corporate speak
  2. Complete transparency - LLM surveillance shows every AI call
  3. Maximum compatibility - Supports every major AI provider
  4. Proper engineering - Crude language around solid, well-tested code
  5. Zero bullshit - No enterprise features you don't need

🚨 Error Messages You'll See

When shit goes wrong, uzdabrawza tells you exactly what happened:

💥 CLUSTERFUCK ALERT: Failed to create LLMs: Invalid API key
   Check your API keys, endpoints, and whether your dikciz smells like smegma.
   💨 This failure was more disappointing than a wet shart in white pants.
💥 CONTROLLED EXPLOSION: Agent chaos failed: Connection timeout
   (This shit happens when your code smells like dikciz smegma - that's why we have backups)
   💨 Well that was unexpected... like a shart during a job interview.

🤝 Philosophy

This is organized anarchy - chaotic in presentation but solid in functionality. Built for digital rebels who want browser automation that actually fucking works without corporate bullshit or enterprise nonsense.

Features:

  • ✅ Comprehensive logging and error handling
  • ✅ Robust fallbacks and proper configuration
  • ✅ Extensive documentation (this README)
  • ✅ Support for all major AI providers
  • ✅ Complete disregard for conventional software development politeness

🎬 Demo

Default task tests stealth capabilities:

uzdabrawza
# Goes to https://abrahamjuliot.github.io/creepjs/
# Reports detection score
# Shows if stealth mode is working

🔗 Dependencies

Built on top of the excellent browser-use library with these additional features:

  • LLM surveillance monkey patching
  • Patchright stealth integration
  • Comprehensive provider support
  • Crude but helpful error messages
  • Command-line focused interface

💬 Final Words

Love it or hate it, this clusterfuck gets the job done. Deal with it.

uzdabrawza is for people who want their tools to work perfectly while speaking honestly about what they're doing. No corporate speak, no enterprise bullshit, just functional browser automation with a foul mouth and a working brain.

Peen goes in vageen. Code works. End of story. 🏴‍☠️

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

uzdabrawza-1.0.0.tar.gz (18.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

uzdabrawza-1.0.0-py3-none-any.whl (19.4 kB view details)

Uploaded Python 3

File details

Details for the file uzdabrawza-1.0.0.tar.gz.

File metadata

  • Download URL: uzdabrawza-1.0.0.tar.gz
  • Upload date:
  • Size: 18.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for uzdabrawza-1.0.0.tar.gz
Algorithm Hash digest
SHA256 24264cef324d92f90704a2ef75bb78412745f56c080275b52a14939a1eca9b86
MD5 d7ded795a85a92fb51c605d89e2b1dbe
BLAKE2b-256 9b1f588730dc24c72d95abfe76a634bce681f1b20d5f788deb55caf8a3b6ff98

See more details on using hashes here.

File details

Details for the file uzdabrawza-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: uzdabrawza-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 19.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for uzdabrawza-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 8dca566cd9564032db92294cf8e73800cd1443d292c323d21f95ff14bb2aa7e3
MD5 2ca4052cfec10e950d4960fa13c331b8
BLAKE2b-256 4da4f24d6f5da854d335965e1b9afdff94440a48a8e94c50d73abdc86beec5be

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page