Solve AWS WAF CAPTCHAs using Modern AI! 🚀
Using powerful AI models like Groq and Moondream to solve AWS WAF CAPTCHAs quickly and accurately
A joint development effort by LaProp and XAhai
⚠️ Alpha Status Notice: This library is currently in alpha stage. Many features are still under development, requiring thorough testing and integration. Expect frequent updates and potential breaking changes. We welcome feedback and contributions to help improve stability and functionality!
Features • Installation • Configuration • Quick Start • Documentation • Contributing
pip install captchai-
🧠 Modern AI Models
- Groq LLM for smart solving
- Moondream for image analysis
- High accuracy rates
-
🛡️ AWS WAF CAPTCHA Support
- Built for AWS WAF challenges
- Solves image puzzles
- Handles audio challenges
-
⚡ High Performance
- Quick processing
- Smart backup options
- Reliable results
-
🛠️ Easy to Use
- Simple API
- Easy setup
- Built to extend
- Full Python typing
Note: Currently, CaptchAI only works with AWS WAF CAPTCHAs. We plan to add support for other CAPTCHAs in the future.
pip install captchaifrom captchai.core.models.config import CaptchaGlobalConfig, AWSProviderConfig, AvailableResolvers
config = CaptchaGlobalConfig(
groq_api_key="your-groq-api-key",
moondream_api_key="your-moondream-api-key",
aws_provider_config=AWSProviderConfig(
image_size=(640, 640), # Customize image size
grid_size=3, # Grid dimensions
resolver=AvailableResolvers.GROQ_IMAGE_ONE_SHOOT,
# Fallback resolvers for resilience
list_resolver_image_fallback=[
AvailableResolvers.MOONDREAM_IMAGE_ONE_SHOOT,
AvailableResolvers.GROQ_IMAGE_ONE_SHOOT,
AvailableResolvers.GROQ_IMAGE_MULTI_SHOOT,
AvailableResolvers.MOONDREAM_IMAGE_MULTI_SHOOT
]
)
)GROQ_IMAGE_ONE_SHOOT: Single-shot solving with GroqGROQ_IMAGE_MULTI_SHOOT: Multi-shot approach with GroqMOONDREAM_IMAGE_ONE_SHOOT: Quick Moondream vision modelMOONDREAM_IMAGE_MULTI_SHOOT: Advanced Moondream processing
GROQ_AUDIO: Advanced audio CAPTCHA processing
Here's a complete example of how to use Captchai to solve different types of CAPTCHAs:
from captchai import CaptchaSolver
from captchai.core.models.config import (
CaptchaGlobalConfig,
AWSProviderConfig,
AvailableResolvers
)
import base64
def solve_captcha_example():
# 1. Configure the solver
config = CaptchaGlobalConfig(
groq_api_key="your-groq-api-key",
moondream_api_key="your-moondream-api-key",
aws_provider_config=AWSProviderConfig(
resolver=AvailableResolvers.GROQ_IMAGE_ONE_SHOOT,
grid_size=3,
image_size=(640, 640)
)
)
# 2. Initialize the solver
solver = CaptchaSolver(config)
# 3. Solve different types of CAPTCHAs
# Image CAPTCHA (using base64 string)
with open("path/to/captcha.png", "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
# For image CAPTCHAs, query is required - it specifies what type of object to identify
image_result = solver.solve_aws_captcha_image(
data=image_base64,
query="bucket" # Required: Specify the type of object to identify
)
print(f"Image CAPTCHA Solution: {image_result}")
# Audio CAPTCHA (using base64 string)
with open("path/to/audio.mp3", "rb") as audio_file:
audio_base64 = base64.b64encode(audio_file.read()).decode('utf-8')
# For audio CAPTCHAs, query is optional
audio_result = solver.solve_aws_captcha(
data=audio_base64
)
print(f"Audio CAPTCHA Solution: {audio_result}")
if __name__ == "__main__":
solve_captcha_example()Note: For image CAPTCHAs, the
queryparameter is required - it specifies what type of object to identify (e.g., "Select all images with traffic lights", "Select all squares with buses"). For audio CAPTCHAs, thequeryparameter is optional.
- Python 3.12+
- Dependencies:
- groq >= 0.15.0
- moondream >= 0.0.6
- pydantic >= 2.10.5
- pydub >= 0.25.1
- pytest >= 8.3.4
-
Core Features
- Groq LLM integration
- Moondream vision model integration
- AWS WAF CAPTCHA image solving
- AWS WAF CAPTCHA audio solving
- Fallback resolver system
-
Direct AWS Integration
- Handle AWS CAPTCHA request/response flow directly
- Implement automatic token extraction and submission
-
Browser Automation
- Selenium integration for automated CAPTCHA solving
- Playwright integration
- Browser extension support
-
Future Enhancements
- Support for additional CAPTCHA providers
- More AI model options
- Performance optimizations
- Improved error handling and retries
- Comprehensive logging system
We welcome your contributions! We want to make it easy for you to help improve Captchai. Check out our Contributing Guidelines to get started.
- Fork the repo
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
IMPORTANT: FOR RESEARCH AND EDUCATIONAL PURPOSES ONLY
This library is for research and educational purposes only. It helps study and understand how AI can solve CAPTCHA challenges.
By using this library, you agree that:
- This tool is only for research, testing, and learning
- Using this library to bypass CAPTCHAs on real websites may break their terms of service
- The developers and contributors are NOT responsible for:
- Any misuse of the library
- Breaking any website's terms of service
- Any legal issues from using this library
- Any problems or losses from using this library
- You are responsible for using this library in a way that follows all laws, rules, and terms of service
DO NOT use this library to:
- Bypass security on real websites
- Break any website's terms of service
- Do any kind of automated abuse or spam
Give a ⭐️ if this project helped you!