crawl4ai
🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https://discord.gg/mEkkMXFG
Stars: 32374
Crawl4AI is a powerful and free web crawling service that extracts valuable data from websites and provides LLM-friendly output formats. It supports crawling multiple URLs simultaneously, replaces media tags with ALT, and is completely free to use and open-source. Users can integrate Crawl4AI into Python projects as a library or run it as a standalone local server. The tool allows users to crawl and extract data from specified URLs using different providers and models, with options to include raw HTML content, force fresh crawls, and extract meaningful text blocks. Configuration settings can be adjusted in the `crawler/config.py` file to customize providers, API keys, chunk processing, and word thresholds. Contributions to Crawl4AI are welcome from the open-source community to enhance its value for AI enthusiasts and developers.
README:
Crawl4AI is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.
✨ Check out latest update v0.5.0
🎉 Version 0.5.0 is out! This major release introduces Deep Crawling with BFS/DFS/BestFirst strategies, Memory-Adaptive Dispatcher, Multiple Crawling Strategies (Playwright and HTTP), Docker Deployment with FastAPI, Command-Line Interface (CLI), and more! Read the release notes →
🤓 My Personal Story
My journey with computers started in childhood when my dad, a computer scientist, introduced me to an Amstrad computer. Those early days sparked a fascination with technology, leading me to pursue computer science and specialize in NLP during my postgraduate studies. It was during this time that I first delved into web crawling, building tools to help researchers organize papers and extract information from publications a challenging yet rewarding experience that honed my skills in data extraction.
Fast forward to 2023, I was working on a tool for a project and needed a crawler to convert a webpage into markdown. While exploring solutions, I found one that claimed to be open-source but required creating an account and generating an API token. Worse, it turned out to be a SaaS model charging $16, and its quality didn’t meet my standards. Frustrated, I realized this was a deeper problem. That frustration turned into turbo anger mode, and I decided to build my own solution. In just a few days, I created Crawl4AI. To my surprise, it went viral, earning thousands of GitHub stars and resonating with a global community.
I made Crawl4AI open-source for two reasons. First, it’s my way of giving back to the open-source community that has supported me throughout my career. Second, I believe data should be accessible to everyone, not locked behind paywalls or monopolized by a few. Open access to data lays the foundation for the democratization of AI, a vision where individuals can train their own models and take ownership of their information. This library is the first step in a larger journey to create the best open-source data extraction and generation tool the world has ever seen, built collaboratively by a passionate community.
Thank you to everyone who has supported this project, used it, and shared feedback. Your encouragement motivates me to dream even bigger. Join us, file issues, submit PRs, or spread the word. Together, we can build a tool that truly empowers people to access their own data and reshape the future of AI.
- Built for LLMs: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.
- Lightning Fast: Delivers results 6x faster with real-time, cost-efficient performance.
- Flexible Browser Control: Offers session management, proxies, and custom hooks for seamless data access.
- Heuristic Intelligence: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.
- Open Source & Deployable: Fully open-source with no API keys—ready for Docker and cloud integration.
- Thriving Community: Actively maintained by a vibrant community and the #1 trending GitHub repository.
- Install Crawl4AI:
# Install the package
pip install -U crawl4ai
# For pre release versions
pip install crawl4ai --pre
# Run post-installation setup
crawl4ai-setup
# Verify your installation
crawl4ai-doctorIf you encounter any browser-related issues, you can install them manually:
python -m playwright install --with-deps chromium- Run a simple web crawl with Python:
import asyncio
from crawl4ai import *
async def main():
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url="https://www.nbcnews.com/business",
)
print(result.markdown)
if __name__ == "__main__":
asyncio.run(main())- Or use the new command-line interface:
# Basic crawl with markdown output
crwl https://www.nbcnews.com/business -o markdown
# Deep crawl with BFS strategy, max 10 pages
crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10
# Use LLM extraction with a specific question
crwl https://www.example.com/products -q "Extract all product prices"📝 Markdown Generation
- 🧹 Clean Markdown: Generates clean, structured Markdown with accurate formatting.
- 🎯 Fit Markdown: Heuristic-based filtering to remove noise and irrelevant parts for AI-friendly processing.
- 🔗 Citations and References: Converts page links into a numbered reference list with clean citations.
- 🛠️ Custom Strategies: Users can create their own Markdown generation strategies tailored to specific needs.
- 📚 BM25 Algorithm: Employs BM25-based filtering for extracting core information and removing irrelevant content.
📊 Structured Data Extraction
- 🤖 LLM-Driven Extraction: Supports all LLMs (open-source and proprietary) for structured data extraction.
- 🧱 Chunking Strategies: Implements chunking (topic-based, regex, sentence-level) for targeted content processing.
- 🌌 Cosine Similarity: Find relevant content chunks based on user queries for semantic extraction.
- 🔎 CSS-Based Extraction: Fast schema-based data extraction using XPath and CSS selectors.
- 🔧 Schema Definition: Define custom schemas for extracting structured JSON from repetitive patterns.
🌐 Browser Integration
- 🖥️ Managed Browser: Use user-owned browsers with full control, avoiding bot detection.
- 🔄 Remote Browser Control: Connect to Chrome Developer Tools Protocol for remote, large-scale data extraction.
- 👤 Browser Profiler: Create and manage persistent profiles with saved authentication states, cookies, and settings.
- 🔒 Session Management: Preserve browser states and reuse them for multi-step crawling.
- 🧩 Proxy Support: Seamlessly connect to proxies with authentication for secure access.
- ⚙️ Full Browser Control: Modify headers, cookies, user agents, and more for tailored crawling setups.
- 🌍 Multi-Browser Support: Compatible with Chromium, Firefox, and WebKit.
- 📐 Dynamic Viewport Adjustment: Automatically adjusts the browser viewport to match page content, ensuring complete rendering and capturing of all elements.
🔎 Crawling & Scraping
- 🖼️ Media Support: Extract images, audio, videos, and responsive image formats like
srcsetandpicture. - 🚀 Dynamic Crawling: Execute JS and wait for async or sync for dynamic content extraction.
- 📸 Screenshots: Capture page screenshots during crawling for debugging or analysis.
- 📂 Raw Data Crawling: Directly process raw HTML (
raw:) or local files (file://). - 🔗 Comprehensive Link Extraction: Extracts internal, external links, and embedded iframe content.
- 🛠️ Customizable Hooks: Define hooks at every step to customize crawling behavior.
- 💾 Caching: Cache data for improved speed and to avoid redundant fetches.
- 📄 Metadata Extraction: Retrieve structured metadata from web pages.
- 📡 IFrame Content Extraction: Seamless extraction from embedded iframe content.
- 🕵️ Lazy Load Handling: Waits for images to fully load, ensuring no content is missed due to lazy loading.
- 🔄 Full-Page Scanning: Simulates scrolling to load and capture all dynamic content, perfect for infinite scroll pages.
🚀 Deployment
- 🐳 Dockerized Setup: Optimized Docker image with FastAPI server for easy deployment.
- 🔑 Secure Authentication: Built-in JWT token authentication for API security.
- 🔄 API Gateway: One-click deployment with secure token authentication for API-based workflows.
- 🌐 Scalable Architecture: Designed for mass-scale production and optimized server performance.
- ☁️ Cloud Deployment: Ready-to-deploy configurations for major cloud platforms.
🎯 Additional Features
- 🕶️ Stealth Mode: Avoid bot detection by mimicking real users.
- 🏷️ Tag-Based Content Extraction: Refine crawling based on custom tags, headers, or metadata.
- 🔗 Link Analysis: Extract and analyze all links for detailed data exploration.
- 🛡️ Error Handling: Robust error management for seamless execution.
- 🔐 CORS & Static Serving: Supports filesystem-based caching and cross-origin requests.
- 📖 Clear Documentation: Simplified and updated guides for onboarding and advanced usage.
- 🙌 Community Recognition: Acknowledges contributors and pull requests for transparency.
✨ Play around with this
✨ Visit our Documentation Website
Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.
🐍 Using pip
Choose the installation option that best fits your needs:
For basic web crawling and scraping tasks:
pip install crawl4ai
crawl4ai-setup # Setup the browserBy default, this will install the asynchronous version of Crawl4AI, using Playwright for web crawling.
👉 Note: When you install Crawl4AI, the crawl4ai-setup should automatically install and set up Playwright. However, if you encounter any Playwright-related errors, you can manually install it using one of these methods:
-
Through the command line:
playwright install
-
If the above doesn't work, try this more specific command:
python -m playwright install chromium
This second method has proven to be more reliable in some cases.
The sync version is deprecated and will be removed in future versions. If you need the synchronous version using Selenium:
pip install crawl4ai[sync]For contributors who plan to modify the source code:
git clone https://github.com/unclecode/crawl4ai.git
cd crawl4ai
pip install -e . # Basic installation in editable modeInstall optional features:
pip install -e ".[torch]" # With PyTorch features
pip install -e ".[transformer]" # With Transformer features
pip install -e ".[cosine]" # With cosine similarity features
pip install -e ".[sync]" # With synchronous crawling (Selenium)
pip install -e ".[all]" # Install all optional features🐳 Docker Deployment
🚀 Major Changes Coming! We're developing a completely new Docker implementation that will make deployment even more efficient and seamless. The current Docker setup is being deprecated in favor of this new solution.
The existing Docker implementation is being deprecated and will be replaced soon. If you still need to use Docker with the current version:
- 📚 Deprecated Docker Setup - Instructions for the current Docker implementation
⚠️ Note: This setup will be replaced in the next major release
Our new Docker implementation will bring:
- Improved performance and resource efficiency
- Streamlined deployment process
- Better integration with Crawl4AI features
- Enhanced scalability options
Stay connected with our GitHub repository for updates!
Run a quick test (works for both Docker options):
import requests
# Submit a crawl job
response = requests.post(
"http://localhost:11235/crawl",
json={"urls": "https://example.com", "priority": 10}
)
task_id = response.json()["task_id"]
# Continue polling until the task is complete (status="completed")
result = requests.get(f"http://localhost:11235/task/{task_id}")For more examples, see our Docker Examples. For advanced configuration, environment variables, and usage examples, see our Docker Deployment Guide.
You can check the project structure in the directory https://github.com/unclecode/crawl4ai/docs/examples. Over there, you can find a variety of examples; here, some popular examples are shared.
📝 Heuristic Markdown Generation with Clean and Fit Markdown
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai.content_filter_strategy import PruningContentFilter, BM25ContentFilter
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
async def main():
browser_config = BrowserConfig(
headless=True,
verbose=True,
)
run_config = CrawlerRunConfig(
cache_mode=CacheMode.ENABLED,
markdown_generator=DefaultMarkdownGenerator(
content_filter=PruningContentFilter(threshold=0.48, threshold_type="fixed", min_word_threshold=0)
),
# markdown_generator=DefaultMarkdownGenerator(
# content_filter=BM25ContentFilter(user_query="WHEN_WE_FOCUS_BASED_ON_A_USER_QUERY", bm25_threshold=1.0)
# ),
)
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(
url="https://docs.micronaut.io/4.7.6/guide/",
config=run_config
)
print(len(result.markdown.raw_markdown))
print(len(result.markdown.fit_markdown))
if __name__ == "__main__":
asyncio.run(main())🖥️ Executing JavaScript & Extract Structured Data without LLMs
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
import json
async def main():
schema = {
"name": "KidoCode Courses",
"baseSelector": "section.charge-methodology .w-tab-content > div",
"fields": [
{
"name": "section_title",
"selector": "h3.heading-50",
"type": "text",
},
{
"name": "section_description",
"selector": ".charge-content",
"type": "text",
},
{
"name": "course_name",
"selector": ".text-block-93",
"type": "text",
},
{
"name": "course_description",
"selector": ".course-content-text",
"type": "text",
},
{
"name": "course_icon",
"selector": ".image-92",
"type": "attribute",
"attribute": "src"
}
}
}
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
browser_config = BrowserConfig(
headless=False,
verbose=True
)
run_config = CrawlerRunConfig(
extraction_strategy=extraction_strategy,
js_code=["""(async () => {const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");for(let tab of tabs) {tab.scrollIntoView();tab.click();await new Promise(r => setTimeout(r, 500));}})();"""],
cache_mode=CacheMode.BYPASS
)
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(
url="https://www.kidocode.com/degrees/technology",
config=run_config
)
companies = json.loads(result.extracted_content)
print(f"Successfully extracted {len(companies)} companies")
print(json.dumps(companies[0], indent=2))
if __name__ == "__main__":
asyncio.run(main())📚 Extracting Structured Data with LLMs
import os
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
from crawl4ai.extraction_strategy import LLMExtractionStrategy
from pydantic import BaseModel, Field
class OpenAIModelFee(BaseModel):
model_name: str = Field(..., description="Name of the OpenAI model.")
input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
async def main():
browser_config = BrowserConfig(verbose=True)
run_config = CrawlerRunConfig(
word_count_threshold=1,
extraction_strategy=LLMExtractionStrategy(
# Here you can use any provider that Litellm library supports, for instance: ollama/qwen2
# provider="ollama/qwen2", api_token="no-token",
llm_config = LLMConfig(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY')),
schema=OpenAIModelFee.schema(),
extraction_type="schema",
instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens.
Do not miss any models in the entire content. One extracted model JSON format should look like this:
{"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
),
cache_mode=CacheMode.BYPASS,
)
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(
url='https://openai.com/api/pricing/',
config=run_config
)
print(result.extracted_content)
if __name__ == "__main__":
asyncio.run(main())🤖 Using You own Browser with Custom User Profile
import os, sys
from pathlib import Path
import asyncio, time
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
async def test_news_crawl():
# Create a persistent user data directory
user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
os.makedirs(user_data_dir, exist_ok=True)
browser_config = BrowserConfig(
verbose=True,
headless=True,
user_data_dir=user_data_dir,
use_persistent_context=True,
)
run_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS
)
async with AsyncWebCrawler(config=browser_config) as crawler:
url = "ADDRESS_OF_A_CHALLENGING_WEBSITE"
result = await crawler.arun(
url,
config=run_config,
magic=True,
)
print(f"Successfully crawled {url}")
print(f"Content length: {len(result.markdown)}")-
🚀 Deep Crawling System: Explore websites beyond initial URLs with three strategies:
- BFS Strategy: Breadth-first search explores websites level by level
- DFS Strategy: Depth-first search explores each branch deeply before backtracking
- BestFirst Strategy: Uses scoring functions to prioritize which URLs to crawl next
-
Page Limiting: Control the maximum number of pages to crawl with
max_pagesparameter - Score Thresholds: Filter URLs based on relevance scores
- ⚡ Memory-Adaptive Dispatcher: Dynamically adjusts concurrency based on system memory with built-in rate limiting
-
🔄 Multiple Crawling Strategies:
- AsyncPlaywrightCrawlerStrategy: Browser-based crawling with JavaScript support (Default)
- AsyncHTTPCrawlerStrategy: Fast, lightweight HTTP-only crawler for simple tasks
- 🐳 Docker Deployment: Easy deployment with FastAPI server and streaming/non-streaming endpoints
-
💻 Command-Line Interface: New
crwlCLI provides convenient terminal access to all features with intuitive commands and configuration options - 👤 Browser Profiler: Create and manage persistent browser profiles to save authentication states, cookies, and settings for seamless crawling of protected content
- 🧠 Crawl4AI Coding Assistant: AI-powered coding assistant to answer your question for Crawl4ai, and generate proper code for crawling.
-
🏎️ LXML Scraping Mode: Fast HTML parsing using the
lxmllibrary for improved performance -
🌐 Proxy Rotation: Built-in support for proxy switching with
RoundRobinProxyStrategy - 🤖 LLM Content Filter: Intelligent markdown generation using LLMs
- 📄 PDF Processing: Extract text, images, and metadata from PDF files
- 🔗 URL Redirection Tracking: Automatically follow and record HTTP redirects
- 🤖 LLM Schema Generation: Easily create extraction schemas with LLM assistance
- 🔍 robots.txt Compliance: Respect website crawling rules
Read the full details in our 0.5.0 Release Notes or check the CHANGELOG.
Crawl4AI follows standard Python version numbering conventions (PEP 440) to help users understand the stability and features of each release.
Our version numbers follow this pattern: MAJOR.MINOR.PATCH (e.g., 0.4.3)
We use different suffixes to indicate development stages:
-
dev(0.4.3dev1): Development versions, unstable -
a(0.4.3a1): Alpha releases, experimental features -
b(0.4.3b1): Beta releases, feature complete but needs testing -
rc(0.4.3rc1): Release candidates, potential final version
-
Regular installation (stable version):
pip install -U crawl4ai
-
Install pre-release versions:
pip install crawl4ai --pre
-
Install specific version:
pip install crawl4ai==0.4.3b1
We use pre-releases to:
- Test new features in real-world scenarios
- Gather feedback before final releases
- Ensure stability for production users
- Allow early adopters to try new features
For production environments, we recommend using the stable version. For testing new features, you can opt-in to pre-releases using the --pre flag.
🚨 Documentation Update Alert: We're undertaking a major documentation overhaul next week to reflect recent updates and improvements. Stay tuned for a more comprehensive and up-to-date guide!
For current documentation, including installation instructions, advanced features, and API reference, visit our Documentation Website.
To check our development plans and upcoming features, visit our Roadmap.
📈 Development TODOs
- [x] 0. Graph Crawler: Smart website traversal using graph search algorithms for comprehensive nested page extraction
- [ ] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
- [ ] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
- [ ] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
- [ ] 4. Automated Schema Generator: Convert natural language to extraction schemas
- [ ] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
- [ ] 6. Web Embedding Index: Semantic search infrastructure for crawled content
- [ ] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
- [ ] 8. Performance Monitor: Real-time insights into crawler operations
- [ ] 9. Cloud Integration: One-click deployment solutions across cloud providers
- [ ] 10. Sponsorship Program: Structured support system with tiered benefits
- [ ] 11. Educational Content: "How to Crawl" video series and interactive tutorials
We welcome contributions from the open-source community. Check out our contribution guidelines for more information.
I'll help modify the license section with badges. For the halftone effect, here's a version with it:
Here's the updated license section:
This project is licensed under the Apache License 2.0 with a required attribution clause. See the Apache 2.0 License file for details.
When using Crawl4AI, you must include one of the following attribution methods:
Add one of these badges to your README, documentation, or website:
| Theme | Badge |
|---|---|
| Disco Theme (Animated) |  |
| Night Theme (Dark with Neon) |  |
| Dark Theme (Classic) |  |
| Light Theme (Classic) |  |
HTML code for adding the badges:
<!-- Disco Theme (Animated) -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-disco.svg" alt="Powered by Crawl4AI" width="200"/>
</a>
<!-- Night Theme (Dark with Neon) -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-night.svg" alt="Powered by Crawl4AI" width="200"/>
</a>
<!-- Dark Theme (Classic) -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-dark.svg" alt="Powered by Crawl4AI" width="200"/>
</a>
<!-- Light Theme (Classic) -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-light.svg" alt="Powered by Crawl4AI" width="200"/>
</a>
<!-- Simple Shield Badge -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://img.shields.io/badge/Powered%20by-Crawl4AI-blue?style=flat-square" alt="Powered by Crawl4AI"/>
</a>Add this line to your documentation:
This project uses Crawl4AI (https://github.com/unclecode/crawl4ai) for web data extraction.
If you use Crawl4AI in your research or project, please cite:
@software{crawl4ai2024,
author = {UncleCode},
title = {Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper},
year = {2024},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/unclecode/crawl4ai}},
commit = {Please use the commit hash you're working with}
}Text citation format:
UncleCode. (2024). Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper [Computer software].
GitHub. https://github.com/unclecode/crawl4ai
For questions, suggestions, or feedback, feel free to reach out:
- GitHub: unclecode
- Twitter: @unclecode
- Website: crawl4ai.com
Happy Crawling! 🕸️🚀
Our mission is to unlock the value of personal and enterprise data by transforming digital footprints into structured, tradeable assets. Crawl4AI empowers individuals and organizations with open-source tools to extract and structure data, fostering a shared data economy.
We envision a future where AI is powered by real human knowledge, ensuring data creators directly benefit from their contributions. By democratizing data and enabling ethical sharing, we are laying the foundation for authentic AI advancement.
🔑 Key Opportunities
- Data Capitalization: Transform digital footprints into measurable, valuable assets.
- Authentic AI Data: Provide AI systems with real human insights.
- Shared Economy: Create a fair data marketplace that benefits data creators.
🚀 Development Pathway
- Open-Source Tools: Community-driven platforms for transparent data extraction.
- Digital Asset Structuring: Tools to organize and value digital knowledge.
- Ethical Data Marketplace: A secure, fair platform for exchanging structured data.
For more details, see our full mission statement.
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Inferable is an open source platform that helps users build reliable LLM-powered agentic automations at scale. It offers a managed agent runtime, durable tool calling, zero network configuration, multiple language support, and is fully open source under the MIT license. Users can define functions, register them with Inferable, and create runs that utilize these functions to automate tasks. The platform supports Node.js/TypeScript, Go, .NET, and React, and provides SDKs, core services, and bootstrap templates for various languages.
daydreams
Daydreams is a generative agent library designed for playing onchain games by injecting context. It is chain agnostic and allows users to perform onchain tasks, including playing any onchain game. The tool is lightweight and powerful, enabling users to define game context, register actions, set goals, monitor progress, and integrate with external agents. Daydreams aims to be 'lite' and 'composable', dynamically generating code needed to play games. It is currently in pre-alpha stage, seeking feedback and collaboration for further development.
jido
Jido is a toolkit for building autonomous, distributed agent systems in Elixir. It provides the foundation for creating smart, composable workflows that can evolve and respond to their environment. Geared towards Agent builders, it contains core state primitives, composable actions, agent data structures, real-time sensors, signal system, skills, and testing tools. Jido is designed for multi-node Elixir clusters and offers rich helpers for unit and property-based testing.
booster
Booster is a powerful inference accelerator designed for scaling large language models within production environments or for experimental purposes. It is built with performance and scaling in mind, supporting various CPUs and GPUs, including Nvidia CUDA, Apple Metal, and OpenCL cards. The tool can split large models across multiple GPUs, offering fast inference on machines with beefy GPUs. It supports both regular FP16/FP32 models and quantised versions, along with popular LLM architectures. Additionally, Booster features proprietary Janus Sampling for code generation and non-English languages.
ExtractThinker
ExtractThinker is a library designed for extracting data from files and documents using Language Model Models (LLMs). It offers ORM-style interaction between files and LLMs, supporting multiple document loaders such as Tesseract OCR, Azure Form Recognizer, AWS TextExtract, and Google Document AI. Users can customize extraction using contract definitions, process documents asynchronously, handle various document formats efficiently, and split and process documents. The project is inspired by the LangChain ecosystem and focuses on Intelligent Document Processing (IDP) using LLMs to achieve high accuracy in document extraction tasks.
rigging
Rigging is a lightweight LLM framework designed to simplify the usage of language models in production code. It offers structured Pydantic models for text output, supports various models like LiteLLM and transformers, and provides features such as defining prompts as python functions, simple tool use, storing models as connection strings, async batching for large scale generation, and modern Python support with type hints and async capabilities. Rigging is developed by dreadnode and is suitable for tasks like building chat pipelines, running completions, tracking behavior with tracing, playing with generation parameters, and scaling up with iterating and batching.
Agentarium
Agentarium is a powerful Python framework for managing and orchestrating AI agents with ease. It provides a flexible and intuitive way to create, manage, and coordinate interactions between multiple AI agents in various environments. The framework offers advanced agent management, robust interaction management, a checkpoint system for saving and restoring agent states, data generation through agent interactions, performance optimization, flexible environment configuration, and an extensible architecture for customization.
Learn_Prompting
Learn Prompting is a platform offering free resources, courses, and webinars to master prompt engineering and generative AI. It provides a Prompt Engineering Guide, courses on Generative AI, workshops, and the HackAPrompt competition. The platform also offers AI Red Teaming and AI Safety courses, research reports on prompting techniques, and welcomes contributions in various forms such as content suggestions, translations, artwork, and typo fixes. Users can locally develop the website using Visual Studio Code, Git, and Node.js, and run it in development mode to preview changes.
simba
Simba is an open source, portable Knowledge Management System (KMS) designed to seamlessly integrate with any Retrieval-Augmented Generation (RAG) system. It features a modern UI and modular architecture, allowing developers to focus on building advanced AI solutions without the complexities of knowledge management. Simba offers a user-friendly interface to visualize and modify document chunks, supports various vector stores and embedding models, and simplifies knowledge management for developers. It is community-driven, extensible, and aims to enhance AI functionality by providing a seamless integration with RAG-based systems.
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Forza-Mods-AIO
Forza Mods AIO is a free and open-source tool that enhances the gaming experience in Forza Horizon 4 and 5. It offers a range of time-saving and quality-of-life features, making gameplay more enjoyable and efficient. The tool is designed to streamline various aspects of the game, improving user satisfaction and overall enjoyment.
hass-ollama-conversation
The Ollama Conversation integration adds a conversation agent powered by Ollama in Home Assistant. This agent can be used in automations to query information provided by Home Assistant about your house, including areas, devices, and their states. Users can install the integration via HACS and configure settings such as API timeout, model selection, context size, maximum tokens, and other parameters to fine-tune the responses generated by the AI language model. Contributions to the project are welcome, and discussions can be held on the Home Assistant Community platform.
crawl4ai
Crawl4AI is a powerful and free web crawling service that extracts valuable data from websites and provides LLM-friendly output formats. It supports crawling multiple URLs simultaneously, replaces media tags with ALT, and is completely free to use and open-source. Users can integrate Crawl4AI into Python projects as a library or run it as a standalone local server. The tool allows users to crawl and extract data from specified URLs using different providers and models, with options to include raw HTML content, force fresh crawls, and extract meaningful text blocks. Configuration settings can be adjusted in the `crawler/config.py` file to customize providers, API keys, chunk processing, and word thresholds. Contributions to Crawl4AI are welcome from the open-source community to enhance its value for AI enthusiasts and developers.
MaterialSearch
MaterialSearch is a tool for searching local images and videos using natural language. It provides functionalities such as text search for images, image search for images, text search for videos (providing matching video clips), image search for videos (searching for the segment in a video through a screenshot), image-text similarity calculation, and Pexels video search. The tool can be deployed through the source code or Docker image, and it supports GPU acceleration. Users can configure the tool through environment variables or a .env file. The tool is still under development, and configurations may change frequently. Users can report issues or suggest improvements through issues or pull requests.
tenere
Tenere is a TUI interface for Language Model Libraries (LLMs) written in Rust. It provides syntax highlighting, chat history, saving chats to files, Vim keybindings, copying text from/to clipboard, and supports multiple backends. Users can configure Tenere using a TOML configuration file, set key bindings, and use different LLMs such as ChatGPT, llama.cpp, and ollama. Tenere offers default key bindings for global and prompt modes, with features like starting a new chat, saving chats, scrolling, showing chat history, and quitting the app. Users can interact with the prompt in different modes like Normal, Visual, and Insert, with various key bindings for navigation, editing, and text manipulation.
openkore
OpenKore is a custom client and intelligent automated assistant for Ragnarok Online. It is a free, open source, and cross-platform program (Linux, Windows, and MacOS are supported). To run OpenKore, you need to download and extract it or clone the repository using Git. Configure OpenKore according to the documentation and run openkore.pl to start. The tool provides a FAQ section for troubleshooting, guidelines for reporting issues, and information about botting status on official servers. OpenKore is developed by a global team, and contributions are welcome through pull requests. Various community resources are available for support and communication. Users are advised to comply with the GNU General Public License when using and distributing the software.
QA-Pilot
QA-Pilot is an interactive chat project that leverages online/local LLM for rapid understanding and navigation of GitHub code repository. It allows users to chat with GitHub public repositories using a git clone approach, store chat history, configure settings easily, manage multiple chat sessions, and quickly locate sessions with a search function. The tool integrates with `codegraph` to view Python files and supports various LLM models such as ollama, openai, mistralai, and localai. The project is continuously updated with new features and improvements, such as converting from `flask` to `fastapi`, adding `localai` API support, and upgrading dependencies like `langchain` and `Streamlit` to enhance performance.
extension-gen-ai
The Looker GenAI Extension provides code examples and resources for building a Looker Extension that integrates with Vertex AI Large Language Models (LLMs). Users can leverage the power of LLMs to enhance data exploration and analysis within Looker. The extension offers generative explore functionality to ask natural language questions about data and generative insights on dashboards to analyze data by asking questions. It leverages components like BQML Remote Models, BQML Remote UDF with Vertex AI, and Custom Fine Tune Model for different integration options. Deployment involves setting up infrastructure with Terraform and deploying the Looker Extension by creating a Looker project, copying extension files, configuring BigQuery connection, connecting to Git, and testing the extension. Users can save example prompts and configure user settings for the extension. Development of the Looker Extension environment includes installing dependencies, starting the development server, and building for production.
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sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.
teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.
ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.
classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.
chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.
BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.