Search Results for "include"
Sort By:
Showing 22 open source projects for "include"
View related business solutions-
The Most Awarded Employee Time Clock SoftwareCloud based time clock solution that pre-populates reports for payroll. Employees can punch in on their desktop or mobile devices. Punching in & out is intuitive for your employees & easy for you to view & export time. Employees can clock in using a browser or our Google, iOS, & Android apps. You can view who's working, their GPS position or even limit where they can punch. We integrate with QuickBooks, ADP, Paychex, & SurePayroll while also offering Excel exports. Advanced features such as PTO Accrual Tracking, Punch Rounding, Job Codes, QR Codes, Automatic Breaks, & SSO are all included in our cloud based time clock.
-
The AI coach for teams, built on validated assessments.Give managers and teams proactive, contextual coaching to lead effectively, communicate clearly, and navigate real work situations as they happen.
-
1
OpenClaude
Claude Code opened to any LLM
...It likely includes support for executing tasks, managing context, and interacting with external tools, allowing agents to perform real-world actions beyond simple text generation. The architecture emphasizes flexibility, enabling developers to adapt the system to different use cases and integrate it with various models or APIs. It may also include modular components for extending functionality, such as plugins or skills. The project reflects a broader trend toward open and decentralized AI systems that prioritize transparency and control. -
2
Neuron AI
The PHP Agentic Framework to build production-ready AI driven apps
...Its design is modular, so developers can swap model providers with minimal changes to their application code, which makes it practical for teams that need flexibility across vendors. The project also supports structured output, monitoring, MCP connectivity, and workflow patterns that include human-in-the-loop intervention when automated flows need review or correction. -
3
Qwen
The official repo of Qwen chat & pretrained large language model
...These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a versatile tool for developers looking to integrate advanced AI functionalities into their applications. -
4
Lobe Icons
Brings AI/LLM brand logos to your React & React Native apps
Lobe Icons is an open-source icon library designed to provide developers with a comprehensive collection of logos and visual assets representing popular artificial intelligence platforms, language models, and related technologies. The project focuses on making it easy for developers to include recognizable AI brand icons in applications such as dashboards, AI tools, documentation sites, or developer portals. The library includes icons for a wide range of AI providers and models, allowing developers to visually represent integrations with tools such as large language models, AI APIs, and machine learning platforms. These icons are distributed in multiple formats including SVG, PNG, and WebP so they can be used in both web and mobile applications. -
Feroot AI automates website security with 24/7 monitoringFeroot unifies JavaScript behavior analysis, web compliance scanning, third-party script monitoring, consent enforcement, and data privacy posture management to stop Magecart, formjacking, and unauthorized tracking.
-
5
LangChain
⚡ Building applications with LLMs through composability ⚡
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. -
6
Rogue
AI Agent Evaluator & Red Team Platform
...The system allows developers to define specific scenarios, expected outcomes, and business rules so that the framework can verify whether an agent behaves according to required policies. During testing, Rogue records conversations and produces detailed reports that explain whether the agent passed or failed each scenario. These reports include reasoning and evidence, helping developers understand why a particular failure occurred. -
7
files-to-prompt
Concatenate a directory full of files into a single prompt
...The tool is aimed at workflows where you want to ask an LLM questions about a whole codebase, documentation set, or notes folder without manually copying files together. It includes rich filtering controls, letting you limit by extension, include or skip hidden files, and ignore paths that match glob patterns or .gitignore rules. The output format is flexible: you can emit plain text, Markdown with fenced code blocks, or a Claude-XML style format designed for structured multi-file prompts. It can read file paths from stdin (including NUL-separated paths), which makes it easy to combine with find, rg, or other shell tools. -
8
Bedrock Chat
AWS-native chatbot using Bedrock
...The mirror hosted on SourceForge exists primarily to ensure long-term accessibility of the source code and provide alternative download options for developers. Chat platforms of this type frequently include tools for maintaining conversation history, managing prompts, and connecting to multiple model providers. By combining a chat interface with backend AI services, these systems allow users to explore generative AI capabilities in a structured environment. -
9
MemoryOS
MemoryOS is designed to provide a memory operating system
...MemoryOS introduces a hierarchical memory architecture inspired by operating system memory management principles, allowing agents to store, update, retrieve, and generate information from multiple layers of memory. These layers typically include short-term memory for immediate conversation context, mid-term memory for topic-level grouping, and long-term personal memory for persistent knowledge about users or tasks. The system dynamically updates and promotes information between these layers using structured algorithms that prioritize relevance and recency. -
Deliver trusted data with dbtData teams use dbt to codify business logic and make it accessible to the entire organization—for use in reporting, ML modeling, and operational workflows.
-
10
NVIDIA NeMo
Toolkit for conversational AI
...NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training. ... -
11
ML Retreat
Machine Learning Journal for Intermediate to Advanced Topics
...Rather than functioning as a traditional tutorial series, the repository is organized as a learning journey that progressively explores increasingly advanced subjects. Topics include large language models, graph neural networks, mechanistic interpretability, transformer architectures, and emerging research areas such as quantum machine learning. The repository includes references to influential research papers, lectures, and educational content from well-known machine learning educators. -
12
apfel
Apple Intelligence from the command line
...The project appears to follow a philosophy of reducing unnecessary complexity while still enabling practical functionality for developers who prefer lean systems over heavy frameworks. It is designed to be adaptable, allowing developers to extend or modify its behavior depending on their specific use case. Apfel may include utilities or structural patterns that streamline development workflows, particularly in environments where speed and clarity are more important than feature richness. Its architecture likely avoids over-engineering, making it suitable for small projects, prototypes, or educational purposes. The project encourages direct interaction with code rather than relying on extensive abstraction layers, giving developers more control over implementation details. -
13
Extractous
Fast and efficient unstructured data extraction
...For broader format support, the system combines its Rust core with ahead-of-time compiled Apache Tika shared libraries, which allows it to extend parsing coverage while still avoiding traditional server-based overhead. It also supports OCR for images and scanned documents through Tesseract, making it useful for document ingestion pipelines that include image-based or scanned inputs. -
14
Kernel Memory
Research project. A Memory solution for users, teams, and applications
...Kernel Memory can ingest documents in multiple formats, process them into embeddings, and store them in searchable indexes. Applications can then query these indexed data sources to retrieve relevant information and include it as context for AI responses. -
15
AI Engineering Transition Path
Research papers and blogs to transition to AI Engineering
...Instead of presenting isolated tutorials, the repository provides a structured pathway that guides engineers through the technical knowledge needed to build and deploy large language model systems. The materials include curated research papers, blog posts, and code examples that explain both theoretical foundations and practical implementation strategies. By consolidating these resources into a single repository, the project helps developers navigate the rapidly expanding AI ecosystem without needing to search through scattered materials. -
16
All Agentic Architectures
Implementation of 17+ agentic architectures
...Each architecture is explained through runnable notebooks that illustrate how the agent works internally and how it interacts with tools, data sources, or other agents. The repository organizes the architectures into a structured learning path that progresses from simple reasoning agents to complex multi-agent systems. Examples include planning agents, tool-using agents, tree-of-thought reasoning systems, and collaborative multi-agent environments. -
17
Generative AI for beginners with JS
Join a time-traveling adventure where you meet history’s legends
...The project is structured as a multi-lesson curriculum that introduces the concepts, tools, and practical techniques required to create generative AI applications. Each lesson includes written explanations, hands-on exercises, quizzes, and supporting videos to help developers learn the material progressively. Topics covered include prompt engineering, building AI-powered applications, working with structured outputs, integrating retrieval-augmented generation, and enabling tool or function calling in AI systems. The repository focuses specifically on how generative AI can be integrated into web, mobile, or desktop applications using JavaScript frameworks and APIs. -
18
PKU Beaver
Constrained Value Alignment via Safe Reinforcement Learning
...To support this process, the project provides datasets containing human-labeled examples that encode both performance preferences and safety constraints across multiple dimensions. These annotations include categories such as harmful language, unethical behavior, privacy violations, and other sensitive topics. By incorporating constraint-based optimization methods, Safe-RLHF trains models that balance reward objectives with safety requirements, ensuring that harmful outputs are penalized during training. -
19
Daily Interview
Datawhale members have compiled a book covering machine learning
...The repository is organized in a structured format that encourages developers to practice solving problems regularly while building a deeper understanding of underlying concepts. Many of the problems include explanations, references, or links to additional learning resources that help users study relevant theory and improve problem-solving skills. The project is particularly useful for developers preparing for coding interviews at technology companies, as it provides a continuous stream of practice material organized by topic and difficulty level. -
20
architecture.of.internet-product
Internet company technical architecture
...It aggregates architectural information about well-known companies such as Google, Facebook, Amazon, Alibaba, and Tencent, as well as other large internet services. The repository organizes materials into categories that include system architecture diagrams, engineering papers, technical presentations, and case studies from major technology conferences. By studying these examples, developers can gain insight into distributed systems design, scalability strategies, and the infrastructure patterns used by high-traffic platforms. -
21
Chat with GPT
An open-source ChatGPT app with a voice
...The platform also integrates speech capabilities by connecting to text-to-speech systems and speech recognition engines, enabling voice-based conversations with the AI assistant. Additional features include message editing, response regeneration, and the ability to share conversations through public links. -
22
aigc
An e-book about the real-world application of LLM
"Building Large Language Model Applications: Application Development and Architecture Design" is an open source e-book about the real-world application of LLM. It introduces the basics and applications of large language models, as well as how to build your own models. These include writing, developing, and managing prompts, exploring what the best large language models can bring, and pattern and architecture design for LLM application development.
- Previous
- You're on page 1
- Next
