Search Results for "define" - Page 3
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Showing 200 open source projects for "define"
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1
Ruler AI
Centralize and sync AI coding rules across tools and projects
...Instead of maintaining separate configuration files for tools like GitHub Copilot, Claude, or Cursor, it stores all rules in a single .ruler/ directory and distributes them automatically. This reduces duplication, avoids inconsistent outputs, and keeps guidance aligned as projects evolve. Ruler supports nested rule loading, allowing teams to define context-specific instructions for different parts of a codebase. It also manages MCP server settings, automates .gitignore updates, and provides simple commands to initialize, apply, and revert configurations. Designed for teams using multiple AI agents, it improves workflow consistency, simplifies onboarding, and ensures all assistants follow the same standards across projects. -
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TensorRT LLM
TensorRT LLM provides users with an easy-to-use Python API
TensorRT-LLM is an open-source high-performance inference library specifically designed to optimize and accelerate large language model deployment on NVIDIA GPUs. It provides a Python-based API built on top of PyTorch that allows developers to define, customize, and deploy LLMs efficiently across a variety of hardware configurations, from single GPUs to large multi-node clusters. The library focuses on maximizing throughput and minimizing latency through advanced techniques such as quantization, custom attention kernels, and optimized memory management strategies. It includes support for cutting-edge inference methods like speculative decoding and inflight batching, enabling real-time and large-scale AI applications. ... -
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AG2
Framework for building and orchestrating multi-agent AI systems
AG2 is an open source framework designed to support the creation and coordination of multiple AI agents working together to solve complex tasks. It provides abstractions that allow developers to define agents with distinct roles, responsibilities, and communication patterns, enabling collaborative problem-solving workflows. AG2 focuses on making multi-agent systems more accessible by simplifying how agents are configured, connected, and executed. It includes mechanisms for agent-to-agent interaction, task delegation, and iterative reasoning, which are essential for building advanced AI-driven applications. ... -
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runprompt
Run LLM prompts from your shell
...It functions as a lightweight, launcher-centric interface where you can type a phrase, partial command, or alias and have RunPrompt suggest or execute relevant actions instantly, reducing the need to memorize long commands or navigate complex directory structures. The project emphasizes extensibility, letting users define custom actions, integrate with existing shell environments, and even leverage fuzzy matching or contextual prompts to narrow down options as you type. Designed to be cross-platform, RunPrompt works with standard shells on Windows, macOS, and Linux while honoring the user’s preferred environment and configurations. -
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Excalibur
Excalibur is a highly opinionated agent harness
...The system likely provides tools for managing tasks, orchestrating processes, or enhancing decision-making capabilities in automated systems. Its design suggests an emphasis on control and precision, allowing users to define how tasks are executed and monitored. It may include abstractions for handling inputs, outputs, and intermediate steps, enabling more predictable behavior in complex workflows. The architecture is likely modular, supporting customization and extension for different use cases. This makes it suitable for experimentation as well as integration into larger systems. ... -
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Ralph Wiggum Marketer
A Claude Code Plugin that provides an autonomous AI copywriter
...It leverages the Ralph Wiggum loop concept — a continuous iteration pattern named after the iconic character that symbolizes persistent, repeated refinement — to let Claude Code keep iterating on content until predefined completion criteria are met, rather than stopping after a single output. This plugin integrates with Claude Code workflows so that marketers or product teams can define high-level goals, seed their product or service context, and then have the AI autonomously draft, refine, and polish marketing materials with minimal human intervention. -
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spacy-llm
Integrating LLMs into structured NLP pipelines
Large Language Models (LLMs) feature powerful natural language understanding capabilities. With only a few (and sometimes no) examples, an LLM can be prompted to perform custom NLP tasks such as text categorization, named entity recognition, coreference resolution, information extraction and more. This package integrates Large Language Models (LLMs) into spaCy, featuring a modular system for fast prototyping and prompting, and turning unstructured responses into robust outputs for various... -
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HunyuanWorld-Voyager
RGBD video generation model conditioned on camera input
HunyuanWorld-Voyager is a next-generation video diffusion framework developed by Tencent-Hunyuan for generating world-consistent 3D scene videos from a single input image. By leveraging user-defined camera paths, it enables immersive scene exploration and supports controllable video synthesis with high realism. The system jointly produces aligned RGB and depth video sequences, making it directly applicable to 3D reconstruction tasks. At its core, Voyager integrates a world-consistent video... -
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Ars Contexta
Claude Code plugin that generates individualized knowledge systems
...The system emphasizes long-term knowledge retention by structuring information into reusable and evolving documents rather than ephemeral chat logs. It allows users to define their thinking style and workflows, which the system uses to tailor how knowledge is captured and organized. Arscontexta integrates directly into coding workflows, making it particularly useful for developers who want to accumulate insights over time. The resulting knowledge base can be reused across sessions, improving productivity and reducing repeated explanations. -
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Nexent
Zero-code platform for building AI agents from natural language input
Nexent is an open source platform designed to enable users to create intelligent agents using natural language instead of traditional programming or visual orchestration tools. It focuses on a zero-code approach, allowing users to define workflows and agent behavior purely through language prompts, significantly lowering the barrier to entry for AI development. Built on the MCP ecosystem, Nexent integrates a wide range of tools, models, and data sources into a unified environment for agent creation and execution. Nexent supports multi-agent collaboration, enabling multiple intelligent agents to interact and coordinate tasks within complex workflows. ... -
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AIPex
AI browser automation assistant, no migration and privacy first
...It supports modular plugin architecture so teams can extend or customize how assistants behave based on project conventions, code standards, or tooling preferences. AIPex also includes orchestration pipelines that let teams define multi-step AI-driven transformations — for example, generating code then running validation, producing documentation, and opening change requests — all within a unified pattern. -
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Agent Control
Centralized agent control plane for governing runtime agent behavior
Agent Control is a centralized control plane for governing AI agent behavior at runtime across different frameworks and deployment environments. It lets teams define controls once and apply them consistently to agents without rewriting the agent’s core code. The platform evaluates agent inputs and outputs against configurable policies to reduce risks such as prompt injection, unsafe responses, sensitive data exposure, and policy drift. It is designed for production environments where organizations need observability, enforcement, and governance around autonomous or semi-autonomous AI systems. ... -
13
typeui.sh
Design system skills for agentic tools
...It focuses on improving developer productivity by reducing runtime errors and ensuring consistency across UI components. The framework integrates closely with TypeScript, allowing developers to define interfaces and components with strict type validation. It supports component-based architecture, making it easier to build scalable and maintainable applications. TypeUI also emphasizes reusability, enabling developers to create standardized components that can be shared across projects. Its design encourages clean and predictable code, which is especially valuable in large-scale applications. ... -
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Markdown Viewer Agent Skills
Opinionated skills for AI coding agents to create stunning diagrams
Markdown Viewer Agent Skills is a repository that provides a collection of modular AI “skills” designed to extend the capabilities of agents or tools that operate on markdown-based workflows. These skills are typically structured as self-contained instruction sets that define how an AI should perform specific tasks, such as formatting, analysis, or content transformation. The project emphasizes simplicity and composability, allowing developers to integrate these skills into existing systems without requiring complex infrastructure. Each skill is designed to be reusable and adaptable, making it easy to customize behavior for different use cases. ... -
15
GoGogot
Lightweight self-hosted AI agent
...The system is likely designed to enable rapid execution of commands and processes, acting as a bridge between manual scripting and more advanced agent frameworks. It emphasizes simplicity and speed, allowing developers to define and run tasks without heavy configuration or overhead. The architecture suggests a modular approach where tasks can be composed and reused across different contexts. It may also incorporate elements of automation pipelines, enabling sequential or conditional execution of operations. The project is particularly suited for developers who want to experiment with automation concepts without adopting complex infrastructure. ... -
16
ChainForge
An open-source visual programming environment
...The platform enables rapid experimentation by generating permutations of prompts and inputs, making it possible to test hundreds of variations in parallel and analyze performance trends more effectively. It also includes evaluation nodes that allow developers to define scoring functions, enabling automated benchmarking of outputs based on custom criteria such as accuracy, formatting, or relevance. -
17
Nimbalyst
Run multiple Codex and Claude Code AI sessions
Crystal is an open-source project focused on building a lightweight and flexible system for managing structured data, workflows, or automation pipelines, typically oriented toward developer productivity and extensible backend tooling. It is designed with modularity in mind, allowing developers to define reusable components and compose them into larger workflows that can adapt to different use cases. The project emphasizes simplicity and clarity, making it easier to understand and extend compared to heavier enterprise frameworks. Crystal often leverages modern programming practices and clean architecture principles to ensure maintainability and scalability as projects grow. ... -
18
Kener
Kener is a Modern Self hosted Status Page, batteries included
Kener: Open-source Node.js status page tool, designed to make service monitoring and incident handling a breeze. It offers a sleek and user-friendly interface that simplifies tracking service outages and improves how we communicate during incidents. And the best part? Kener integrates seamlessly with GitHub, making incident management a team effort—making it easier for us to track and fix issues together in a collaborative and friendly environment. -
19
Fairlearn
A Python package to assess and improve fairness of ML models
...Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harm. Fairness of AI systems is about more than simply running lines of code. In each use case, both societal and technical aspects shape who might be harmed by AI systems and how. There are many complex sources of unfairness and a variety of societal and technical processes for mitigation, not just the mitigation algorithms in our library. -
20
Sacred
Sacred is a tool to help you configure, andorganize IDSIA experiments
...It is designed to do all the tedious overhead work that you need to do around your actual experiment. A very convenient way of the local variables in a function to define the parameters your experiment uses. You can access all parameters of your configuration from every function. They are automatically injected by name. You get a powerful command-line interface for each experiment that you can use to change parameters and run different variants. Observers that log all kinds of information about your experiment, its dependencies, the configuration you used, the machine it is run on, and of course the result. ... -
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Flux.jl
Relax! Flux is the ML library that doesn't make you tensor
...It's a 100% pure Julia stack and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable. Flux provides a single, intuitive way to define models, just like mathematical notation. Julia transparently compiles your code, optimizing and fusing kernels for the GPU, for the best performance. Existing Julia libraries are differentiable and can be incorporated directly into Flux models. Cutting-edge models such as Neural ODEs are first class, and Zygote enables overhead-free gradients. ... -
22
Polyglot
Cross-platform AI language practice app
...It is built around conversational large language models and Azure based text to speech services, turning them into an interactive environment for speaking practice in multiple languages. Users can define custom AI personas, choose languages, and configure their own OpenAI and Azure keys so they retain control over which backends they use. The app supports speech recognition with quick keyboard shortcuts, allowing learners to hold down a key to speak and release it to submit for recognition and response. It includes translation features, dark mode, playback of the user’s own recorded speech, and word highlighting that tracks the progress of synthesized audio to make following along easier. ... -
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Codex Autoresearch
A codex plugin for running optimization loops inside a codebase
...It generalizes the concept of autoresearch beyond machine learning, allowing optimization of test coverage, latency, lint errors, and overall code quality. Developers define a goal and verification command, and the agent continuously runs experiments to reach the desired outcome. The framework supports multiple operational modes, including debugging, planning, security auditing, and release validation. It can run unattended for extended periods, producing logs of experiments and improvements. This approach transforms software development into an iterative, evidence-driven optimization process rather than manual trial and error. -
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AutoAgent AI
Autonomous harness engineering
AutoAgent is an experimental AI framework focused on autonomous agent engineering, where a meta-agent iteratively improves another agent’s architecture without direct human intervention. Instead of manually tuning prompts or workflows, developers define high-level goals in a configuration file, and the system continuously modifies its own tools, orchestration, and logic based on benchmark performance. It operates through a loop of testing, analyzing failures, and refining the agent’s configuration to maximize a scoring metric. The framework uses a single-file agent harness combined with structured tasks and evaluation suites to guide optimization. ... -
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adversarial-spec
A Claude Code plugin that iteratively refines product specifications
adversarial-spec is a framework focused on designing and testing systems using adversarial thinking to uncover weaknesses and improve robustness. It encourages developers to define specifications that anticipate failure modes, edge cases, and malicious inputs before implementing solutions. The project emphasizes proactive design, ensuring that systems are built with resilience in mind from the beginning. It provides structured approaches for identifying vulnerabilities and stress-testing assumptions. The framework can be applied across domains, including software development, AI systems, and security workflows. ...
