Search Results for "define" - Page 2
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Showing 187 open source projects for "define"
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KerasTuner
A Hyperparameter Tuning Library for Keras
KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms. -
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Flow-Next
Plan-first AI workflow plugin for Claude Code, OpenAI Codex
Flow-Next is a workflow orchestration tool designed to manage complex processes by structuring tasks into organized and repeatable pipelines. It focuses on improving productivity by allowing users to define workflows that can be executed step by step or in parallel. The system emphasizes modularity, enabling tasks to be broken down into smaller components that can be reused across different workflows. It supports integration with various tools and services, making it adaptable to different environments. The project is designed to handle both simple and complex workflows, providing flexibility for a wide range of use cases. ... -
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Dagger
Containerized automation engine for programmable CI/CD workflows
Dagger is an open source automation engine designed to build, test, and deliver software in a consistent and programmable way. It enables developers to define software delivery workflows using code instead of complex shell scripts or configuration files. Dagger executes tasks inside containers, ensuring that automation runs in identical environments across local machines, CI servers, or cloud infrastructure. Dagger provides a core execution engine and system API that orchestrates containers, filesystems, secrets, repositories, and other resources needed during development pipelines. ... -
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ClawRouter
Smart LLM router
...Because distributed AI systems often involve many services, agents, and runtime components interacting with each other and with external APIs, ClawRouter helps ensure that communication paths remain clear, efficient, and adaptable as systems scale. The framework supports plugin-based extensions so developers can define custom protocols, transformation hooks, and monitoring handlers without modifying core routing logic. It also offers operational features like health checking, metrics reporting, and failure handling that make production deployments more reliable. -
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LangGraph
Build resilient language agents as graphs
LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features. -
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CrewAI
Framework for orchestrating role-playing, autonomous AI agents
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. The power of AI collaboration has too much to offer. CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI... -
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Open Multi-Agent
One runTeam() call from goal to result
...It focuses on distributing responsibilities across specialized agents, each handling a specific part of a problem, such as planning, execution, or validation. The system emphasizes modularity, allowing developers to define agent roles, communication protocols, and workflows. It supports iterative collaboration, where agents exchange information and refine outputs collectively. The architecture is designed to be extensible, enabling integration with external tools and APIs to expand agent capabilities. It is particularly useful for research, automation, and development workflows that require multiple perspectives or stages of processing. ... -
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Cua
Open-source infrastructure for Computer-Use Agents. Sandboxes
Cua is an open-source command-line utility and workflow orchestrator designed to help developers define, compose, and run common tasks with a unified interface, promoting consistency and reuse across projects. It introduces a declarative syntax for specifying build scripts, automation pipelines, environment setups, and project-specific commands so contributors don’t need to memorize disparate scripts or tooling across languages and ecosystems. -
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Cofounder
AI tool that generates full-stack web apps with generative UI systems
...Generated projects are stored locally and can be launched using standard development commands, allowing developers to run and modify the code that the system produces. Cofounder relies on configurable nodes and sequences that define how AI operations are executed. -
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LangGraph.js
Framework to build resilient language agents as graphs
LangGraphJS is a JavaScript framework designed to build stateful AI applications and autonomous agents using graph-based execution models. Developed as part of the LangChain ecosystem, the framework allows developers to represent complex AI workflows as graphs where nodes represent tasks and edges define the flow of execution. This structure makes it easier to implement long-running agents, multi-step reasoning pipelines, and workflows that require persistent state. LangGraphJS supports advanced capabilities such as branching logic, loops, and conditional execution, enabling developers to build sophisticated AI systems that can adapt to dynamic conditions. ... -
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BAML
The AI framework that adds the engineering to prompt engineering
...This design allows developers to treat language model interactions as predictable software components rather than ad-hoc prompt strings. The framework enables developers to define prompt logic in a dedicated language while integrating it into applications written in various programming languages such as Python, TypeScript, Ruby, and Go. BAML also allows developers to specify which models are used for each prompt and how outputs should be validated or structured. By converting prompt engineering into a more formal programming workflow, the framework improves reliability, debugging, and maintainability of AI systems. -
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CyberStrikeAI
CyberStrikeAI is an AI-native security testing platform built in Go
...The platform integrates over 100 security tools out of the box and pairs them with an intelligent orchestration engine that can be directed via natural language or policy definitions, allowing users to automate reconnaissance, scanning, exploitation, and reporting without manual sequencing of tools. It supports role-based testing, letting teams define security roles with tailored tool access and prompts, and includes a skills system that encapsulates specialized testing strategies that the AI can incorporate into its planning. Through comprehensive lifecycle management, results are tracked, aggregated, and visualized, with support for versioned persistence, search, and risk severity scoring. -
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Agency Agents
A complete AI agency at your fingertips
...It is designed around the idea of “agency,” where each agent has a defined role, responsibility, and interaction pattern within a larger system. The framework enables developers to define workflows where agents communicate, delegate tasks, and share context, creating a distributed problem-solving environment. It supports modular design, allowing agents to be composed into reusable systems that can be adapted across different use cases. The project emphasizes clarity in agent responsibilities, which helps reduce ambiguity and improve reliability in multi-agent workflows. ... -
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GenericAgent
Self-evolving autonomous agent framework
The GenericAgent project is a flexible framework for building autonomous AI agents that can operate across diverse tasks and environments. It is designed around modularity, allowing developers to define agents with interchangeable components such as tools, memory systems, and reasoning strategies. The architecture emphasizes generality, enabling the same agent framework to be adapted for different domains including coding, research, and task automation. It integrates with modern language models to provide planning, execution, and iterative reasoning capabilities, making it suitable for complex workflows. ... -
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Pluely
The Open Source Alternative to Cluely
Pluely is an open-source AI automation framework designed to simplify the development and deployment of AI-driven workflows across applications and services. The system focuses on orchestrating tasks performed by large language models and other AI components, allowing developers to define structured workflows where models interact with tools, APIs, and external systems. By providing a modular architecture for building AI pipelines, the platform enables developers to connect multiple processing steps such as data retrieval, prompt execution, analysis, and response generation. The project emphasizes flexibility, allowing developers to extend the platform with custom integrations and automation logic. ... -
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ALLWEONE
AI tool that generates custom presentations with real-time editing
Presentation AI by ALLWEONE is an open source tool that uses artificial intelligence to generate complete slide decks from a simple prompt. It helps users create professional presentations quickly, with support for customizable themes, layouts, and styles. You can define slide count, language, and tone, then review or edit the AI-generated outline before finalising. Slides are built in real time, allowing you to watch content develop as the system works. Presentation AI by ALLWEONE includes image generation, rich text editing, and drag-and-drop functionality for easy adjustments. It also supports presentation mode, so you can present directly within the app. ... -
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Opik
Debug, evaluate, and monitor your LLMapps, RAG systems, and agentic AI
...Log traces during development and in production. Run experiments with different prompts and evaluate against a test set. Choose and run pre-configured evaluation metrics or define your own with our convenient SDK library. Consult built-in LLM judges for complex issues like hallucination detection, factuality, and moderation. -
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SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch
...It provides implementations of various state-of-the-art RL algorithms and emphasizes reproducibility, scalability, and detailed experiment tracking. SLM Lab is structured around a flexible experiment management system, allowing users to define, run, and analyze RL experiments efficiently. -
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Inbox Zero
AI assistant that automates email tasks to help achieve inbox zero
...It aims to reduce the time spent handling email by automatically organizing, prioritizing, and responding to messages using customizable automation rules and artificial intelligence. Users can define prompts or rule-based actions that guide how the assistant processes incoming messages, enabling automated workflows for sorting, replying, or handling routine communication. Inbox Zero is structured as a modern web application built with a monorepo architecture that contains multiple applications and shared packages, allowing modular development and easier maintenance. ... -
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TreeQuest
A Tree Search Library with Flexible API for LLM Inference-Time Scaling
TreeQuest, developed by SakanaAI, is a versatile Python library implementing adaptive tree search algorithms—such as AB‑MCTS—for enhancing inference-time performance of large language models (LLMs). It allows developers to define custom state-generation and scoring functions (e.g., via LLMs), and then efficiently explores possible answer trees during runtime. With support for multi-LLM collaboration, checkpointing, and mixed policies, TreeQuest enables smarter, trial‑and‑error question answering by leveraging both breadth (multiple attempts) and depth (iterative refinement) strategies to find better outputs dynamically -
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Pixeltable
Data Infrastructure providing an approach to multimodal AI workloads
...Unlike traditional architectures that require multiple tools such as databases, vector stores, and workflow orchestrators, Pixeltable unifies these functions within a table-based abstraction. Developers define data transformations and AI operations using computed columns on tables, allowing pipelines to evolve incrementally as new data or models are added. The framework supports multimodal content including images, video, text, and audio, enabling applications such as retrieval-augmented generation systems, semantic search, and multimedia analytics. -
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Defang
Defang CLI and sample projects
...The Defang Command Line Interface (CLI) facilitates interactions with the platform, offering installation options via shell scripts, Homebrew, Winget, Nix, or direct download. Developers can define services using compose.yaml files, which Defang utilizes to deploy applications to the cloud. -
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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. ...
