Search Results for "parallel"
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Showing 163 open source projects for "parallel"
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Failed Payment Recovery for Subscription BusinessesFlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
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Loan management software that makes it easy.Bryt Software is ideal for lending professionals who are looking for a feature rich loan management system that is intuitive and easy to use. We are 100% cloud-based, software as a service. We believe in providing our customers with fair and honest pricing. Our monthly fees are based on your number of users and we have a minimal implementation charge.
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EnvPool
C++-based high-performance parallel environment execution engine
EnvPool is a fast, asynchronous, and parallel RL environment library designed for scaling reinforcement learning experiments. Developed by SAIL at Singapore, it leverages C++ backend and Python frontend for extremely high-speed environment interaction, supporting thousands of environments running in parallel on a single machine. It's compatible with Gymnasium API and RLlib, making it suitable for scalable training pipelines. -
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MiroFish
A Simple and Universal Swarm Intelligence Engine
MiroFish is a next-generation artificial intelligence prediction engine that leverages multi-agent technology and swarm-intelligence simulation to model, simulate, and forecast complex real-world scenarios. The system extracts “seed” information from sources such as breaking news, policy documents, and market signals to construct a high-fidelity digital parallel world populated by thousands of virtual agents with independent memory and behavior rules. Users can inject variables or conditions into this simulated environment from a “god’s eye view,” enabling iterative prediction of future trends under different assumptions, which can be useful for decision support, scenario planning, or creative exploration. ... -
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Emdash
Emdash is the Open-Source Agentic Development Environment
Emdash is an open-source agentic development environment that allows developers to orchestrate multiple AI coding agents in parallel within a unified desktop application. It introduces a new paradigm where coding tasks are delegated to independent AI agents, each operating in its own isolated Git worktree, enabling concurrent development workflows without conflicts. The platform is provider-agnostic, supporting a wide range of CLI-based AI coding tools such as Claude Code, Codex, and others, allowing developers to switch or combine models based on their needs. ... -
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multi-agent-shogun
Samurai-inspired multi-agent system for Claude Code
multi-agent-shogun is a multi-agent orchestration system designed to coordinate multiple AI coding agents working in parallel. Inspired by the hierarchy of a feudal Japanese military structure, the system organizes agents into roles such as Shogun, Karo, and Ashigaru, which correspond to strategist, coordinator, and worker agents. A user interacts primarily with the Shogun agent by issuing natural language instructions that describe the desired tasks. -
AestheticsPro Medical Spa SoftwareAestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
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LightGBM
Gradient boosting framework based on decision tree algorithms
LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory. LightGBM supports parallel and GPU learning, and can handle large-scale data. It’s become widely-used for ranking, classification and many other machine learning tasks. -
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LLMCompiler
An LLM Compiler for Parallel Function Calling
...LLMCompiler addresses this limitation by applying principles from classical compilers to analyze a task and construct an execution plan that allows multiple functions to run in parallel whenever possible. The framework builds a dependency graph of required operations, identifying which tasks must run sequentially and which can be executed simultaneously. Its architecture includes components such as a planning module that constructs the task graph, a task dispatcher that manages dependencies, and an executor that performs parallel calls. -
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Brax
Massively parallel rigidbody physics simulation
Brax is a fast and fully differentiable physics engine for large-scale rigid body simulations, built on JAX. It is designed for research in reinforcement learning and robotics, enabling efficient simulations and gradient-based optimization. -
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Maestro AI Orchestration
Agent Orchestration Command Center
Maestro is a cross-platform desktop application designed for power users to orchestrate and manage fleets of AI agents and project workflows from a keyboard-centric interface. It provides a high-performance experience for running multiple agent sessions in parallel, integrating with tools such as Claude Code, OpenAI Codex, and other agent tooling to automate tasks, perform unattended execution, and organize long-running work flows. Users can collaborate with AI to draft specifications, break tasks into playbooks, and run them sequentially or concurrently in isolated contexts, each with clean session history. ... -
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LobeHub
Workspace to find, build, and collaborate with AI agents
...LobeHub brings multiple models, tools, and modalities into a single unified environment under the user’s control. With built-in collaboration features, agents can work in parallel, share context, and support complex projects seamlessly. The platform is built around the idea of co-evolution, where both humans and agents continuously learn and improve together. -
The Most Powerful Software Platform for EHSQ and ESG ManagementChoose from a complete set of software solutions across EHSQ that address all aspects of top performing Environmental, Health and Safety, and Quality management programs.
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vLLM
A high-throughput and memory-efficient inference and serving engine
vLLM is a fast and easy-to-use library for LLM inference and serving. High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more. -
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Superset LLM
Run an army of Claude Code, Codex, etc. on your machine
Superset is a development environment and terminal-based platform designed to orchestrate multiple AI coding agents simultaneously within a single workspace. The tool enables developers to run many autonomous coding agents in parallel without the typical overhead of manually managing multiple terminals, repositories, or branches. Each agent task is isolated in its own Git worktree, ensuring that code changes from different agents do not interfere with each other while allowing developers to track their progress independently. The platform includes built-in monitoring capabilities so users can observe the activity of each agent, receive notifications when tasks are completed, and quickly review changes produced by automated coding workflows. ... -
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Kimi K2.5
Moonshot's most powerful AI model
...K2.5 supports both “Thinking” and “Instant” modes, enabling either deep step-by-step reasoning or low-latency responses depending on the task. Designed for agentic workflows, it features an Agent Swarm mechanism that decomposes complex problems into coordinated sub-agents executing in parallel. With a 256K context length and MoonViT vision encoder, the model excels across reasoning, coding, long-context comprehension, image, and video benchmarks. Kimi K2.5 is available via Moonshot’s API (OpenAI/Anthropic-compatible) and supports deployment through vLLM, SGLang, and KTransformers. -
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HunyuanVideo-I2V
A Customizable Image-to-Video Model based on HunyuanVideo
...It extends video generation so that given a static reference image plus an optional prompt, it generates a video sequence that preserves the reference image’s identity (especially in the first frame) and allows stylized effects via LoRA adapters. The repository includes pretrained weights, inference and sampling scripts, training code for LoRA effects, and support for parallel inference via xDiT. Resolution, video length, stability mode, flow shift, seed, CPU offload etc. Parallel inference support using xDiT for multi-GPU speedups. LoRA training / fine-tuning support to add special effects or customize generation. -
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CodeMachine
CLI tool for multi-agent workflows and automated code generation
...It enables developers to transform high-level specifications into production-ready code by managing planning, architecture, implementation, testing, and validation within a unified environment. CodeMachine CLI supports parallel execution through multiple specialized agents, allowing faster development cycles and scalable automation. Built for flexibility, it can handle anything from simple scripts to complex, long-running workflows that span hours or days. CodeMachine also integrates with various AI engines, assigning roles such as planning, coding, and review to different models for efficient collaboration. -
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ChainForge
An open-source visual programming environment
...Instead of relying on isolated prompt experimentation, it introduces a dataflow-based interface that allows users to create complex prompt pipelines and evaluate them across different models, parameters, and datasets simultaneously. 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. -
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CGraph
A general, three-party dependency-free, cross-platform
CGraph is a high-performance, cross-platform Directed Acyclic Graph (DAG) framework implemented in pure C++ with no third-party dependencies, designed for building complex task pipelines and parallel execution workflows. It allows developers to model computational processes as graph structures, where nodes represent tasks and edges define dependencies, enabling efficient scheduling and execution. The framework includes a pipeline system that supports sequential and parallel execution, conditional branching, aggregation, and loop control, making it highly flexible for advanced workflows. ... -
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Loki Mode
Multi-agent autonomous startup system for Claude Code
...It orchestrates dozens of agent types across swarms that handle designated roles — such as architecture, coding, QA, deployment, and business workflows — running in parallel to cover both engineering and operational tasks without continuous human intervention. By supporting multiple AI providers (like Claude Code, OpenAI Codex CLI, and Google Gemini CLI), loki-mode dynamically selects and spawns only the needed agents for a given project, optimizing computational resources and task throughput. Its Reason-Act-Reflect-Verify (RARV) cycle with self-verification loops emphasizes quality and resilience, automating end-to-end development lifecycles. -
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HunyuanVideo
HunyuanVideo: A Systematic Framework For Large Video Generation Model
...The framework aims to push the boundaries of video generation quality, incorporating multiple innovative approaches to improve the realism and coherence of the generated content. Release of FP8 model weights to reduce GPU memory usage / improve efficiency. Parallel inference code to speed up sampling, utilities and tests included. -
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Agent Orchestrator
Agentic orchestrator for parallel coding agents
Agent Orchestrator from Composio is an open-source orchestration layer designed to manage fleets of parallel AI coding agents working on a shared codebase. It enables each agent to operate independently in isolated git worktrees, handling tasks like fixing CI failures, addressing code review comments, and creating pull requests. The platform automates the coordination of multiple agents, reducing the need for manual oversight in complex development workflows. -
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Oh My codeX (OMX)
Your codex is not alone. Add hooks, agent teams, HUDs
...It addresses limitations in the base Codex environment, such as the lack of hooks, agent coordination, and persistent execution, by layering a shell-based system that enables richer interaction patterns. The project transforms a single AI coding assistant into a coordinated system of specialized agents that can collaborate in parallel, improving both speed and reliability of development tasks. It leverages tools like tmux to manage multiple agent sessions simultaneously, enabling a “team mode” where different agents handle distinct responsibilities within a shared workflow. The system also introduces staged pipelines, allowing tasks to move through phases such as planning, execution, verification, and refinement in a structured manner. -
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mosaicml composer
Supercharge Your Model Training
...The framework is intended for modern workloads that may span anything from a single GPU to very large distributed training environments, which makes it suitable for both experimentation and production-scale development. It includes built-in support for distributed training strategies such as Fully Sharded Data Parallelism and standard Distributed Data Parallel execution, helping teams scale models without having to assemble as much infrastructure by hand. -
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JavaCV
Java interface to OpenCV, FFmpeg, and more
JavaCV uses wrappers from the JavaCPP Presets of commonly used libraries by researchers in the field of computer vision (OpenCV, FFmpeg, libdc1394, FlyCapture, Spinnaker, OpenKinect, librealsense, CL PS3 Eye Driver, videoInput, ARToolKitPlus, flandmark, Leptonica, and Tesseract) and provides utility classes to make their functionality easier to use on the Java platform, including Android. JavaCV also comes with hardware accelerated full-screen image display (CanvasFrame and GLCanvasFrame), easy-to-use methods to execute code in parallel on multiple cores (Parallel), user-friendly geometric and color calibration of cameras and projectors (GeometricCalibrator, ProCamGeometricCalibrator, ProCamColorCalibrator), detection and matching of feature points (ObjectFinder), a set of classes that implement direct image alignment of projector-camera systems (mainly GNImageAligner, ProjectiveTransformer, ProjectiveColorTransformer, ProCamTransformer, and ReflectanceInitializer), and more. -
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Swarms
Enterprise multi-agent orchestration framework for scalable AI apps
Swarms is an enterprise-grade multi-agent orchestration framework designed to help developers build, manage, and scale collaborative AI systems composed of multiple agents. It provides a structured infrastructure for coordinating agents in hierarchical, parallel, or sequential workflows, enabling complex task execution across distributed components. It emphasizes production readiness, offering modular architecture, high availability, and observability features suitable for large-scale deployments. It supports integration with multiple model providers and existing ecosystems, allowing developers to combine different AI tools and frameworks within a unified system. ... -
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nndeploy
An Easy-to-Use and High-Performance AI Deployment Framework
...The system supports multiple inference engines and hardware accelerators, allowing the same AI workflow to run on different platforms without significant modifications. nndeploy also includes performance optimization techniques such as parallel execution, memory reuse, and hardware-accelerated operations to improve inference speed. -
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DFlash
Block Diffusion for Ultra-Fast Speculative Decoding
DFlash is an open-source framework for ultra-fast speculative decoding using a lightweight block diffusion model to draft text in parallel with a target large language model, dramatically improving inference speed without sacrificing generation quality. It acts as a “drafter” that proposes likely continuations which the main model then verifies, enabling significant throughput gains compared to traditional autoregressive decoding methods that generate token by token.
