Search Results for "design analysis algorithm"
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Showing 53 open source projects for "design analysis algorithm"
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Propel Software: Product Value Management Platform for ManufacturersPropel is a cloud-native Product Value Management platform that unifies PLM, QMS, and PIM in one connected system, giving manufacturers complete visibility and control across the entire product lifecycle. It provides a single source of truth for all product data, streamlines change management, strengthens quality and compliance processes, and accelerates time-to-market by eliminating the silos and manual steps that slow teams down.
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Composable, Open Source Payments PlatformJuspay's Payments Orchestration Platform offers a comprehensive product suite for businesses, including open-source payment orchestration, global payouts, seamless authentication, payment tokenization, fraud & risk management, end-to-end reconciliation, unified payment analytics & more. The company’s offerings also include end-to-end white label payment gateway solutions & real-time payments infrastructure for banks. These solutions help businesses achieve superior conversion rates, reduce fraud, optimize costs, and deliver seamless customer experiences at scale.
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Lama Cleaner
Image inpainting tool powered by SOTA AI Model
Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, or people from your pictures or erase and replace(powered by stable diffusion) anything on your pictures. Lama Cleaner is a free, open-source and fully self-hostable inpainting tool powered by state-of-the-art AI models. You can use it to remove any unwanted object, defect, or people from your pictures or erase and replace anything on your pictures. Many AICG creators are using Lama Cleaner to clean-up their... -
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AReal
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible
...It is intended to facilitate reproducible RL training on reasoning / agentic tasks, supporting scaling from single nodes to large GPU clusters. It can streamline the development of AI agents and reasoning systems. Support for algorithm and system co-design optimizations (to improve efficiency and stability). -
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Torch Pruning
DepGraph: Towards Any Structural Pruning
...The library focuses on reducing the size and computational cost of neural networks by removing redundant parameters and channels while maintaining model performance. It introduces a graph-based algorithm called DepGraph that automatically identifies dependencies between layers, allowing parameters to be pruned safely across complex architectures. This dependency analysis makes it possible to prune large networks such as transformers, convolutional networks, and diffusion models without breaking the computational graph. Torch-Pruning physically removes parameters rather than masking them, which results in smaller and faster models during both training and inference. ... -
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sktime
A unified framework for machine learning with time series
...It features dedicated time series algorithms and tools for composite model building such as pipelining, ensembling, tuning, and reduction, empowering users to apply an algorithm designed for one task to another. -
Enterprise-Class Managed File Transfer.Diplomat MFT by Coviant Software is a secure, reliable managed file transfer solution designed to simplify and automate SFTP, FTPS, and HTTPS file transfers. Built for seamless integration, Diplomat MFT works across major cloud storage platforms, including AWS S3, Azure Blob, Google Cloud, Oracle Cloud, SharePoint, Dropbox, Box, and more.
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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BioNeMo
BioNeMo Framework: For building and adapting AI models
BioNeMo is an AI-powered framework developed by NVIDIA for protein and molecular generation using deep learning models. It provides researchers and developers with tools to design, analyze, and optimize biological molecules, aiding in drug discovery and synthetic biology applications. -
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MathModelAgent
An Agent Designed for Mathematical Modeling
MathModelAgent is an AI agent system designed specifically for assisting with mathematical modeling tasks and academic problem solving. The platform automates the process of analyzing mathematical problems, constructing models, generating code for simulations or computations, and producing a complete research-style report. The project uses a multi-agent architecture where different specialized agents handle tasks such as problem interpretation, modeling design, programming implementation,... -
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MemU
MemU is an open-source memory framework for AI companions
...Powerful tools and dedicated support to scale your AI applications with confidence. Full proprietary features, commercial usage rights, and white-labeling options for your enterprise needs. SSO/RBAC integration and a dedicated algorithm team for scenario-specific optimization. User behavior analysis, real-time monitoring, and automated agent optimization tools. 24/7 dedicated support team, custom SLAs, and professional implementation services. -
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Claude Skills
Public repository for Agent Skills
...Rather than relying on handcrafted prompts every time, Skills teach an AI agent procedural knowledge and task-specific workflows so it can apply that expertise reliably, whether the task involves document creation, data analysis, design generation, or technical automation. Each Skill lives in its own directory with a SKILL.md file containing metadata and instructions, and can include supplemental scripts or assets that the agent uses to perform complex operations when relevant. -
EHS Software and Management SystemERA Environmental Software Solutions develops web-based EHS management software for small, medium, and large manufacturers needing to comply with federal, provincial, and state regulations, monitor their air, water, and waste emissions and other environmental outputs, author and manage Safety Data Sheets (SDS) in more than 40 languages, or standardize their Health and Safety procedures for incident and inspection tracking, training delivery, and audit management. The platform also supports comprehensive reporting for programs like TRI, Tier II, Title V, NEI, and NPRI. Companies across the automotive, aerospace, general manufacturing, and paints and coatings industries, to name a few, rely on ERA’s all-in-one, SOC 2 Type II certified SaaS for complete coverage of their EHS needs.
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BettaFish
Public opinion analysis system
...With a design that lets users pose questions in natural language and receive structured reports, charts, and visualizations, the system aims to break information cocoons and provide comprehensive views of trends and public sentiment. Unlike simpler analytics tools, BettaFish employs agent collaboration and a “forum” style internal mechanism to combine diverse model outputs, making the analysis richer and more robust. -
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OpenClaw Medical Skills
The largest open-source medical AI skills library for OpenClaw
...This modular design allows developers and researchers to build AI systems that can access specialized medical reasoning processes, retrieve relevant biomedical information, and generate structured outputs suitable for analysis or downstream processing. -
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BruteForceAI
Advanced LLM-powered brute-force tool combining AI intelligence
...The repository emphasizes features such as threaded execution, logging, and notification integrations, which position it as an automation-oriented project for controlled security assessment environments. From a software design perspective, its distinguishing idea is the use of language models as a front-end analysis layer that interprets a target page before the rest of the workflow proceeds. -
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HDBSCAN
A high performance implementation of HDBSCAN clustering
...In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any). -
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FlexLLMGen
Running large language models on a single GPU
...This design allows organizations to deploy powerful language models for high-volume tasks without the infrastructure costs typically associated with large-scale AI systems. The project is particularly useful for workloads that prioritize throughput over latency, including benchmarking experiments and large corpus analysis. -
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Biomni
Biomni: a general-purpose biomedical AI agent
...It integrates retrieval-augmented generation with code-based execution, allowing it to access external knowledge, process data, and generate testable hypotheses in scientific workflows. The system is built to support researchers by automating repetitive and time-consuming tasks such as literature review, data analysis, and experimental design. Biomni operates within a comprehensive environment that includes tools, APIs, and datasets, enabling it to execute multi-step research processes rather than just generating text responses. It supports integration with multiple AI models, allowing flexibility in selecting the most appropriate model for specific tasks. -
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SGR Agent Core
Schema-Guided Reasoning (SGR) has agentic system design
SGR Agent Core is an open-source framework for building intelligent AI research agents based on a methodology known as Schema-Guided Reasoning (SGR). The framework provides a core library that allows developers to design autonomous agents capable of structured reasoning and complex task execution. Instead of relying solely on free-form prompts, the system organizes reasoning processes around schemas that guide how agents analyze problems, gather information, and generate outputs. This... -
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SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch
SLM Lab is a modular and extensible deep reinforcement learning framework designed for research and practical applications. 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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RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
RL with PyTorch is a research-oriented repository that provides implementations of deep reinforcement learning algorithms using the PyTorch framework. The project focuses on helping developers and researchers understand reinforcement learning methods by providing clean and reproducible implementations of well-known algorithms. It includes code for popular deep reinforcement learning techniques such as Deep Q-Networks, policy gradient methods, actor-critic architectures, and other modern RL... -
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MiroFlow
Agent framework that enables tool-use agent tasks
...One of the core innovations of MiroFlow is its use of agent graphs, which enable flexible orchestration of multiple sub-agents and tools in order to complete complex workflows. This architecture allows agents to perform advanced reasoning tasks such as deep research, future event prediction, and multi-step knowledge analysis. The framework emphasizes reliability and scalability by incorporating robust workflow execution, concurrency management, and fault-tolerant design to handle unstable APIs or network conditions. -
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Databend
Cloud-native open source data warehouse for analytics and AI queries
...Databend provides a unified engine capable of handling analytics, vector search, and full-text search within a single platform. Databend supports SQL-based workflows and enables real-time data ingestion, transformation, and analysis through streaming and task orchestration features. With its cloud-native design and distributed architecture, Databend can run both as a self-hosted system or within managed environments to power data analytics, AI workloads, and large-scale data. -
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UltraRAG
Less Code, Lower Barrier, Faster Deployment
...The toolkit comes with built-in support for popular RAG datasets, large corpora, and canonical baselines, plus documentation that walks from “quick start” to debugging and case analysis. It encourages pipeline composition via configuration, enabling researchers to swap retrievers, rerankers, and generators without heavy refactoring. Community posts highlight its focus on reducing engineering overhead so more effort goes to experimental design. Backed by the OpenBMB org, it is actively maintained with tutorials and updates. -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior... -
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MetaGPT
The Multi-Agent Framework
The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo. Assign different roles to GPTs to form a collaborative software entity for complex tasks. MetaGPT takes a one-line requirement as input and outputs user stories / competitive analysis/requirements/data structures / APIs / documents, etc. Internally, MetaGPT includes product managers/architects/project managers/engineers. -
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Janus
Unified Multimodal Understanding and Generation Models
...Rather than having separate systems for “look and describe” and “prompt and generate”, Janus uses an autoregressive transformer framework with a decoupled visual encoder—allowing it to ingest images for comprehension and to produce images from text prompts with shared internal representations. The design tackles long-standing conflicts in multimodal models: namely that the visual encoder has to serve both analysis (understanding) and synthesis (generation) roles. By splitting those pathways but keeping one unified core transformer, Janus maintains flexibility and achieves strong performance across tasks previously requiring distinct architectures. ... -
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GLM-4.5V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
GLM-4.5V is the preceding iteration in the GLM-V series that laid much of the groundwork for general multimodal reasoning and vision-language understanding. It embodies the design philosophy of mixing visual and textual modalities into a unified model capable of general-purpose reasoning, content understanding, and generation, while already supporting a wide variety of tasks: from image captioning and visual question answering to content recognition, GUI-based agents, video understanding,...
