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Showing 102 open source projects for "node"
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MCP Server Chart
A visualization mcp contains 25+ visual charts
mcp-server-chart is a TypeScript Model Context Protocol (MCP) server that turns AntV’s visualization stack into agent-callable tools for automatic chart generation and lightweight data analysis. Out of the box it exposes more than 20–25 chart generators—covering staples like bar, line, area, histogram and pie, plus advanced visuals such as dual-axes, heatmaps, radar, flow and fishbone diagrams—so an AI client can request a chart and receive an image URL in return. The server can run over... -
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NVIDIA NeMo Framework
Scalable generative AI framework built for researchers and developers
...It provides collections of domain-specific modules and reference implementations that make it easier to pre-train, fine-tune, and deploy very large models on multi-GPU and multi-node infrastructure. NeMo 2.0 introduces a Python-based configuration system, replacing YAML with more flexible, programmable configs that can be versioned and composed for different experiments. The framework builds on PyTorch Lightning–style modular abstractions, so training scripts are composed from reusable components for data loading, models, optimizers, and schedulers, which simplifies experimentation and adaptation. ... -
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NVIDIA NeMo
Toolkit for conversational AI
...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. Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC. NGC collection of pre-trained speech processing models. -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. ... -
Junie, the AI coding agent by JetBrainsJunie is an AI-powered coding agent developed by JetBrains designed to enhance developer productivity by integrating directly into popular IDEs such as IntelliJ IDEA, PyCharm, and Android Studio. It supports developers by assisting with code completion, testing, and inspections, ensuring code quality and reducing debugging time.
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Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph representing the pipeline, allowing the system to execute transformations in the correct order. This approach encourages modular, testable, and maintainable data pipelines because each transformation is isolated and easily unit tested. ... -
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Cube Studio
Cube Studio open source cloud native one-stop machine learning
Cube Studio is an open-source, cloud-native end-to-end machine learning and AI platform designed to support the full lifecycle of AI development — from data preparation and interactive notebook coding to distributed training, model tuning, and deployment in production-ready environments. It provides a unified interface where teams can manage data sources, track datasets, and build pipelines using drag-and-drop workflow orchestration, making it accessible for both engineers and data... -
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Fractals
Fractals is a recursive task orchestrator for agent swarm
...The system takes a high-level goal and decomposes it into a hierarchy of smaller subtasks, forming a self-similar tree that resembles a fractal structure. Each leaf node of this tree represents a specific executable task that can be processed independently by an AI agent. The framework runs these subtasks in isolated Git worktrees so agents can work on separate components of a project without interfering with one another. By coordinating these agents through a recursive task decomposition strategy, the system allows large problems to be solved collaboratively by multiple AI processes. ... -
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OmAgent
Build multimodal language agents for fast prototype and production
...The framework provides abstractions and infrastructure for building AI agents that operate on text, images, video, and audio while maintaining a relatively simple interface for developers. Instead of forcing developers to implement complex orchestration logic manually, the system manages task scheduling, worker coordination, and node optimization behind the scenes. Its architecture uses a graph-based workflow engine where tasks are represented as nodes in a directed workflow, enabling modular composition of complex reasoning pipelines. The framework also includes support for various reasoning strategies commonly used in language agents, such as chain-of-thought prompting, self-consistency reasoning, and ReAct-style decision loops. -
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Agents 2.0
An Open-source Framework for Data-centric Language Agents
...The project introduces a concept known as agent symbolic learning, which treats an agent pipeline similarly to a neural network computational graph. In this framework, each node in the pipeline represents a step in the reasoning or action process, while prompts and tools act as adjustable parameters analogous to neural network weights. During training, the system performs a forward execution where the agent completes a task and records the trajectory of prompts, outputs, and tool usage. A prompt-based loss function is then applied to evaluate the quality of the outcome, generating language-based gradients that guide improvements to the agent pipeline. -
Complete Data Management for NonprofitsNewOrg is a robust platform built with enhanced features to help non-profit organizations that capture and integrate the information from all of their operational areas to better manage volunteers, clients, programs, outcome reporting, activity sign-ups & scheduling, communications, surveys, fundraising activities and Development campaigns. NewOrg can truly deliver an intuitive product that will help manage your Committees, Donors, Events, and Memberships so that the organization runs efficiently.
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DLRM
An implementation of a deep learning recommendation model (DLRM)
...The architecture combines dense (MLP) and sparse (embedding) branches, then interacts features via dot product or feature interactions before passing through further dense layers to predict click-through, ranking scores, or conversion probabilities. The implementation is optimized for performance at scale, supporting multi-GPU and multi-node execution, quantization, embedding partitioning, and pipelined I/O to feed huge embeddings efficiently. It includes data loaders for standard benchmarks (like Criteo), training scripts, evaluation tools, and capabilities like mixed precision, gradient compression, and memory fusion to maximize throughput. -
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amrlib
A python library that makes AMR parsing, generation and visualization
...Sentence to Graph alignment routines FAA_Aligner (Fast_Align Algorithm), based on the ISI aligner code detailed in this paper. RBW_Aligner (Rule Based Word) for a simple, single token to single node alignment. -
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Botonic
Build chatbots and conversational experiences using React
Botonic is a full-stack Javascript framework to create chatbots and modern conversational apps that work on multiple platforms, web, mobile and messaging apps (Messenger, Whatsapp, Telegram, etc). Building modern applications on top of messaging apps like Whatsapp or Messenger is much more than creating simple text-based chatbots. Botonic is a full-stack serverless framework that combines the power of React and Tensorflow.js to create amazing experiences at the intersection of text and... -
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Flock
Flock is a workflow-based low-code platform for building chatbots
Flock is a workflow-based low-code platform designed for building AI applications such as chatbots, retrieval-augmented generation systems, and multi-agent workflows. The platform uses a visual workflow architecture where different nodes represent processing steps such as input processing, model inference, retrieval operations, and tool execution. Developers can connect these nodes to create complex pipelines that orchestrate multiple language models and external services. Built on... -
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ERNIE
The official repository for ERNIE 4.5 and ERNIEKit
...The project also emphasizes optimization techniques for large-scale training, including mixed-precision and hybrid-parallel strategies that are commonly needed for multi-node GPU clusters. In addition to training, it includes guidance and example materials intended to help developers adopt ERNIE models for real product scenarios rather than only research demonstrations. -
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TorchRec
Pytorch domain library for recommendation systems
...It allows authors to train models with large embedding tables sharded across many GPUs. Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, and column-wise sharding. The TorchRec planner can automatically generate optimized sharding plans for models. Pipelined training overlaps dataloading device transfer (copy to GPU), inter-device communications (input_dist), and computation (forward, backward) for increased performance. ... -
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Matrix
Multi-Agent daTa geneRation Infra and eXperimentation framework
Matrix is a distributed, large-scale engine for multi-agent synthetic data generation and experiments: it provides the infrastructure to run thousands of “agentic” workflows concurrently (e.g. multiple LLMs interacting, reasoning, generating content, data-processing pipelines) by leveraging distributed computing (like Ray + cluster management). The idea is to treat data generation as a “data-to-data” transformation: each input item defines a task, and the runtime orchestrates asynchronous,... -
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Mastra
The TypeScript AI agent framework
Mastra is a TypeScript-first framework for building AI-powered applications and agents, designed to take projects from prototype to production on a modern JavaScript/TypeScript stack. It integrates cleanly with React, Next.js, and Node-based backends, but can also run as a standalone server, giving teams flexibility in how they deploy their AI logic. At its core, Mastra provides abstractions for agents, workflows, tools, memory, retrieval, and model routing, so developers can focus on specifying behavior rather than wiring infrastructure from scratch. Model routing lets you connect to dozens of providers (OpenAI, Anthropic, Gemini, and others) through a single standardized interface, while agents orchestrate LLM calls and tools to solve open-ended tasks with internal reasoning loops. ... -
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AIConfig
AIConfig is a config-based framework to build generative AI apps
...AIConfig supports multiple model providers and modalities, enabling developers to experiment with different models without rewriting application logic. The configuration format is JSON-serializable and integrates with tools such as Python and Node SDKs, allowing the same configuration file to be used across multiple environments. -
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Bert-VITS2
VITS2 backbone with multilingual-bert
...The repository includes everything needed to train, fine-tune, and run the model, from configuration files to preprocessing scripts, spectrogram utilities, and training entrypoints for multi-GPU and multi-node setups. It provides emotional modeling through “emo embeddings,” allowing voices to be conditioned on different affective states during synthesis. Releases include optimizations for Japanese and English alignment, expanded training data, spec caching and pre-generation tools, as well as ONNX export for more lightweight inference deployments. -
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Aviary
Ray Aviary - evaluate multiple LLMs easily
...Providing an extensive suite of pre-configured open source LLMs, with defaults that work out of the box. Supporting Transformer models hosted on Hugging Face Hub or present on local disk. Aviary has native support for autoscaling and multi-node deployments thanks to Ray and Ray Serve. Aviary can scale to zero and create new model replicas (each composed of multiple GPU workers) in response to demand. Ray ensures that the orchestration and resource management is handled automatically. Aviary is able to support hundreds of replicas and clusters of hundreds of nodes, deployed either in the cloud or on-prem. -
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cordum
Enterprise AI Agent Orchestration & Governance Platform.
...This architecture ensures "Zero-Copy" security (keeping PII off the wire) and provides a centralized Safety Kernel to intercept hallucinations and unauthorized actions before execution. Key Features: Protocol-First: Language-agnostic orchestration (Python, Go, Node, Rust). Safety Kernel: Deterministic guardrails enforced at the infrastructure level. Human-in-the-Loop: Native approval workflows for critical agent actions. Observability: Real-time tracing of agent thoughts, decisions, and tool usage. Stop building fragile scripts. Start engineering governed agent fleets with Cordum. -
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Astrape
Optical-packet node transceiver frequency allocation
In an optical network scenario which consists of multiple nodes (whiteboxes) at its edges and ROADMs in-between, the coherent transceiver average laser configuration time is improved. The process is evaluated according to a testbed setup. This is facilitated in the appropriate lab equipment (or via simulation when required). For that purpose, a software agent (Netconf server) residing at the whiteboxes, is developed receiving input from the Software-Defined Networking (SDN) packet... -
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InternLM
Official release of InternLM series
...The broader InternLM ecosystem also includes training tooling and guidance aimed at making fine-tuning and adaptation more accessible across hardware setups, including smaller single-GPU environments and larger multi-node configurations. -
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ChatGPT Web
ChatGPT demo webpage built with Express and Vue3
ChatGPT demo webpage built with Express and Vue3. Supports dual models, providing two unofficial ChatGPT API methods. You should first use the API way. If the network is not available, it means that the country is blocked, and you need to build your own proxy. Never use other people's public proxy, it is dangerous. The reverse proxy will expose your access tokens to third parties when using the accessTokenthe method, and doing so should have no ill effects, but consider the risks before... -
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Node ChatGPT API
A client implementation for ChatGPT and Bing AI
A client implementation for ChatGPT and Bing AI. Available as a Node.js module, REST API server, and CLI app. Support for the official ChatGPT model has been added! You can now use the gpt-3.5-turbo model with the official OpenAI API, using ChatGPTClient. This is the same model that ChatGPT uses, and it's the most powerful model available right now. Usage of this model is not free, however it is 10x cheaper than text-davinci-003. The default model used in ChatGPTClient is now gpt-3.5-turbo....
