Search Results for "frameworks" - Page 14
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Showing 1157 open source projects for "frameworks"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Data-Juicer
Data processing for and with foundation models
Data-Juicer is an open-source data processing and augmentation framework designed to enhance the quality and diversity of datasets for machine learning tasks. It includes a modular pipeline for scalable data transformation. -
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CTranslate2
Fast inference engine for Transformer models
...The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many advanced optimizations: layer fusion, padding removal, batch reordering, in-place operations, caching mechanism, etc. The model serialization and computation support weights with reduced precision: 16-bit floating points (FP16), 16-bit integers (INT16), and 8-bit integers (INT8). The project supports x86-64 and AArch64/ARM64 processors and integrates multiple backends that are optimized for these platforms: Intel MKL, oneDNN, OpenBLAS, Ruy, and Apple Accelerate. -
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The Web MCP
A powerful Model Context Protocol (MCP) server
...Bright Data also advertises a getting-started tier with a free monthly allotment, plus options for remote or self-hosted operation depending on governance needs. Ecosystem materials and examples show how it plugs into MCP-capable runtimes and agent frameworks. Overall, the project is aimed at making web intelligence a reliable building block for agent workflows. -
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FormCreate
The easy-to-use Vue low-code visual AI form designer
...The tool is designed to significantly reduce development time by allowing users to visually assemble forms while automatically generating the underlying configuration and logic. It supports multiple UI frameworks such as Element Plus and Vant, making it adaptable for both desktop and mobile applications. The platform has been widely applied in enterprise systems such as ERP, OA, and e-commerce platforms, demonstrating its scalability and reliability in production environments. Additionally, it includes support for custom components and extensibility, allowing developers to tailor form behavior to complex business requirements. -
Your monitoring isn't a stack. It's a pile. Fix that.Replace Datadog, New Relic, and Sentry without adding three more dashboards.
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Cosmos-RL
Cosmos-RL is a flexible and scalable Reinforcement Learning framework
Cosmos-RL is a scalable reinforcement learning framework designed specifically for physical AI systems such as robotics, autonomous agents, and multimodal models. It provides a distributed training architecture that separates policy learning and environment rollout processes, enabling efficient and asynchronous reinforcement learning at scale. The framework supports multiple parallelism strategies, including tensor, pipeline, and data parallelism, allowing it to leverage large GPU clusters... -
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Swarms
Enterprise multi-agent orchestration framework for scalable AI apps
...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. Swarms also includes mechanisms for agent lifecycle management, memory handling, and dynamic composition, making it adaptable to evolving workloads. Additionally, it focuses on developer productivity through APIs, CLI tools, and templates that simplify building and deploying agent-based applications. -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
...Many of the tutorials focus on building AI agents, multi-agent systems, and workflows that integrate language models with external tools or APIs. The codebase acts as a hands-on learning resource, allowing users to experiment with new frameworks, architectures, and machine learning workflows through guided examples. -
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The Machine & Deep Learning Compendium
List of references in my private & single document
The Machine & Deep Learning Compendium is an open-source knowledge repository that compiles summaries, references, and learning materials related to machine learning and deep learning. The project functions as a comprehensive compendium that organizes hundreds of topics covering algorithms, frameworks, research areas, and practical machine learning workflows. Originally created as a personal knowledge base, the repository evolved into a public educational resource designed to help learners explore the rapidly expanding machine learning ecosystem. The compendium includes explanations of concepts across multiple domains such as natural language processing, computer vision, time-series analysis, anomaly detection, and graph learning. ... -
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Cog
Package and deploy machine learning models using Docker containers
...Developers can define the runtime environment, dependencies, and Python versions required for their models, allowing Cog to build a consistent container environment that follows best practices. Cog also resolves compatibility issues between frameworks and GPU libraries by automatically selecting compatible combinations of CUDA, cuDNN, and machine learning frameworks such as PyTorch or TensorFlow. Cog automatically generates a RESTful HTTP API for running predictions, enabling models to be accessed programmatically through a built-in prediction server. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Kodezi Chronos
Kodezi Chronos is a debugging-first language model
...Another component, Persistent Debug Memory, allows the system to learn patterns from past debugging sessions and apply that knowledge to future problems. The repository mainly contains research documentation, evaluation benchmarks, and experimental frameworks rather than the full proprietary model implementation. -
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Autonomous Agents
Autonomous Agents (LLMs) research papers. Updated Daily
...The project explores how multiple agents can cooperate and interact with complex environments through machine learning, imitation learning, and multimodal sensing. It includes frameworks that integrate visual perception, tactile sensing, and spatial reasoning to guide the actions of robotic agents during manipulation or collaborative tasks. One of the central concepts explored in the repository is the integration of different sensory modalities using advanced machine learning techniques such as Feature-wise Linear Modulation and graph-based attention mechanisms. ... -
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DATAGEN
AI-driven multi-agent research assistant automating hypothesis
...Instead of requiring users to manually orchestrate each stage of a research process, the platform allows these agents to coordinate automatically and handle the workflow end-to-end. The project integrates several modern AI frameworks including LangChain, LangGraph, and large language models to manage reasoning and data processing tasks. Through this architecture, the system can combine structured data analysis with natural language reasoning to generate insights and research outputs. The platform is designed for researchers, analysts, and developers who want to accelerate data exploration and automate parts of the research lifecycle. -
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All Agentic Architectures
Implementation of 17+ agentic architectures
...The project acts as a practical learning resource that bridges the gap between theoretical research on autonomous agents and real software implementations. It contains more than a dozen agent architectures implemented using frameworks such as LangChain and LangGraph. Each architecture is explained through runnable notebooks that illustrate how the agent works internally and how it interacts with tools, data sources, or other agents. The repository organizes the architectures into a structured learning path that progresses from simple reasoning agents to complex multi-agent systems. ... -
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Quint Code
Structured reasoning framework for Claude Code, Gemini, and Cursor
Quint Code is a structured reasoning and decision-support framework aimed at making AI-assisted software engineering and decision workflows more rigorous and auditable. It implements the First Principles Framework (FPF) to guide users and AI tools through hypothesis generation, logical verification, evidence gathering, and documented decision making, reducing reliance on ad hoc or “vibe” coding. Instead of accepting the first plausible answer generated by an AI assistant, Quint Code... -
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mcpo
A simple, secure MCP-to-OpenAPI proxy server
mcpo is a minimal bridge that exposes any MCP tool as an OpenAPI-compatible HTTP server. Instead of writing glue code, you point mcpo at an MCP server command and it generates REST endpoints and an OpenAPI spec that other systems (or LLM agent frameworks) can call immediately. This design lets you reuse a growing library of MCP servers with platforms that only understand HTTP+OpenAPI, unifying tool access across ecosystems. The project emphasizes “dead-simple” setup and pairs with Open WebUI documentation that shows end-to-end integration. It supports running multiple tools and makes them discoverable to clients that expect Swagger/JSON schemas. ... -
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NVIDIA AgentIQ
The NVIDIA AgentIQ toolkit is an open-source library
NVIDIA AgentIQ is an open-source toolkit designed to efficiently connect, evaluate, and accelerate teams of AI agents. It provides a framework-agnostic platform that integrates seamlessly with various data sources and tools, enabling developers to build composable and reusable agentic workflows. By treating agents, tools, and workflows as simple function calls, AgentIQ facilitates rapid development and optimization of AI-driven applications, enhancing collaboration and efficiency in complex... -
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EdgeChains
EdgeChains.js is Full-Stack GenAI library
EdgeChains.js is a full-stack generative AI library that provides front-end, back-end, APIs, prompt management, and distributed computing capabilities, with core prompts and chains managed declaratively in Jsonnet. At EdgeChains, we take a unique approach to Generative AI - we think Generative AI is a deployment and configuration management challenge rather than a UI and library design pattern challenge. We build on top of a tech that has solved this problem in a different domain -... -
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Superduper
Superduper: Integrate AI models and machine learning workflows
Superduper is a Python-based framework for building end-2-end AI-data workflows and applications on your own data, integrating with major databases. It supports the latest technologies and techniques, including LLMs, vector-search, RAG, and multimodality as well as classical AI and ML paradigms. Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This... -
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Tribe AI
Low code tool to rapidly build and coordinate multi-agent teams
Low code tool to rapidly build and coordinate multi-agent teams. Have you heard the saying, 'Two minds are better than one'? That's true for agents too. Tribe leverages on the langgraph framework to let you customize and coordinate teams of agents easily. By splitting up tough tasks among agents who are good at different things, each one can focus on what it does best. This makes solving problems faster and better. -
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gse
Go efficient multilingual NLP and text segmentation
Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others. Gse is implements jieba by golang, and try add NLP support and more feature. Support common, search engine, full mode, precise mode and HMM mode multiple word segmentation modes. Support user and embed dictionary, Part-of-speech/POS tagging, analyze segment info, stop and trim words. Support multilingual: English, Chinese, Japanese and others. Support Traditional Chinese. Support HMM cut text... -
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Determined
Determined, deep learning training platform
The fastest and easiest way to build deep learning models. Distributed training without changing your model code. Determined takes care of provisioning machines, networking, data loading, and fault tolerance. Build more accurate models faster with scalable hyperparameter search, seamlessly orchestrated by Determined. Use state-of-the-art algorithms and explore results with our hyperparameter search visualizations. Interpret your experiment results using the Determined UI and TensorBoard, and... -
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zvt
Modular quant framework
For practical trading, a complex algorithm is fragile, a complex algorithm building on a complex facility is more fragile, complex algorithm building on a complex facility by a complex team is more and more fragile. zvt wants to provide a simple facility for building a straightforward algorithm. Technologies come and technologies go, but market insight is forever. Your world is built by core concepts inside you, so it’s you. zvt world is built by core concepts inside the market, so it’s zvt.... -
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Qwen-Agent
Agent framework and applications built upon Qwen>=3.0
Qwen-Agent is a framework for building applications / agents using Qwen models (version 3.0+). It provides components for instruction following, tool usage (function calling), planning, memory, RAG (retrieval augmented generation), code interpreter, etc. It ships with example applications (Browser Assistant, Code Interpreter, Custom Assistant), supports GUI front-ends, backends, server setups. Agent workflow can maintain context / memory to perform multi-turn or more complex logic over time.... -
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Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training... -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep...
