Search Results for "lifecycle"
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Showing 41 open source projects for "lifecycle"
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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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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
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Wanwu AI Agent Platform
Enterprise AI agent platform for workflows, models, and RAG apps
...Wanwu integrates large language models with business process automation, allowing developers to design complex, production-ready AI solutions tailored to enterprise needs. It includes comprehensive model lifecycle management capabilities, enabling users to configure, monitor, and manage different models efficiently. Wanwu also supports knowledge base construction, allowing organizations to incorporate structured and unstructured data into their AI applications. With a focus on openness and extensibility, it encourages developers to build on top of its ecosystem while maintaining a secure and compliant architecture for business use cases. -
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SimpleMem
SimpleMem: Efficient Lifelong Memory for LLM Agents
...It provides easy-to-use APIs for storing structured memory entries, querying those memories using semantic search, and retrieving context to augment prompt inputs for downstream processing. Unlike monolithic systems where memory management is ad-hoc, SimpleMem formalizes a memory lifecycle—write, index, retrieve, refine—so applications can handle user history, document collections, or dynamic contextual state systematically. It supports customizable embedding models, efficient vector indexes, and relevance weighting, making it practical for building assistants, personal agents, or domain-specific retrieval systems that need persistent knowledge. -
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lightning AI
The most intuitive, flexible, way for researchers to build models
Build in days not months with the most intuitive, flexible framework for building models and Lightning Apps (ie: ML workflow templates) which "glue" together your favorite ML lifecycle tools. Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any cluster. Find templates (Lightning Apps), modify them and publish your own. ... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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Spec Kit
Toolkit to help you get started with Spec-Driven Development
...It introduces a structured process in which developers define detailed specifications first, then allow AI tools to generate plans, tasks, and implementation code aligned with those requirements. The toolkit provides scaffolding, prompt templates, and automation scripts that help teams maintain a clear source of truth throughout the development lifecycle. By emphasizing intent before code, Spec Kit reduces ambiguity and improves the reliability of AI-generated output. It integrates with popular AI coding tools such as GitHub Copilot and similar assistants, allowing developers to embed spec-driven practices directly into their existing workflows. Overall, the project aims to improve collaboration between humans and AI by making software development more predictable, traceable, and maintainable. -
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HackerRepo.org
Collection of cybersecurity-related references, scripts, tools, code
...The repository is continuously maintained and categorized into specialized directories so users can quickly locate relevant learning material or utilities. Rather than being a single tool, it functions as an extensive reference library that supports skill development across the entire cybersecurity lifecycle. Overall, h4cker serves as a comprehensive open knowledge repository for practitioners building or expanding professional security expertise. -
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ChatDev
Create Customized Software using Natural Language Idea
ChatDev is an AI-powered development tool designed to simulate the software development lifecycle using multi-agent collaboration. It allows multiple AI agents to take on roles such as product managers, developers, and testers to collaboratively generate, refine, and evaluate software code. This project explores how AI can be leveraged to automate and optimize development workflows. -
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Claude Blog
Claude Code blog skill suite
Claude Blog is a Claude Code blog skill suite for planning, writing, optimizing, and auditing long-form content. It is built as a full-lifecycle blog engine with 30 sub-skills, 5 agents, 12 content templates, on-demand references, Python scripts, and automated tests. The workflow is designed to produce production-ready content rather than one-shot AI drafts. It uses a five-gate delivery contract covering capability, format, visual quality, content review, and asset integrity. -
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TapeAgents
A framework that facilitates all stages of LLM development
TapeAgents is a framework that facilitates all stages of the Large Language Model (LLM) agent development lifecycle, providing tools for building, testing, and deploying AI agents. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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AI Marketing Skills
Open-source AI marketing skills for Claude Code
...The system is organized into multiple domains such as growth experimentation, sales pipeline generation, content production, outbound marketing, SEO optimization, and financial analysis, effectively covering the entire revenue lifecycle of a business. Each skill functions as an executable capability that can be invoked on demand, enabling users to perform tasks like running A/B tests, generating high-quality content, or analyzing conversion funnels with minimal manual effort. -
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Khazix Skills
Digital Life Kazik Open Source AI Skills Collection
...It acts as a pipeline that analyzes a repository’s metadata, extracts relevant information such as README content and commit hashes, and converts it into a standardized skill format that can be integrated into agent ecosystems. The system emphasizes lifecycle management by embedding versioning, traceability, and metadata directly into generated skill files, allowing future updates and synchronization with the original repository. It also generates wrapper scripts that enable AI agents to interact with the underlying repository functionality without requiring deep manual integration. ... -
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MLRun
Machine Learning automation and tracking
MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements. ... -
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AG-UI
The Agent-User Interaction Protocol
...It is designed to make agent behavior visible, interactive, and controllable inside real-time front-end experiences. Instead of treating an AI agent as a black-box chat endpoint, AG-UI defines structured events for messages, tool calls, state changes, lifecycle updates, and user interactions. This makes it easier for developers to build agent-powered apps that stream progress, request human input, update UI state, and coordinate complex workflows. The project is especially useful for teams building copilots, workflow assistants, multi-agent products, or custom AI interfaces. Overall, AG-UI provides a shared communication layer between autonomous systems and the interfaces where people actually use them. -
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ComfyUI-Copilot
AI assistant for ComfyUI workflow generation, debugging, and tuning
...ComfyUI-Copilot focuses on reducing the complexity of building node-based pipelines for generative AI tasks such as image generation, making it more accessible to both beginners and experienced users. It supports the entire workflow lifecycle, including generation, debugging, rewriting, and parameter optimization, helping users iterate more efficiently. ComfyUI-Copilot leverages large language model capabilities to analyze user intent, recommend nodes, and suggest models that match specific requirements. It also provides automated error detection and repair suggestions, improving reliability during development. -
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LMOps
General technology for enabling AI capabilities w/ LLMs and MLLMs
...The project explores the technologies and methodologies required to move foundation models from research environments into production-grade AI products. It includes experimental tools and frameworks that help developers optimize prompts, design workflows for generative models, and manage the lifecycle of LLM-based systems. The initiative also investigates techniques for improving the reliability, scalability, and maintainability of applications powered by large models. By addressing challenges such as prompt engineering, evaluation strategies, and deployment infrastructure, LMOps aims to establish best practices for operating large language model systems in real-world environments. -
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ContextForge MCP Gateway
A Model Context Protocol (MCP) Gateway & Registry
...Packaged for quick starts via PyPI and Docker, it targets production reliability with health checks, metrics, and structured logs. The project positions itself as an integration hub so agentic apps can “connect once, use many” backends with consistent policy and lifecycle control. -
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
...It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model using a single language throughout the lifecycle of model exploration and production. TFP is open source and available on GitHub. Tools to build deep probabilistic models, including probabilistic layers and a `JointDistribution` abstraction. Variational inference and Markov chain Monte Carlo. A wide selection of probability distributions and bijectors. Optimizers such as Nelder-Mead, BFGS, and SGLD. -
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EverMemOS
Long-term memory OS for AI with structured recall and context awarenes
...EverMemOS goes beyond simple retrieval by actively applying stored knowledge to current tasks, improving personalization and consistency. EverMemOS uses a multi-stage memory lifecycle to convert raw dialogue into structured semantic data, supporting long-horizon reasoning and adaptive behavior across sessions. -
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AI-DLC
AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI
AI-DLC is an open-source workflow framework from AWS Labs designed to structure software development around AI-assisted engineering processes. The project promotes an “AI-Driven Life Cycle” methodology where coding assistants, IDE agents, and automation systems participate directly in planning, implementation, testing, and operational workflows. Rather than focusing on a single model or IDE, the framework provides reusable rules, templates, and orchestration patterns compatible with tools... -
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Machine Learning Engineering Open Book
Machine Learning Engineering Open Book
...It is heavily oriented toward practitioners who need hands-on solutions, including copy-paste commands, infrastructure comparisons, and performance tuning strategies. The material spans the full ML lifecycle, from hardware selection and distributed training to inference optimization and debugging. Rather than focusing purely on theory, the project emphasizes engineering tradeoffs and production realities that often determine success at scale. It is continuously updated as a knowledge dump, making it especially valuable for engineers operating complex AI systems in the wild. -
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GPT PILOT
The first real AI developer
GPT PILOT is an open-source AI developer assistant designed to build full applications by collaborating with a human developer throughout the software lifecycle. Unlike simple autocomplete tools, it aims to function as a true AI engineer that can generate features, set up environments, debug code, and request feedback when necessary. The system works by asking clarifying questions, producing product requirements, and then implementing the application step by step while the user supervises. ... -
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Devon
Open source AI pair programmer for coding, debugging, automation
Devon is an open source AI-powered pair programming tool designed to assist developers with software engineering tasks through natural language interaction. It operates as an agent-based system that can explore codebases, edit files, and execute development workflows with minimal manual intervention. Devon uses a client-server architecture with a Python backend and multiple user interfaces, including a terminal interface and an Electron-based desktop application. Devon integrates with... -
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MineContext
MineContext is your proactive context-aware AI partner
...Unlike traditional chat-based assistants, MineContext operates in the background and delivers proactive outputs such as daily summaries, task suggestions, and contextual reminders without requiring explicit prompts. It is built around a context engineering framework that manages the full lifecycle of data, including capture, processing, storage, retrieval, and consumption. The platform emphasizes privacy through a local-first architecture, allowing users to keep their data stored and processed on their own device rather than relying on external cloud services. -
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nanochat
The best ChatGPT that $100 can buy
nanochat is a from-scratch, end-to-end “mini ChatGPT” that shows the entire path from raw text to a chatty web app in one small, dependency-lean codebase. The repository stitches together every stage of the lifecycle: tokenizer training, pretraining a Transformer on a large web corpus, mid-training on dialogue and multiple-choice tasks, supervised fine-tuning, optional reinforcement learning for alignment, and finally efficient inference with caching. Its north star is approachability and speed: you can boot a fresh GPU box and drive the whole pipeline via a single script, producing a usable chat model in hours and a clear markdown report of what happened. ... -
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Instill Core
Instill Core is a full-stack AI infrastructure tool for data
...It provides an end-to-end solution that enables developers to build, deploy, and manage AI-powered applications without needing to manually stitch together multiple tools across the data and model lifecycle. The platform focuses heavily on handling unstructured data such as documents, images, audio, and video, transforming them into AI-ready formats through integrated ETL pipelines and processing workflows. Instill Core includes modular components such as pipelines, artifacts, and model services, which work together to enable flexible and scalable AI system design. ...
