Search Results for "lifecycle"
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Showing 19 open source projects for "lifecycle"
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Factory AI
Agent-Native Software Development
Factory is an agent-native software development platform designed to automate and accelerate the entire software engineering lifecycle by leveraging AI agents that can understand, modify, and manage codebases in real time. At its core, the system introduces the concept of “Droids,” specialized AI agents that operate across development environments such as the command line, web interfaces, project management tools, and communication platforms, enabling seamless interaction with engineering workflows. ... -
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Vibium
Browser automation for AI agents and humans
Vibium is an open-source browser automation infrastructure built to serve both AI agents and human developers by simplifying control and interaction with real browsers. It integrates a single lightweight binary that manages browser lifecycle, implements a WebDriver BiDi proxy, and exposes a Model Context Protocol (MCP) server so language models or automation clients can control browser behavior without complex setup. This design makes it ideal for AI agents that need to interact with the web, perform tasks, or simulate human interactions in a browser environment, and it also works well for traditional testing and automation workflows. ... -
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MiMo Code
Where Models and Agents Co-Evolve
MiMo-Code is a terminal-native AI coding assistant built for real software projects. It can read and edit code, run commands, manage Git, and preserve project knowledge across sessions. The tool includes multiple agent modes, including a build mode for development, a plan mode for read-only analysis, and a compose mode for structured workflows. Its persistent memory system stores project notes, checkpoints, scratch notes, and task progress so the assistant can resume work with context. It... -
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Coze Loop
Next-generation AI Agent Optimization Platform
Coze Loop is a developer-oriented platform that provides full lifecycle management for AI agents, covering everything from prompt engineering to production monitoring. The project aims to simplify the increasingly complex workflow of building reliable AI agents by offering integrated tools for debugging, evaluation, observability, and optimization. Through its visual playground, developers can test prompts interactively and compare outputs across different language models. -
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PentAGI
Perform penetration testing tasks
PentAGI is a fully autonomous AI agent system designed to perform complex penetration testing tasks by orchestrating multiple intelligent components into a coordinated offensive security workflow. The platform aims to automate significant portions of the penetration testing lifecycle, including reconnaissance, vulnerability discovery, and exploitation planning, reducing the amount of manual effort required from security professionals. It leverages agent-based architecture and AI reasoning to chain together tools and strategies in a way that mimics experienced human testers. The project is built to be modular and extensible so researchers and red teams can customize behavior or integrate additional tools as needed. ... -
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OpenShell
OpenShell is the safe, private runtime for autonomous AI agents.
OpenShell is an open-source runtime designed to safely run autonomous AI agents in isolated environments. Developed by NVIDIA, it provides sandboxed execution spaces that protect system resources, credentials, and data from unauthorized access. Each agent runs inside a containerized sandbox governed by declarative YAML security policies that control network access, file permissions, and process behavior. The platform includes a gateway service that manages sandbox lifecycles and routes AI... -
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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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Sublayer
A model-agnostic Ruby Generative AI DSL and framework
Sublayer is a platform that enables developers to build and deploy machine learning models with ease, focusing on simplifying the ML lifecycle from development to production. -
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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. -
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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. ... -
12
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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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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NVIDIA NeMo Agent Toolkit
Library for efficiently connecting and optimizing teams of AI agents
NVIDIA NeMo Agent Toolkit is an open-source framework designed to build, optimize, and manage AI agents across different development ecosystems. It provides enterprise-grade tools for improving agent performance, reliability, and observability throughout the development lifecycle. The toolkit integrates with popular agent frameworks such as LangChain, LlamaIndex, CrewAI, Microsoft Semantic Kernel, and Google ADK. Developers can monitor agent execution, trace workflows, and analyze token-level performance to identify bottlenecks and improve efficiency. NeMo Agent Toolkit also supports evaluation systems, prompt optimization, and reinforcement learning techniques to enhance agent behavior over time. ... -
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agentmemory
#1 Persistent memory for AI coding agents
...The system automatically records what agents do, compresses that activity into searchable memory, and injects relevant context when a new coding session begins. It combines keyword search, vector search, confidence scoring, lifecycle management, and knowledge graph querying to retrieve useful memories without overloading the context window. The project also includes hooks, an API, import and export support, audit trails, and team sharing workflows. Overall, agentmemory is designed to make AI coding assistants more continuous, context-aware, and useful across long-running software projects. -
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clawhip
claw + whip: Event-to-channel notification router
Clawhip is an open-source daemon-first notification router designed to deliver structured events from development workflows directly to platforms like Discord and Slack. It acts as a central event-processing system that listens to sources such as Git, GitHub, tmux sessions, and custom CLI events, then routes them through a typed pipeline. Built with a clean separation between routing, rendering, and delivery, Clawhip ensures reliable and organized notifications without polluting AI agent... -
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GitClaw
A universal git-native AI agent framework
GitClaw is an open-source framework for building AI agents whose entire identity, configuration, memory, and capabilities live inside a Git repository. Instead of storing agent state in databases or application code, the framework treats a repository itself as the agent’s environment, allowing developers to version, inspect, and collaborate on agents using standard Git workflows. The system defines structured files that represent the agent’s personality, rules, configuration, and operational... -
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aictx
Repo-local continuity runtime for AI coding agents. Helps them continu
...AICTX keeps operational continuity inside the repo: - active Work State and next action; - execution summaries and handoffs; - explicit decisions; - known failures and resolved failure patterns; - strategy hints from successful prior work; - execution contracts and contract-compliance signals; - optional RepoMap structural entry points; - continuity quality signals for stale, missing, demoted, obsolete, or unverified context; - read-only Task Context Packs for focused task-specific context; - lifecycle diagnostics for incomplete or unfinalized sessions; - optional Git-portable continuity for small teams. It also makes continuity shared across agents. -
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uuv e2e accessibility testing
Open-source platform for automated accessibility testing.
...The UUV Dashboard centralizes results from multiple projects, enabling teams to monitor accessibility metrics over time, identify regressions, compare executions, and track compliance progress through interactive reports. UUV integrates into CI/CD pipelines to support continuous accessibility testing throughout the software development lifecycle, reducing remediation costs while making digital services .
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