Search Results for "loop linux"
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Showing 119 open source projects for "loop linux"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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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. The platform also... -
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Loop Engineering
Practical patterns, starters & CLI tools for loop engineering with AI
Loop Engineering is a practical reference repository for designing loop-based workflows with AI coding agents. It focuses on replacing repeated manual prompting with systems that prompt, verify, schedule, and hand off work over time. The project is aimed at developers using tools such as Grok, Claude Code, Codex, Cursor, and other coding agents. It explains core building blocks such as scheduling, worktrees, skills, plugins, connectors, sub-agents, and persistent memory or state. The... -
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Loop Library
A library of practical AI-agent loops and an installable skill
Loop Library is a library and installable skill for designing repeatable AI-agent workflows. It is built around the idea of loops, which are bounded playbooks that tell an agent what to do, how to check progress, what to try next, and when to stop. The repository contains both a public loop library website and the Loopy skill that agents can use to discover, audit, adapt, run, and prepare loops. It helps turn open-ended prompts into measurable workflows with evidence, stopping criteria, and... -
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ralph-loop-agent
Continuous Autonomy for the AI SDK
ralph-loop-agent is an experimental autonomous agent framework from Vercel Labs that brings continuous autonomy to the AI SDK, enabling AI solutions to perform long-running, iterative tasks without manual stop/start intervention. Rather than simply answering a single request and stopping, Ralph Loop implements a loop control architecture that allows an agent to repeatedly evaluate its progress, adjust its approach, and continue working toward a defined completion criteria until tasks are... -
Demo Series - Small Business Backup By VeeamWatch this on-demand demo series and learn how to protect your Microsoft 365 data with clear, simple, actionable steps that are easy to implement for businesses of all sizes.
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pi-autoresearch
Autonomous experiment loop extension for pi
pi-autoresearch is an automation-oriented research assistant project that focuses on orchestrating iterative information gathering, analysis, and synthesis workflows with minimal human intervention. It is designed to simulate a continuous research loop where queries are generated, refined, and expanded based on previous outputs, enabling deeper exploration of complex topics. The system likely integrates with external data sources or APIs to retrieve information and process it into structured... -
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RecursiveMAS
Offical Implementation for "Recursive Multi-Agent Systems"
RecursiveMAS is an advanced multi-agent AI framework that introduces a recursive collaboration mechanism to improve reasoning and problem-solving across multiple agents. Instead of treating agents as independent units exchanging text outputs, it connects them through a shared latent computation loop, allowing internal “thought states” to be passed and refined iteratively. This recursive structure enables agents to build on each other’s intermediate reasoning, leading to deeper and more... -
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AgentScope Java
Agent-Oriented Programming for Building LLM Applications
AgentScope Java is an agent-oriented programming framework that enables Java developers to build intelligent, LLM-powered applications using a dynamic reasoning-acting (ReAct) paradigm. It provides a comprehensive toolkit for creating autonomous agents that can plan, execute, and adjust complex workflows, making decisions about which tools to invoke and how to solve multi-step problems. The framework includes runtime controls such as safe interruption and graceful cancellation to manage... -
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OpenJarvis
Personal AI, On Personal Devices
OpenJarvis is an open-source framework designed to build personal AI agents that run primarily on local devices rather than relying on cloud infrastructure. Developed as part of the Intelligence Per Watt research initiative, it focuses on improving the efficiency and practicality of on-device AI systems. The framework provides shared primitives for building local-first agents, along with evaluation tools that measure performance using metrics such as energy consumption, latency, cost, and... -
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Phantasm
Toolkits to create a human-in-the-loop approval layer
Phantasm offers toolkits to create a human-in-the-loop approval layer to monitor and guide AI agents' workflows in real-time, ensuring safety and reliability in AI operations. -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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OpenSquilla
Token-Efficient AI Agent with same budget, higher intelligence density
OpenSquilla is a token-efficient microkernel AI agent runtime designed for CLI, web UI, and chat-based workflows. It routes each turn through a shared loop that can select lower-cost models when appropriate while preserving tool dispatch, retries, memory, and decision logging. The project supports multiple LLM providers through a pluggable provider layer, making it adaptable to different model ecosystems. It includes persistent memory, built-in web search, on-device embeddings, and... -
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LangGraph
Build resilient language agents as graphs
LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application,... -
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AgentScript
Build AI agents that think in code
AgentScript is a TypeScript SDK for building reliable AI agents that express their plans as code, enabling stop/start workflows, tool-level state management, and enhanced observability. -
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eve
The Framework for Building Agents
eve is Vercel’s open-source framework for building, running, and scaling durable backend AI agents. It uses a filesystem-first model where an agent is defined as a directory of files, making the project structure itself the agent contract. The framework is designed to include production concerns from the start instead of leaving developers to assemble durability, sandboxing, approvals, and deployment separately. Eve supports durable execution, sandboxed compute, human-in-the-loop approvals,... -
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J-Space Cognition Suite V3.6
AI cognitive-enhancement Skills based on Anthropic's J-space
J-Space Cognition Suite is a model-agnostic inference-time control system for improving deep reasoning, long-horizon work, tool use, verification, and recovery in AI agents. It is distributed as a cross-platform Skill that leaves model weights and training unchanged. The suite manages an agent's accessible working representations through selective loading instead of applying every mechanism to every task. Its fast, full, and loop modes scale from simple checks to multi-stage work requiring... -
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Better Harness
Help your coding agents get better at getting better
Better Harness is an open-source evaluation system for improving the workflows used by AI coding agents. Instead of reviewing only the final code change, it examines how an agent understood the task, executed work, validated results, delivered safely, and captured reusable lessons. It gathers project and session evidence while marking missing or unobserved behavior explicitly. Reports organize findings across five Agent Work Loop dimensions and connect each conclusion to visible supporting... -
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Future AGI
Open-source platform for evaluating, observing, and improving LLM
Future AGI is an open-source, end-to-end platform for evaluating, observing, protecting, and improving AI agent applications. It is built for teams that need more than basic tracing, combining evaluations, simulations, datasets, guardrails, gateway routing, and optimization in one feedback loop. The platform helps developers detect hallucinations, measure agent quality, monitor production behavior, and use evaluation results to improve prompts or workflows over time. It supports both cloud... -
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deepclaude
Use Claude Code's agent loop with DeepSeek V4 Pro, OpenRouter & more
deepclaude is a lightweight proxy tool that enables developers to run Claude Code’s autonomous coding agent loop using alternative AI backends like DeepSeek V4 Pro, OpenRouter, or other Anthropic-compatible models. It preserves the full Claude Code experience—including file editing, terminal execution, and multi-step agent workflows—while dramatically reducing operational costs. By swapping out the underlying model instead of the interface, deepclaude delivers the same familiar UX with... -
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AgentScope
Build and run agents you can see, understand and trust
AgentScope is a production-ready agent framework designed to help developers build, deploy, and scale intelligent agentic applications. It provides essential abstractions that evolve with advancing LLM capabilities, emphasizing reasoning, tool use, and flexible orchestration rather than rigid prompt constraints. With built-in support for ReAct agents, memory, planning, human-in-the-loop control, and real-time voice interaction, developers can create powerful agents in minutes. AgentScope... -
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Ralph Wiggum Marketer
A Claude Code Plugin that provides an autonomous AI copywriter
Ralph Wiggum Marketer is a Claude Code plugin that serves as an autonomous AI copywriter tailored for SaaS content marketing, enabling automated generation of marketing copy such as landing pages, taglines, feature summaries, and promotional messaging. It leverages the Ralph Wiggum loop concept — a continuous iteration pattern named after the iconic character that symbolizes persistent, repeated refinement — to let Claude Code keep iterating on content until predefined completion criteria... -
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Zypher Agent
A minimal yet powerful framework for creating AI agents
Zypher Agent is an open-source framework for building full-featured AI agents that can be embedded directly into applications, enabling reactive decision loops where the agent dynamically chooses its next actions. Unlike workflow-style orchestrators, it uses a reactive agent loop that interprets the task, reasons about next steps via LLMs, and integrates directly with extensible tools and external services. Zypher prioritizes native support for multiple model providers such as OpenAI and... -
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darwin-skill
Autoresearch-inspired autonomous skill optimization for Claude Code
darwin-skill is an experimental framework designed to automatically improve AI agent “skills” through iterative evaluation and optimization loops inspired by machine learning training processes. Instead of treating prompts or skill definitions as static assets, the system applies a continuous improvement cycle that evaluates performance, proposes changes, tests outcomes, and either retains or reverts modifications. The framework introduces a scoring system across multiple dimensions,... -
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Aden Hive
Outcome driven agent development framework that evolves
Hive is an open-source agent development framework that helps developers build autonomous, reliable, self-improving AI agents by letting them describe goals in ordinary natural language instead of hand-coding detailed workflows. Rather than manually defining execution graphs, Hive’s coding agent generates the agent graph, connection code, and test cases based on your high-level objectives, enabling outcome-driven agent creation that fits real business processes. Once deployed, agents can... -
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CUDA Agent
Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
CUDA Agent is a research-driven agentic reinforcement learning system designed to automatically generate and optimize high-performance CUDA kernels for GPU workloads. The project addresses the long-standing challenge that efficient CUDA programming typically requires deep hardware expertise by training an autonomous coding agent capable of iterative improvement through execution feedback. Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment,... -
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WorldGen
Generate Any 3D Scene in Seconds
WorldGen is an AI model and library that can generate full 3D scenes in a matter of seconds from either text prompts or reference images. It is designed to create interactive environments suitable for games, simulations, robotics research, and virtual reality, rather than just static 3D assets. The core idea is that you describe a world in natural language and WorldGen produces a navigable 3D scene that you can freely explore in 360 degrees, with loop closure so that the space remains... -
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Build Your Own OpenClaw
A step-by-step guide to build your own AI agent
Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable...
