Trending repositories: loop-engineering

8 tracked repositories tagged with loop-engineering, ordered by stars. Use the topic filters below to narrow further.

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8 of 8 repositories

  • agentscope-ai/QwenPaw

    Your Personal AI Assistant; easy to install, deploy on your own machine or on the cloud; supports multiple chat apps with easily extensible capabilities.

    AI summary: A versatile personal AI assistant built on the Qwen model family.

    34,301productivityPythonApache-2.0

  • agentscope-ai/QwenPaw

    Your Personal AI Assistant; easy to install, deploy on your own machine or on the cloud; supports multiple chat apps with easily extensible capabilities.

    AI summary: A versatile personal AI assistant framework with extensible capabilities.

    29,700productivityPythonApache-2.0

  • NevaMind-AI/memU

    Personal memory across agents

    AI summary: An agent-driven memory system that curates a shared LLM wiki across different sessions, coding agents, and devices.

    14,267ai-mlPythonOther

  • cobusgreyling/loop-engineering

    Practical patterns, starters & CLI tools for loop engineering with AI coding agents. Design systems that prompt and orchestrate agents (inspired by Addy Osmani and Boris Cherny). Includes loop-audit, loop-init, loop-cost.

    AI summary: A framework for building robust agentic loops and evaluation pipelines.

    9,942ai-mlJavaScriptMIT

  • huangruiteng/loopx

    Lightweight loop engineering state kernel for long-running AI agent teams. Agent-loop agnostic across Codex, Claude Code, and other coding agents, with durable goals, quota-aware auto-wake, executable todos, evidence logs, and verifiable handoffs.

    AI summary: A local control plane for managing and reviewing long-running AI agent workflows and loops.

    3,303ai-mlPythonMIT

  • ray-r-ren/agent-apprenticeship

    The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.

    AI summary: Ecosystem framework for capturing and utilizing AI agent work traces for iterative learning.

    1,340ai-mlPythonMIT

  • ray-r-ren/agent-apprenticeship

    The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.

    AI summary: Ecosystem framework for capturing and utilizing AI agent work traces for iterative learning.

    1,339ai-mlPythonMIT

  • modiqo/waggle

    Attributed, resolvable artifact references for agent handoffs — a ~30-byte token instead of pasted context. MCP-native; the reference layer for the agent-harness world.

    AI summary: An MCP-native reference layer that replaces raw context pasting with resolvable, 30-byte artifact tokens for AI agents.

    754developer-toolsRustApache-2.0