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⚡ huangruiteng/loopx is making waves. Here's the full picture.
🔗 https://github.com/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.
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LoopX is a lightweight, local control plane for loop engineering, designed to keep long-running AI agent work reviewable, restartable, and easier to hand off across turns, tools, and agents. It maintains a compact, durable control state, including objectives, gates, todos, evidence, and quota, ensuring that human judgment is always involved when needed. Key features include a simple, agent-agnostic design, support for multiple AI runtimes (e.g., Codex, Claude Code), and a
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🧠 Channel: https://xn--r1a.website/GithubRe
🔗 https://github.com/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.
──────────────────────────────
LoopX is a lightweight, local control plane for loop engineering, designed to keep long-running AI agent work reviewable, restartable, and easier to hand off across turns, tools, and agents. It maintains a compact, durable control state, including objectives, gates, todos, evidence, and quota, ensuring that human judgment is always involved when needed. Key features include a simple, agent-agnostic design, support for multiple AI runtimes (e.g., Codex, Claude Code), and a
quota system that decides whether a turn should deliver, ask, wait, or stop. Usage involves installing LoopX, connecting it to your project, and using the loopx command to manage the loop. Technical highlights include a small core tick, deliberate separation of concerns, and a focus on local-first design. LoopX is suitable for developers, researchers, and operators working with long-running AI agent projects, such as multi-day engineering, research, or experiments. In short, LoopX helps you keep the loop moving while keeping human judgment in the driver's seat.──────────────────────────────
🧠 Channel: https://xn--r1a.website/GithubRe
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Github Top Repositories
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🔍 Deep-diving into TencentCloud/TencentDB-Agent-Memory — fresh off the trending list.
🔗 https://github.com/TencentCloud/TencentDB-Agent-Memory
📝 TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.
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The TencentDB Agent Memory is a cutting-edge solution designed to enhance the capabilities of AI agents by providing them with a robust memory system. This system combines symbolic short-term memory and layered long-term memory to enable agents to learn from workflows, retain task context, and reuse past experiences. By offloading heavy tool logs and condensing them into compact Mermaid symbols, the system reduces token usage and improves task success rates.
The key features of TencentDB Agent Memory include:
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The system has been integrated with OpenClaw and Hermes agents, with impressive results, including a 61.38% reduction in token usage and a 51.52% improvement in pass rate.
To get started with TencentDB Agent Memory, users can follow the
In summary, TencentDB Agent Memory is a powerful solution that enables AI agents to remember what's important, so humans can focus on what truly matters – and that's a game-changer!
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🧠 Channel: https://xn--r1a.website/GithubRe
🔗 https://github.com/TencentCloud/TencentDB-Agent-Memory
📝 TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.
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The TencentDB Agent Memory is a cutting-edge solution designed to enhance the capabilities of AI agents by providing them with a robust memory system. This system combines symbolic short-term memory and layered long-term memory to enable agents to learn from workflows, retain task context, and reuse past experiences. By offloading heavy tool logs and condensing them into compact Mermaid symbols, the system reduces token usage and improves task success rates.
The key features of TencentDB Agent Memory include:
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Memory layering: a hierarchical approach to memory formation and recall, allowing for progressive disclosure and heterogeneous storage.*
Symbolic memory: a Mermaid symbol graph that encodes task state transitions, enabling precise and concise memory representation.*
Context offloading: the ability to offload full tool logs to external files, reducing token cost while preserving traceability.The system has been integrated with OpenClaw and Hermes agents, with impressive results, including a 61.38% reduction in token usage and a 51.52% improvement in pass rate.
To get started with TencentDB Agent Memory, users can follow the
Quick Start guide, which provides step-by-step instructions for installing and configuring the plugin with OpenClaw or Hermes agents.In summary, TencentDB Agent Memory is a powerful solution that enables AI agents to remember what's important, so humans can focus on what truly matters – and that's a game-changer!
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