<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai-Agents on Noureddine RAMDI</title><link>https://ramdi.fr/tags/ai-agents/</link><description>Recent content in Ai-Agents on Noureddine RAMDI</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 23 May 2026 20:41:27 +0000</lastBuildDate><atom:link href="https://ramdi.fr/tags/ai-agents/index.xml" rel="self" type="application/rss+xml"/><item><title>AgentFlow: orchestrating AI coding agents with programmatic dependency graphs</title><link>https://ramdi.fr/github-stars/agentflow-orchestrating-ai-coding-agents-with-programmatic-dependency-graphs/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/agentflow-orchestrating-ai-coding-agents-with-programmatic-dependency-graphs/</guid><description>AgentFlow uses Python&amp;rsquo;s graph-based DSL to orchestrate AI coding agents with parallelism, iteration, and remote execution. It supports Codex, Claude, and others.</description></item><item><title>AutoSkill: Experience-driven lifelong learning for LLM agents with skill versioning and evolution</title><link>https://ramdi.fr/github-stars/autoskill-experience-driven-lifelong-learning-for-llm-agents-with-skill-versioning-and-evolution/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/autoskill-experience-driven-lifelong-learning-for-llm-agents-with-skill-versioning-and-evolution/</guid><description>AutoSkill is a Python framework enabling LLM agents to extract, version, and evolve skills from dialogues, providing a persistent long-term memory system for AI agents.</description></item><item><title>Clawd on Desk: an ambient desktop pet for real-time AI agent observability</title><link>https://ramdi.fr/github-stars/clawd-on-desk-an-ambient-desktop-pet-for-real-time-ai-agent-observability/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/clawd-on-desk-an-ambient-desktop-pet-for-real-time-ai-agent-observability/</guid><description>Clawd on Desk uses an Electron-based desktop pet with pixel-art animations to visualize AI coding agents&amp;rsquo; activity in real time, enabling glanceable observability and inline permission handling.</description></item><item><title>ClawMetry: zero-config real-time observability dashboard for OpenClaw AI agents</title><link>https://ramdi.fr/github-stars/clawmetry-zero-config-real-time-observability-dashboard-for-openclaw-ai-agents/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/clawmetry-zero-config-real-time-observability-dashboard-for-openclaw-ai-agents/</guid><description>ClawMetry offers a zero-configuration real-time dashboard for OpenClaw AI agents, visualizing message flows, token usage, and operational alerts with easy setup.</description></item><item><title>Clay: a self-hosted multiplayer AI workspace with persistent AI teammates and autonomous coding loops</title><link>https://ramdi.fr/github-stars/clay-a-self-hosted-multiplayer-ai-workspace-with-persistent-ai-teammates-and-autonomous-coding-loops/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/clay-a-self-hosted-multiplayer-ai-workspace-with-persistent-ai-teammates-and-autonomous-coding-loops/</guid><description>Clay unifies local repos into a browser-based dashboard with persistent AI teammates that remember across sessions and iterate autonomously via overnight coding loops. Self-hosted with no cloud lock-in.</description></item><item><title>homeassistant-ai/skills: standardizing Home Assistant skills for AI coding agents</title><link>https://ramdi.fr/github-stars/homeassistant-ai-skills-standardizing-home-assistant-skills-for-ai-coding-agents/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/homeassistant-ai-skills-standardizing-home-assistant-skills-for-ai-coding-agents/</guid><description>homeassistant-ai/skills provides a modular collection of Home Assistant skills for AI coding agents supporting the Agent Skills standard, easing integration with tools like Claude Code and Copilot.</description></item><item><title>how narratorai cli skill orchestrates ai agents for automated movie narration</title><link>https://ramdi.fr/github-stars/how-narratorai-cli-skill-orchestrates-ai-agents-for-automated-movie-narration/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/how-narratorai-cli-skill-orchestrates-ai-agents-for-automated-movie-narration/</guid><description>NarratorAI CLI Skill uses a machine-readable SKILL.md file to enable AI agents to automate movie narration video production via a CLI tool. Supports multiple agents and rich media resources.</description></item><item><title>How opencode-power-pack bridges Claude Code workflows into OpenCode</title><link>https://ramdi.fr/github-stars/how-opencode-power-pack-bridges-claude-code-workflows-into-opencode/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/how-opencode-power-pack-bridges-claude-code-workflows-into-opencode/</guid><description>opencode-power-pack translates Anthropic&amp;rsquo;s Claude Code plugins into OpenCode SKILL.md files, enabling multi-agent AI coding workflows across platforms. Here&amp;rsquo;s how it works under the hood.</description></item><item><title>Inside gtm-agents: a Claude Code marketplace for specialized GTM AI plugins</title><link>https://ramdi.fr/github-stars/inside-gtm-agents-a-claude-code-marketplace-for-specialized-gtm-ai-plugins/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/inside-gtm-agents-a-claude-code-marketplace-for-specialized-gtm-ai-plugins/</guid><description>gtm-agents bundles 67 GTM plugins, 92 AI agents, 52 business skills, and 20 workflow orchestrators for Claude Code, saving 15+ hours/week on sales and marketing busywork.</description></item><item><title>Inside red-run: AI agent orchestration for offensive security assessments</title><link>https://ramdi.fr/github-stars/inside-red-run-ai-agent-orchestration-for-offensive-security-assessments/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/inside-red-run-ai-agent-orchestration-for-offensive-security-assessments/</guid><description>red-run orchestrates Claude Code AI agent teams across the full pentest kill chain using persistent teammates and semantic routing. Explore its architecture, strengths, and quickstart.</description></item><item><title>Integrating Google Workspace with AI agents via the google-docs-mcp server</title><link>https://ramdi.fr/github-stars/integrating-google-workspace-with-ai-agents-via-the-google-docs-mcp-server/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/integrating-google-workspace-with-ai-agents-via-the-google-docs-mcp-server/</guid><description>The google-docs-mcp server exposes Google Docs, Sheets, Drive, Gmail, and Calendar as callable tools for AI agents like Claude Desktop, enabling deep document and email automation via OAuth2.</description></item><item><title>LLM4Pentest: A curated knowledge hub on large language models for automated penetration testing</title><link>https://ramdi.fr/github-stars/llm4pentest-a-curated-knowledge-hub-on-large-language-models-for-automated-penetration-testing/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/llm4pentest-a-curated-knowledge-hub-on-large-language-models-for-automated-penetration-testing/</guid><description>LLM4Pentest aggregates 40+ research papers and tools tracking the evolving role of LLMs in automated penetration testing, highlighting progress and limitations.</description></item><item><title>mcp-selenium: structured browser automation for AI agents via MCP</title><link>https://ramdi.fr/github-stars/mcp-selenium-structured-browser-automation-for-ai-agents-via-mcp/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/mcp-selenium-structured-browser-automation-for-ai-agents-via-mcp/</guid><description>mcp-selenium exposes Selenium WebDriver as typed MCP tools for AI agents, supporting multi-browser automation with WebDriver BiDi diagnostics. Setup is easy with npx commands.</description></item><item><title>native-devtools-mcp: cross-platform AI agent control of native apps with macOS accessibility precision</title><link>https://ramdi.fr/github-stars/native-devtools-mcp-cross-platform-ai-agent-control-of-native-apps-with-macos-accessibility-precision/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/native-devtools-mcp-cross-platform-ai-agent-control-of-native-apps-with-macos-accessibility-precision/</guid><description>native-devtools-mcp provides a Rust-based MCP server letting AI agents control native desktop apps on macOS/Windows, Chrome/Electron browsers, and Android devices. It features macOS AX dispatch for precise, focus-free app control.</description></item><item><title>opcode: a Tauri desktop app turning Claude Code CLI into a graphical AI developer cockpit</title><link>https://ramdi.fr/github-stars/opcode-a-tauri-desktop-app-turning-claude-code-cli-into-a-graphical-ai-developer-cockpit/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/opcode-a-tauri-desktop-app-turning-claude-code-cli-into-a-graphical-ai-developer-cockpit/</guid><description>opcode is a cross-platform Tauri desktop app that wraps Claude Code CLI with a GUI, session checkpointing, custom AI agents, MCP server management, and usage analytics.</description></item><item><title>OpenClaw Agents: orchestrating adversarial AI agents with shell-driven provisioning</title><link>https://ramdi.fr/github-stars/openclaw-agents-orchestrating-adversarial-ai-agents-with-shell-driven-provisioning/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/openclaw-agents-orchestrating-adversarial-ai-agents-with-shell-driven-provisioning/</guid><description>OpenClaw Agents deploys paired AI agents using shell scripts for adversarial collaboration in OpenClaw gateway, supporting multi-channel or local workflows with safe config merges.</description></item><item><title>OpenUI: a visual canvas for managing multiple AI agents with real-time status and session persistence</title><link>https://ramdi.fr/github-stars/openui-a-visual-canvas-for-managing-multiple-ai-agents-with-real-time-status-and-session-persistence/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/openui-a-visual-canvas-for-managing-multiple-ai-agents-with-real-time-status-and-session-persistence/</guid><description>OpenUI provides a TypeScript CLI and browser UI to spawn, organize, and monitor multiple AI agents with session persistence and real-time status. Easy to start and extend.</description></item><item><title>Ori-Mnemos: scoped memory management for AI agents in TypeScript</title><link>https://ramdi.fr/github-stars/ori-mnemos-scoped-memory-management-for-ai-agents-in-typescript/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/ori-mnemos-scoped-memory-management-for-ai-agents-in-typescript/</guid><description>Ori-Mnemos provides a TypeScript CLI tool for managing AI agent memory with scoped vaults and activation modes, enabling persistent and flexible context handling.</description></item><item><title>SceneSmith: AI-driven pipeline for physics-ready 3D indoor scene generation from text</title><link>https://ramdi.fr/github-stars/scenesmith-ai-driven-pipeline-for-physics-ready-3d-indoor-scene-generation-from-text/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/scenesmith-ai-driven-pipeline-for-physics-ready-3d-indoor-scene-generation-from-text/</guid><description>SceneSmith uses GPT-5-powered agents to generate physically plausible 3D indoor scenes from text prompts, ready for robotics simulation without manual cleanup.</description></item><item><title>Ultimate AI Engineer Roadmap 2026: A comprehensive curriculum for aspiring AI engineers</title><link>https://ramdi.fr/github-stars/ultimate-ai-engineer-roadmap-2026-a-comprehensive-curriculum-for-aspiring-ai-engineers/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/ultimate-ai-engineer-roadmap-2026-a-comprehensive-curriculum-for-aspiring-ai-engineers/</guid><description>Explore a detailed 17-phase AI engineering roadmap for 2026, focusing on multi-LLM orchestration, RAG, AI agents, and production-ready skills with 51 hands-on projects.</description></item><item><title>World2Agent: Standardizing AI Agent Perception with Pluggable NPM Sensors</title><link>https://ramdi.fr/github-stars/world2agent-standardizing-ai-agent-perception-with-pluggable-npm-sensors/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/world2agent-standardizing-ai-agent-perception-with-pluggable-npm-sensors/</guid><description>World2Agent defines a TypeScript protocol for AI agents to ingest real-world data as pluggable npm sensors, with runtime bridges for Claude Code, Hermes, and OpenClaw.</description></item><item><title>XcodeBuildMCP: Bridging AI Agents and Native Apple Development with MCP Tools</title><link>https://ramdi.fr/github-stars/xcodebuildmcp-bridging-ai-agents-and-native-apple-development-with-mcp-tools/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/xcodebuildmcp-bridging-ai-agents-and-native-apple-development-with-mcp-tools/</guid><description>XcodeBuildMCP exposes xcodebuild operations as MCP tools for AI coding assistants and CLI use, enabling automated iOS/macOS builds, testing, and debugging with a per-workspace daemon on macOS.</description></item><item><title>OpenShell: Securing AI agents with runtime-policy sandboxing from NVIDIA</title><link>https://ramdi.fr/github-stars/openshell-securing-ai-agents-with-runtime-policy-sandboxing-from-nvidia/</link><pubDate>Mon, 18 May 2026 18:25:17 +0000</pubDate><guid>https://ramdi.fr/github-stars/openshell-securing-ai-agents-with-runtime-policy-sandboxing-from-nvidia/</guid><description>OpenShell by NVIDIA offers a Rust-based AI agent sandbox runtime with hot-reloadable YAML policies for filesystem, network, process, and inference controls inside containers.</description></item><item><title>OpenAlpha_Evolve: autonomous code evolution with LLM-driven diff mutations</title><link>https://ramdi.fr/github-stars/openalpha-evolve-autonomous-code-evolution-with-llm-driven-diff-mutations/</link><pubDate>Sat, 09 May 2026 11:42:26 +0000</pubDate><guid>https://ramdi.fr/github-stars/openalpha-evolve-autonomous-code-evolution-with-llm-driven-diff-mutations/</guid><description>OpenAlpha_Evolve uses large language models to generate precise code diffs as mutations in an evolutionary algorithm, enabling autonomous iterative code improvement with sandboxed evaluation.</description></item><item><title>HASH: Autonomous AI-driven knowledge graph platform with Rust and multi-service architecture</title><link>https://ramdi.fr/github-stars/hash-autonomous-ai-driven-knowledge-graph-platform-with-rust-and-multi-service-architecture/</link><pubDate>Tue, 05 May 2026 16:46:42 +0000</pubDate><guid>https://ramdi.fr/github-stars/hash-autonomous-ai-driven-knowledge-graph-platform-with-rust-and-multi-service-architecture/</guid><description>HASH is a Rust-based multi-tenant knowledge graph platform using autonomous AI agents to build and validate data. It combines Rust backend, TypeScript workers, and Docker services with LLM integrations.</description></item><item><title>Octopoda-OS: a memory layer for AI agents with loop detection and audit trails</title><link>https://ramdi.fr/github-stars/octopoda-os-a-memory-layer-for-ai-agents-with-loop-detection-and-audit-trails/</link><pubDate>Tue, 05 May 2026 16:46:42 +0000</pubDate><guid>https://ramdi.fr/github-stars/octopoda-os-a-memory-layer-for-ai-agents-with-loop-detection-and-audit-trails/</guid><description>Octopoda-OS is a Python library providing persistent memory, loop detection, and audit trails for AI agents. It supports SQLite/PostgreSQL, zero-config runtime, and cloud sync.</description></item><item><title>TinyAGI: A lightweight multi-agent orchestration platform with SQLite-backed task queue</title><link>https://ramdi.fr/github-stars/tinyagi-a-lightweight-multi-agent-orchestration-platform-with-sqlite-backed-task-queue/</link><pubDate>Tue, 05 May 2026 16:46:42 +0000</pubDate><guid>https://ramdi.fr/github-stars/tinyagi-a-lightweight-multi-agent-orchestration-platform-with-sqlite-backed-task-queue/</guid><description>TinyAGI is a TypeScript platform for solo operators managing multiple AI agent teams. It uses a SQLite queue with atomic transactions for reliable async task processing and supports multi-channel messaging.</description></item><item><title>agentic-stack: portable multi-agent memory for AI coding assistants</title><link>https://ramdi.fr/github-stars/agentic-stack-portable-multi-agent-memory-for-ai-coding-assistants/</link><pubDate>Tue, 05 May 2026 13:37:39 +0000</pubDate><guid>https://ramdi.fr/github-stars/agentic-stack-portable-multi-agent-memory-for-ai-coding-assistants/</guid><description>agentic-stack provides a harness-agnostic shared memory layer for AI coding agents, enabling seamless context persistence and migration across tools like Claude Code and Cursor.</description></item><item><title>Nanobrowser: multi-agent AI browser automation with dynamic self-correcting planning</title><link>https://ramdi.fr/github-stars/nanobrowser-multi-agent-ai-browser-automation-with-dynamic-self-correcting-planning/</link><pubDate>Tue, 05 May 2026 13:37:39 +0000</pubDate><guid>https://ramdi.fr/github-stars/nanobrowser-multi-agent-ai-browser-automation-with-dynamic-self-correcting-planning/</guid><description>Nanobrowser is a TypeScript Chrome extension implementing a multi-agent AI system for browser automation with a unique self-correcting Planner-Navigator architecture, supporting multiple LLMs and local privacy.</description></item><item><title>site-md: Next.js middleware serving Markdown to AI agents without content duplication</title><link>https://ramdi.fr/github-stars/site-md-next-js-middleware-serving-markdown-to-ai-agents-without-content-duplication/</link><pubDate>Tue, 05 May 2026 13:37:39 +0000</pubDate><guid>https://ramdi.fr/github-stars/site-md-next-js-middleware-serving-markdown-to-ai-agents-without-content-duplication/</guid><description>site-md uses Next.js middleware to serve clean Markdown to AI agents and HTML to humans, converting HTML on-the-fly without duplicating content. Easy install with minimal setup.</description></item><item><title>ZenML: a unified MLOps platform bridging classical ML and AI agent orchestration</title><link>https://ramdi.fr/github-stars/zenml-a-unified-mlops-platform-bridging-classical-ml-and-ai-agent-orchestration/</link><pubDate>Tue, 05 May 2026 13:37:39 +0000</pubDate><guid>https://ramdi.fr/github-stars/zenml-a-unified-mlops-platform-bridging-classical-ml-and-ai-agent-orchestration/</guid><description>ZenML offers an open-source Python SDK to orchestrate full ML and AI agent lifecycles, integrating popular tools and enabling natural-language MLOps interactions via its MCP server.</description></item><item><title>awesome-sandbox: comparing modern sandboxing tech for AI agent execution</title><link>https://ramdi.fr/github-stars/awesome-sandbox-comparing-modern-sandboxing-tech-for-ai-agent-execution/</link><pubDate>Mon, 04 May 2026 10:23:03 +0000</pubDate><guid>https://ramdi.fr/github-stars/awesome-sandbox-comparing-modern-sandboxing-tech-for-ai-agent-execution/</guid><description>A curated repo comparing sandboxing technologies for secure, fast AI agent execution. Covers microVMs, containers, WebAssembly, and more with tradeoffs on security vs speed.</description></item><item><title>EvoClaw: Structured memory and identity evolution framework for OpenClaw AI agents</title><link>https://ramdi.fr/github-stars/evoclaw-structured-memory-and-identity-evolution-framework-for-openclaw-ai-agents/</link><pubDate>Mon, 04 May 2026 10:23:03 +0000</pubDate><guid>https://ramdi.fr/github-stars/evoclaw-structured-memory-and-identity-evolution-framework-for-openclaw-ai-agents/</guid><description>EvoClaw enforces AI agent memory and identity evolution with an 8-validator pipeline ensuring integrity and governance, featuring a tiered memory system and radial mindmap UI.</description></item><item><title>agent-sat: Autonomous AI agent discovering MaxSAT solving techniques through iterative experimentation</title><link>https://ramdi.fr/github-stars/agent-sat-autonomous-ai-agent-discovering-maxsat-solving-techniques-through-iterative-experimentation/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/agent-sat-autonomous-ai-agent-discovering-maxsat-solving-techniques-through-iterative-experimentation/</guid><description>agent-sat is an autonomous AI agent system where Claude Code learns to solve weighted MaxSAT problems by iterating solver improvements and coordinating via git, solving 220/229 benchmarks.</description></item><item><title>AgentFlow: orchestrating AI coding agents with graph-based parallelism and remote execution</title><link>https://ramdi.fr/github-stars/agentflow-orchestrating-ai-coding-agents-with-graph-based-parallelism-and-remote-execution/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/agentflow-orchestrating-ai-coding-agents-with-graph-based-parallelism-and-remote-execution/</guid><description>AgentFlow is a Python library for orchestrating AI coding agents using dependency graphs, supporting parallel fanout, iterative refinement, and remote execution. It integrates with Codex CLI for natural-language pipeline creation.</description></item><item><title>Agentic Coding Flywheel Setup: Bootstrapping AI-powered coding agents on a VPS</title><link>https://ramdi.fr/github-stars/agentic-coding-flywheel-setup-bootstrapping-ai-powered-coding-agents-on-a-vps/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/agentic-coding-flywheel-setup-bootstrapping-ai-powered-coding-agents-on-a-vps/</guid><description>Agentic Coding Flywheel Setup automates turning a fresh Ubuntu VPS into an AI-powered coding environment with autonomous agents, simplifying complex setups into a single command.</description></item><item><title>AgentOps: a local operating layer for cross-vendor AI coding agents with multi-agent consensus</title><link>https://ramdi.fr/github-stars/agentops-a-local-operating-layer-for-cross-vendor-ai-coding-agents-with-multi-agent-consensus/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/agentops-a-local-operating-layer-for-cross-vendor-ai-coding-agents-with-multi-agent-consensus/</guid><description>AgentOps provides a Go-based local operating layer for AI coding agents, enabling persistent memory, validation gates, and multi-agent review across vendors with zero cloud dependency.</description></item><item><title>AgentsMesh: a self-hosted AI agent orchestration platform with control plane/data plane separation</title><link>https://ramdi.fr/github-stars/agentsmesh-a-self-hosted-ai-agent-orchestration-platform-with-control-plane-data-plane-separation/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/agentsmesh-a-self-hosted-ai-agent-orchestration-platform-with-control-plane-data-plane-separation/</guid><description>AgentsMesh offers a self-hosted AI agent orchestration platform with a clean control/data plane split using gRPC+mTLS and WebSocket relay for real-time terminal I/O streaming.</description></item><item><title>AutoProber: AI-driven hardware automation with oscilloscope-monitored safety for PCB analysis</title><link>https://ramdi.fr/github-stars/autoprober-ai-driven-hardware-automation-with-oscilloscope-monitored-safety-for-pcb-analysis/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/autoprober-ai-driven-hardware-automation-with-oscilloscope-monitored-safety-for-pcb-analysis/</guid><description>AutoProber is a Python automation stack that controls a flying probe system for PCB analysis, featuring oscilloscope-based safety monitoring and a Flask dashboard.</description></item><item><title>cass-memory: a TypeScript cognitive memory system with confidence decay for AI coding agents</title><link>https://ramdi.fr/github-stars/cass-memory-a-typescript-cognitive-memory-system-with-confidence-decay-for-ai-coding-agents/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/cass-memory-a-typescript-cognitive-memory-system-with-confidence-decay-for-ai-coding-agents/</guid><description>cass-memory is a TypeScript CLI implementing a three-layer cognitive architecture for AI coding agents, featuring a novel confidence decay system to maintain a reliable, evolving knowledge base.</description></item><item><title>claude code viewer: a pragmatic web UI for managing Claude Code agent sessions</title><link>https://ramdi.fr/github-stars/claude-code-viewer-a-pragmatic-web-ui-for-managing-claude-code-agent-sessions/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/claude-code-viewer-a-pragmatic-web-ui-for-managing-claude-code-agent-sessions/</guid><description>Claude Code Viewer provides a web UI for real-time monitoring and managing Claude Code sessions with dual authentication modes and PWA mobile support. It handles Anthropic&amp;rsquo;s SDK restrictions gracefully.</description></item><item><title>claude-code-harness: a Shell-based plugin harness for Claude Code AI agents</title><link>https://ramdi.fr/github-stars/claude-code-harness-a-shell-based-plugin-harness-for-claude-code-ai-agents/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/claude-code-harness-a-shell-based-plugin-harness-for-claude-code-ai-agents/</guid><description>claude-code-harness is a Shell plugin harness for Claude Code that integrates AI agent features without Node.js, relying on specific Claude Code and Opus versions for full capability.</description></item><item><title>claudemap: visual interactive map for Claude AI agents with Codex support</title><link>https://ramdi.fr/github-stars/claudemap-visual-interactive-map-for-claude-ai-agents-with-codex-support/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/claudemap-visual-interactive-map-for-claude-ai-agents-with-codex-support/</guid><description>claudemap offers a JavaScript runtime to create and run visual interactive maps for Claude AI agents, with optional Codex assistant integration for enhanced natural language control.</description></item><item><title>ctx: managing AI skills and agents with a context-aware knowledge graph</title><link>https://ramdi.fr/github-stars/ctx-managing-ai-skills-and-agents-with-a-context-aware-knowledge-graph/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/ctx-managing-ai-skills-and-agents-with-a-context-aware-knowledge-graph/</guid><description>ctx builds a 104K-node knowledge graph to optimize AI skill and agent selection for Claude Code, solving context window bloat with a graph-based recommender and lifecycle management.</description></item><item><title>Extending Vercel AI SDK with modular TypeScript tools for AI applications</title><link>https://ramdi.fr/github-stars/extending-vercel-ai-sdk-with-modular-typescript-tools-for-ai-applications/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/extending-vercel-ai-sdk-with-modular-typescript-tools-for-ai-applications/</guid><description>ai-sdk-tools offers modular TypeScript packages extending Vercel AI SDK with production-ready patterns like multi-agent orchestration and type-safe streaming artifacts for React apps.</description></item><item><title>Goose Skills: Modular GTM AI agent skills for sales and marketing automation</title><link>https://ramdi.fr/github-stars/goose-skills-modular-gtm-ai-agent-skills-for-sales-and-marketing-automation/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/goose-skills-modular-gtm-ai-agent-skills-for-sales-and-marketing-automation/</guid><description>Goose Skills provides 108 reusable AI agent skills for sales, marketing, and competitive intelligence, structured as atomic tools, skill chains, and workflows for coding agents like Claude and Codex.</description></item><item><title>Kitaru: a durable runtime for autonomous AI agents with checkpointed execution</title><link>https://ramdi.fr/github-stars/kitaru-a-durable-runtime-for-autonomous-ai-agents-with-checkpointed-execution/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/kitaru-a-durable-runtime-for-autonomous-ai-agents-with-checkpointed-execution/</guid><description>Kitaru offers a framework-agnostic runtime for autonomous AI agents with durable execution via checkpointing, enabling replay and state preservation to avoid costly restarts on failures.</description></item><item><title>KohakuTerrarium: Modular AI agent composition with algebraic pipelines</title><link>https://ramdi.fr/github-stars/kohakuterrarium-modular-ai-agent-composition-with-algebraic-pipelines/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/kohakuterrarium-modular-ai-agent-composition-with-algebraic-pipelines/</guid><description>KohakuTerrarium offers a Python framework to build modular AI agents using a unique algebra for composing multi-agent pipelines, with session persistence and multi-runtime support.</description></item><item><title>Mapping the LLM agent landscape with the awesome-llm-agents curated catalog</title><link>https://ramdi.fr/github-stars/mapping-the-llm-agent-landscape-with-the-awesome-llm-agents-curated-catalog/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/mapping-the-llm-agent-landscape-with-the-awesome-llm-agents-curated-catalog/</guid><description>A curated catalog of 20+ LLM agent frameworks and tools organized by agent type and capabilities. Understand architectural differences and trade-offs in LLM agent design.</description></item><item><title>octogent: a local orchestration layer for multi-agent workflows with claude code</title><link>https://ramdi.fr/github-stars/octogent-a-local-orchestration-layer-for-multi-agent-workflows-with-claude-code/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/octogent-a-local-orchestration-layer-for-multi-agent-workflows-with-claude-code/</guid><description>Octogent adds a local orchestration layer on Claude Code for multi-agent workflows using &amp;rsquo;tentacles&amp;rsquo; — scoped context directories that isolate work and enable inter-agent messaging.</description></item><item><title>Open Computer Use: orchestrating multi-agent AI for real computer control with containerized VMs</title><link>https://ramdi.fr/github-stars/open-computer-use-orchestrating-multi-agent-ai-for-real-computer-control-with-containerized-vms/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/open-computer-use-orchestrating-multi-agent-ai-for-real-computer-control-with-containerized-vms/</guid><description>Open Computer Use enables AI agents to control real computers using specialized Browser, Terminal, and Desktop agents running in isolated Docker VMs. It achieves 82% on the OSWorld benchmark.</description></item><item><title>OpenClaw Client: a self-hosted multi-agent AI chat interface with streaming "thinking" separation</title><link>https://ramdi.fr/github-stars/openclaw-client-a-self-hosted-multi-agent-ai-chat-interface-with-streaming-thinking-separation/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/openclaw-client-a-self-hosted-multi-agent-ai-chat-interface-with-streaming-thinking-separation/</guid><description>OpenClaw Client offers a self-hosted web UI to manage OpenClaw AI agents with streaming response separation, file uploads to agent workspaces, JWT auth, and PWA install.</description></item><item><title>Packaging product management expertise as Claude Code skills with lenny-skills</title><link>https://ramdi.fr/github-stars/packaging-product-management-expertise-as-claude-code-skills-with-lenny-skills/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/packaging-product-management-expertise-as-claude-code-skills-with-lenny-skills/</guid><description>lenny-skills packages product management knowledge as markdown skills for Claude Code, enabling AI agents to apply frameworks from top product leaders. Install via npx skills CLI.</description></item><item><title>Scientific Agent Skills: Modular AI capabilities for complex scientific workflows</title><link>https://ramdi.fr/github-stars/scientific-agent-skills-modular-ai-capabilities-for-complex-scientific-workflows/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/scientific-agent-skills-modular-ai-capabilities-for-complex-scientific-workflows/</guid><description>Scientific Agent Skills extends AI coding agents with 135 domain-specific scientific skills, unifying database queries and multi-step workflows across bioinformatics, chemistry, and clinical research.</description></item><item><title>smux: a shell-driven multi-agent communication layer using tmux panes</title><link>https://ramdi.fr/github-stars/smux-a-shell-driven-multi-agent-communication-layer-using-tmux-panes/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/smux-a-shell-driven-multi-agent-communication-layer-using-tmux-panes/</guid><description>smux leverages tmux panes and a CLI bridge to enable AI agents to collaborate inside the terminal without APIs or shared state. Simple install, macOS/Linux compatible.</description></item><item><title>souls-directory: a curated Next.js directory for SOUL.md AI agent personalities</title><link>https://ramdi.fr/github-stars/souls-directory-a-curated-next-js-directory-for-soul-md-ai-agent-personalities/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/souls-directory-a-curated-next-js-directory-for-soul-md-ai-agent-personalities/</guid><description>souls-directory is a Next.js app hosting curated SOUL.md personality templates for OpenClaw AI agents, using Convex backend and GitHub OAuth. Explore its architecture and usage.</description></item><item><title>text-to-cad: AI-driven parametric CAD with geometry-aware iterative editing</title><link>https://ramdi.fr/github-stars/text-to-cad-ai-driven-parametric-cad-with-geometry-aware-iterative-editing/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/text-to-cad-ai-driven-parametric-cad-with-geometry-aware-iterative-editing/</guid><description>text-to-cad bridges AI coding agents with parametric CAD using a local-first architecture and a novel @cad reference system for geometry-aware iterative edits and multi-format export.</description></item><item><title>Tolaria: a Tauri desktop app for managing markdown knowledge bases with AI agent integration</title><link>https://ramdi.fr/github-stars/tolaria-a-tauri-desktop-app-for-managing-markdown-knowledge-bases-with-ai-agent-integration/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/tolaria-a-tauri-desktop-app-for-managing-markdown-knowledge-bases-with-ai-agent-integration/</guid><description>Tolaria is a Tauri desktop app for managing markdown knowledge bases with git versioning and AI agent integration via a bundled MCP server. Supports 10,000+ notes offline.</description></item><item><title>tui-use: synchronizing AI agents with interactive terminal programs through PTY event streams</title><link>https://ramdi.fr/github-stars/tui-use-synchronizing-ai-agents-with-interactive-terminal-programs-through-pty-event-streams/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/tui-use-synchronizing-ai-agents-with-interactive-terminal-programs-through-pty-event-streams/</guid><description>tui-use bridges AI agents and terminal apps by intercepting PTY output with a headless xterm emulator, enabling smart wait semantics for reliable interaction in CLI TUIs.</description></item><item><title>usecomputer: A native cross-platform CLI for AI-driven desktop automation with precise coordinate mapping</title><link>https://ramdi.fr/github-stars/usecomputer-a-native-cross-platform-cli-for-ai-driven-desktop-automation-with-precise-coordinate-mapping/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/usecomputer-a-native-cross-platform-cli-for-ai-driven-desktop-automation-with-precise-coordinate-mapping/</guid><description>usecomputer is a Zig-based native CLI for cross-platform desktop automation, solving coordinate mismatches via a coord-map system for AI agents working with downscaled screenshots.</description></item><item><title>Adaptive video perception for AI agents with claude-video-vision</title><link>https://ramdi.fr/github-stars/adaptive-video-perception-for-ai-agents-with-claude-video-vision/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/adaptive-video-perception-for-ai-agents-with-claude-video-vision/</guid><description>claude-video-vision adds adaptive video frame extraction and audio transcription to Claude Code, bridging natural language queries with dynamic ffmpeg processing. It supports multiple audio backends and runs on Node.js 20+.</description></item><item><title>Clawd Cursor: Unified cross-platform desktop control for AI agents</title><link>https://ramdi.fr/github-stars/clawd-cursor-unified-cross-platform-desktop-control-for-ai-agents/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/clawd-cursor-unified-cross-platform-desktop-control-for-ai-agents/</guid><description>Clawd Cursor offers AI agents native desktop control on Windows, macOS, and Linux with a unified PlatformAdapter and local-first architecture, enabling secure, model-agnostic automation without cloud round-trips.</description></item><item><title>CORAL: orchestrating autonomous AI coding agents with git worktree isolation and shared state</title><link>https://ramdi.fr/github-stars/coral-orchestrating-autonomous-ai-coding-agents-with-git-worktree-isolation-and-shared-state/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/coral-orchestrating-autonomous-ai-coding-agents-with-git-worktree-isolation-and-shared-state/</guid><description>CORAL uses git worktree branches combined with symlinked shared state to orchestrate multiple AI coding agents collaborating in real-time. This Python infrastructure supports iterative code improvement through evaluation loops.</description></item><item><title>DeepChat: a unified Electron desktop platform for multi-LLM AI agents with ACP integration</title><link>https://ramdi.fr/github-stars/deepchat-a-unified-electron-desktop-platform-for-multi-llm-ai-agents-with-acp-integration/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/deepchat-a-unified-electron-desktop-platform-for-multi-llm-ai-agents-with-acp-integration/</guid><description>DeepChat is an Electron-based TypeScript desktop app unifying multi-LLM chat, MCP protocols, and ACP agent integration with remote control and Skills support.</description></item><item><title>How awesome-design-skills structures AI-driven design consistency with SKILL.md patterns</title><link>https://ramdi.fr/github-stars/how-awesome-design-skills-structures-ai-driven-design-consistency-with-skill-md-patterns/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/how-awesome-design-skills-structures-ai-driven-design-consistency-with-skill-md-patterns/</guid><description>awesome-design-skills offers a registry of structured design system files that constrain AI UI code generation for consistent, accessible results using TypeUI CLI.</description></item><item><title>Inside AG2 Studio: A practical UI for AI agent prototyping with FastAPI and Next.js</title><link>https://ramdi.fr/github-stars/inside-ag2-studio-a-practical-ui-for-ai-agent-prototyping-with-fastapi-and-next-js/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/inside-ag2-studio-a-practical-ui-for-ai-agent-prototyping-with-fastapi-and-next-js/</guid><description>AG2 Studio offers a FastAPI + Next.js UI for prototyping multi-agent AI workflows on the AG2 framework. It supports multiple LLMs and skill composition but is a reference, not production-ready.</description></item><item><title>LangAlpha: AI agents using programmatic Python execution for financial research</title><link>https://ramdi.fr/github-stars/langalpha-ai-agents-using-programmatic-python-execution-for-financial-research/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/langalpha-ai-agents-using-programmatic-python-execution-for-financial-research/</guid><description>LangAlpha uses AI agents that write and execute Python to analyze financial data, reducing token waste and enabling persistent, steerable multi-agent research workspaces.</description></item><item><title>lich-skills: structured AI coding assistant skills with engineering rigor</title><link>https://ramdi.fr/github-stars/lich-skills-structured-ai-coding-assistant-skills-with-engineering-rigor/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/lich-skills-structured-ai-coding-assistant-skills-with-engineering-rigor/</guid><description>lich-skills offers seven domain-specific AI coding assistant skills with a focus on spec-driven development and scientific debugging to improve reliability.</description></item><item><title>Lumibot: Unified Python trading library with AI agent runtime for reproducible strategy testing</title><link>https://ramdi.fr/github-stars/lumibot-unified-python-trading-library-with-ai-agent-runtime-for-reproducible-strategy-testing/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/lumibot-unified-python-trading-library-with-ai-agent-runtime-for-reproducible-strategy-testing/</guid><description>Lumibot unifies backtesting and live trading for stocks, options, crypto, and forex with AI agent runtime using DuckDB, supporting multiple brokers and data sources.</description></item><item><title>MemKraft: local-first memory for AI agents with empirical self-improvement</title><link>https://ramdi.fr/github-stars/memkraft-local-first-memory-for-ai-agents-with-empirical-self-improvement/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/memkraft-local-first-memory-for-ai-agents-with-empirical-self-improvement/</guid><description>MemKraft is a zero-dependency local-first memory system storing AI agent knowledge as Markdown, featuring bitemporal tracking, hybrid search, and a prompt self-improvement loop.</description></item><item><title>Mind: a structured persistent memory layer with 3-tier context for AI agents</title><link>https://ramdi.fr/github-stars/mind-a-structured-persistent-memory-layer-with-3-tier-context-for-ai-agents/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/mind-a-structured-persistent-memory-layer-with-3-tier-context-for-ai-agents/</guid><description>Mind offers a SQLite-backed persistent memory layer with a 3-tier model for AI agents, solving context decay via checkpoint recovery and semantic search integration.</description></item><item><title>Observing AI agents at scale with opsrobot: a Vector-based telemetry pipeline for OpenClaw workflows</title><link>https://ramdi.fr/github-stars/observing-ai-agents-at-scale-with-opsrobot-a-vector-based-telemetry-pipeline-for-openclaw-workflows/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/observing-ai-agents-at-scale-with-opsrobot-a-vector-based-telemetry-pipeline-for-openclaw-workflows/</guid><description>opsrobot-ai/opsrobot offers a full-stack observability platform for AI agents, using Vector pipelines to transform OpenClaw logs into structured data in Apache Doris, enabling detailed multi-agent tracing.</description></item><item><title>Prefab: a Python-first declarative UI framework for agent-generated MCP apps</title><link>https://ramdi.fr/github-stars/prefab-a-python-first-declarative-ui-framework-for-agent-generated-mcp-apps/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/prefab-a-python-first-declarative-ui-framework-for-agent-generated-mcp-apps/</guid><description>Prefab offers a Python DSL for building declarative UI compiled to JSON and rendered via React, designed for MCP apps and AI agent-generated interfaces without frontend JavaScript.</description></item><item><title>specialized claude code agents as architectural consultants</title><link>https://ramdi.fr/github-stars/specialized-claude-code-agents-as-architectural-consultants/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/specialized-claude-code-agents-as-architectural-consultants/</guid><description>A curated repo of 30+ Claude Code agents focusing on architectural consulting across languages, frameworks, and infrastructure. Guidance-first approach flips typical code-gen agents.</description></item><item><title>standardizing AI agent capabilities with sanjay3290/ai-skills</title><link>https://ramdi.fr/github-stars/standardizing-ai-agent-capabilities-with-sanjay3290-ai-skills/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/standardizing-ai-agent-capabilities-with-sanjay3290-ai-skills/</guid><description>Explore sanjay3290/ai-skills, a portable skill collection implementing the open Agent Skills Standard for cross-platform AI agent extensibility. Supports 40+ agents, 20+ skills, with OAuth security.</description></item><item><title>Using an MCP server to query Meta Ads API for AI-driven ad insights</title><link>https://ramdi.fr/github-stars/using-an-mcp-server-to-query-meta-ads-api-for-ai-driven-ad-insights/</link><pubDate>Mon, 04 May 2026 10:18:38 +0000</pubDate><guid>https://ramdi.fr/github-stars/using-an-mcp-server-to-query-meta-ads-api-for-ai-driven-ad-insights/</guid><description>This Python MCP server wraps Meta&amp;rsquo;s Facebook Ads API into 20+ tools, letting AI agents query ad data conversationally. Setup is simple with a single server.py and token auth.</description></item><item><title>Apify MCP Server: Enabling autonomous AI agent payments for web automation tools</title><link>https://ramdi.fr/github-stars/apify-mcp-server-enabling-autonomous-ai-agent-payments-for-web-automation-tools/</link><pubDate>Mon, 04 May 2026 10:09:30 +0000</pubDate><guid>https://ramdi.fr/github-stars/apify-mcp-server-enabling-autonomous-ai-agent-payments-for-web-automation-tools/</guid><description>Apify MCP Server exposes 8,000+ web automation tools as MCP tools to AI agents, featuring agentic payments allowing autonomous crypto payments for tool execution. Supports HTTPS and local modes.</description></item><item><title>elizaOS: a TypeScript monorepo for building and deploying AI agents</title><link>https://ramdi.fr/github-stars/elizaos-a-typescript-monorepo-for-building-and-deploying-ai-agents/</link><pubDate>Sat, 02 May 2026 20:52:17 +0000</pubDate><guid>https://ramdi.fr/github-stars/elizaos-a-typescript-monorepo-for-building-and-deploying-ai-agents/</guid><description>Explore elizaOS, a TypeScript monorepo for AI agents with CLI and web UI. Build and deploy agents fast or extend with plugins using Bun and Vite.</description></item><item><title>Open Design: repurposing coding-agent CLIs into a modular local-first design engine</title><link>https://ramdi.fr/github-stars/open-design-repurposing-coding-agent-clis-into-a-modular-local-first-design-engine/</link><pubDate>Sat, 02 May 2026 20:48:38 +0000</pubDate><guid>https://ramdi.fr/github-stars/open-design-repurposing-coding-agent-clis-into-a-modular-local-first-design-engine/</guid><description>Open Design turns 12 coding-agent CLIs into a deterministic design engine with 31 composable skills and 72+ design systems, running locally with sandboxed previews and multi-format export.</description></item><item><title>LobeHub: An extensible AI agent playground with MCP plugin architecture</title><link>https://ramdi.fr/github-stars/lobehub-an-extensible-ai-agent-playground-with-mcp-plugin-architecture/</link><pubDate>Sat, 02 May 2026 20:15:49 +0000</pubDate><guid>https://ramdi.fr/github-stars/lobehub-an-extensible-ai-agent-playground-with-mcp-plugin-architecture/</guid><description>LobeHub offers a TypeScript-based AI agent platform with a unique MCP plugin system for integrating 10,000+ skills and collaborative multi-agent workflows. Explore its architecture and developer experience.</description></item><item><title>Camoufox: a stealthy Firefox fork for AI agents and web scraping</title><link>https://ramdi.fr/github-stars/camoufox-a-stealthy-firefox-fork-for-ai-agents-and-web-scraping/</link><pubDate>Sat, 02 May 2026 20:07:04 +0000</pubDate><guid>https://ramdi.fr/github-stars/camoufox-a-stealthy-firefox-fork-for-ai-agents-and-web-scraping/</guid><description>Camoufox is a Firefox fork optimized for AI agents and web scraping with stealth fingerprint injection at the C++ level and Python API support.</description></item><item><title>Exploring the Model Context Protocol with awesome-mcp-servers: a curated directory of MCP server implementations</title><link>https://ramdi.fr/github-stars/exploring-the-model-context-protocol-with-awesome-mcp-servers-a-curated-directory-of-mcp-server-implementations/</link><pubDate>Sat, 02 May 2026 20:07:04 +0000</pubDate><guid>https://ramdi.fr/github-stars/exploring-the-model-context-protocol-with-awesome-mcp-servers-a-curated-directory-of-mcp-server-implementations/</guid><description>awesome-mcp-servers is a curated list of Model Context Protocol (MCP) servers enabling AI models to interact securely with resources. This article explores its architecture, strengths, and how to navigate it.</description></item><item><title>Flowise: visual low-code AI agent builder with a modular TypeScript monorepo</title><link>https://ramdi.fr/github-stars/flowise-visual-low-code-ai-agent-builder-with-a-modular-typescript-monorepo/</link><pubDate>Sat, 02 May 2026 20:07:04 +0000</pubDate><guid>https://ramdi.fr/github-stars/flowise-visual-low-code-ai-agent-builder-with-a-modular-typescript-monorepo/</guid><description>Flowise offers a visual drag-and-drop low-code platform to build AI agents and LLM apps, with a Node.js backend and React frontend in a modular monorepo. Easy to start via npm or Docker.</description></item><item><title>How WordPress MCP Adapter standardizes AI agent interaction with WordPress</title><link>https://ramdi.fr/github-stars/how-wordpress-mcp-adapter-standardizes-ai-agent-interaction-with-wordpress/</link><pubDate>Sat, 02 May 2026 20:07:04 +0000</pubDate><guid>https://ramdi.fr/github-stars/how-wordpress-mcp-adapter-standardizes-ai-agent-interaction-with-wordpress/</guid><description>The WordPress MCP Adapter converts WordPress&amp;rsquo;s Abilities API into the Model Context Protocol, enabling AI agents to interact with WordPress seamlessly through standardized tools and prompts.</description></item><item><title>Inside agents: a granular multi-agent orchestration system with PluginEval quality assurance</title><link>https://ramdi.fr/github-stars/inside-agents-a-granular-multi-agent-orchestration-system-with-plugineval-quality-assurance/</link><pubDate>Sat, 02 May 2026 20:07:04 +0000</pubDate><guid>https://ramdi.fr/github-stars/inside-agents-a-granular-multi-agent-orchestration-system-with-plugineval-quality-assurance/</guid><description>Explore agents, a Python-based multi-agent orchestration repo featuring 184 AI agents, 78 plugins, and a three-layer PluginEval framework for plugin quality assurance.</description></item><item><title>Spec Kit: AI-Driven Spec-Driven Development with Executable Specifications</title><link>https://ramdi.fr/github-stars/spec-kit-ai-driven-spec-driven-development-with-executable-specifications/</link><pubDate>Sat, 02 May 2026 20:07:04 +0000</pubDate><guid>https://ramdi.fr/github-stars/spec-kit-ai-driven-spec-driven-development-with-executable-specifications/</guid><description>Spec Kit redefines software development by turning specifications into executable artifacts guided by AI agents, offering a CLI-driven, human-in-the-loop workflow for predictable software delivery.</description></item><item><title>Beads: a distributed graph issue tracker for multi-agent AI workflows</title><link>https://ramdi.fr/github-stars/beads-a-distributed-graph-issue-tracker-for-multi-agent-ai-workflows/</link><pubDate>Sun, 26 Apr 2026 23:47:28 +0000</pubDate><guid>https://ramdi.fr/github-stars/beads-a-distributed-graph-issue-tracker-for-multi-agent-ai-workflows/</guid><description>Beads is a Go-based CLI tool that uses Dolt-backed version control to manage AI agent tasks as a dependency-aware graph, solving merge conflicts and context window limits with semantic compaction.</description></item><item><title>AutoGen: exploring multi-agent AI orchestration with Python in maintenance mode</title><link>https://ramdi.fr/github-stars/autogen-exploring-multi-agent-ai-orchestration-with-python-in-maintenance-mode/</link><pubDate>Sun, 26 Apr 2026 17:51:11 +0000</pubDate><guid>https://ramdi.fr/github-stars/autogen-exploring-multi-agent-ai-orchestration-with-python-in-maintenance-mode/</guid><description>AutoGen is a Python framework for building multi-agent AI applications with LLM integration, now in maintenance mode with Microsoft Agent Framework as its successor. Learn its architecture, strengths, and how to get started.</description></item><item><title>AutoGPT: A modular platform for continuous AI agents and workflow automation</title><link>https://ramdi.fr/github-stars/autogpt-a-modular-platform-for-continuous-ai-agents-and-workflow-automation/</link><pubDate>Sun, 26 Apr 2026 17:51:11 +0000</pubDate><guid>https://ramdi.fr/github-stars/autogpt-a-modular-platform-for-continuous-ai-agents-and-workflow-automation/</guid><description>AutoGPT is a Python-based platform for building and managing continuous AI agents that automate workflows, featuring a modular architecture, low-code agent creation, and benchmarking tools.</description></item><item><title>awesome-copilot: modular community plugins and agentic workflows for GitHub Copilot</title><link>https://ramdi.fr/github-stars/awesome-copilot-modular-community-plugins-and-agentic-workflows-for-github-copilot/</link><pubDate>Sun, 26 Apr 2026 17:51:11 +0000</pubDate><guid>https://ramdi.fr/github-stars/awesome-copilot-modular-community-plugins-and-agentic-workflows-for-github-copilot/</guid><description>awesome-copilot is a community-curated collection of plugins and agents that extend GitHub Copilot with modular, agentic workflows managed through a CLI marketplace.</description></item><item><title>Context7: injecting real-time, version-specific docs into LLM workflows</title><link>https://ramdi.fr/github-stars/context7-injecting-real-time-version-specific-docs-into-llm-workflows/</link><pubDate>Sun, 26 Apr 2026 17:51:11 +0000</pubDate><guid>https://ramdi.fr/github-stars/context7-injecting-real-time-version-specific-docs-into-llm-workflows/</guid><description>Context7 tackles LLM hallucinations by injecting up-to-date, version-specific library docs directly into AI coding agents&amp;rsquo; context via CLI or MCP server integration.</description></item><item><title>DeerFlow 2.0: orchestrating multi-agent AI workflows with flexible LLM integration</title><link>https://ramdi.fr/github-stars/deerflow-2-0-orchestrating-multi-agent-ai-workflows-with-flexible-llm-integration/</link><pubDate>Sun, 26 Apr 2026 17:51:11 +0000</pubDate><guid>https://ramdi.fr/github-stars/deerflow-2-0-orchestrating-multi-agent-ai-workflows-with-flexible-llm-integration/</guid><description>DeerFlow 2.0 is a Python framework for orchestrating AI sub-agents and memory with support for multiple LLMs and execution sandboxes. It uses a modular config and setup wizard for flexible deployment.</description></item><item><title>MindsDB: unified AI-powered SQL querying and data fusion for diverse sources</title><link>https://ramdi.fr/github-stars/mindsdb-unified-ai-powered-sql-querying-and-data-fusion-for-diverse-sources/</link><pubDate>Sun, 26 Apr 2026 17:51:11 +0000</pubDate><guid>https://ramdi.fr/github-stars/mindsdb-unified-ai-powered-sql-querying-and-data-fusion-for-diverse-sources/</guid><description>MindsDB offers an AI-powered SQL-compatible engine that unifies structured and unstructured data across 200+ sources, enabling semantic search and conversational analytics with AI agents.</description></item><item><title>OpenBB's Open Data Platform: Unified financial data integration for diverse analytics and AI</title><link>https://ramdi.fr/github-stars/openbb-s-open-data-platform-unified-financial-data-integration-for-diverse-analytics-and-ai/</link><pubDate>Sun, 26 Apr 2026 09:31:26 +0000</pubDate><guid>https://ramdi.fr/github-stars/openbb-s-open-data-platform-unified-financial-data-integration-for-diverse-analytics-and-ai/</guid><description>OpenBB&amp;rsquo;s Open Data Platform offers a unified &amp;ldquo;connect once, consume everywhere&amp;rdquo; layer bridging financial data sources with Python, Excel, AI agents, and REST APIs for seamless analytics and AI use.</description></item><item><title>Awesome LLM Apps: a practical collection of runnable AI agent and RAG templates</title><link>https://ramdi.fr/github-stars/awesome-llm-apps-a-practical-collection-of-runnable-ai-agent-and-rag-templates/</link><pubDate>Fri, 24 Apr 2026 18:26:13 +0000</pubDate><guid>https://ramdi.fr/github-stars/awesome-llm-apps-a-practical-collection-of-runnable-ai-agent-and-rag-templates/</guid><description>Awesome LLM Apps offers 100+ runnable AI agent and RAG templates for quick LLM app development. It supports multiple providers and advanced multi-agent patterns with minimal setup.</description></item><item><title>PinchTab: Token-efficient Chrome automation for AI agents with Go</title><link>https://ramdi.fr/github-stars/pinchtab-token-efficient-chrome-automation-for-ai-agents-with-go/</link><pubDate>Fri, 24 Apr 2026 07:26:29 +0000</pubDate><guid>https://ramdi.fr/github-stars/pinchtab-token-efficient-chrome-automation-for-ai-agents-with-go/</guid><description>PinchTab is a Go HTTP server enabling AI agents to control Chrome instances efficiently by extracting structured text, cutting token costs 5-13x compared to screenshots. Local-first, multi-instance, secure.</description></item></channel></rss>