Multi-agent systems, memory, planning, reasoning loops
- Stars
- 2,939
- Star gain
- +10
- Growth score
- 0.0034
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Explore AI open-source repositories ranked by positive star growth over 7d. Compare recent momentum instead of lifetime popularity.
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Multi-agent systems, memory, planning, reasoning loops
Technical overview of the Model Context Protocol, current to the 2026-07-28 spec.
Unified Emacs interface supporting OpenAI Codex, Antigravity CLI, Claude Code, Pi agent, Muse Code, and more
ClawTeam fork fully adapted for OpenClaw — multi-agent swarm coordination with OpenClaw as the default agent
✨ A curated list of awesome GitHub instructions, prompt, skills, MCPs and agent markdown files for enhancing your GitHub Copilot AI experience.
SkillX: Automatically Constructing Skill Knowledge Bases for Agents
YoLuster Shorts 釉光影:An independently developed AI short-drama creation workspace — from initial ideas to storyboard scripts, images, videos, and reusable asset management. Provides a Docker-based local demo package.
High performance and CommonMark compliant HTML to Markdown converter. Maintained by the Kreuzberg team. Kreuzberg is a fast, polyglot document intelligence engine with a Rust core. It extracts structured data from 98+ document formats using streaming parsers and built-in OCR.
Open-Source Personal Cloud OS for Always-On Agents
🐢 Open-Source Evaluation & Testing library for LLM Agents
Build, test, and deploy intelligent AI agents the Laravel way
:sunglasses: All the required resources to build your own startup
A curated list for Self-Improvement in Foundation Model Based Agentic Systems.
Build, evaluate and observe LLM apps
🪞 Make your agents recursively self-improve
The original Tiny Claw as your personal autonomous AI companion. 🐜
Run OpenClaw on Android with a single command — no proot, no Linux
A powerful Model Context Protocol (MCP) server that provides an all-in-one solution for public web access.
Self-documenting AGENTS.md
A simple and powerful content-driven static site generator.
Momentum Score compares the latest star count with a baseline for the selected window: ln((current stars + 1) / (baseline stars + 1)). Rankings include repositories with positive star gain.
Seven-day and thirty-day history may use estimates reconstructed from GitHub star creation history; estimated values are labeled. The twenty-four-hour ranking uses measured snapshots only.