AI Repos Worth Watching
GitHub's trending AI repos ranked and scored. Updated daily.
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
An agentic skills framework & software development methodology that works.
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
open-source coding agent
Skills for Real Engineers. Straight from my .claude directory.
Open-source Android/Desktop remake of Civ V
The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra
Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. 🔥💬
World's first open-source, agentic video production system. 12 pipelines, 52 tools, 500+ agent skills. Turn your AI coding assistant into a full video production studio.
General plug-and-play inference library for Recursive Language Models (RLMs), supporting various sandboxes.
🚀 Run and manage AI models effortlessly with Allew, an open-source tool offering a powerful CLI and compatibility with major AI providers.
Allew is an open-source CLI tool to run and manage AI models, supporting major AI providers and local and remote LLMs.
🤖 Enhance AI collaboration with these five reusable skills, fostering effective teamwork in software development while minimizing process overhead.
This GitHub repo offers five reusable AI agent skills designed to enhance collaboration and efficiency in software development by reducing process overhead.
📄 Enhance document processing by implementing Recursive Language Models with Claude Code to exceed typical context limits and manage larger inputs effectively.
A Python implementation of Recursive Language Models using Claude Code aimed at surpassing standard context limits for enhanced document processing.
Semantic code intelligence for AI coding agents - 45% fewer tool calls, 17% faster exploration, 100% local. A fork of codegraph.
Cartograph is a fork of codegraph providing semantic code intelligence for AI coding agents that reduces tool calls by 45% and improves exploration speed by 17%, operating fully locally.
🛒 Connect AI agents to Walmart's ecosystem using the Model Context Protocol for real-time data access and enhanced product search capabilities.
An open-source project enables AI agents to connect to Walmart's ecosystem via the Model Context Protocol, providing real-time data access and improved product search.
📝 Fetch Twitter/X content and convert it into blog posts using the MCP server for seamless integration and easy content management.
A TypeScript tool that uses an AI-powered MCP server to fetch Twitter/X content and convert it into blog posts for easy content management integration.
Enable AI agents to access Tor and .onion sites with a simple Python layer supporting built-in search engines and OSINT tools.
A Python framework called Sicry enables AI agents to access Tor and .onion sites, integrating search engines and OSINT tools for enhanced intelligence gathering.
Search 1C BSL code templates with semantic search, MCP tools, and a Monaco-based CRUD web UI.
A GitHub repo offers semantic search for 1C BSL code templates using embeddings and MCP tools, integrated with a Monaco editor-based CRUD UI.
Pragmatic AI Labs MCP Agent Toolkit - An MCP Server designed to make code with agents more deterministic
The paiml-mcp-agent-toolkit is an MCP server built to improve determinism in code using AI agents.
Transform probabilistic LLMs into deterministic state machines to improve logic, prevent context loss, and generate unique, reliable responses.
The re-think_protocol project proposes transforming probabilistic large language models into deterministic state machines to enhance logical consistency, prevent context loss, and produce unique, reliable responses.