Agent-MCP is a framework for creating multi-agent systems that enables coordinated, efficient AI collaboration through the Model Context Protocol (MCP). The system is designed for developers building AI applications that benefit from multiple specialized agents working in parallel on different aspects of a project.

This system takes an unstructured text document, and uses an LLM of your choice to extract knowledge in the form of Subject-Predicate-Object (SPO) triplets, and visualizes the relationships as an interactive knowledge graph.

🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & Scrapper

Real-time face swap for PC streaming or video calls

real time face swap and one-click video deepfake with only a single image

A TTS model capable of generating ultra-realistic dialogue in one pass.

🎨 Diagram as Code for prototyping cloud system architectures

The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.

DroidRun is a powerful framework for controlling Android and iOS devices through LLM agents. It allows you to automate device interactions using natural language commands. Checkout our benchmark results

mcp server for gitingest

Glances an Eye on your system. A top/htop alternative for GNU/Linux, BSD, Mac OS and Windows operating systems.

Graphiti is a framework for building and querying temporally-aware knowledge graphs, specifically tailored for AI agents operating in dynamic environments. Unlike traditional retrieval-augmented generation (RAG) methods, Graphiti continuously integrates user interactions, structured and unstructured enterprise data, and external information into a coherent, queryable graph. The framework supports incremental data updates, efficient retrieval, and precise historical queries without requiring complete graph recomputation, making it suitable for developing interactive, context-aware AI applications.