GitHub Weekly Top 10 Trends (13 Jul 2025)

This week’s GitHub Top 10 Trends highlights the growing diversity and innovation within the open-source community. Developers are increasingly exploring new technologies and frameworks, showcasing projects that not only solve specific problems but also push the boundaries of what’s possible in software development. From advanced AI tools to enhanced data management systems, the trends reflect a strong emphasis on collaboration, creativity, and practical applications that cater to a wide range of user needs. As the landscape continues to evolve, these projects exemplify the spirit of experimentation and the commitment to building a more interconnected digital ecosystem.

For past weekly trending, please view Weekly Tags.

GenAI Toolbox

GenAI Toolbox (also known as MCP Toolbox for Databases) is an open-source MCP server designed to offer a unified interface for large language model (LLM) agents to interact seamlessly with databases. This innovative tool simplifies the development of AI applications by managing essential database functionalities such as connection pooling, authentication, and observability, thereby alleviating developers from the intricacies of database infrastructure. By streamlining these processes, GenAI Toolbox enables developers to concentrate on building and enhancing AI applications without the overhead of database management.

RustFS

RustFS is a high-performance distributed object storage system that offers a range of advanced features designed for efficiency and reliability. It provides a fully S3-compatible API, ensuring seamless integration with Amazon S3 services. The system incorporates erasure coding through its ECStore component, utilizing Reed-Solomon encoding for robust data protection. Users can manage their storage through a user-friendly web console accessible via port 9001. RustFS supports a distributed architecture, allowing for multi-node deployment with automatic data healing and rebalancing capabilities. Additionally, it ensures secure communication with HTTPS/TLS encryption for both API and console endpoints, and includes OpenTelemetry integration for comprehensive observability through metrics, traces, and logs.

Sniffnet

Sniffnet is a powerful network traffic monitoring tool that enables users to capture packets from selected network adapters, analyze them in real-time, and display the results through an interactive graphical interface. Key features include real-time traffic visualization via charts and tables, as well as robust filtering options by IP version, protocol, and address/port for precise analysis. The tool allows for the identification of hosts, services, and geographic locations, alongside customizable notifications for significant traffic events. Users can perform detailed inspections of individual connections and export data for comprehensive analysis reports in PCAP format. Additionally, Sniffnet offers a customizable appearance with multiple themes and internationalization support, enhancing user experience across diverse environments.

PocketBase

PocketBase is an open-source backend framework and application written in Go, designed to provide a comprehensive solution for building backend applications with minimal setup. It integrates an embedded SQLite database, a file management system, authentication features, and a REST API into a single executable, streamlining the development process. With PocketBase, developers can quickly create robust backend applications without the need for extensive configuration, making it an efficient choice for projects that require a lightweight yet powerful backend solution.

ML-For-Beginners

ML-For-Beginners is an educational resource aimed at teaching classical machine learning through a project-based approach. It encompasses various key topics, including regression, classification, clustering, natural language processing, time series forecasting, and reinforcement learning, providing learners with a solid foundation in machine learning fundamentals. The curriculum is designed to introduce machine learning concepts to beginners, offer hands-on learning experiences through practical projects, and teach essential ML techniques using real-world datasets. Additionally, it aims to make machine learning education accessible globally by providing multilingual support, ensuring that learners from diverse backgrounds can benefit from the materials offered.

Agents From Scratch

Agents From Scratch is an educational resource that illustrates the process of building increasingly sophisticated email management agents using the LangGraph framework. This project showcases a progression that starts with basic email handling and culminates in the development of a fully-featured “ambient” agent capable of managing emails through integration with the Gmail API. By following the examples and implementations provided in this repository, users can gain practical experience in constructing intelligent agents that enhance email management capabilities.

Golang WhatsApp

Golang WhatsApp is a Go-based application that facilitates programmatic interaction with WhatsApp, offering dual operational modes: a REST API server and an MCP (Model Context Protocol) server. This comprehensive system allows users to send messages, manage groups, handle media, and integrate with AI agents using standardized protocols. By leveraging this API, developers can create robust applications that enhance the functionality of WhatsApp, enabling seamless communication and interaction across multiple devices.

Biomni

Biomni is a versatile biomedical AI agent designed to autonomously perform a wide array of research tasks across various biomedical subfields. By integrating advanced large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, Biomni empowers scientists to significantly boost research productivity and formulate testable hypotheses. Unlike traditional biomedical software solutions that depend on predefined templates or domain-specific implementations, Biomni stands out by dynamically composing and executing research workflows across multiple biomedical domains without the need for task-specific tuning, making it a powerful tool for innovative research in the biomedical field.

OpenTelemetry Go

OpenTelemetry Go is a comprehensive project that provides an overview of its architecture and key components, serving as an essential starting point for understanding how the different parts of the OpenTelemetry Go SDK interact and function together. This repository introduces users to the foundational elements of the SDK, enabling them to grasp the overall structure and capabilities of OpenTelemetry in the Go programming environment.

Graphiti

Graphiti is a Python library designed to empower AI agents in building and querying temporally-aware knowledge graphs from dynamic data sources. Unlike traditional retrieval-augmented generation (RAG) approaches that depend on batch processing and static summarization, Graphiti offers real-time incremental updates while maintaining historical context through explicit temporal tracking. The framework effectively addresses critical challenges in agent memory and knowledge management by enabling real-time knowledge integration that continuously ingests user interactions, structured data, and external information without the need for batch recomputation. Additionally, it supports temporal reasoning by tracking both the occurrence and recording times of events, facilitating precise point-in-time queries. Graphiti also features a hybrid search mechanism that combines semantic embeddings, keyword search (BM25), and graph traversal for efficient retrieval, while allowing developers to define domain-specific entities and relationships using Pydantic models.