🌟 rohitg00/agentmemory caught my eye on GitHub Trending today.

🔗 https://github.com/rohitg00/agentmemory
📝 #1 Persistent memory for AI coding agents based on real-world benchmarks
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The agentmemory GitHub repository provides a persistent memory solution for AI coding agents, allowing them to recall previous interactions and maintain context. The key features of agentmemory include auto-capture of interactions, retrieval accuracy of 95.2%, and support for multiple agents through MCP and REST API.

To use npx @agentmemory/agentmemory, simply run the command, and the agent will start capturing interactions and maintaining context. This eliminates the need for re-explaining previous interactions, making it a valuable tool for developers.

The technical highlights of agentmemory include its use of BM25, vector, and graph search algorithms, as well as its support for leases, signals, and SQLite storage. The repository also includes benchmarks and comparisons with other competitors, demonstrating the effectiveness of agentmemory.

agentmemory is designed for developers who work with AI coding agents, particularly those who use Claude Code, Cursor, Gemini CLI, Codex CLI, and other MCP clients. With its easy-to-use interface and robust features, agentmemory is an essential tool for anyone looking to improve their productivity and efficiency when working with AI coding agents.

In summary, agentmemory is a game-changer for AI coding agents, providing persistent memory and recall capabilities that make development faster and more efficient - with agentmemory, your coding agent remembers everything, so you don't have to.

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💡 datawhalechina/hello-agents just hit the trending charts — here's why it matters.

🔗 https://github.com/datawhalechina/hello-agents
📝 📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
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Hello-Agents is a comprehensive open-source project that aims to provide a systematic guide to building intelligent agents from scratch. The project is designed for AI developers, software engineers, and students who want to gain a deep understanding of intelligent agents and their applications.

The project covers a wide range of topics, including the basics of intelligent agents, language models, and their applications. It provides a step-by-step guide on how to build intelligent agents, from the basics to advanced topics such as memory and retrieval, context engineering, and agent training.

One of the key features of Hello-Agents is its emphasis on practical implementation. The project provides a fully executable codebase that allows users to implement and experiment with intelligent agents. The codebase is written in Python and is designed to be easy to understand and modify.

The project also includes a range of real-world examples and case studies that demonstrate the application of intelligent agents in various domains. These examples include travel assistants, research agents, and social simulation agents.

Overall, Hello-Agents is a valuable resource for anyone interested in building intelligent agents and gaining a deep understanding of the underlying technologies. With its comprehensive coverage of topics, practical implementation, and real-world examples, it is an ideal project for researchers, developers, and students alike.

Start building your own intelligent agents today and join the Hello-Agents community to learn from and contribute to this exciting project!

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⚡ datawhalechina/easy-vibe is making waves. Here's the full picture.

🔗 https://github.com/datawhalechina/easy-vibe
📝 💻 vibe coding 2026 | Your first modern programming course for beginners to master step by step.
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The datawhalechina/easy-vibe GitHub repository is a treasure trove for anyone looking to dive into the world of coding and AI development. Its primary purpose is to provide a beginner-friendly learning platform where users can turn their ideas into real products by conversing with AI. The key features include a step-by-step visual tutorial, immersive simulated coding, and visible AI principles.

To get started, users can simply describe what they want, and the platform will guide them through the process. The repository offers various learning paths tailored to different needs, including a fast first win, turning an idea into a product prototype, building full-stack products, and advancing Claude Code and agent workflows.

Technical highlights of the repository include interactive demos, cross-platform project tutorials, and a comprehensive knowledge base covering computer fundamentals, AI principles, and engineering practices.

The platform is designed for a wide range of audiences, from complete beginners to mid-level and senior developers, as well as product managers and founders.

In short, datawhalechina/easy-vibe is your one-stop-shop for learning AI-assisted coding, and with its interactive approach, you can turn your ideas into products in no time. So, what are you waiting for? Dive into the world of Easy-Vibe and start building your dream projects today - if you can talk, you can build apps!

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🌟 rowboatlabs/rowboat caught my eye on GitHub Trending today.

🔗 https://github.com/rowboatlabs/rowboat
📝 Open-source AI coworker, with memory
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Rowboat is an open-source AI coworker that helps you get work done by turning your work into a knowledge graph and acting on it. It connects to your email and meeting notes, builds a long-lived knowledge graph, and uses that context to assist you in various tasks. You can use Rowboat to generate decks, prep for meetings, track people or topics, and visualize your knowledge graph.

The tool is local-first, meaning all your data is stored locally as plain Markdown, giving you full control over your information. Rowboat also supports integrations with various tools and services, including Gmail, Google Calendar, and Composio.

Whether you're looking to boost your productivity or simply want a more organized workflow, Rowboat is definitely worth checking out. Here's a glimpse of what you can do with Rowboat in Python:

import rowboat

# Connect to your email and meeting notes
rowboat.connect_to_email()
rowboat.connect_to_meeting_notes()

# Generate a deck about your next quarter roadmap
rowboat.generate_deck("Next Quarter Roadmap")

Rowboat is perfect for anyone looking for a powerful AI coworker to help them stay organized and productive. Get started with Rowboat today and experience the power of a local-first AI coworker - your new best work buddy that keeps all your secrets, and helps you get work done!

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⚡ ChromeDevTools/chrome-devtools-mcp is making waves. Here's the full picture.

🔗 https://github.com/ChromeDevTools/chrome-devtools-mcp
📝 Chrome DevTools for coding agents
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The Chrome DevTools for Agents (chrome-devtools-mcp) repository provides a Model-Context-Protocol (MCP) server that enables coding agents to control and inspect a live Chrome browser. Key features include performance insights, advanced browser debugging, and reliable automation.

To use chrome-devtools-mcp, you'll need Node.js and Chrome installed. The repository provides a CLI for use without MCP. You can configure your MCP client to use the server by adding the provided config.

The server supports various MCP clients, including Gemini, Claude, Copilot, and Cursor. It also provides a slim mode for basic browser tasks.

Technical highlights include the use of puppeteer for automation and Chrome DevTools for performance insights. The server also sends usage statistics to Google by default, which can be disabled.

The target audience for this repository includes developers and users of coding agents who want to leverage the power of Chrome DevTools for automation, debugging, and performance analysis.

In short, chrome-devtools-mcp empowers your coding agent to supercharge your development workflow with the full power of Chrome DevTools - automate, debug, and optimize your way to better coding.

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🔍 Deep-diving into masterking32/MasterDnsVPN — fresh off the trending list.

🔗 https://github.com/masterking32/MasterDnsVPN
📝 Advanced DNS tunneling VPN for censorship bypass, optimized beyond DNSTT and SlipStream with low-overhead ARQ, resolver load balancing, high packet-loss stability and speed.
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README not available for this repository.

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🧠 Channel: https://t.iss.one/GithubRe