Close Menu
    DevStackTipsDevStackTips
    • Home
    • News & Updates
      1. Tech & Work
      2. View All

      How To Prevent WordPress SQL Injection Attacks

      June 14, 2025

      This week in AI dev tools: Apple’s Foundations Model framework, Mistral’s first reasoning model, and more (June 13, 2025)

      June 13, 2025

      Open Talent platforms emerging to match skilled workers to needs, study finds

      June 13, 2025

      Java never goes out of style: Celebrating 30 years of the language

      June 12, 2025

      6 registry tweaks every tech-savvy user must apply on Windows 11

      June 14, 2025

      Here’s why network infrastructure is vital to maximizing your company’s AI adoption

      June 14, 2025

      The AI video tool behind the most viral social trends right now

      June 14, 2025

      Got a new password manager? How to clean up the password mess you left in the cloud

      June 14, 2025
    • Development
      1. Algorithms & Data Structures
      2. Artificial Intelligence
      3. Back-End Development
      4. Databases
      5. Front-End Development
      6. Libraries & Frameworks
      7. Machine Learning
      8. Security
      9. Software Engineering
      10. Tools & IDEs
      11. Web Design
      12. Web Development
      13. Web Security
      14. Programming Languages
        • PHP
        • JavaScript
      Featured

      Right Invoicing App for iPhone: InvoiceTemple

      June 14, 2025
      Recent

      Right Invoicing App for iPhone: InvoiceTemple

      June 14, 2025

      Tunnel Run game in 170 lines of pure JS

      June 14, 2025

      Integrating Drupal with Salesforce SSO via SAML and Dynamic User Sync

      June 14, 2025
    • Operating Systems
      1. Windows
      2. Linux
      3. macOS
      Featured

      Windows 11 24H2 tests toggle to turn off Recommended feed in the Start menu

      June 14, 2025
      Recent

      Windows 11 24H2 tests toggle to turn off Recommended feed in the Start menu

      June 14, 2025

      User calls Windows 11 “pure horror,” Microsoft says it’s listening to feedback

      June 14, 2025

      John the Ripper is an advanced offline password cracker

      June 14, 2025
    • Learning Resources
      • Books
      • Cheatsheets
      • Tutorials & Guides
    Home»News & Updates»From MCP to multi-agents: The top 10 open source AI projects on GitHub right now and why they matter

    From MCP to multi-agents: The top 10 open source AI projects on GitHub right now and why they matter

    April 30, 2025

    Every day, new public and open source repositories appear on GitHub, and navigating the sheer amount of activity can be a challenge for the best of us. Luckily, we’ve done the heavy lifting for you.

    Together with our panel of GitHub experts—who have experience across open source and developer relations—we analyzed every open source project created in the last 99 days (as of March 29, 2025), and ranked them in consideration of a number of factors including stars-per-day, forks, traffic spikes, and contributor velocity.

    Our GitHub panel includes:

    • Abigail Cabunoc Mayes, aka @abbycabs, who works on open source maintainer programs and serves as a Director on the OpenJS foundation.
    • Kara Sowles, aka @karasowles, who works with maintainers and the open source community.
    • Kevin Crosby, aka @kevincrosby, who runs GitHub’s open source funding program.
    • Jeff Luszcz, aka @jeffrey-luszcz, who helps manage GitHub’s open source program office (OSPO).

    Below, we’ll give you a rundown of these projects—and also discuss why we believe they’re the ones developers keep coming back to. Let’s dive in.

    Top trends, at a glance

    • Agents are becoming key: A year ago the question was “What model can I fine tune?” Now it’s “What agent can I put to work?”
    • Model Context Protocol (MCP) is helping integrate AI (and becoming the USB‑C of AI tooling): More projects are exposing their functions via MCP so any LLM can call them.
    • Multi‑agent orchestration is no longer research only: Frameworks like OWL let several specialized agents cooperate on a task.
    • Speech generation is leveling up: Projects push TTS/STT beyond “read this text aloud” into precise duration control and natural rhythm and sound.
    • An increasing number of experiments with digital twins: There’s interest in personal AI that carries your context and voice across apps.

    Get the full analysis >

    1. Open WebUI MCP: simplifying AI tool integrations 🔌

    Website

    Source

    Python


    @open-webui

    📜 MIT license

    First up is a proxy server that turns MCP tools into OpenAPI-compatible HTTP servers. Developers building AI-powered apps are using the server to easily connect MCP-based tools with anything that uses standard RESTful OpenAPI interfaces.

    Why it matters

    “This new project from OpenWebUI (an alumni of 2024 GitHub Accelerator) is a great example of a growing trend in AI around integration—especially its use of MCP,” explains Abigail. “It’s highlighting that people in AI need more integration, and more standards like MCP will help.”

    2. Unbody: the “Supabase of AI” 🧩

    A diagram showing chatbot, mobile, website, and smartwatch feeding into API, which then feeds into data.

    Website

    Source

    TypeScript


    @unbody-io

    📜 Apache 2.0 license

    Think Supabase, but for AI: that’s Unbody in a nutshell. It’s a modular backend that lets you build AI-native software that actually understands and reasons about knowledge, instead of just shuffling data around.

    The project breaks things down into four layers that you can mix and match:

    1. Perception: Ingests, parses, enhances, and vectorizes raw data.
    2. Memory: Stores structured knowledge in vector databases and persistent storage.
    3. Reasoning: Generates content, calls functions, and plans actions.
    4. Action: Exposes knowledge via APIs, SDKs, and triggers.

    Why it matters

    “It’s an interesting question: How does agent coding become more abstracted from the backend?” asks Kevin. “With Unbody, you can write in any framework and get a backend that’s automatically managed. If you look at companies like E2E, you see work being done to create much more advanced agents that show how the backend stack is being abstracted.”

    3. OWL: multi-agent collaboration in action 🦉

    An image showing an OWL System Architecture.

    Source

    Python


    @camel-ai

    📜 Apache 2.0 license

    When one AI agent isn’t enough, OWL enters the chat. Built on the CAMEL-AI framework—best known for popularizing multi-agent role-play and releasing a trove of synthetic “task + data” bundles—OWL lets several specialized agents cooperate through browsers, terminals, function calls, and MCP tools. It even tops the open-source leaderboard on the GAIA benchmark (58.18).

    Why it matters

    “It’s not just agentic, it’s also multi-model—and multi-agent architectures like OWL,” notes Abigail. “A year ago, it was all about people building models—now it’s all about agents and what they can do. OWL is doing multi-agent work which is quickly emerging.”

    4. F/mcptools: Command-line power for MCP developers 💻

    A screenshot showing MCP Tools Shell.

    Source

    Go


    @f

    📜 MIT license

    CLI fans: Here’s a command-line interface for working with MCP servers that’ll make you feel right at home. Built by GitHub Star Fatih Kadir Akin (who also shipped the GitHub Copilot prompts feature), it lets you discover and call tools, access resources, and manage prompts from any MCP-compatible server.

    MCP Tools supports input/output over stdin/stdout or HTTP, and spits out results in JSON or table views. It even lets you create mock servers for testing or proxy MCP requests to shell scripts.

    Why it matters

    Why it matters: This turns MCP into something you can “git clone && mcp call.” It offers a familiar CLI workflow plus a built-in guard mode that lets you prototype tools fast and lock them down for prod.

    5. Nutlope/self.so: Build your personal site with AI in seconds ⚡

    A screenshot of Self.so turning a LinkedIn profile into a website.

    Website

    Source

    TypeScript


    @Nutlope

    📜 MIT license

    If putting together a personal website isn’t your idea of fun, Nutlope/self.so can lend a hand. Upload your résumé or LinkedIn profile, and the tool will pull together a straightforward site for you, using AI to handle the layout so you can skip the CSS headaches.

    The tech stack includes Together.ai for language modeling, Vercel’s AI SDK, Clerk for auth, Next.js for the framework, Helicone for observability, S3 for storage, Upstash Redis for the database, and Vercel for hosting.

    Why it matters

    The project speaks to composable “AI Lego” stacks. That’s because it chains Vercel AI SDK, Clerk auth, Upstash Redis, S3, and Tailwind—with each service doing one job well—which illustrates how modern AI apps are often stitched together from small, specialized services instead of monoliths.

    6. VoiceStar: precise control for text-to-speech applications 🎙️

    Source

    Python


    @jasonppy

    📜 MIT code license, CC-BY-4.0 model license

    If your project needs speech that lands within a specific time window, VoiceStar’s duration-controllable synthesis can help. It lets developers set target lengths so voice output fits time-sensitive use cases—like fixed-length prompts or narration—without extra audio editing.

    The project includes both CLI and Gradio interfaces for inference, plus pre-trained models you can use right away. As voice interfaces become more important in apps, having open source models with this level of control is a helpful step forward

    Why it matters

    It’s an open TTS model that lets you pin speech to an exact duration—which is helpful for dubbing, ads, and accessibility overlays where every millisecond counts. This is AI-assisted broadcast-grade timing from the open source community.

    7. Create your digital twin with Second-Me 🤖

    A screenshot of Second Me.

    Website

    Source

    Python


    @mindverse

    📜 Apache 2.0 license

    Interested in experimenting with an AI stand-in? Second-Me lets you try a basic “digital twin”—an agent that aims to reflect some of your knowledge, communication style, and preferences.

    The possibilities range from personal assistants that actually understand how you think to innovative ways to share your expertise with others. One example of Second-Me in action: Have your digital twin manage your LinkedIn or Airbnb account, playing the role of the professional or host.

    Why it matters

    This is a prime example of the shift we’re seeing from models to agents. “If we looked a year ago, it was all about model creation. ‘How do you do X or Y?’ You’d make a new model,” explains Jeff. “This project and others on the show a shift towards agentic motions and how people are using AI to do things.”

    8. SesameAILabs/csm: reimagining speech synthesis 🔊

    Source

    Python


    @SesameAILabs

    📜 Apache 2.0 code license (model has restrictions on Abuse)

    The Conversational Speech Model (CSM) brings a fresh approach to speech generation. It converts text and audio inputs into Residual Vector Quantization (RVQ) audio codes using a Llama-based architecture. Its dedicated audio decoder produces Mimi audio codes that result in surprisingly natural-sounding speech.

    What’s interesting here is how CSM merges language model architecture with specialized audio decoding—giving you an open alternative to the proprietary text-to-speech options that dominate the market.

    Why it matters

    CSM fuses a Llama-based text backbone with a lightweight audio decoder that outputs Mimi RVQ codes—proving multimodal mash-ups can run locally on a single GPU. This tells us model-level multimodality is starting to take on API chains while permissive Apache-2.0 licensing is accelerating community R&D on billion-parameter speech systems.

    9. Letta: a universal standard for portable AI agents 📦

    Source

    Python


    @letta-ai

    📜 Apache 2.0 license

    Letta introduces an open file format (.af) for packaging up AI agents with their memory and behavior intact. Think of it as a portable container for agents. You can share, checkpoint, and version control them across different frameworks.

    For developers juggling multiple agent frameworks, this could be a time-saver. Want to move an agent from one system to another without rebuilding it from scratch? That’s the problem Letta is solving.

    Notably, the Letta project is an offshoot of the cpacker/memgpt project—it spun out the serialization layer that MemGPT originally used to snapshot its “virtual-context” agents. The team carved that code into a clean, framework-agnostic spec (agent-file) so any stack—MemGPT/Letta, LangGraph, CrewAI, you name it—can import or export a fully stateful agent with a single .af archive.

    Why it matters

    Think “Docker image for AI agents.” The .af spec snapshots memory, tools, and prompts so you can version-control, share, and hot-swap agents across frameworks (MemGPT, LangGraph, CrewAI, etc.), solving the “how do I move my agent?” headache.

    10. Blender meets Claude: bridging 3D creation and AI 🎨

    Source

    Python


    @ahujasid

    📜 MIT license

    Blender artists, this one’s for you: a third party tool that connects the popular open source 3D creation suite Blender with Claude AI through the MCP. With Blender-MCP, developers can control Blender operations with natural language—or add AI assistance to their 3D workflow.

    Blender-MCP shows how the MCP can act as a universal “tool port” for LLM agents: today it’s Blender; tomorrow it could be Unity, Unreal, or any complex desktop app. For 3D artists and prototypers, that means faster scene blocking, easy style experiments, and a brand-new way to teach beginners. Just describe what you want and watch the software build it.

    Interested? Installing Blender-MCP is as simple as running a bash command.

    Why it matters

    This shows how MCP can wire LLMs into heavyweight desktop apps—in this case, giving Claude the keys to Blender for natural-language scene creation and asset management. Its rapid growth hints that the next UX leap for 3-D (and maybe CAD, Unity, Unreal) might be chat-driven.

    What these projects tell us about AI’s evolution in open source

    These patterns not only reflect the current state of AI in open source, but also hint at the challenges and opportunities that lie ahead. Here’s what GitHub experts have to say about the rapidly evolving space:

    Integration in AI via MCP is the new frontier 🔗

    The prominence of MCP across multiple projects highlights the growing importance of standardized integration patterns in AI development.

    “A big pattern that I saw is the pain point around AI and integration,” notes Abigail. “More standards like MCP will help with this.”

    Multi-agent collaboration emerges 👥

    Projects like OWL point to a future where multiple specialized AI agents work together to solve complex problems.

    “You have to think about it in the construct of person to person, agent to agent, and then having multiple agents working in tandem,” explains Kevin, highlighting the complexity and potential of this approach.

    Speech generation is advancing 🗣️

    Speech tech certainly isn’t new—but large language models are reshaping both text-to-speech (TTS) and speech-to-text (STT) so dramatically that a fresh wave of possibilities is opening up.

    The bigger story is what this means downstream with implications across media, customer support, and product UX.

    The evolving landscape of open source participation 🌱

    AI has drawn a fresh wave of maintainers and contributors to open source, bringing new energy and approaches. Kara Deloss, senior program manager of developer relations at GitHub, notes: “We’re seeing a new generation, or a new type of maintainer” in the AI space. Kevin adds that “if you have a big community on day one, that’s valuable,” highlighting how the ecosystem is evolving.

    This blend of established and emerging development practices is creating exciting opportunities for collaboration across the community.

    The importance of OSI-approved licenses 📄

    Every top project in our list uses OSI-approved licenses (mostly MIT and Apache 2.0), and that’s no accident. “In general, you won’t get a lot of positive sentiment from the community if you call yourself open source but aren’t using an OSI-approved license,” Jeff points out. “These licenses matter because they provide clear guarantees around usage, modification, and redistribution rights that build trust in the community.”

    Jeff also notes an emerging challenge: “As AI powered services and tools become more powerful, we are seeing a trend where some projects attach Abuse and Fraud related restrictions on model or service use. This may not make them completely open source under an OSI approved license, and the projects and the community will continue to have an intense conversation about these conditions.”

    He continues: “It’s important to understand and document any restrictions in place before using a model or service,” which is a timely reminder as the open source AI community navigates these evolving licensing questions.

    Explore and contribute to tomorrow’s AI tooling 🛠️

    The projects we’ve highlighted here are just the tip of the iceberg. As AI keeps evolving, the open source ecosystem is where many of the most exciting standards, tools, and techniques are popping up first.

    Here’s how to get involved:

    • Check out these projects to see how they might fit into your workflow
    • Join in and contribute to projects that spark your interest
    • Keep an eye on MCP and other emerging standards

    Find more cutting-edge repos on GitHub’s trending page >

    📣 Call for Speakers: Git Merge 2025

    Have a story, tool, or hard-won lesson that can level-up the Git community? Git Merge 2025 is now accepting talk proposals—especially from first-time speakers, maintainers, educators, and voices from under-represented groups. Submit your idea by May 13, 2025 and help shape the future of distributed version control. 👉 Propose your talk >

    The post From MCP to multi-agents: The top 10 open source AI projects on GitHub right now and why they matter appeared first on The GitHub Blog.

    Source: Read More 

    Facebook Twitter Reddit Email Copy Link
    Previous ArticleDaiser partners with UK government’s EdgeAI hub
    Next Article Build Adobe Express Add-Ons and Get Funded

    Related Posts

    News & Updates

    6 registry tweaks every tech-savvy user must apply on Windows 11

    June 14, 2025
    News & Updates

    Here’s why network infrastructure is vital to maximizing your company’s AI adoption

    June 14, 2025
    Leave A Reply Cancel Reply

    For security, use of Google's reCAPTCHA service is required which is subject to the Google Privacy Policy and Terms of Use.

    Continue Reading

    Outside Processing vs Contract Manufacturing

    Development

    CVE-2025-43716 – Ivanti LANDesk Management Gateway Directory Traversal Vulnerability

    Common Vulnerabilities and Exposures (CVEs)

    These $60 headphones have no business sounding this good (and they’re on sale)

    News & Updates

    What is Pen Testing as a Service (PTaaS), and Do You Need it?

    Web Development

    Highlights

    CVE-2025-4282 – SourceCodester Oretnom23 Stock Management System CSRF Vulnerability

    May 5, 2025

    CVE ID : CVE-2025-4282

    Published : May 5, 2025, 6:15 p.m. | 36 minutes ago

    Description : A vulnerability has been found in SourceCodester/oretnom23 Stock Management System 1.0 and classified as problematic. This vulnerability affects unknown code of the file /classes/Users.php?f=save. The manipulation leads to cross-site request forgery. The attack can be initiated remotely. The exploit has been disclosed to the public and may be used.

    Severity: 4.3 | MEDIUM

    Visit the link for more details, such as CVSS details, affected products, timeline, and more…

    Xbox co-founder now leads mysterious ‘ZeroOne’ team at Amazon

    May 30, 2025

    10 Strumenti GNU/Linux Poco Conosciuti per Massimizzare la Produttività al Terminale

    June 8, 2025

    8 ways I use Microsoft’s Copilot Vision AI to save time on my phone and PC

    May 1, 2025
    © DevStackTips 2025. All rights reserved.
    • Contact
    • Privacy Policy

    Type above and press Enter to search. Press Esc to cancel.