From Mcp To Multi Agents The Top 10 New Open Source Ai Projects On

Bonisiwe Shabane

-Dec 28, 2025, 12:32 PM

from mcp to multi agents the top 10 new open source ai projects on

Run multiple Copilot agents from one place. Learn prompt techniques, how to spot drift early, and how to review agent work efficiently. Find out about the latest custom models powering the completions experience in GitHub Copilot. Looking ahead to the New Year? These GitHub Podcast episodes help you cut through the noise and build with more confidence across AI, open source, and developer tools. From Appwrite to Zulip, Universe 2025’s Open Source Zone was stacked with standout projects showing just how far open source can go.

Meet the maintainers—and if you want to join them in 2026, you can now apply for next year’s cohort. We envision the future of AI-enabled tooling to look like near-effortless engineering for sustainability. We call it Continuous Efficiency. AI agent frameworks have exploded in popularity as developers shift from simply calling LLMs to building autonomous systems that can reason, plan, use tools, and even collaborate with other agents. The past year has seen remarkable innovation in this space, with new frameworks emerging to address different aspects of agent development. Whether you're building a RAG-based assistant, a multi-agent research system, or enterprise AI workflows, finding the right framework is crucial.

This guide compares the top open-source AI agent frameworks of 2025, analyzing their architectures, capabilities, and optimal use cases. Modern AI agent frameworks provide the infrastructure for language models to: Let's examine how the top frameworks deliver these capabilities. Language: Python Agent Style: Multi-agent conversation framework Execution Logic: Event-driven, asynchronous Memory Support: Conversation context, extensible Tool Use: Flexible, delegated to specialized agents Notable Features: Multi-agent collaboration, tool integration, code execution Why it Stands... Instead of a single agent loop, you create multiple agents (e.g., an assistant, a user proxy, a coding agent) that communicate asynchronously to solve complex tasks. ModelingNLP & LLMsAgentic AIAI Agentposted by ODSC Team April 17, 2025 ODSC Team

As AI agents continue to evolve from research concepts into production-ready solutions, open-source frameworks are playing a pivotal role in accelerating adoption. Whether you’re building autonomous systems, LLM-powered applications, or orchestrating multi-agent collaboration, having the right AI agent framework is essential. In this blog, we’ve curated 10 of the most notable open-source AI agent frameworks to watch in 2025. These projects are actively maintained, widely adopted, and cater to diverse use cases in the AI development lifecycle. Meet leading experts, upskill with hands-on workshops, and connect with thousands of data science and AI practitioners shaping the next wave of innovation. Use case: LLM-powered applications, prompt chaining, tool usage, memory management

LangChain has established itself as the go-to toolkit for building applications with large language models. Its modular structure makes it easy to chain prompts, integrate external tools, and manage conversational memory. LangChain supports both synchronous and asynchronous workflows, making it suitable for production-grade pipelines. With a vibrant ecosystem and extensive documentation, it remains a foundational framework for LLM application development. About a month ago, I came across a highly discussed post on Hacker News — “Stop Building AI Agents” In the post, the author shared a personal experience: he built a “research crew” with CrewAI: three agents, five tools, perfect coordination on paper.

But in practice, the researcher ignored the web scraper, the summarizer forgot to use the citation tool and the coordinator gave up entirely when processing longer documents. It was a beautiful plan falling apart in spectacular ways. The flowchart below was created by the author after countless rounds of debugging and failed attempts, summarizing his decision guide for Should I use an Agent. Image source: https://decodingml.substack.com/p/stop-building-ai-agents The article distilled an important principle: agents work best in unstable processes where humans remain in the loop for oversight — in these scenarios, an agent’s exploratory and creative capabilities often outperform a rigid... AI Agents are moving from hype to reality.They browse the web, write code, take actions, use tools, schedule meetings, analyze documents, execute multi-step workflows, and collaborate with other agents.

If you want to learn how to build them, there is one problem.The information is scattered everywhere. This is a clean, curated, human-readable roadmap of the best AI agent projects, RAG systems, multi-agent teams, memory-based apps, voice agents, MCP tools, and LLM optimization projects you can explore right now. Subscribe for free to receive more AI resources! 1. Starter AI AgentsSimple, ready-to-run agents for learning core patterns. Stay ahead of the curve in AI development.

What are the key open source AI projects gaining traction on GitHub right now, and what makes them significant? 📈 We dive into the Top 10, covering advancements from Model Context Protocol (MCP) integration to multi-agent systems, and explain why these projects matter for developers and the broader AI landscape. Discover these impactful tools and gain insights into current AI trends. 👇 https://lnkd.in/gtxs5wrG GitHub The article nails the shift in AI from just training models to building agentic, interoperable systems. Projects like MCP, LangGraph, and A2A are gaining traction because they enable real-world intelligence.

#MonkDB is built for this wave with native agent support, MCP integration, and real-time context sync. The future is smarter, not just bigger. Great to see a spotlight on the top GitHub projects driving innovation—from MCP to multi-agent systems. A must-read for devs and AI enthusiasts alike. Exciting news: Today, Anaconda announced that David DeSanto is joining as our new CEO. David led GitLab—a fellow open source ISV—pioneering their AI-native DevSecOps platform used by millions of developers worldwide.

His deep belief that open source is the foundation of AI innovation, combined with his focus on helping builders move from experiments to real business outcomes, makes this an exciting next chapter for Anaconda... I’m excited and honored to share that I have joined Anaconda, Inc. as CEO leading the open source AI leader into its next chapter! Enterprises are increasingly recognizing the challenge of scaling AI initiatives, with industry research showing that 80% of AI projects fail (http://bit.ly/47tBCje) with 95% of generative AI pilot programs failing to deliver meaningful results (http://bit.ly/4o3LkPd)... Built on a rich history of open source and Python, Anaconda is the guardian of open innovation, helping builders utilize AI to turn ideas into impact. 95% of the Fortune 500 and over 50 million users worldwide rely on the value Anaconda’s AI Platform delivers through a centralized approach to sourcing, securing, building, and deploying AI.

In this new chapter, I'm committed to helping builders and organizations move faster from AI experiments to real-world results. We'll work together to remove the friction that slows down great ideas and help our customers build solutions that actually work. A big thank you to Anaconda’s board of directors and CEO search team (Peter Wang, George Mathew, and Ganesh Bell) as well as the Anaconda executive team for the trust and partnership you have... I look forward to what we accomplish together delivering real value to our customers and the broader Anaconda community! Full announcement: https://bit.ly/4hd44cp Learn how to build your own agentic AI application with free tutorials, guides, courses, projects, example code, research papers, and more.

AI agents are autonomous software entities that perceive their environment, make decisions, and take actions to achieve specific goals. They are fundamental to modern artificial intelligence applications, ranging from chatbots to complex multi-agent systems. The Model Context Protocol (MCP) is an open standard designed for connecting AI models with external tools, APIs, and data sources. Both of these technologies are dominating the AI space, and companies are using them to automate repetitive tasks and reduce workforce, as agentic AI can outperform junior-level employees in certain cases. In this article, we will review ten GitHub repositories that can help you learn the basics of AI agents and guide you in building agent-based applications. These repositories include tutorials, code samples, hands-on projects, valuable resources, and even YouTube guides to accelerate your learning journey.

This repository provides a structured path to understanding AI and large language models (LLMs) from the ground up, using only free resources. Whether you are a beginner or brushing up on the basics, you will find valuable guides and links. The #1 early-stage company on the Enterprise Tech 30 is hiring across Engineering & GTM. Browserbase powers web browsing capabilities for AI agents and applications. GTM: Sales Engineer, Account Executive, Customer Engineer, Brand Designer, Demand Generation Lead, Technical Product Marketing Lead, Developer Advocate Lead. Engineering: Distributed System Software Engineer, Dashboard Software Engineer, DevOps Engineer, Infrastructure Engineer.

Thanks to Browserbase for partnering today! Each GitHub repository offers real code, clear structure, and step-by-step guidance to help you understand and build agent systems hands-on. Whether you’re a beginner exploring AI agents or an experienced developer diving into MCP frameworks, these projects cover every stage of the learning curve. The innovation driving AI agents and MCPs in 2025 is happening on GitHub, powered by a community that shares, builds, and evolves together. People build online tools to optimize their work for greater efficiency. Some projects teach while showing patterns.

Other platforms offer playgrounds where users learn concepts fast and then build something that really helps. Let’s take a look at some of the best GitHub repositories that allow users to experience solid, hands-on grounding in agent systems and the Model Context Protocol ecosystem in 2025.

From Mcp To Multi Agents The Top 10 New Open Source Ai Projects On

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