5 Fully Open-Source AI Agents Frameworks for Building Clever Agents
- Nishant
- Jun 13
- 4 min read
Tech giants are always in the headlines, showcasing their bigger and more advanced models that are completely closed-source. Closed-source software has plenty of restrictions, as users can't see or modify the source code. The majority of developers cannot work with closed-source software, causing them to lean toward open-source projects and tools, which allow them to modify and use code however they want.
Closed and open-source are fundamentally different philosophies on how intelligent AI agents should be built, function, and interact with the world. The real story of AI's advancement isn't just about the models themselves but about the infrastructure that brings them to life. Open-source frameworks offer a more diverse, customizable, and developer-centric platform.
What is Open-Source?
Open source means software whose source code is made available for public use, modification, and redistribution. It is a collaborative model that allows developers to access, adapt, and build upon the code freely, allowing open collaboration, transparency, and fast innovation.
In this article, we'll introduce you to the 5 fully open-source AI agent frameworks you might not know but definitely should for building clever agents. Instead of a one-size-fits-all approach, pick the style that best suits your project's vibe.
Here are the 5 fully open-source AI agent frameworks you need to know:
1. Agent Zero
Agent Zero offers a personal and organic take on AI agents, providing a framework that grows and learns with the user. It treats your computer as a tool for creating and executing commands on the fly, emphasizing transparency and customization.
Agent Zero is not a one-size-fits-all solution; it learns and adapts based on your individual usage.
It directly uses the host computer's resources and terminal commands to perform different tasks.
Its design is meant to be readable and comprehensible, giving users complete control and understanding.
2. Motia
Motia simplifies backend development by unifying APIs, background jobs, events, and AI agents into one cohesive system. Its core concept, the "Step," acts like a React Component for backend processes, allowing developers to create complex, event-driven workflows with languages like JavaScript, Python, and Ruby. This approach allows the development of mixed-language AI agents and real-time process visualization.
Combines different backend components into one system, reducing complexity.
Allows developers to build agents using JavaScript, TypeScript, Python, and Ruby, even within the same workflow.
Uses "Steps" as modular building blocks for creating backend and agent logic.
Provides tools to see your agent's workflow as it executes, simplifying debugging and development.
3. Dapr Agents
Dapr Agents is a framework built on the Dapr project that allows the creation of resilient, production-grade AI agent systems at scale. It allows developers to build agents that can reason, act, and collaborate using large language models (LLMs). It ensures complex tasks are completed successfully with built-in observability and stateful workflow execution. Its efficiency allows thousands of agents to run on a single core and scale down to zero when not in use.
Designed to ensure that agent workflows are completed successfully, no matter their complexity.
Capable of running thousands of agents on a single CPU core.
Agents can scale down completely when idle to conserve resources and boot up in milliseconds when needed.
Provides tools to monitor and understand the behavior of your agent systems.
AgentKit, developed by BCG X, is a starter kit for building agent applications using the LangChain library. It allows developers to quickly experiment with constrained agent architectures through an intuitive interface. AgentKit lays the groundwork for scalable, chat-based agent applications, making it ideal for creating interactive and user-friendly MVPs.
Built on the widely used LangChain library, making it familiar to many AI developers.
Allows for quick testing of different agent architectures with a user-friendly interface.
Provides the tools to build a complete chat-based agent app from frontend to backend.
The UI is specifically designed to be flexible and responsive for agent interactions.
Cloudflare's agent SDK allows anyone to build intelligent, stateful agents that operate at the network's edge, allowing AI agents to maintain conversation history, browse the web in real-time, and use AI models. These agents last over time, learning from interactions, and benefit from low-latency performance on Cloudflare's global network.
Agents run on Cloudflare's distributed global network for faster response times.
Agents maintain their state across multiple conversations, allowing for continuous learning.
Can access and process information from the live web to inform their actions.
Designed to persist, learn, and grow more capable over time through interactions.
Conclusion
The five open-source frameworks highlighted here, Agent Zero, Motia, Dapr Agents, AgentKit, and Cloudflare's agents, show the vibrant diversity of approaches available to developers. There is no single "best" option. The open-source community offers tools to build a personalized agent that learns on your machine, a resilient system for large-scale operations, or an intelligent presence at the network's edge. The right open-source tool choice depends entirely on the specific goals of your project. Get hands‑on and creative, and experiment with AI agents on your terms.
