Top 5 Open-Source Frameworks to Build Multi-Agent AI Systems in 2025
- Nishant
- Jul 2
- 4 min read
Artificial intelligence (AI) agents are getting more popular, and 2025 is clearly the year of AI agents. If you are familiar with this recent trend, you would know that AI agents are autonomous systems that can reason and act to complete tasks. There are several different types of AI agents, such as simple reflex, model-based reflex, goal-based, utility-based, learning, hierarchical, and multi-agent AI systems. In this article, we will focus on multi-agent systems (MAS) and check out open-source tools to build multi-agent AI systems.
What are Multi-Agent Systems (MAS)?
Multi-agent systems (MAS) contain multiple AI agents that interact, cooperate, compete, or coordinate to complete individual or collective objectives within a shared environment.
Building multi-agent systems (MAS) requires specialized frameworks made specifically to build, manage, and deploy teams of AI agents that can collaborate to solve complicated problems.
In this article, we have listed the top 5 fully open-source frameworks to build multi-agent AI systems in 2025. Each of these has its own unique system and features that allow you to choose the one that best suits your project's needs.
Here are the top 5 fully open-source frameworks to build multi-agent AI systems in 2025:
1. Motia
Motia is a backend framework that integrates different components of a modern application, including APIs, background jobs, and AI agents, into a single, unified system. This open-source framework simplifies backend development by providing a cohesive structure for different programming languages to work together in event-driven workflows.
The main concept of Motia is the "Step," which functions similarly to a React Component but for the backend, allowing for modular and reusable code. Motia's framework is particularly useful for developers who want to build complex, multi-language applications with built-in state management and easy deployment.
Combines APIs, background jobs, events, and AI agents into one system.
Allows developers to use Python, TypeScript, and Ruby within a single AI agent.
Provides tools to visualize your agent's activity as it happens.
Simplifies the process of getting your application up and running.
2. Agno
Agno is a full-stack framework designed to build multi-agent AI systems with a focus on memory, knowledge, and reasoning. It provides a complete set of tools to help developers create high-performing agentic systems without having to build everything from scratch.
Agno is model-agnostic, meaning it can work with different model providers, and it's built to be highly performant, with agents that incorporate quickly and use minimal memory. The framework also has built-in support for multi-modal inputs and outputs, allowing agents to work with text, images, audio, and video.
Offers a unified interface for over 23 model providers.
Supports multiple approaches to reasoning, including reasoning models and a custom chain-of-thought process.
Accepts and generates text, image, audio, and video.
Provides a multi-agent architecture with reasoning, memory, and shared context.
Allows agents to search for information at runtime using over 20 vector databases.
3. Pydantic AI
Pydantic AI is a Python agent framework created by the team behind the popular Pydantic library. This fully open-source framework to build multi-agent AI systems is designed to make building production-grade applications with generative AI a more straightforward and less painful process.
The framework is built on the same principles as FastAPI, offering a developer-friendly design that leverages Python's familiar control flow and type-checking capabilities. Pydantic AI is model-agnostic and integrates seamlessly with Pydantic Logfire for real-time debugging and performance monitoring.
Uses familiar Python control flow and composition to build AI projects.
Uses Pydantic to validate and structure model outputs for consistency.
Offers an optional system for providing data and services to agents, which is useful for testing.
Provides the ability to stream and validate LLM outputs in real-time.
4. AWS Multi-Agent Orchestrator
The AWS Multi-Agent Orchestrator, also known as Agent Squad, is a flexible framework for managing multiple AI agents and handling complex conversations. It features an intelligent intent classification system that can actively route user queries to the most appropriate AI agent based on the context of the conversation.
The open-source framework is implemented in both Python and TypeScript and supports both streaming and non-streaming responses from different agents. It is designed to be extensible and allows developers to easily integrate new agents and customize the system to fit their needs.
The intelligent intent classification can actively route queries to the best-suited agent.
Fully implemented in both Python and TypeScript.
Maintains conversation context across multiple agents for more coherent interactions.
Can be run in different environments, including AWS Lambda, local machines, or other cloud platforms.
Comes with a variety of ready-to-use agents and classifiers to speed up development.
5. AutoAgent
AutoAgent is a zero-code LLM agent framework that allows users to build and deploy agents using only natural language. It is a fully automated and self-developing system, making it accessible to users without a technical or coding background.
AutoAgent includes a native self-managing vector database for Agentic-RAG and supports different large language models (LLMs). The open-source framework is also flexible, supporting both function-calling and reasoning and action (ReAct) interaction modes, and it is made to be lightweight and extensible.
Build tools, agents, and workflows using natural language (zero-code).
Features a self-managing vector database that outperforms many industry solutions.
Integrates with a wide variety of LLMs, including those from OpenAI, Anthropic, and Hugging Face.
Supports both function-calling and ReAct interaction modes.
Designed to be a personal AI assistant that is dynamic and customizable.
Final Thoughts:
These were the 5 fully open-source frameworks to build multi-agent AI systems you can use today. Building multi-agent AI systems no longer requires you to reinvent the wheel to get basic workflows running. These five open-source frameworks offer you different options, from lightweight and visual (Motia), to deeply customizable (Pydantic AI), to no-code deployments (AutoAgent). These tools give you a solid foundation to start building multi-agent AI systems, so try them for yourself and use what fits you the best.