Pros and Cons of Popular Multi-Agent Orchestration Frameworks
The topic of multi-agent systems in generative AI is heating up, with every major tech giant releasing some related frameworks.
But which multi-agent framework should you choose?
There are just too many options!!
With OpenAI releasing Swarm and Microsoft’s Magentic-One, this field has become very crowded. Therefore, to eliminate any doubts, I will try to explain the key features, pros, and cons of each framework to help you decide which one is best for you. We will discuss:
AutoGen (Microsoft)
LangGraph (LangChain)
CrewAI
OpenAI Swarm (OpenAI)
Magentic-One (Microsoft)
Let’s get started!!
1. AutoGen
AutoGen is the most popular and one of the earliest frameworks from Microsoft in this field, better suited for software development tasks.
Features:
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• It primarily involves two agents, the user and the assistant.
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• User Agent and Assistant Agent Interaction: In the AutoGen user-assistant agent model, the User Agent can provide prompts or requests, while the Assistant Agent generates and executes code.
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• The Assistant Agent not only handles code generation but is also responsible for execution, feeding back results to the user or other agents in the setup.
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• Focused on code tasks in multi-agent orchestration but can handle other tasks as well.
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• Human guidance can be provided during the interaction.
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• Backed by Microsoft’s strong community support.
Limitations:
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• Not intuitive enough, unsuitable for non-programmers.
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• Complex setup, especially when using local LLMs; requires an agent server.
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• Performance may be quite mediocre if not for software development tasks.
2. CrewAI
CrewAI is often the preferred choice for quickly demonstrating any multi-AI agent task because it is very intuitive and easy to set up.
Features:
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• Very intuitive, mainly relies on prompt writing.
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• Creating new agents and adding them to the ecosystem is very simple. You can create hundreds of agents in minutes.
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• Non-technical users find it easy to use.
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• Thanks to integration with LangChain, it works well with most LLM providers and local LLMs.
Limitations:
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• Limited flexibility and customization capabilities.
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• Suitable for basic use cases, not for complex programming tasks.
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• Some bugs in the interaction process between agents.
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• Limited community support.
3. LangGraph
My personal favorite, LangGraph, can be used for any multi-agent task and offers great flexibility.
Features:
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• Built on LangChain; based on the concept of directed cyclic graphs.
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• It is not just a multi-agent framework but has more functionalities.
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• Highly flexible and customizable, supporting nearly all multi-agent orchestration applications.
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• It is an extension of LangChain and thus has good community support.
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• Works well with open-source LLMs and any API.
Limitations:
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• Lacks comprehensive documentation.
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• Not user-friendly for non-programmers or beginner programmers.
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• Requires considerable programming skills, especially in understanding graphs and logical flows.
4. OpenAI Swarm
OpenAI recently released Swarm, and I must say, if you want to get started, this is the simplest multi-AI agent framework.
Features
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• Suitable for beginners in the multi-agent field.
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• Primarily focuses on simplifying “agent creation” and context switching between agents (called handoff).
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• Creating a short demo is very simple.
Limitations
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• Does not support LLMs other than the OpenAI API.
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• Not suitable for production environment deployment.
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• Lacks flexibility.
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• Poor community support. You can’t even ask questions on GitHub!
5. Magentic-One
The newest member on this list is Microsoft’s Magentic-One (their second framework), which is also an attempt to simplify their existing AutoGen framework.
Features
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• Similar to Swarm, suitable for non-programmers and easy to run.
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• Comes with a default package of 5 agents, one of which is a management agent, and the other 4 are: WebSurfer for browsing and interacting with web pages, FileSurfer for managing and navigating local files, Coder for writing and analyzing code, and ComputerTerminal for console access to run programs and install libraries.
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• Built on AutoGen, more like a general-purpose framework.
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• Includes AutoGenBench, a tool specifically for analyzing agent performance.
Limitations
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• Support for open-source LLMs is complex.
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• Lacks flexibility; feels more like an application than a framework to me.
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• Currently, documentation and community support are almost non-existent.
So, which is the best multi-agent framework?
In my opinion (having used all these packages),
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• Software Development: AutoGen (Microsoft) — Best suited for tasks involving code generation and complex multi-agent coding workflows.
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• Best for Beginners: OpenAI Swarm and CrewAI — User-friendly, suitable for those unfamiliar with multi-agent AI and who do not need complex setups.
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• Best for Complex Tasks: LangGraph — Offers high flexibility, built for advanced users, allowing for custom logic and orchestration.
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• Open-Source LLM: LangGraph — Integrates well with open-source LLMs and supports various APIs, unlike some other frameworks. Even CrewAI is decent.
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• Best Community Support: AutoGen has good community support to help you solve various issues.
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• Ready to Go: CrewAI — Quick setup and intuitive, suitable for demos or tasks that require rapid agent creation. Even Swarm and Magentic-One are quite good, but lack community support.
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• Cost-Effective: Magentic-One — Offers a pre-packaged setup and general approach, potentially saving initial costs. Even Swarm and CrewAI are worth considering.
I hope this blog helps you choose the right multi-agent AI orchestration framework.