Which Agentic AI Framework to Pick? LangGraph vs. CrewAI vs. AutoGen

Summary

This video compares three popular agentic frameworks: Crew AI, Autogen, and LangGraph, evaluating their flexibility, documentation, and unique features. Crew AI stands out for its task-based approach, ease of use, and excellent documentation, making it ideal for task-focused problems. LangGraph excels in flexibility and streaming support, making it better for complex, conversational agents. Autogen is a good choice for .NET users but lags behind in flexibility. The video provides clear guidance on which framework to choose based on user needs, highlighting the strengths and limitations of each.

Takeaways

• 😀 Crew AI is a task-based agent framework best suited for solving problems that can be broken down into clear tasks.
• 😀 Crew AI is flexible, but not as much as Langgraph, especially for complex tasks or when a conversational agent is needed.
• 😀 Crew AI excels in documentation, with a well-organized website and easy-to-follow guides and resources.
• 😀 Crew AI's documentation includes useful community resources and courses on DeepLearning.AI, making it an excellent resource for learners.
• 😀 Crew AI lacks support for streaming, but its task-based nature makes this limitation less critical.
• 😀 Crew AI supports human-in-the-loop functionality but only at the end of a task, limiting its real-time interaction capabilities.
• 😀 Crew AI offers time travel, allowing users to go back and review previous runs and explore changes in task outcomes.
• 😀 Crew AI includes built-in memory support (short-term, long-term, and user memory), which enhances its functionality.
• 😀 Crew AI currently only supports Python, which may limit its usability for users seeking multi-language support.
• 😀 Crew AI has an unofficial low-code interface, Crew Studio, which works well for task-based configurations but lacks an official version.
• 😀 When comparing frameworks, Crew AI is ideal for task-based problems, while Langgraph is better for complex tasks and conversational agents, and Autogen is recommended for Microsoft .NET integration.

Q & A

What is Crew AI's primary use case?

Crew AI is primarily a task-based agent framework, which means it is designed to break down problems into discrete tasks for solving. It works well for task-driven problems but can be limiting for more complex or conversational workflows.

How does Crew AI compare in terms of flexibility to other frameworks like Autogen and LangGraph?

Crew AI is less flexible than LangGraph but more flexible than Autogen. It works well for task-based problems, but if the problem is more complex or requires a conversational agent, it may not be as suitable.

What does Crew AI’s documentation look like?

Crew AI’s documentation is highly regarded for being well-organized, with separate sections for conceptual understanding, getting started, and detailed how-to guides. It also includes a blog for community insights and issues, as well as courses on DeepLearning.ai for further learning.

Does Crew AI support streaming?

No, Crew AI does not support streaming. However, since it’s based on a task-driven framework, streaming isn't critical for its use case, and the lack of streaming is less of an issue in many cases.

Can Crew AI be used for conversational agents or voice-based tasks?

While Crew AI is not specifically built for conversational agents or voice-based tasks, it can handle simple task-based workflows. For more complex or dynamic conversational needs, other frameworks like LangGraph may be more appropriate.

What memory capabilities does Crew AI have?

Crew AI includes built-in support for short-term, long-term, and user memory. This makes it easier to manage state over time and provide more personalized interactions.

Is Crew AI compatible with languages other than Python?

Currently, Crew AI only supports Python. This could limit its flexibility for teams working with other programming languages.

What is Crew AI’s approach to human-in-the-loop functionality?

Crew AI supports human-in-the-loop functionality, but it is limited to asking for human input at the end of a task, just before returning the final response to the user.

Does Crew AI provide a low-code interface?

Crew AI offers a low-code interface, but it is not officially supported. There is an unofficial tool called 'Crei Studio' built with Streamlit, which allows users to create tasks and agents in a low-code environment.

What is Crew AI’s rating compared to other frameworks like Autogen and LangGraph?

Crew AI received a rating of 7 for flexibility, 10 for documentation, and 7 for other features. It is seen as the best choice for task-based problems, while LangGraph excels in flexibility and streaming, and Autogen is best for .NET infrastructure.