AutoGen vs CrewAI: Choosing the Right AI Framework for Your Project

In the dynamically evolving world of artificial intelligence, selecting the right framework can significantly affect the success of your projects. Today, we will explore two prominent AI frameworks: AutoGen by Microsoft and the newer contender, CrewAI. Both have garnered attention in the tech community, evidenced by their GitHub popularity, with AutoGen boasting over 25,000 stars and CrewAI rapidly gaining traction with over 12,000 stars. Let’s dive into their capabilities, nuances, and which might be the best fit for your needs.

AutoGen: The Established Contender

AutoGen is notable for its robust functionality, particularly its execution of code within Docker containers. This ensures that potentially harmful code is isolated, providing a safe environment for operation—a crucial feature when dealing with generated code that runs on local machines.

Features of AutoGen:

• Multi-Agent Collaboration: Through patterns like two-agent chat, sequential chat, group chat, and nested chat, AutoGen offers versatile ways for agents to interact and solve problems collaboratively.
• Containerized Code Execution: By running code in Docker containers, AutoGen ensures high security and isolation, protecting your system from unwanted side effects.
• Integration with Local LLMs: You can integrate AutoGen with local large language models like Llama 3, enhancing its capability without relying on external AI services.

CrewAI: The New Challenger

CrewAI, the fresher face in the AI framework arena, has rapidly become a favorite for developers looking to implement multi-agent or agentic applications. Its simplicity and innovative features for task management and execution provide a compelling alternative to more established frameworks.

Unique Features of Crew:AI:

• Expected Output: This feature allows specifying the desired outcome format for tasks, providing clarity and precision in task execution.
• Task Delegation: Agents in CrewAI can assign tasks to other agents, facilitating more natural collaboration and task handling within the system.
• Built-in Tools and Task Patterns: With tools like a built-in website search and support for sequential and hierarchical task patterns, CrewAI simplifies complex tasks.

Similarities and Differences

Both AutoGen and CrewAI cater to developers who need robust, intelligent agent-based systems with the following similarities:

• Python-based: Knowledge of Python is essential to operate both frameworks effectively.
• Support for Multi-Agent Conversations: Each framework allows multiple agents to work in concert to handle complex tasks.
• Integration with Local LLMs: They eliminate the need for integration with large-scale commercial models like OpenAI, instead allowing the use of localized models.

However, their approach to managing tasks and agent interactions shows distinct philosophies:

• Tool Utilization: AutoGen uses Python functions as tools directly, while CrewAI supplements custom tools with an additional array of built-in functionalities, thanks to its integration with Lang chain.
• Communication Patterns: AutoGen offers more diverse interaction patterns among agents, including complex nested chats, making it suitable for intricate workflows. CrewAI focuses on streamlined, hierarchical task management that suits straightforward, yet dynamic task delegations.

Selecting the Right Framework for Your Application

When deciding between AutoGen and CrewAI, consider the following aspects:

• Complexity of Tasks: AutoGen’s diverse chat patterns make it ideal for complex applications involving multiple interacting processes. CrewAI, with its clear focus on task delegation and expected outcomes, might be better for straightforward, outcome-focused projects.
• Security Needs: For projects involving sensitive or potentially risky code execution, AutoGen’s containerized execution offers an additional security layer that CrewAI does not.
• Community and Support: AutoGen, being established for longer, might offer more extensive community support and resources. CrewAI, however, is catching up, buoyed by its modern approach and rapid adoption rates.

Conclusion

Both AutoGen and CrewAI offer powerful features for developing AI-driven applications, each with its strengths. AutoGen’s security features and complex interaction capabilities make it suitable for high-stake and intricate projects. In contrast, CrewAI’s ease of use and task-focused features cater well to projects needing clear, concise task execution with less setup complexity.

As AI continues to merge more deeply into business and technological processes, the choice between AutoGen and CrewAI will increasingly hinge on specific project needs, security considerations, and personal preference in workflow organization. What will your choice be? Share your thoughts and experiences in the comments below, and don’t forget to like and subscribe for more insights on choosing the right tools for your tech needs!

[h3]Watch this video for the full details:[/h3]


This video will make it easier for you to choose between AutoGen and crewAI!

We’ll look at how both frameworks are similar, how they’re different, and which features make each of them unique so that you can quickly choose which one works for you.

We’ll look at the AutoGen conversation patterns, tool usage, and other important information. Similarly, we’ll take a look at crewAI’s processes and tools. Finally, I will wrap up and share my opinion about both frameworks.

Ready? Jump right in!

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Update: crewAI 0.35.0+ agents can now execute code.

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Other resources:

Read on the blog: https://www.gettingstarted.ai/autogen-vs-crewai/
Agents Overview: https://youtu.be/a5iVvrkIk3s
AutoGen Workflows: https://microsoft.github.io/autogen/docs/tutorial/conversation-patterns
crewAI Workflows: https://docs.crewai.com/core-concepts/Processes/

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Timeline

00:00 Introduction
00:45 How AutoGen and crewAI are similar
01:43 Overview of tool usage
02:15 AutoGen Features
02:35 AutoGen Workflows
02:40 AutoGen Workflows: Two-Agent Chat
03:02 AutoGen Workflows: Sequential Chat
03:23 AutoGen Workflows: Group Chat
03:47 AutoGen: Nested Chat
04:15 crewAI Features
04:48 crewAI Agent Tools
05:22 crewAI Workflows
05:30 crewAI Workflows: Sequential
05:48 crewAI Workflows: Hierarchical
06:13 Which framework to choose?
07:13 Conclusion and thoughts

[h3]Transcript[/h3]
on the left side we have Auto Jan by Microsoft with over 25,000 stars on GitHub and on the right side we have the new kit on the block with over 12,000 stars on GitHub crei today we’re going to see which framework knocks the other one out based on how fitting it is for your application so without further Ado let’s get started now since you’re here there’s a couple of things that you can do to support this Channel and my work first first give this video a thumbs up and then subscribe to the channel so you don’t miss out on any future videos also make sure to check out my blog getting started. I’m going to leave a link in the description so that you can get access to all of the code that you see on this channel once you create your free membership okay let’s go back to the fight I’m going to start with the similarities what both Frameworks can do then after I’m going to share with you what I think is good about each one so both Frameworks are written in Python Meaning you’re going to need to know some python to work with the Frameworks also they’re both designed to create AI agents now if you don’t know what agents are I’m going to leave a link in the description for another video that goes over the basics of what agents are and how they talk to each other so make sure to check it out both Frameworks support multi-agent conversations which means you can have one or more agents doing the work for you both of them integrate with local large language models meaning you don’t need to integrate with open AI or Gemini you could use a local llm like llama 3 for example both Frameworks allow for human input during EX execution so if one of the agents requires your input for a specific decision or for some help you can Implement that and then you could provide the feedback that it needs finally both Frameworks use tools or functions to complete tasks and if we take a quick look at this diagram here we can see that the request first comes in so let’s say we want to send an email to Jamie and then this request goes to the agent the agent will check do I have a tool that can send an email or do I need to generate one and then if it has the tool it’s just going to use it to send the email so the tool is send email which is just a python function and then if it doesn’t have the tool it’s going to check whether uh the agent has access to a large language model and it’s going to request from the large language model to generate the code to send an email and then it’s going to proceed with that okay autogen out of the two this one came out first so we’re going to start with it one of the most important features of autogen is its ability to execute code in a Docker container now you may think that it’s not critical but it’s extremely important when you’re dealing with llm generat code that is running on your machine since execution is containerized no harm is done to the environment there are four main workflows that autogen agents used to communicate with one another first we have the simplest pattern which involves two agents called two agent chat usually a user proxy agent and an assistant agent are working together to solve a problem now I have a video about this and I’m going to share the link in the description so check it out if you want to know more how the two agent chat Works within autogen as it is the most popular and I think it works for for most cases that I’ve seen so far now next we have the sequential chat makes sense when you have tasks that need to be completed one after the other for example suppose that you need to scrape the contents of a web page so that’s task number one and then you need to summarize the content task number two and finally send it by email task number three so this is a sequential linear order and that’s where the sequential chat pattern makes sense the most next we have the group chat and this pattern involves a bunch of Agents usually more than two that are working together managed by a special agent called group chat manager which coordinates when and which agent handles a message now the group chat manager is customizable and it supports a bunch of strategies like Auto manual random and Round Robin I’m going to include a link to the docs so you can read more about this topic finally we have nested chat this workflow includes two or more agents that initiate a side conversation and then its summary is sent back to the conversation initiator kind of like when you order food for delivery you are the initiator in this case and then the restaurant and the driver kick off a side conversation without you but then you’ll get the summary of the conversation that the order is ready to be delivered and the driver is on their way does it make sense let me know in the comments all right crew AI the new kit on the Block it’s gaining popularity and it’s the main competitor to autogen for creating multi-agent or agentic applications now there are two nice features that I like about it first one is expected output which specifies how a tasks should look like when it’s done for example if you want a summary of a long text in bullet points you can simply specify this to the task when creating it second task delegation so you can program agents in crei to delegate tasks to other agents and that’s a cool feature because it enables natural collaboration between your agents all right there’s a little bit of a difference between how crei and autogen use tools basically in autogen we have python functions which are the tools that give our agents extra capabilities like sending an email now in crei we have the same thing which is called custom tools because we are defining these functions but we also have some built-in tools things like website Search tool which lets you search the contents of a website for example that’s builtin also since creai is built on top of L chain you’re going to get access to all of the L chain tools right out of the box so that’s something extra all right just like we did with autogen let’s take a look at how agents in crew AI communicate with each other first we have two supported patterns the first one is sequential and this basically means that tasks must run in a linear order so task number one task number two task number three just like we’ve seen before those should run one after the other and this is exactly like the sequential chat pattern that we’ve seen with autogen before now the next one is hierarchical this pattern is a bit more advanced than the sequential one where you have a bunch of agents that need to talk to each other to solve a problem but you have a manager that kind of of orchestrates the communication between these agents delegating tasks and validating outputs so it kind of manages the conversation now this is also very similar to the group chat pattern that we’ve seen with autogen so we’re done and the question is what should you choose which framework is it crew AI or is it autogen now personally I found it easier to understand how things are structured when I was fiddling around with crew AI That’s because of how tasks are split from Agents now I’m not saying that autogen is complicated but it just felt more organized somehow I also like the task delegation feature and the fact that it’s built on top of Lang chain so you get all the goodies from the Lang chain Community now on the other hand auten’s containerized code execution capability is a top feature when your agents need to run llm generated code it’s definitely a winner in terms of safety since it completely isolates any potential harmful code from destroying your environment I also think that auten’s feedback cycle that’s when one agent is having an issue whether it’s executing code or something else and another agent is helping find a solution is an excellent problem solving capability it gives the agents autonomy and forces them to figure out a solution in comparison crew AI agents were more Guided by specific tasks but the choice is yours so which framework will you choose I’d love to know in the comments you know what I’m going to say now so don’t forget to subscribe to the channel and like this video thank you for watching and I’ll see you soon