CrewAI Vs AutoGen: Which is Best to Build AI Agents?

AutoGen and CrewAI are the most widely used multi-agent development frameworks, each known for distinctive features & offerings. 

CrewAI, as a multi-agent framework, is built on the top of LangChain and enables the development of role-defined AI agents. Through its collaborative intelligence, this framework streamlines complex multi agent interactions. 

On the other hand, the AutoGen framework has gained huge popularity because of highly conversational agents that can work in groups and improve their functionalities on the basis of gathered feedback. 

As both CrewAI and AutoGen share great similarities such as their open-source nature and python-based structure, we won’t blame you if you, as a developer, find yourself at a cross-road-selecting one out of these two.  

In this blog, we will help you explore the distinctive functionalities of both these frameworks so that you can make a concise choice. 

Before we help you learn which is better, CrewAI or AutoGen, let’s learn the basics. 

Must Read: AI Agents: Definition, Types, Benefits & Use Cases

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What is CrewAI?

CrewAI is an open-source AI agent development framework, used for the development of multi-agent systems. It leverages collaborative intelligence and is mainly used for the orchestration of role-playing autonomous AI agents. It facilitates sophisticated multi-agent interactions and allows AI agents to assume their specific roles, share responsibilities, and work collaboratively, just like a well-coordinated crew.  

It’s just a month younger than AutoGen as it was released by Joao Moura in November 2023. It soon gained notable popularity on GitHub due to its simple usage and organized processing and is also known as AutoGen 2.0 in the developer community. 

Deep learning and neural networks are the founding elements of CrewAI and enable the framework to generate natural and coherent outputs against the provided human inputs.
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Features of CrewAI

Below are certain distinctive features of CrewAI that make this framework stand out from the crowd. 

Collaborative Intelligence

CrewAI’s agents are designed to work collaboratively. They can review, dictate, and oversee each other’s work. They can even provide feedback to improve the performance of a specific agent. Through this collaborative workflow, CrewAI enables seamless communication and coordination among numerous agents, leading to seamless teamwork and unmatched problem-solving capabilities. 

Role Customization 

CrewAI agents can play specific roles. They can be your data engineer, marketer, or customer service representative and handle designated tasks. 

Must Read: AI Agents in Customer Service 

Task-Oriented Behavior 

CrewAI presents you with a group of task-oriented agents that can concentrate on acquiring distinctive objectives. These agents can break down complex tasks into easy-to-manage subtasks while utilizing language modelling abilities to analyze prompts and extract parameters. 

Structured Process Approach

The processes in CrewAI are designed to be highly dynamic and adaptable. Because of this feature, these processes can fit seamlessly into both development and production workflows. 

Integration Capabilities 

CrewAI is designed to work seamlessly with a wide range of tools such as social media schedulers, content management systems, and email marketing tools. 
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Components of CrewAI 

CrewAI framework is made up of certain components which are:  

Agents

These are the core units of the entire framework. Each agent has a distinguishing role to play, a background story to tell, a unique goal to achieve, and a distinctive memory to store data. More than one agent co-exists in the framework and they all have different work efficiencies and can communicate with each other while executing specific tasks. 

Tools

CrewAI agents use a wide range of tools to achieve their goals. CrewAI integrates with LangChain, which provides a wide range of tools for natural language processing, data manipulation, and other AI tasks. Additionally, developers can create custom tools tailored to their distinct needs.

Learn About: LangChain v/s Haystack: Which Is Best For AI Development?

Tasks

Tasks are the individual units of work that each agent in CrewAI has to complete. These tasks are small, specific, and highly intrusive so that agents can easily accomplish them. 

Process

The process is the strategy or workflow that CrewAI’s agents follow to complete the assigned task. It involves the sequence of steps, the coordination between agents, and the overall approach to problem-solving. At present, CrewAI only supports sequential and hierarchical work processes. 

Crew

A crew in CrewAI refers to the container layer where every agent collaborates to perform tasks.  It orchestrates the interactions between agents and supervises the execution of tasks according to the defined process. 

Memory

The CrewAI framework also uses a highly sophisticated memory system to improve the abilities of the AI agents. This memory system encompasses: 

1. Short-term memory stores the current interactions and outcomes temporarily and enables agents to recall this information and use it in the current context. 
2. Long-term memory conserves only the high-valued learnings and insights, which occurred from past interactions. This enables agents to refine their understanding. 
3. Entity memory is used to seize highly organized information about different entities such as people, places, and concepts. This fosters deeper understanding and relationship mapping abilities in the CrewAI’s agents. 
4. Contextual memory is used to maintain the relevant contexts of the recent interactions so that agents can have a highly coherent and relevant understanding of a specific task sequence. 

These components collectively form the foundation of CrewAI, enabling engineers to harness the collective power of AI agents, promote collaborative decision-making, enhance creativity, and solve complex problems efficiently. 
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How to Build AI Agents with CrewAI?

For In-depth Understanding:: What are AI Agents? How To Build an AI Agent For Your Business?

Building AI agents using CrewAI is a simple process with basic prerequisites such as a good understanding of Python and Large Language Models( LLMs). Let’s present you with a step-by-step guide to developing an AI agent using CrewAI. Suppose you’re developing a FAQ AI agent system featuring two AI agents, a researcher for conducting relevant research and a writer to create content. 

Step #1- Sep your development environment using any IDE tool. 

Step #2- Start installing required libraries to support the development. First,  you must go to the Colab notebook of your development environment and use the below-mentioned command to install CrewAI and other required libraries such as DuckDuckGo. 

Step #3 - Next, you need to set up CrewAI in your development environment and this requires importing necessary modules using the below-mentioned command.

Step #4- Set up API keys to access essential models and development tools. For instance, if you plan to use GPT- 4 for text generation, you need to set up the OpenAI API key in your development environment. 

Step #5- Define the distinctive roles and tasks for your agents. In this example, we will assign one AI agent to a Research with key responsibilities such as conducting research and collecting relevant information.

The other agent will be assigned the role of a Writer bearing the task responsibilities of generating FAQs, creating blogs for detailed queries, and even creating interactive social media posts for common queries. 

Step #6- Once the roles and responsibilities of each agent are clearly defined, you must decide the task execution sequence. You need to create a Crew to manage this entire process.  For example, we’re setting up a sequential process for task execution using this command. 

Step #7-  Start the process and closely monitor the performance of your agents. We recommend printing the results of each agent in the first few executions and analyzing the quality of the output. To take the output printout, use the below-mentioned command. 

Step #8- Review the results of the AI agents and adjust, if required. 

That’s it! You’ve successfully created a FAQ Multi-agent system using CrewAI.
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What is AutoGen?

AutoGen is a multi-agent conversation framework that leverages Natural Language Processing algorithms to build LLM applications using multiple agents. This open-source framework is developed as a result of collaborative studies from renowned researchers from Microsoft, the University of Washington, and Penn State University. Microsoft officially released it in Oct 2023. 

AI developers can use AutoGen to define the flexible agent interactions that are crucial for a wide range of applications such as coding, operation research, question answering, and online decision-making. 

It eliminates complexities from orchestration, automation and optimization of a complex LLM workflow while improving their performance. It deploys diverse conversation patterns to simplify complex workflows. 

As the AutoGen framework features customizable and conversable agents, developers can use it to design assorted conversation patterns with different complexities. It offers a highly user-friendly interface with a wide range of utilities such as API unification, caching,  error handling, context programming, and many more. 

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Features of AutoGen

AutoGen comes with distinctive features such as:  

Multi-agent Conversations

It enables different agents to communicate with each other to solve a specific task with the help of highly customizable agents that conduct human-machine conversation in the long form and allow seamless human participation.  

AutoGen agents have built-in chat automation capabilities that allow them to execute specific tasks with or without any human intervention. 

Conversational Programming

In place of conventional programming, AutoGen utilizes a conversational programming approach wherein developers define a set of conversable agents with specific capabilities and roles. These agents are programmed using conversation-centric computation and control. This type of programming makes the AI agent development process highly intuitive and enables code reuse. 

Human Integration 

AutoGen is integrated with an inventive Human Proxy Agent that can include human feedback in the key workflows.  

Flexible Conversations 

AutoGen can work with static and dynamic conversation workflows with the same ease and perfection. With the help of this feature, this framework can handle conversation workflows, based on a predefined structure or a dynamic flow efficiently. 
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Components of AutoGen 

AutoGen’s functionality revolves around the below-mentioned components that include: 

Agent

Agent in AutoGen refers to an independent entity that can send or receive messages to communicate with other agents. Each agent is powered by LLM models, code executors, tool executors, and human-in-the-loop and is designed to perform distinctive tasks. 

Must Read: How LLMs are Transforming Enterprise Applications? 

Role & Conversations 

Each AutoGen agent has a specific role and is able to converse with other agents. A conversation in this context is the message sequence exchanges between two or more agents to ensure seamless task execution. 

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How to Build AI Agents with AutoGen?

AutoGen has a simplified generative AI development process to offer. Follow below-mentioned steps to build AI agents using AutoGen. 

Step #1- Install AutoGen Studio and its dependencies to start the development process using the following command in your installation environment. 

Step #2- Integrate the API keys of your language models in your environment. If you’re using OpenAI’s LLMs like GPT- 4 or GPT Turbo, the below command is required to load the API keys.

Step #3- Define the skills of the AI agents. For instance, if we’re building a YouTube video translator AI agent then we have to define skills such as fetching the video from YouTube, translation abilities, and so on. Use ChatGPT or any other tool for generating codes for defining your AI agent. 

Step #4- Create AI agents for different tasks. You need to first export the AgentBuilder class from AutoGen to start the development process using the following command. 

Use the below command to create an AgentBuilder instance with your configuration.

Make sure you define your agents and capabilities clearly and concisely to avoid mistakes in task processing. 

Step #5- Start communicating with your agents by sending a message to your agents using 

UserProxyAgent of AutoGen. The following command is required for this action.

Step #6- Run your agents in AutoGen Studio to check their real-life functionalities. In our case, we provided a YouTube video URL as input for the translation and observed the interaction between the agents. 

Step #7– If you find any issues such as missed API keys, broken environment variables, and erroneous codes, fix them. You can easily find the operational issues in the AutoGen Studio's logs and error messages. 

Step #8: Continue refinding your agents and adjust their workflow based on your  evolving needs.

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AutoGen v/s CrewAI: Which is Best for Building AI Agents?

When we talk about AutoGen vs CrewAI, many developers find it difficult to choose because both these frameworks have great similarities. For instance, they both are written in Python, enable multi-agent development, integrate seamlessly with the local LLMs, and allow human inputs during task execution. 

Now that the fundamentals of both these frameworks are clear, it’s time to start the epic war; AutoGen v/s CrewAI- which one is better? We will review both these frameworks on aspects such as code execution, integration, customization, etc. 

AutoGen v/s CrewAI: Code Execution 

Code execution in AutoGen takes place in a Docker container and results are saved in a PDF file, ensuring better code isolation and safety. This may seem a bit tedious to beginner AI developers. But, it offers unmatched code security, which you can’t overlook. 

CrewAI is built on top of LangChain and can perform code execution for LLM-generated codes in a simple manner by integrating with code snippets. It allows developers to use specific tools such as Pandas Dataframe, Python REPL, and Barely Code Interpreter for complex code execution. 

AutoGen v/s CrewAI: Communication 

AutoGen and CrewAI took a different communication approach. In AutoGen, agents follow a linear communication pattern. Agents will process one request at a time. But, developers will have better control over each agent and how it will communicate.

CrewAI is more flexible on the communication front. Agents in CrewAI can communicate in a hierarchical pattern where the basic agent will handle easy questions while superior agents will handle complex questions. They can also communicate within a group to solve a query. 

AutoGen v/s CrewAI: Dialogue Generation 

While both these frameworks leverage LLMs for dialogue generation, the user experience is different in each case. AutoGen specializes in generating long-form dialogues for content like articles and blog posts. It leverages NLP algorithms for generating coherent and high-quality content. 

You can consider AutoGen as a collaborator who can brainstorm ideas for you, conduct arguments, and even build a long-form narrative using past conversations. It’s a great option for content creators, educators, and writers. 

CrewAI, on the other hand, empowers its users to create various content formats, including poems, code, scripts, musical pieces, and email replies. Its collaborative intelligence allows you to get more creative and diverse outputs. Marketers, designers, artists, and script writers can use AI agents built over CrewAI to create content ideas. 

AutoGen v/s CrewAI: Collaboration

Both frameworks lack direct collaboration features such as real-time editing and shared workspaces. But, they both promote asynchronous collaboration in different manners. 

For instance, AutoGen allows sequential or group chat-style collaboration. Agents will take turns to complete the given tasks and even contribute to a conversation. It even has the provision for a feedback loop, allowing agents to agents to identify and address issues in each other's work.

Conversely to this, CrewAI allows hierarchical collaboration, along with sequential. In this collaboration approach, a central agent will manage and oversee the tasks and communication of other agents. 

In simple language, AutoGen collaborates like a store-writing group where each member writes a single line or sentence when their turn comes, based upon others’ ideas whereas CrewAI collaborates like a team working on a single project working under the mentorship of their team lead. 

AutoGen v/s CrewAI: User Interaction 

The next comparison criterion for CrewAI v/s AutoGen is user interaction. They both take a different user interaction approach. For instance, users are allowed to participate in an ongoing chat using agents and have better control over the entire interaction. This makes humans easily jump in, provide feedback, and tweak the agents to meet specific requirements. 

However, CrewAI has a very narrow scope for human interaction. This framework promotes the fully independent and autonomous processing of the agents. While you can interact with the agents, you hardly have any control over their interactions. It focuses on orchestrating autonomous AI agents for specific purposes, leaving bare scope for human intervention and inputs. 

AutoGen v/s CrewAI: Agent Types 

Before you start using any of these frameworks, you must learn the fact that you will get entirely different sets of agents. 

AutoGen mainly offers general-purpose agent types focused on user interaction, assistance, and open-ended conversation. For instance, you have a User Proxy Agent, Assistant Agent, and Conversable Agent that you can use to partake in a conversation, create a helping assistance like a chatbot, and strike open-ended discussions. 

You don’t get a set of agents with defined roles with CrewAI. Instead, this framework prioritizes role-playing scenarios and provides agents to meet your specific needs. For instance, you have customer service agents, teacher agents, and virtual character agents with distinctive roles and responsibilities. 

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Which Is The Best?

Well, there is no specific answer to this. AutoGen and CrewAI differ in their development focus, agent capabilities, integration capabilities, user interaction features, and the types of agents they offer for specific functionalities and scenarios. 

But, they both are doing a great job in offering a code-free and user-friendly interface for developing the multi-agents for your business. 

Yet, we can help you make a choice. 

Prefer CrewAI when: 

1. Task delegation is important for you. CrewAI enables you to design AI agents that can delegate tasks to each other, leading to a highly streamlined workflow. Imagine you have an agent specializing in data analysis and another in creative text generation. With the help of CrewAI, you can easily designate the data analysis agent for tasks involving number crunching while enabling them to create content. 
2. You need LangChain integration. As CrewAI is built upon LangChain, you will have no hassles in using it and its related tools. 
3. You need to have better control over the development process. You can define and control the "who, what, and when" aspects of your multi-agent system.
4. You’re building a prototype as CrewAI has a faster set-up and highly modular design that allows you to test-drive complex agent interactions easily. 
5. You know how to solve a problem and simply want to automate the process. For example, if you know how to run an e-commerce store and need AI agents to automate order processing, customer service, and track order history then CrewAI is an ideal approach to decide. 
6. You want your AI agents to deliver a specified and expected outcome. For instance, you want your AI agent to summarize customer reviews in the form of a list instead of paragraphs.  
7. You want your AI agents to delegate the tasks in a manner where tasks are allotted to the most suited agents. 

Prefer AutoGen when: 

1. You need to focus on specialized tasks and code integration capabilities in your AI agent development process. AutoGen allows you to precisely control the information process, external APIs, and custom scripting. 
2. You need to integrate a feedback loop in your AI agents. AutoGen incorporates a feedback loop within its system to enhance user interactions and improve the accuracy and reliability of responses. This functionality is missing in CrewAI. 
3. You don‘t know the process of solving the problem and want an agent for this. For example, you don’t know about your customer preferences and you need to build AI agents to track customers’ buying habits and analyze the negative reviews to identify recurring issues. In this case, AutoGen is a better choice to make as you can incorporate dynamic conversations and customize each agent for specific tasks. 
4. You prefer isolation and code security. AutoGen is capable of executing codes in a docker container, allowing developers to isolate the codes and avoid unwanted hassles. 
5. You need to maintain consistency across the content creation process. AutoGen enables you to define dialogue structures for competitive formats like interviews or Q&A sections. 

How Can Ampcome Help?

Building an AI agent might seem straightforward at first glance But, its real-world execution demands a deep understanding of your project goals, a firm grasp of relevant technologies, and hands-on experience with key AI agent development frameworks such as CrewAI and AutoGen. 

Ampcome owns an in-house fleet of seasoned AI agent developers possessing the expertise you need to navigate the complexities of AI agent development with ease. We will closely work with you to understand your key objectives, select the optimal framework (AutoGen or CrewAI), and ensure the seamless integration of developed AI agents into your existing infrastructure. 

So, why wrestle with AI agent development struggles when you can have time-bound, inventive, and customized AI agent development assistance from Ampcome? Book a free consultation to know how you can harness the true potential of AI agents within your ecosystem.