Agent Cloud vs CrewAI: An Indepth Comparison

Introduction

The demand for collaborative and generative artificial intelligence is increasing as more companies are interested in interacting with their data in a natural language, gaining valuable insights from the interaction, and automating complex business processes.

AI collaboration or collaborative intelligence, refers to humans and AI working together to build powerful and efficient systems that automate tasks, make decisions, interpret data, and generate relevant outputs. Generative AI and collaborative intelligence tools like Agent Cloud and CrewAI are important for conversing with your data and automating processes.

Agent Cloud is an open-source platform that enables companies to build LLM-powered conversational chat apps to talk with their data. These conversational chat apps are private and secure. They can retrieve information from hundreds of data sources. Furthermore, Agent Cloud allows you to build process apps for automating tasks and complex business processes. 

It uses multiple AI agents to ensure autonomous, collaborative, and scalable process applications that can access third-party APIs. The built-in data pipeline of Agent Cloud allows for the splitting, chunking, and embedding of data from more than 300 data sources including Postgres, Docs, Slack Google Drive, etc, making your RAG app seamless.

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CrewAI is a collaborative process automation framework that utilizes multiple AI agents working together to automate assigned tasks. It simplifies the processes of building multi-agent automation. You can deploy your multi-agent automation within seconds using the prebuilt templates. CrewAI also provides a flexible approach for building your automation locally.

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In this article, we will compare Agent Cloud and CrewAI exhaustively using indicators like architectural paradigm, key features, use cases, similarities, and differences to help you understand how these tools work and how to select the more appropriate one for your organization's needs. 

Architectural Philosophy

Next, let's go into the architectural philosophy of both tools - Agent Cloud and CrewAI - to understand how things work at their core. 

Agent Cloud

Agent Cloud is an open-source generative AI platform that uses a modular architecture designed to scale with your organization. This means users can go fully private by deploying to their own cloud environment or connecting it to their locally hosted model. 

With Agent Cloud being LLM agnostic, you are not constrained to any LLM. It also connects to any open-source model or defaults to OpenAI. 

Agent Cloud has a built-in RAG as a Service that can ingest data from over 300 sources for knowledge retrieval for conversational chat apps. Alternatively, you can upload your own files. It supports pdf, docx, txt, xlsx, and csv files.

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Cron expressions help you control the sync frequency of your data from their various sources. Agent Cloud allows you to select what tables and fields get synced. You can also specify how you want your data to be split and chunked. 

The tool supports both character splitting and semantic chunking for files. For data sources like Bigquery and others, it automatically chunks the messages that come through the message bus. Soon, Agent Cloud users can select the fields to embed and fields to store as metadata from their data sources, and shortly, it will enable a vector upsert to give more flexibility for custom chunking.

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The Agent Cloud multi-agent engine is an abstraction of CrewAI. The multi-agent engine powers the AI agents that help you automate manual processes.

Under the hood, Agent Cloud uses the following open-source stack:

• Airtbyte for its ELT pipeline
• RabbitMQ for message bus.
• Qdrant for vector database.

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Although you can access Airbyte and Qdrant (the vector database instance) locally, you don’t have to manage any of these apps. Agent Cloud is fully abstracted. 

For installation, Agent Cloud currently has a docker install.sh script for Mac/Linux users. WSL is recommended for users on Windows.

CrewAI

CrewAI is an open-source framework for multi-agent collaboration built on Langchain. As a multi-agent runtime, Its entire architecture relies heavily on Langchain.

Building multi-agent automation with CrewAI involves the following: 

• Installation: Install CrewAI and other dependencies using pip.

pip install crewai 
pip install 'crewai[tools]

• Agents Assembly: CrewAI allows you to define your agents and assign distinct roles and backstories to them. Doing so helps agents in the way they execute tasks and how they interact in the crew.
• Task Definition: After assembling and defining your agents, next is to define tasks. Defining tasks helps set the objectives for your agents. 
• Crew Formation: A crew is a group of agents working collaboratively to complete an assigned task. Form your agents into a crew and set the workflow process then start the task execution process.

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Functionalities like verbose mode, language model configuration, and memory capabilities can be incorporated into your crew to improve performance. You can choose to let your crew perform tasks based on either a sequential process or a hierarchical process. The former process executes tasks one after the other while the latter follows the hierarchical approach. 

Although CrewAI uses OpenAI’s GPT-4 model by default for language processing, it allows connection with any LLM. Because it was built on top of Lanchain, CrewAI is compatible with all Langchain LLM components thus enabling diverse integrations. It also allows the flexibility of customizing agents and integrating human input into agent execution. 

Key Features

Let’s do a breakdown of the key features of Agent Cloud and CrewAI

Key Features of Agent Cloud:

• RAG as a Service: Agent Cloud has a built-in RAG as a Service feature for building conversational chat apps that interact with your data. The RAG as a Service feature can reference multiple data sources to mitigate hallucination.
• Data Ingestion/Syncing: Agent Cloud allows you to sync data from multiple sources with a built-in Qdrant vector database. You can also specify the sync frequency for your data.
• Conversation Management: Agent Cloud allows you to build chatbots that interact with users based on the information retrieved from your data. Because it uses connected data sources as knowledge points, it is a powerful internal search engine for your data. 
• Multi-Agent Engine: Automate complex business processes with multiple AI agents working together to complete tasks. The agents can specialize in different tasks and also collaborate to complete a task. Single-agent applications like chatbots can also be created with Agent Cloud.
• Permissions: Agent Cloud allows team and user permissions. 
• Data Privacy: You can deploy Agent Cloud to your cloud servers, keeping your data within your control.

Key Features of CrewAI:

The following are the key features of CrewAI:

• Multi-Agent Collaboration: Multi-agent collaboration is the core of CrewAI’s strength. It allows you to define agents, assign distinct roles, and define tasks. Agents can communicate and collaborate to achieve their shared objective.
• Role-Based Design: Assign distinct roles to agents to promote efficiency and avoid redundant efforts. For example, you could have an “analyst” agent analyzing data and a “summary” agent summarizing the data.
• Shared Goals: Agents in CrewAI can work together to complete an assigned task. They exchange information and share resources to achieve their objective. 
• Process Execution: CrewAI allows the execution of agents in both a sequential and a hierarchical process. You can seamlessly delegate tasks and validate results.
• Privacy and Security: CrewAI runs each crew in standalone virtual private servers (VPSs) making it private and secure.

Similarities and Differences

Next, let's explore the similarities and differences between Agent Cloud and CrewAI

Similarities

• They both allow you to connect to any LLM including local LLMs like Ollama.
• Both Agent Cloud and CrewAI are open source.
• They both take privacy and security seriously.
• They both leverage multi-agent collaboration.

Differences

• Core Focus: Agent Cloud is dual-focused. It offers RAG as a Service (single agent conversational chat app) for interacting with your data and also offers process automation using its multi-agent engine. CrewAI’s only focus is process automation.
•  Underlying Technology: Agent Cloud uses a modular architecture that is scalable with your organization. The open-source stack like Airbyte and Qdrant that it uses under the hood are well abstracted so you don’t need to manage any of them. CrewAI was built on top of Lanchain making it easy to access pre-trained LLMs thereby reducing your development workload. 
• Use Cases: Agent Cloud shines in information processing from multiple data sources and task automation. It therefore has a more diverse use case than CrewAI. The use cases for CrewAI are streamlined to process automation. 
• Community and Support: Agent Cloud currently has a small but growing community compared to CrewAI which benefits from the larger Langchain community and resources.

Choosing Between Agent Cloud and CrewAI: What to Consider

Choosing between Agent Cloud and CrewAI depends largely on your use cases and needs.

Unlike CrewAI, the use cases for Agent Cloud are more robust. It combines the collaborative process automation in CrewAI with a built-in RAG as a Service for building private and secure conversational chat apps.

CrewAI shines in facilitating communication, task delegation, result validation, and resource sharing among agents to achieve an objective. Agent Cloud on the other hand shines for both process automation and RAG app use cases. It can sync to hundreds of data sources to retrieve information and present it through conversational interfaces like chatbots. The ability to reference data from multiple sources mitigates hallucination. 

Ultimately, Agent Cloud is a more robust solution that covers your conversational chat apps and process automation needs.

Conclusion

Tools like Agent Cloud and CrewAI leverage generative artificial intelligence and collaborative intelligence to facilitate natural language interaction with data and automate complex and redundant business processes.

While both tools share similarities such as LLM compatibility, open-source nature, and emphasis on privacy and security, they differ in their core focus, underlying technology, use cases, and community support. Agent Cloud offers a diverse range of use cases, combining conversational chat apps and process automation. At the same time, CrewAI excels in facilitating communication and collaboration among agents for process automation tasks.

In conclusion, the choice between Agent Cloud and CrewAI depends on your organization’s specific use cases and needs. However, if you are looking for a robust solution for your RAG and process automation needs, you should consider trying out Agent Cloud.

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Learn more about Agent Cloud’s capabilities:

• 🚀 Compare with Vertex AI Agents Builder
• 🧠 Evaluate against OpenAI
• 👋 Quickstart with Agent Cloud on How to Build a RAG Chat App with Agent Cloud and BigQuery