Comparing CrewAI vs. Dify - Which is the Best AI Agent Framework?

Lina Lam· December 17, 2024

Choosing the proper framework to build your AI agent is essential for your team's productivity and app scalability.

As more and more frameworks emerge in a competitive market, developers and non-technical users alike are looking for the best AI agent framework to get started.

In this blog, we will explore the key differences between CrewAI and Dify—two popular open-source AI agent frameworks, their strengths and limitations, how to get started with them, and a step-by-step guide to monitoring your AI agent applications.

Table of Contents

• CrewAI vs Dify: Quick Compare
• What is CrewAI?
• How to Build an AI Agent in CrewAI
• What is Dify?
• How to Build a Dify Agent
• Dify vs CrewAI: Which is better for building AI Agents?
• How to Monitor Your CrewAI or Dify Agents
• Bottom Line

CrewAI vs Dify: Quick Compare

What is CrewAI?

CrewAI is a multi-agent automation tool for building AI agentic workflows. CrewAI's comprehensive tools simplify the building, management, and deployment of AI agents.

These agents are typically powered by large language models (LLMs) and can be integrated with external tools to improve functionality.

CrewAI allows developers to create role-based AI agents for defined workflows. Users can assign agent roles, goals, and backstories while specifying skill mappings, configuring interaction patterns, etc.

Key Features

• Role-based schema for determining distinct roles for AI agents
• Supports multi-agent workflows
• Customizable framework that supports integration with LangChain and more.
• Built-in error handling and safety management

Limitations

• Less robust at handling complex code execution compared to alternatives like AutoGen from Microsoft
• Less suited for tasks that require heavy computation or are highly specialized
• Less flexibility than other developer-centric platforms

Pricing

• Free tier available for exploring limited features
• Enterprise plans include templates, built-in access controls, and more.

For up-to-date pricing information, we recommend checking CrewAI's official website.

How to Build an AI Agent in CrewAI

CrewAI offers a structured approach to building AI agents.

CrewAI's CLI provides a streamlined way to scaffold and organize your agent projects. Install it by following the instructions in the installation guide, then:

1. Create a new crew project:

   crewai create crew your-project-name
   cd your-project-name
   

2. Define your agents in agents.yaml:

   # src/your_project_name/config/agents.yaml
   researcher:
     role: >
       {topic} Senior Data Researcher
     goal: >
       Uncover cutting-edge developments in {topic}
     backstory: >
       You're a seasoned researcher with a knack for uncovering the latest
       developments in {topic}. Known for your ability to find the most relevant
       information and present it in a clear and concise manner.
   

   You can also define agents directly in Python code. Refer to the official CrewAI documentation for more details.

   Note that the YAML approach is recommended, especially for larger projects.

3. Define your tasks in tasks.yaml:

   # src/your_project_name/config/tasks.yaml
    research_task:
      description: >
        Conduct a thorough research about {topic}
      expected_output: >
        A list with 10 bullet points of relevant information
      agent: researcher
   

4. Implement your crew in crew.py:

   from crewai import Agent, Crew, Process, Task
   from crewai.project import CrewBase, agent, crew, task
   from crewai_tools import SerperDevTool
   
   @CrewBase
   class YourProjectCrew():
    @agent
    def researcher(self) -> Agent:
      return Agent(config=self.agents_config['researcher'], tools=[SerperDevTool()])
   
    @task
    def research_task(self) -> Task:
      return Task(config=self.tasks_config['research_task'])
   
    @crew
    def crew(self) -> Crew:
      return Crew(agents=self.agents, tasks=self.tasks, process=Process.sequential)
   

5. Run your crew:

   crewai install
   crewai run
   

For the most complete and up-to-date instructions, always refer to CrewAI's documentation.

What is Dify?

Dify is a no-code platform that allows cross-functional teams to build AI agents rapidly and collaboratively. It supports LLM-based workflows and can integrate Backend-as-a-Service (BaaS) APIs and tools.

Key Features

• No-code, easy prompt design and management
• Provides pre-built templates for non-technical users
• Has integrated RAG pipeline to increase contextual accuracy
• Flexible APIs to integrate with multiple systems

Limitations

• Limited to built-in components and visual workflows
• Not as robust at handling heavy computational tasks
• Building complex or large-scale tasks is challenging, but Dify is suitable for building most AI apps.

Dify's Pricing

• Sandbox Plan: Free (includes 200 OpenAI calls)
• Professional Plan: $59/month, includes additional capabilities
• Team Plan: $159/month, includes collaboration tools

For up-to-date pricing information, we recommend checking Dify's official website.

How to Build a Dify Agent

Dify.AI offers two primary methods for creating agents: using pre-built templates or building your own from scratch.

Both approaches leverage Dify's visual interface, requiring no coding.

Method 1: Start with a Dify Template

1. Log in to your Dify.AI dashboard
2. Navigate to the Explore section
3. Browse available templates (chatbots, assistants, etc.)
4. Select a template that matches your needs
5. Click "Use Template" to add it to your workspace
6. Customize the template parameters to fit your specific requirements
7. Test your agent in the preview mode
8. Deploy when ready

This method is ideal for quickly launching applications like content generators, or data analysis tools without starting from scratch.

Method 2: Build Your Own Custom Dify Agent

Dify.AI provides a dedicated "Agent" application type for creating intelligent assistants. Here's how to build one using Dify's intuitive interface:

1. Navigate and Create:

   • Go to Dify Studio → Create Application → Select "Agent"

2. Configure Core Components:

   • Definition: Set name and description
   • Instructions: Define skills, workflow, and limitations
   • Tools: Add knowledge bases, search capabilities, and API integrations
   • Settings: Choose inference model (Function Calling/ReAct) and parameters
   • Conversation Opener: Design initial user experience

3. Test and Deploy:

   • Debug using the testing interface
   • Refine instructions and configurations
   • Publish as a web app or API endpoint

For the most up-to-date instructions, please check Dify's documentation.

Dify vs CrewAI: Which is better for building AI Agents?

• For building a multi-agent system with error handling, CrewAI's APIs and LangChain integration make it the better choice.
• For rapid prototyping, Dify is the better option for its no-code Studio with pre-built templates that helps build agents quicker.
• For workflow automation involving defined roles and collaboration, CrewAI is the better option.
• For teams with mixed technical backgrounds, opt for Dify for its user-friendly interface that caters to both non-technical users and developers.
• For developers looking for deeper customization, choose CrewAI over Dify.
• For developers building chatbots or conversational AI, pick Dify for its solid support for NLP tasks and dialogue management.

How to Monitor Your CrewAI or Dify Agents

As you build increasingly complex systems such as agents or complex LLM workflows, monitoring and tracing become critical.

Tools like Helicone's Sessions provide real-time traces and model performance monitoring for both CrewAI and Dify projects. Here's what Sessions look like - companies use Sessions frequently to narrow down errors in a complex LLM workflow, and identify performance improvements.

Integrating Helicone is simple:

Step 1: Create a Helicone account

Getting started is free. You get 10,000 logs/month and access to enterprise-grade features for comprehensive monitoring.

Step 2: Generate an API key

Generate a Helicone API Key in Settings, under API Keys.

Step 3: Configure your framework

For CrewAI users, set OPENAI_BASE_URL as an environment variable:

import os

os.environ["OPENAI_API_BASE"] = f"https://oai.helicone.ai/{HELICONE_API_KEY}/v1"

For detailed documentation, please visit CrewAI Integration.

For Dify.AI users, choose whichever provider you are using that is supported by Helicone. Here is an example using OpenAI:

For detailed documentation, please visit Dify Integration.

Step 4: Monitor the performance

Once integrated, Helicone provides powerful monitoring capabilities for both your CrewAI workflows and Dify applications, where you can:

• Track token usage and costs across agents
• Monitor latency and performance metrics
• View detailed conversation logs
• Trace multi-agent interactions using Sessions
• Analyze prompt effectiveness and model responses

Bottom Line

When choosing between Dify vs CrewAI, think about your team's technical capabilities, project complexity, and deployment requirements. In summary:

• CrewAI excels for developers building complex, role-based agent systems who need deep customization and don't mind writing code.
• Dify.AI shines for teams seeking rapid development through a no-code interface, particularly those with mixed technical expertise.

We encourage you to explore other AI agent building platforms such as AutoGen and LangChain—who knows, you might find them a better fit for your use case!

Other useful comparisons:

• Comparing CrewAI vs. AutoGen - Open-source AI Agent Frameworks

• Comparing LlamaIndex vs. LangChain - AI Agent Frameworks

• 6 Awesome Open-Source AI Agent Frameworks