CrewAI + Groq Tutorial: Crash Course for Beginners

In this tutorial video, the host guides viewers through the process of integrating Gro, an AI startup company's language processing unit (LPU), with Crew AI to create efficient and cost-effective 'Crews'. The video covers the basics of Gro, demonstrates how to generate an API key, and use it to connect with Crew AI. The host then walks through the process of building a Cold Outreach Crew optimized for Gro, which takes a CSV list of customers and a template email to generate personalized emails. The tutorial also includes tips on avoiding rate limits and ensuring efficient operation, highlighting the significant speed and cost advantages of using Gro over other services. The source code for the Crew is provided for free, and the host shares cost calculations to illustrate the savings when using Gro.

Takeaways

• 🚀 **Groq Introduction**: Groq is an AI startup that developed a Language Processing Unit (LPU) designed to run large language models faster and cheaper than existing solutions like Chachi BT and Google's Gemini 1.5.
• 🔑 **API Key Setup**: To start using Groq, you need to sign up on Groq Cloud, create an API key, and integrate it with your Crew AI project.
• 📚 **Project Dependencies**: Install Groq by using either `pip install langchain-grok` or `poetry add langchain-grok` depending on your dependency management tool.
• 💻 **Code Integration**: Update your Crew AI agents to use Groq instead of OpenAI by modifying the agent's file and setting up the API key and model preferences.
• 📧 **Email Personalization**: The tutorial covers building a Cold Outreach Crew that takes a CSV list of customers and a template email to create personalized emails.
• ⚙️ **Optimizing for Groq**: To optimize the Crew for Groq, set the max iterations to a low number (e.g., 2-4) to maintain efficiency and avoid rate limits.
• 🔍 **Task Execution**: Use asynchronous execution for tasks to allow for parallel processing and faster email generation.
• 💡 **Rate Limit Consideration**: Be aware of Groq's rate limit of 30 calls per minute and adjust your Crew's max RPM to stay within this limit.
• 📈 **Cost Efficiency**: Groq offers a cost-effective solution with faster processing speeds, which can lead to significant savings compared to other platforms like OpenAI's Chat GPT.
• 📝 **Documentation and Support**: The source code for the Crew is provided for free, and a school community is available for support and discussion with other AI developers.
• ⏱️ **Speed and Efficiency**: The Crew demonstrated the ability to generate customized emails rapidly, showcasing the potential for high-speed automation in outreach tasks.

Q & A

What is the main focus of the video tutorial?

The main focus of the video tutorial is to teach viewers how to use Groq with Crew AI to build Crews that are faster and cheaper, specifically by creating a cold Outreach Crew optimized for Groq.

What is Groq and what is its primary function?

Groq is an AI startup company that developed a new chip called a Language Processing Unit (LPU), which is specifically designed to run large language models faster and cheaper.

How does the speed of Groq's LPU compare to other models?

Groq's LPU, running Mixl, can process 500 tokens per second, which is 25 times faster than Chachi BT and 10 times faster than Google's Gemini 1.5.

What is the process of creating a new API key for Groq?

To create a new API key for Groq, one needs to sign up on Groq Cloud, go to the API key tab, click on 'Create API key', name the key, and then copy it for use in connecting Groq to Crew AI.

How can Groq be integrated with Crew AI?

Groq can be integrated with Crew AI by installing Groq via pip or poetry, updating the Crew's agents file to use Groq instead of OpenAI, and setting up the API key and model within the code.

What is the purpose of the cold Outreach Crew being built in the tutorial?

The cold Outreach Crew is designed to take in a CSV list of customers and a template email, merge them together to build hyper-personalized emails, and send them to future customers.

How can the source code for the Crew be obtained?

The source code for the Crew is available for free and can be obtained by clicking on the link provided in the video description.

What is the significance of setting the max iterations to two when working with Groq?

Setting the max iterations to two ensures that the agent focuses on the core task of personalizing the email and doesn't get confused or deviate to perform unnecessary complex tasks.

How can the output of personalized emails be saved using Crew AI?

The output of personalized emails can be saved by defining an output file in the task settings of Crew AI, specifying the output folder and the desired file naming convention.

What is the rate limit imposed by Groq and how can it be managed?

Groq imposes a rate limit of 30 calls per minute. This can be managed by setting the max RPM (requests per minute) in the Crew settings to a value below the limit, such as 29, to ensure that the rate limit is not exceeded.

What are some tips for optimizing the use of Groq in a Crew AI project?

Some tips include keeping the max iteration low (around 2-4) for reliable results, managing rate limits by adjusting the async execution and max RPM, and using dynamic task generation to simplify the process.

Outlines

00:00

🚀 Introduction to Gro and Building a Crew AI with Gro

The video begins with an introduction to Gro, an AI startup company that developed a Language Processing Unit (LPU) designed to run large language models faster and more cost-effectively. The host expresses excitement about teaching viewers how to use Gro with Crew AI to create efficient and cost-effective outreach crews. The tutorial will cover the basics of Gro, creating an API key, and building a Crew that takes a CSV list of customers and a template email to generate personalized emails for future customers. The host also mentions providing the source code for the tutorial for free.

05:02

📚 Setting Up Gro and Creating Personalized Email Agents

The host guides viewers through setting up Gro by creating an API key, installing necessary dependencies, and updating the Crew AI project to use Gro instead of OpenAI. The video then focuses on creating two agents: one for personalizing emails and another, named Ghost Rider, to ensure the emails sound like they're coming from the sender. The host emphasizes the importance of setting the max iterations to two for both agents to maintain focus and avoid unnecessary complexity in the task execution.

10:04

🔧 Fine-Tuning Gro Agents for Efficient Email Personalization

The host shares a crucial trick for optimizing the use of Gro agents, which is to limit the max iteration to a small number to prevent the agents from getting confused and to ensure they perform tasks efficiently. The video then moves on to setting up the Ghost Rider agent, which is responsible for styling the email tone and voice to avoid a robotic sound. The host also discusses the importance of keeping the email length similar to the template and using triple quotes for consistency in results.

15:04

🔗 Linking Personalized Email Task with Ghost Writing for Natural Flow

The video explains how to link the personalized email task with the Ghost Writer task to ensure the personalized emails sound natural and like the sender. The host demonstrates how to dynamically create tasks based on the number of clients to be reached out to and how to connect these tasks in sequence. The process involves iterating through a CSV file, creating recipients, and setting up tasks that generate personalized emails, which are then passed to the Ghost Writer for natural tone adjustment.

20:06

💡 Executing the Crew and Addressing Rate Limitations

The host runs the Crew, demonstrating the rapid generation of customized emails. The video also addresses the rate limit of 30 calls per minute when using Gro, which can slow down the process due to the speed at which Crew operates. The host shares cost calculations, showing the savings when using Gro compared to other services like OpenAI's Chat GPT. The video concludes with a reminder to keep the max iteration low and to adjust the asynchronous execution to avoid rate limiting issues.

25:07

🎓 Conclusion and Future Use of Gro in Projects

The host wraps up the tutorial by summarizing the key learnings and tips for using Gro effectively with Crew AI. These include keeping the max iteration low, adjusting asynchronous execution, and dynamically generating tasks to streamline the process. The host also shares future plans to use Gro for initial Crew creation due to its speed and cost-effectiveness. The video ends with an invitation to join a free school community for AI developers and a prompt to check out more Crew AI content on the channel.

Keywords

💡Groq

Groq is an AI startup company that has developed a new chip called a Language Processing Unit (LPU). This chip is specifically designed to run large language models faster and cheaper than other existing technologies. In the context of the video, Groq is used to optimize the performance of the 'Crew' that the tutorial is building, allowing for faster and more cost-effective operations.

💡Crew AI

Crew AI is a platform that allows users to build 'Crews', which are automated workflows for various tasks, such as sending personalized emails. In the video, the creator is teaching how to use Crew AI with Groq to build a 'Cold Outreach Crew' that can send hyper-personalized emails to customers.

💡CSV list

A CSV (Comma Separated Values) list is a file format that stores tabular data, with each row representing a record and each column representing a field. In the video, the CSV list contains customer information that the Crew will use to personalize the outreach emails.

💡Template email

A template email is a pre-written email format that can be customized with specific details to communicate with recipients. In the script, a template email is used as a base for the personalized emails that the Crew will generate and send to customers.

💡API key

An API key is a unique identifier used to authenticate a user, device, or application to access an API (Application Programming Interface). In the context of the video, creating an API key for Groq is a necessary step to connect and use Groq's services within the Crew AI platform.

💡Language Processing Unit (LPU)

The Language Processing Unit (LPU) is a type of chip developed by Groq that is specifically designed for running large language models. It is mentioned in the video as being capable of processing 500 tokens per second, which is significantly faster than other technologies, making it ideal for the Crew AI application.

💡Rate limiting

Rate limiting is a technique used to prevent abuse of APIs by imposing a limit on the number of requests an application can make within a certain period of time. In the video, the creator discusses how the fast operation of Crew AI with Groq can lead to hitting rate limits, which can slow down the process.

💡Personalized email agent

A personalized email agent is a component within the Crew AI platform that is responsible for customizing email templates with recipient information. In the video, setting up this agent involves using Groq to merge customer data with an email template to create personalized emails.

💡Ghost Rider agent

The Ghost Rider agent, as described in the video, is an element of the Crew AI setup that ensures the personalized emails maintain the tone and voice of the sender. It is a part of the process that adds a human-like touch to the emails, making them sound less robotic.

💡Dynamic task generation

Dynamic task generation refers to the process of automatically creating tasks based on data or conditions, rather than manually setting them up. In the video, the creator uses dynamic task generation to automate the creation of personalized email tasks for each client listed in the CSV file.

💡Asynchronous execution

Asynchronous execution is a method of executing tasks concurrently, allowing for increased efficiency and speed. In the context of the video, setting tasks to execute asynchronously allows the Crew AI to work on multiple personalized emails at the same time, taking advantage of Groq's fast processing capabilities.

Highlights

The tutorial introduces Gro, an AI startup company that developed a Language Processing Unit (LPU) for faster and cheaper operations of large language models.

Gro's LPU can process 500 tokens per second, which is significantly faster than other leading models like Chachi BT and Google's Gemini 1.5.

Gro is currently free for developers to start using, offering a cost-effective solution for AI language model operations.

The video demonstrates how to use Gro with Crew AI to build a Cold Outreach Crew for sending personalized emails to customers.

The process involves creating a CSV list of customers, a template email, and using Crew AI to merge them into personalized emails.

The tutorial provides step-by-step guidance, including how to sign up for Gro Cloud, create an API key, and connect it to Crew AI.

Installing Gro involves using pip or poetry to add Lang chain Groq to your project dependencies.

The tutorial explains how to update Crew AI agents to use Gro instead of Open AI by changing the default LLM in the agents' file.

The video emphasizes optimizing Crew AI to work better with Gro and avoiding rate limits by setting max iterations to a small number like two.

The process includes creating a personalized email agent and a Ghost Rider agent to ensure the emails sound like they are from the sender.

The tutorial details how to dynamically generate tasks for each client in the CSV list to create personalized emails.

The use of asynchronous execution in tasks is highlighted to speed up the process by allowing parallel processing.

The video demonstrates how to handle rate limits by adjusting the Max RPM (requests per minute) when running the Crew.

The source code for the Crew is given away for free, allowing viewers to replicate the process and learn from the example.

The tutorial shows how to calculate the cost savings of using Gro compared to other language models like Open AI's Chat GPT.

The video concludes with practical tips for using Gro effectively, such as keeping max iterations low and being mindful of rate limits.

The presenter shares future plans to use Gro for initial Crew creation due to its speed and cost benefits.

A free school community is mentioned for like-minded AI developers to discuss and get support for Crew AI projects.