Exploring AI Agentic Workflows with Groq and CrewAI

Introduction

Andrew Ng’s comment, “AI Agentic workflow will drive massive progress this year,” highlights the significant advancements expected in AI. As large language models gain popularity, Autonomous Agents have become a topic of discussion. This article will take you on a journey to explore Autonomous Agents, understand the components of building an Agentic workflow, and see how to practically implement a Content creation agent using Groq and crewAI.

Learning Objectives

Here are the key things you’ll learn:

Understand how Autonomous Agents work, using a simple example of human task – execution.
Discover the limitations and research areas of autonomous agents.
Explore the core components needed to build an AI agent pipeline.
Build a content creator agent with crewAI, an open – source Agentic framework.
Integrate an open – source large language model within the agentic framework using LangChain and Groq.

What is Agentic Workflow?

Agentic workflow is a novel way of leveraging AI, especially large language models (LLMs). It’s distinct from the traditional method of simply giving an LLM a prompt and getting a response. In an agentic workflow:

Multiple AI agents: Instead of a single LLM, several AI agents work together, each with specific roles.
Iterative process: Tasks are broken down into smaller steps, and agents learn and improve as they progress, with the possibility of feedback.
Collaboration: Agents collaborate, sharing information and completing subtasks to reach the final goal.

Understanding Autonomous Agents

Think of a group of engineers planning a new software app. Each brings unique expertise. To replicate this with LLMs, Autonomous Agents were born. These agents have human – like reasoning and planning capabilities. For example, “Devin AI” has sparked debates about replacing human engineers. While full replacement may be premature due to software development’s complexity, research focuses on areas like self – reasoning (to reduce hallucinations) and memory utilization (to avoid repeating mistakes).

Simple Task Execution Workflow

When humans approach a problem, say building a customer service chatbot, we don’t start coding immediately. We break the task into smaller sub – tasks like fetching data, cleaning data, and building the model. We use our experience and tools for each sub – task, plan carefully, and iterate until the task is done. Agentic workflows operate in a similar way.

AI Agents Component Workflow

The core of the workflow is the Agents. Users provide a task description, and the Agent uses planning and reasoning components, often with prompt techniques like REACT. The process involves task description, planning and reasoning, using tools like web APIs, and storing responses in memory for future reference.

Content Creator Agent using Groq and CrewAI

Now, let’s get into the steps to build an Agentic Workflow with CrewAI and Groq’s Open Source Model:

Step1: Installation

Install crewai, the open – source Agents framework, along with the supported tools integration and langchain_groq for LLM inference.

Use the commands: pip install crewai, pip install 'crewai[tools]', pip install langchain_groq

Step2: Setup the API keys

Securely store your API keys using the getpass module. Obtain the SERPER_API_KEY from serper.dev and the GROQ_API_KEY from console.groq.com.

Step3: Integrate Gemma Open Source Model

Groq is a fast LLM inference engine. Integrate it into crewAI via Langchain to perform inference on open – source LLMs like Gemma.

Step4: Search Tool

Use the SerperDevTool, a search API that provides contextual data from web searches to aid agents in task execution.

Step5: Agent

Agents are the heart of crewAI. Define their role, goal, backstory, tools, and LLM. CrewAI’s multi – agent functionality allows task delegation.

Step6: Task

Provide clear tasks to agents, including description, expected output, and link them with the right agents and tools. CrewAI also offers async execution flexibility.

Step7: Run and Execute the Agent

Define a Crew, a collection of Agents and tasks. Run the kickoff function to execute the multi – agent setup.

Conclusion

The field of Agents is research – driven, with open – source frameworks like crewAI making it easier to build agents without relying on closed – source LLMs. Prompt engineering is crucial for maximizing the potential of LLMs and agents. We hope this article has given you a good understanding of agentic workflows, their components, and how to create content using crewAI and Groq.

Key Takeaways

Understand the importance of Agentic workflows, compare them with human task execution, explore open – source model options with Groq, leverage crewAI’s multi – agent functionality, and recognize the significance of prompt engineering.