Exploring CrewAI, LangFlow | Building an Agentic AI Prototype with Relevance AI

I just completed a course on CrewAI, a powerful framework for designing and orchestrating multi-agent AI systems to handle complex workflows. As a hands-on project, I used Relevance AI—a no-code platform—to build an agentic AI prototype: RouteGenie, an AI-powered road trip itinerary generator. Check it out!

This article covers:

✅ What CrewAI is and how it works 

✅ How I built RouteGenie, an AI-powered road trip planner 

✅ The multi-agent approach and why it’s effective 

✅ The tools I integrated (Google Search, Google Maps, and Summarization APIs) 

✅ A live link to try it out for yourself 

✅ Other exciting agentic AI use cases

Understanding CrewAI: The Power of Multi-Agent Systems

CrewAI is an advanced framework that enables the creation of multi-agent AI systems. Unlike single AI models that handle tasks sequentially, CrewAI allows multiple agents—each specializing in a specific function—to collaborate autonomously, resulting in more efficient workflows.

At its core, CrewAI operates with:

• Agents – Specialized AI units, each responsible for a distinct task.

• Tasks – Defined objectives that agents work on.

• Orchestration – The process of coordinating agents to work seamlessly together.

This method improves modularity, scalability, and performance, making it ideal for complex applications. Unlike traditional generative AI, which primarily focuses on producing content (e.g., text or images), agentic AI emphasizes decision-making and autonomous action while integrating with external tools.

I also experimented with Langflow, a great tool for designing AI workflows. However, I found Relevance AI to be a better fit for my needs due to its simplicity and ease of deployment. Unlike Langflow, which required more setup, Relevance AI enabled to quickly build, test, and publish the application with just a few clicks, making it an ideal choice for a no-code multi-agent system.

Introducing RouteGenie: AI-Powered Road Trip Planner

🚗 RouteGenie is a POC for an AI-powered road trip itinerary generator designed for both cars and motorcycles. It helps travelers plan optimized routes by:

✅ Finding the best path using Google Maps ✅ Identifying top-rated pitstops along the way ✅ Generating a structured itinerary in table format ✅ Providing travel tips based on the user’s vehicle type

This is especially useful for motorcycle riders, who need frequent fuel stops, scenic routes, and specialized travel advice compared to car travelers.

Built Using Relevance AI (No-Code Platform)

I built RouteGenie using Relevance AI, a no-code agentic AI builder that allows the effortless creation of multi-agent workflows. With Relevance AI, I was able to:

• Connect multiple AI agents that specialize in different aspects of itinerary planning.

• Integrate Google Search and Maps for real-time data.

• Use summarization APIs to extract key travel insights.

The result? A fully automated trip planner that tailors routes based on the user’s needs.

Breaking Down the Multi-Agent System in RouteGenie

Instead of using a single AI model for everything, I designed four specialized AI agents, each responsible for a specific function.

1️⃣ Route Planner Agent 🗺️

Task: Determines the optimal route using Google Maps data. ✅ Considers vehicle-specific road restrictions (e.g., motorcycle-only routes) ✅ Analyzes traffic, road conditions, and distances ✅ Suggests alternative paths when necessary

2️⃣ Pitstop Optimizer Agent ⛽

Task: Identifies top-rated pitstops along the route. ✅ Searches for fuel stations, restaurants, and rest areas ✅ Prioritizes highly-rated places based on Google reviews ✅ Ensures frequent stops for motorcycle riders

3️⃣ Itinerary Formatter Agent 📋

Task: Organizes data into a structured itinerary. ✅ Creates a clean, readable table ✅ Includes stop names, distances, travel times, and notes ✅ Adjusts layout based on vehicle type

4️⃣ Travel Tips & Advisory Agent 💡

Task: Provides road trip tips and insights based on the route. ✅ Advises on weather conditions, safety, and gear recommendations ✅ Alerts users about tolls, parking, and traffic rules ✅ Offers tailored recommendations based on vehicle type

Why Use a Multi-Agent Approach?

Instead of a single AI handling all tasks, each agent specializes in one area, resulting in:

✔ More accurate and reliable outputs ✔ Faster processing since tasks are distributed ✔ Better flexibility (easy to scale or update individual agents)

Tools & Integrations Used

To make RouteGenie more intelligent and data-driven, I integrated the following tools:

🔎 Google Search – For retrieving travel-related insights 

📌 Google Maps – For fetching real-time routes and locations

📊 Google Results Summarization – To extract key travel details

These integrations ensure that RouteGenie dynamically pulls the best travel data rather than relying on static information.

Try RouteGenie for Yourself! 🚀

I’m super excited to share this project with the community! You can try RouteGenie prototype here: 👉 RouteGenie – AI Road Trip Planner

What You Can Do With It:

✅ Enter your start and destination locations 

✅ Select your vehicle type (Car/Motorcycle) 

✅ Receive a custom itinerary with top pitstops and travel tips

Click Here to TRY ROUTEGENIE POC

3 More simple and useful Multi-Agent AI Usecases in pipeline

🚀 1️⃣ Interview Helper – Prepares job seekers by analyzing company insights, latest news, interview questions, and employee reviews for a quick research and prepage strategy. 

📚 2️⃣ PM Concept Explainer – Summarizes first principles, case studies, and expert content to deliver personalized and structured PM learning paths. 

🔍 3️⃣ LinkedIn Profile Reviewer – Audits profiles against best practices, industry benchmarks, and job roles to offer tailored linkedin profile update recommendations.

Final Thoughts

Building RouteGenie reinforced my belief that multi-agent AI systems are the future of automation. By dividing complex workflows into specialized AI agents, we can create highly efficient and scalable applications without overloading a single model.

I’m excited to continue experimenting with CrewAI, Relevance AI, and AI-powered workflows to develop even more advanced systems.

If you’re interested in building your own multi-agent AI, I highly recommend checking out:

🔹 CrewAI Course & Documentation – Crew AI 

🔹 Langflow No-Code Platform – Langflow 

🔹 Relevance AI Platform – Relevance AI

Feel free to try out RouteGenie and let me know your thoughts—I’d love your feedback! 🔹 

Are you experimenting with agentic AI? Let’s connect and discuss! 🚀