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URL: https://www.coursera.org/learn/crewai-tools-mcp-agentic-rag

⇱ CrewAI Tools, MCP, and Agentic RAG | Coursera


CrewAI Tools, MCP, and Agentic RAG

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CrewAI Tools, MCP, and Agentic RAG

Instructor: Edureka

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Define how tools, memory, MCP, and Agentic RAG enable intelligent and scalable multi-agent systems

  • Apply CrewAI tools, memory systems, and retrieval techniques to build structured and context-aware agent workflows

  • Analyze agent behavior, workflow design, and knowledge retrieval strategies to improve accuracy and system performance

  • Design secure, scalable multi-agent systems integrating tools, memory, MCP, and Agentic RAG for real-world applications

Details to know

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Recently updated!

April 2026

Assessments

14 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Mastering CrewAI for Multi Agent Systems Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This program introduces you to CrewAI Tools, MCP, and Agentic RAG, designed for developers and AI practitioners looking to build intelligent, production-ready multi-agent systems. You’ll begin by exploring how agents use tools to interact with external systems, including CrewAI’s built-in tools and custom tool development for real-world workflows.

Next, you’ll dive into memory and knowledge systems, learning how agents store, retrieve, and prioritize information across interactions. You’ll explore Agentic RAG to build knowledge-driven agents that retrieve relevant data and generate accurate, context-aware responses. Through hands-on demonstrations, you will design systems that combine memory and retrieval to improve reliability and reduce hallucinations. As you progress, you’ll focus on extending agents using the Model Context Protocol (MCP). You’ll learn how agents discover and interact with tools dynamically through MCP servers, enabling structured communication and scalable system design. You’ll also implement role-based access control, authentication, and secure workflows to ensure safe and controlled agent behavior in real-world environments. By the end of the program, you will be able to: - Identify how tools extend agent capabilities and enable structured workflows in CrewAI. - Apply memory systems and Agentic RAG to build context-aware and knowledge-driven agents. - Analyze how agents retrieve and use knowledge to improve accuracy and reduce hallucinations. - Integrate MCP to enable dynamic tool discovery and structured agent communication. - Design secure agent systems with role-based access control and authentication mechanisms. - Develop scalable multi-agent workflows combining tools, memory, MCP, and retrieval. This program is ideal for developers, AI engineers, and technical professionals interested in building advanced agent systems and intelligent automation workflows. Prior experience with Python programming and basic AI concepts will help maximize your learning experience. Learners need a reliable internet connection, a modern web browser, and access to Python development tools. The course uses CrewAI and related AI technologies, which do not require specialized hardware. Basic familiarity with APIs and Python is recommended. Join us and learn to build intelligent agents that can interact with tools, retain knowledge, operate securely, and power real-world AI systems at scale.

Learn how tools extend agent capabilities beyond text generation in CrewAI. Explore built-in tools for tasks such as web research, data extraction, and reporting, and understand how agents use them within structured workflows. Gain hands-on experience building custom tools and designing multi-step workflows using tool chaining and orchestration. Develop practical skills to create agents that interact with external systems and execute real-world tasks.

What's included

14 videos5 readings4 assignments

14 videosTotal 93 minutes
  • Specialization Introduction6 minutes
  • Course Introduction6 minutes
  • CrewAI Tool Ecosystem Overview6 minutes
  • Demonstration: Building a Web Research Tool with SerperDevTool and ScrapeWebsiteTool7 minutes
  • Demonstration: Assembling the Crew and Interpreting Results6 minutes
  • Demonstration: Sales Reporting Workflow with CrewAI Data Tools7 minutes
  • Demonstration: Running the CrewAI Sales Workflow and Analyzing the Results6 minutes
  • Creating Custom Tools with @tool Decorator6 minutes
  • Demonstration: Developing Job Market Analysis Tools for CrewAI Agents7 minutes
  • Demonstration: Assembling the Job Market Intelligence Crew7 minutes
  • Demonstration: Executing the Job Market Intelligence Workflow7 minutes
  • API Integration Tools for Agents6 minutes
  • Demonstration: Designing Chained Agent Workflows with Tool Hooks7 minutes
  • Demonstration: Running the Chained News Workflow and Generating the Editorial Memo7 minutes
5 readingsTotal 70 minutes
  • Course Syllabus15 minutes
  • Complete Built-in Tool Reference Guide15 minutes
  • Custom Tool Development Best Practices15 minutes
  • Tool Design Patterns for Production15 minutes
  • Module Summary: Agent Tooling and Integration with CrewAI10 minutes
4 assignmentsTotal 33 minutes
  • Knowledge Check: Agent Tooling and Integration with CrewAI15 minutes
  • Practice Assignment: Exploring Built-in Tools in CrewAI6 minutes
  • Practice Assignment: Building Custom Tools for CrewAI Agents6 minutes
  • Practice Assignment: Designing Advanced Tool Workflows for Agents6 minutes

Discover how intelligent agents store, retrieve, and use information through memory systems in CrewAI. Learn how to configure memory for persistence, relevance, and role-specific behavior, enabling agents to maintain context across interactions. Explore Agentic RAG and how agents use external knowledge sources to generate grounded and accurate responses. Develop skills to design context-aware and knowledge-driven agent systems.

What's included

10 videos4 readings4 assignments

10 videosTotal 61 minutes
  • CrewAI Memory Fundamentals and Usage4 minutes
  • Demonstration: Getting Started with CrewAI Unified Memory in a Standalone Workflow7 minutes
  • Demonstration: Recalling, Exploring, and Closing CrewAI Unified Memory6 minutes
  • Advanced Memory Architecture in CrewAI4 minutes
  • Demonstration: Configuring CrewAI Memory with Custom LLM and Embedder Settings7 minutes
  • Demonstartion: Attaching Role-Specific Memory to Agents7 minutes
  • Demonstration: Managing CrewAI Memory Storage, Scopes, and Persistence6 minutes
  • Knowledge Sources and Agentic RAG5 minutes
  • Demonstration: Building a Standalone RAG Agent with CrewAI Knowledge Sources7 minutes
  • Demonstration: Orchestrating the RAG Workflow: Tasks, Shared Knowledge, and Final Output7 minutes
4 readingsTotal 55 minutes
  • Memory Layer Characteristics and Use Cases15 minutes
  • Tuning CrewAI Memory for Performance, Cost, and Accuracy15 minutes
  • Designing Effective Knowledge Pipelines for CrewAI Agents15 minutes
  • Module Summary: Memory and Knowledge Systems for Intelligent Agents10 minutes
4 assignmentsTotal 33 minutes
  • Knowledge Check: Memory and Knowledge Systems for Intelligent Agents15 minutes
  • Practice Assignment: Understanding Memory Architecture in CrewAI6 minutes
  • Practice Assignment: Configuring and Managing Memory for AI Agents6 minutes
  • Practice Assignment: Building Knowledge-Driven Agents with RAG6 minutes

Learn how to extend agents using the Model Context Protocol (MCP) for structured and scalable communication. Explore how agents discover and interact with tools through MCP servers and integrate MCP into CrewAI workflows. Gain hands-on experience designing secure workflows with role-based access control and token validation. Develop the ability to build flexible and production-ready agent systems with controlled tool access.

What's included

12 videos4 readings4 assignments

12 videosTotal 75 minutes
  • Introduction to Model Context Protocol (MCP)5 minutes
  • Demonstration: Discovering and Using MCP Tools via Server Exploration7 minutes
  • Demonstration: Designing an MCP Server to Expose Dynamic News Tools6 minutes
  • Demonstration: MCP Tool Discovery and Agent-Driven Morning Briefing Output5 minutes
  • Understanding the MCP Server–Client Model6 minutes
  • Demonstration: Building a Research Workflow Using MCPs Field in CrewAI7 minutes
  • Demonstration: Designing a Research MCP Server with Custom Tools in CrewAI6 minutes
  • Demonstration: Running an MCP-Driven Research Pipeline in CrewAI7 minutes
  • MCP Integration Approaches in CrewAI6 minutes
  • Demonstration: Role-Based Access Control in MCP: Junior vs Senior Agent Behavior7 minutes
  • Demonstration: Securing MCP Tools with Token Validation and Access Control6 minutes
  • Demonstration: Analyzing Agent Behavior Under MCP Access Restrictions6 minutes
4 readingsTotal 55 minutes
  • Understanding the Foundation of Interoperable Agent Systems15 minutes
  • Optimizing Communication Between CrewAI Agents and MCP Servers15 minutes
  • Designing Scalable and Secure Agent Systems with MCP in CrewAI15 minutes
  • Module Summary: Extending Agents with Model Context Protocol (MCP)10 minutes
4 assignmentsTotal 33 minutes
  • Knowledge Check: Extending Agents with Model Context Protocol (MCP)15 minutes
  • Practice Assignment: Introduction to the Model Context Protocol (MCP)6 minutes
  • Practice Assignment: Integrating MCP with CrewAI Agents6 minutes
  • Practice Assignment: Designing MCP-Powered Agent Workflows6 minutes

Consolidate your learning across tools, memory, MCP, and Agentic RAG. Apply your skills in a hands-on project by building a knowledge-driven agent system that integrates tool usage, memory, and retrieval. Complete a graded assessment to demonstrate your ability to design and implement scalable agent workflows. Reflect on your progress and prepare for more advanced multi-agent system design.

What's included

1 video1 reading2 assignments1 discussion prompt

1 videoTotal 5 minutes
  • Course Summary5 minutes
1 readingTotal 30 minutes
  • Practice Project: Building an AI-Powered Enterprise Research and Intelligence System30 minutes
2 assignmentsTotal 60 minutes
  • End Course Knowledge Check: CrewAI Tools, MCP, and Agentic RAG30 minutes
  • Designing a Secure Multi-Agent Research System with CrewAI, MCP, and Agentic RAG30 minutes
1 discussion promptTotal 5 minutes
  • Describe Your Learning Journey5 minutes

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Instructor

Edureka
203 Courses185,285 learners

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Frequently asked questions

This course is ideal for developers, AI engineers, and technical professionals who want to build intelligent multi-agent systems. It is also suitable for learners interested in automation, AI workflows, and real-world agent-based applications.

You will learn how to build intelligent agents using CrewAI, integrate tools, manage memory, apply Agentic RAG for knowledge retrieval, and design secure workflows using MCP for real-world AI systems.

You will work with CrewAI, Python, built-in and custom tools, memory systems, retrieval-based architectures (RAG), and the Model Context Protocol (MCP) for structured communication.

No prior experience with CrewAI, MCP, or RAG is required. The course introduces all concepts step by step, making it accessible while still covering advanced system design topics.

Yes, the course includes demonstrations and practice assignments where you will build workflows, integrate tools, configure memory, and design agent systems in realistic scenarios.

You will learn how agents store, recall, and prioritize information using memory systems, and how Agentic RAG enables agents to retrieve and use external knowledge for accurate responses.

MCP is a framework that allows agents to discover and interact with tools dynamically. It enables structured communication, secure access control, and scalable system design in multi-agent environments.

Yes, the course covers role-based access control, authentication, and MCP-based workflows, helping you design secure, scalable, and production-ready agent systems.

This course focuses on real-world system design using CrewAI, combining tools, memory, RAG, and MCP. It goes beyond prompting to teach how to build complete, production-ready agent systems.

Yes, you will receive a certificate upon successfully completing the course and its assessments.

This course prepares you for roles such as AI Engineer, Agent Systems Developer, Automation Engineer, or roles focused on building intelligent AI applications.

Yes, as long as you have basic Python knowledge, you can follow along. The course gradually introduces key AI concepts and builds up to advanced agent system design.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.