CrewAI Tools, MCP, and Agentic RAG
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
CrewAI Tools, MCP, and Agentic RAG
This course is part of Mastering CrewAI for Multi Agent Systems Specialization
Included with
Ask Coursera
Recommended experience
Recommended experience
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
Skills you'll gain
Details to know
April 2026
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- 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 videos•Total 93 minutes
- Specialization Introduction•6 minutes
- Course Introduction•6 minutes
- CrewAI Tool Ecosystem Overview•6 minutes
- Demonstration: Building a Web Research Tool with SerperDevTool and ScrapeWebsiteTool•7 minutes
- Demonstration: Assembling the Crew and Interpreting Results•6 minutes
- Demonstration: Sales Reporting Workflow with CrewAI Data Tools•7 minutes
- Demonstration: Running the CrewAI Sales Workflow and Analyzing the Results•6 minutes
- Creating Custom Tools with @tool Decorator•6 minutes
- Demonstration: Developing Job Market Analysis Tools for CrewAI Agents•7 minutes
- Demonstration: Assembling the Job Market Intelligence Crew•7 minutes
- Demonstration: Executing the Job Market Intelligence Workflow•7 minutes
- API Integration Tools for Agents•6 minutes
- Demonstration: Designing Chained Agent Workflows with Tool Hooks•7 minutes
- Demonstration: Running the Chained News Workflow and Generating the Editorial Memo•7 minutes
5 readings•Total 70 minutes
- Course Syllabus•15 minutes
- Complete Built-in Tool Reference Guide•15 minutes
- Custom Tool Development Best Practices•15 minutes
- Tool Design Patterns for Production•15 minutes
- Module Summary: Agent Tooling and Integration with CrewAI•10 minutes
4 assignments•Total 33 minutes
- Knowledge Check: Agent Tooling and Integration with CrewAI•15 minutes
- Practice Assignment: Exploring Built-in Tools in CrewAI•6 minutes
- Practice Assignment: Building Custom Tools for CrewAI Agents•6 minutes
- Practice Assignment: Designing Advanced Tool Workflows for Agents•6 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 videos•Total 61 minutes
- CrewAI Memory Fundamentals and Usage•4 minutes
- Demonstration: Getting Started with CrewAI Unified Memory in a Standalone Workflow•7 minutes
- Demonstration: Recalling, Exploring, and Closing CrewAI Unified Memory•6 minutes
- Advanced Memory Architecture in CrewAI•4 minutes
- Demonstration: Configuring CrewAI Memory with Custom LLM and Embedder Settings•7 minutes
- Demonstartion: Attaching Role-Specific Memory to Agents•7 minutes
- Demonstration: Managing CrewAI Memory Storage, Scopes, and Persistence•6 minutes
- Knowledge Sources and Agentic RAG•5 minutes
- Demonstration: Building a Standalone RAG Agent with CrewAI Knowledge Sources•7 minutes
- Demonstration: Orchestrating the RAG Workflow: Tasks, Shared Knowledge, and Final Output•7 minutes
4 readings•Total 55 minutes
- Memory Layer Characteristics and Use Cases•15 minutes
- Tuning CrewAI Memory for Performance, Cost, and Accuracy•15 minutes
- Designing Effective Knowledge Pipelines for CrewAI Agents•15 minutes
- Module Summary: Memory and Knowledge Systems for Intelligent Agents•10 minutes
4 assignments•Total 33 minutes
- Knowledge Check: Memory and Knowledge Systems for Intelligent Agents•15 minutes
- Practice Assignment: Understanding Memory Architecture in CrewAI•6 minutes
- Practice Assignment: Configuring and Managing Memory for AI Agents•6 minutes
- Practice Assignment: Building Knowledge-Driven Agents with RAG•6 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 videos•Total 75 minutes
- Introduction to Model Context Protocol (MCP)•5 minutes
- Demonstration: Discovering and Using MCP Tools via Server Exploration•7 minutes
- Demonstration: Designing an MCP Server to Expose Dynamic News Tools•6 minutes
- Demonstration: MCP Tool Discovery and Agent-Driven Morning Briefing Output•5 minutes
- Understanding the MCP Server–Client Model•6 minutes
- Demonstration: Building a Research Workflow Using MCPs Field in CrewAI•7 minutes
- Demonstration: Designing a Research MCP Server with Custom Tools in CrewAI•6 minutes
- Demonstration: Running an MCP-Driven Research Pipeline in CrewAI•7 minutes
- MCP Integration Approaches in CrewAI•6 minutes
- Demonstration: Role-Based Access Control in MCP: Junior vs Senior Agent Behavior•7 minutes
- Demonstration: Securing MCP Tools with Token Validation and Access Control•6 minutes
- Demonstration: Analyzing Agent Behavior Under MCP Access Restrictions•6 minutes
4 readings•Total 55 minutes
- Understanding the Foundation of Interoperable Agent Systems•15 minutes
- Optimizing Communication Between CrewAI Agents and MCP Servers•15 minutes
- Designing Scalable and Secure Agent Systems with MCP in CrewAI•15 minutes
- Module Summary: Extending Agents with Model Context Protocol (MCP)•10 minutes
4 assignments•Total 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 Agents•6 minutes
- Practice Assignment: Designing MCP-Powered Agent Workflows•6 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 video•Total 5 minutes
- Course Summary•5 minutes
1 reading•Total 30 minutes
- Practice Project: Building an AI-Powered Enterprise Research and Intelligence System•30 minutes
2 assignments•Total 60 minutes
- End Course Knowledge Check: CrewAI Tools, MCP, and Agentic RAG•30 minutes
- Designing a Secure Multi-Agent Research System with CrewAI, MCP, and Agentic RAG•30 minutes
1 discussion prompt•Total 5 minutes
- Describe Your Learning Journey•5 minutes
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Explore more from Software Development
- E
Edureka
Course
Why people choose Coursera for their career
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.
More questions
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.
