What Frameworks Are Taught In Agentic AI Training?

Artificial Intelligence is evolving quickly, and one of the most exciting developments is Agentic AI. Unlike traditional AI systems that only respond to prompts, Agentic AI systems can plan tasks, make decisions, and take actions to achieve specific goals. These systems can use tools, access information, and work through complex problems with minimal human guidance. 

In this blog, we will explore the key frameworks commonly taught in Agentic AI training. 

If you want to build real-world AI agents, consider enrolling in the Applied Agentic AI Certification Course by upGrad KnowledgeHut to start learning these in-demand skills.

Frameworks Taught in Agentic AI Training

Agentic AI systems use specialized frameworks that help developers build intelligent agents that can plan tasks, make decisions, and use tools. These frameworks simplify development by connecting AI models with APIs, memory, and automated workflows.

Most Agentic AI training programs teach popular frameworks used in real-world AI applications, helping developers build scalable AI agents and automation systems.

Below are some of the common frameworks taught in Agentic AI training:

1. LangChain

LangChain is one of the most widely used frameworks for building applications powered by large language models. It helps developers connect AI models with tools, APIs, databases, and memory systems. This makes it easier to create intelligent agents that can perform multiple tasks and interact with external systems.

Uses of LangChain:

• Building AI assistants and chatbots
• Creating task automation agents
• Integrating AI with APIs and databases
• Developing research and knowledge assistants
• Managing memory and multi-step reasoning in AI systems

2. AutoGen

AutoGen is a framework designed for creating multi-agent systems where different AI agents can communicate and work together. Instead of relying on a single AI model, AutoGen allows multiple agents to collaborate to solve complex problems. This approach helps automate tasks that require planning, discussion, and decision-making.

Uses of AutoGen:

• Building multi-agent collaboration systems
• Creating AI coding assistants
• Automating complex research tasks
• Developing AI systems that plan and solve problems together
• Simulating conversations between AI agents

3. CrewAI

CrewAI is a framework that focuses on role-based AI agents working together as a team. Each agent is assigned a specific role, such as researcher, writer, or analyst, and they collaborate to complete tasks. This makes it easier to design structured AI workflows.

Uses of CrewAI:

• Creating team-based AI workflows
• Automating content research and writing tasks
• Building AI project management assistants
• Developing AI agents with specific roles
• Coordinating multiple AI agents for complex tasks

4. Semantic Kernel

Semantic Kernel is a framework developed to help integrate AI models into applications. It allows developers to combine prompts, AI functions, and traditional programming logic. This framework is commonly used for building AI-powered business applications and copilots.

Uses of Semantic Kernel:

• Building AI copilots for software applications
• Integrating AI with business systems
• Creating automation tools for enterprises
• Connecting AI models with APIs and services
• Developing intelligent workflow systems

5. Haystack

Haystack is a framework designed for building AI systems that search and retrieve information from large datasets. It is commonly used for creating knowledge-based AI applications that can answer questions using documents and databases.

Uses of Haystack:

• Building document search systems
• Creating enterprise knowledge assistants
• Developing question-answering AI tools
• Implementing retrieval-augmented generation (RAG) systems
• Managing large knowledge bases with AI

6. LangGraph

LangGraph is a framework used for building advanced AI agents that require structured workflows and long-running processes. It allows developers to design graph-based systems where AI agents can follow complex decision paths and maintain memory over time.

Uses of LangGraph:

• Building stateful AI agents
• Creating complex decision-making workflows
• Managing long-running AI processes
• Developing advanced automation systems
• Designing multi-step AI reasoning workflows

Choosing the Right Framework to Learn

To build effective Agentic AI systems, it is important to learn the right frameworks. Different frameworks are designed for different purposes, such as building simple AI agents, creating multi-agent systems, or developing enterprise AI applications. Choosing the right framework depends on your goals, project needs, and level of experience.

Here’s how to choose the right Agentic AI framework:

• Start with beginner-friendly frameworks: Many learners begin with frameworks like LangChain because they are widely used and easier to understand.
• Consider your project goals: If you want to build multi-agent systems, frameworks like AutoGen or CrewAI can be more suitable.
• Look at industry adoption: Frameworks that are widely used in real-world applications can provide better career opportunities.
• Think about integration needs: Some frameworks are better for connecting AI models with APIs, databases, and external tools.
• Learn more than one framework: Most Agentic AI training programs introduce multiple frameworks so developers can choose the right one for different projects.

Conclusion 

Agentic AI is changing how intelligent systems are built and used in real-world applications. Frameworks like LangChain, AutoGen, CrewAI, Semantic Kernel, Haystack, and LangGraph help developers create powerful AI agents that can automate tasks, solve problems, and work with different tools. 

Learning these frameworks is an important step for anyone who wants to build modern AI solutions. 

If you want to gain practical skills and start building real AI agents, consider enrolling in the Applied Agentic AI Certification Course  by upGrad KnowledgeHut to begin your learning journey.