Welcome to this video on understanding agentic AI and open-source frameworks.
In this video, you'll identify the core traits that define agentic AI. You'll then
describe the benefits and challenges of multi-agent systems. Next, you'll compare the strengths
and ideal use cases of popular open-source frameworks. And finally, you'll recognize
when to apply each framework so you can choose the right tool for your system's needs.
Understanding agentic AI starts with defining AI agents. At their core, they're autonomous
systems that make decisions and take action to achieve goals. Proactive problem solvers
built to navigate complex environments. AI agents perform multi-step reasoning, breaking
down complex tasks into manageable steps. They also engage in decision-making, helping
choose the best course of action. AI agents can call external tools, APIs, or even other
agents to extend their capabilities. They can also retain context from past interactions.
Finally, their goal-oriented behavior ensures that decisions and actions achieve a defined
objective.
Agentic AI represents a shift from reactive to proactive systems. Think of the difference
between a calculator and a research assistant. A calculator is reactive and responds only
to inputs. Agentic AI is like a research assistant who understands your goal, breaks it into
steps, uses tools to gather information, and keeps working until the problem is solved.
You might wonder, why not build agents from scratch? These frameworks solve infrastructure
problems such as memory, tool integration, and agent coordination so you can focus on
your specific problem. Think of them as the rails of AI development.
Before exploring frameworks, let's understand multi-agent systems. Instead of one AI doing
everything, you have multiple agents working together, such as a software team with front-end
developers, back-end developers, and testers, each contributing their expertise to achieve
better results than anyone could alone.
The key benefits of using multi-agent systems include specialization, where each agent is
designed to excel at a specific task. Next is parallel processing, where multiple agents
work simultaneously. Then comes fault tolerance, where the system continues to function smoothly.
Next is scalability, where more agents are added as complexity grows. And finally, modularity,
where it's easy to update or replace individual components.
Building agentic systems from scratch is far more complex than working with single agents.
Without frameworks, you're stuck managing complex message protocols, manually syncing
state across agents, and writing custom coordination logic. You also need to handle errors across
agents and monitoring or debugging challenges.
Frameworks simplify all of this. Communication protocols are built-in. There's automatic
state management. You can use predefined coordination patterns instead of writing your own, while
error handling is standardized and distributed. Integrated tools make it easier to monitor
and debug your agentic system.
This video introduces various agentic frameworks, such as CrewAI, LangGraph, AutoGen (AG2), Pydantic
AI, and Bee AI. Let's get started.
CrewAI shines at simulating multi-agent collaboration. You define agents with distinct roles, such
as researcher, writer, and editor, each assign specific tasks. Together, they work as a coordinated
crew to accomplish complex goals, such as writing a blog.
CrewAI makes it easy for agents to collaborate by assigning clear roles and tasks. Structured
outputs simplify collaboration among agents.
LangGraph lets you control workflows using a directed graph, where each node represents
a step, such as calling a tool, prompting an LLM, or making a decision. And each edge
defines what happens next, depending on the outcome.
This structure gives you fine-grained control. You define how information flows by designing
interactions between nodes and edges, when decisions happen, and how success or failure
is handled. It's like building a flowchart for agents, perfect for visual debugging.
LangGraph is great for building complex workflows by integrating LLMs, tools, memory, and Lang
Chain functionalities, all in one place. It's best suited for multi-step processes, document
workflows, and decision trees.
AutoGen works by running conversations between agents and between agents and humans. It's
designed for intuitive collaboration, where each agent helps solve problems in real-time.
It excels in conversational interactions, clarifying goals, asking questions, and delegating
work. This makes it ideal for human-in-the-loop workflows, where users can step in to guide
or approve actions.
You can define agent personas and prototype how they work together. You can even embed
live code execution into conversations.
AutoGen is especially useful in education, collaborative coding, and technical support
systems.
Pydantic AI brings structure to every step by combining language generation with type
validation. It ensures that outputs follow clear schemas, reducing errors and ambiguity.
Perfect for APIs, structured data, and enterprise use, especially in fields where stability
matters. Its modular design makes schema management simple. From prototype to production,
Pydantic AI delivers confidence and control.
Note, we will not cover Pydantic AI in this course as it is relatively new.
BeeAI is flexible, letting you design the multi-agent system your use case needs. From simple React
agents to complex setups, you control agent behavior.
It integrates with tools such as Ollama, OpenAI, WatsonX.ai, and Grok.
Plug-in LangChain tools connect via the model context protocol or build your own.
Bee AI scales to production with memory, structured outputs, and state persistence.
Its emitters track workflows while telemetry and logging support debugging.
It's reliable, handling failures with clean exceptions.
For enterprise-grade systems, Bee AI offers power, precision, and control.
Crew AI and AutoGen offer the smoothest onboarding, ideal for rapid prototyping, education, or
lightweight systems that benefit from conversational flow and collaboration.
LangGraph and Pydantic AI bring more complexity but offer greater control.
LangGraph suits structured workflows, state management, and error recovery.
Pydantic AI ensures reliable, type-safe outputs, perfect for enterprise systems and APIs.
BeeAI focuses on enterprise-grade deployment, built for scalability and operational robustness.
These frameworks are already in production.
Crew AI powers content pipelines and automated reporting.
LangGraph handles document workflows and customer service processes.
AutoGen supports educational platforms and technical support systems.
Pydantic AI ensures reliable API services and data validation.
BeeAI manages enterprise automation.
Their applications are diverse and growing.
In this video, you learned that
Agentic AI systems are autonomous systems that make decisions and take action to achieve goals.
Multi-agent systems use specialized agents to work in parallel, offering benefits such
as scalability, modularity, and fault tolerance.
CrewAI helps simulate team-style collaboration by assigning clear roles to agents.
LangGraph lets you build structured workflows using nodes and edges.
AutoGen supports agent-to-agent and agent-to-human conversations.
Pydantic AI ensures that outputs match defined schemas, reducing ambiguity, ideal for API
interactions and production environments.
BeeAI is built for enterprise deployment, offering tool integration, memory management, and scalable
agent coordination.