Top 5 Agentic AI Frameworks You Should Know in 2025

Who would have thought machines would evolve to anticipate our needs, make decisions and take independent action to solve problems? But here we are with AI Agents, the next big thing in AI technology and automation. AI Agents are intelligent systems that perceive their environment, reason through challenges and take autonomous actions. These systems started from basic chatbots to advanced tools powered by large language models (LLMs) and large action models (LAMs). Read our latest article below to explore the top 5 Agentic AI frameworks to automate your workloads.

What are AI Agentic Frameworks? 

AI agentic frameworks are purpose-built toolkits that simplify building autonomous AI systems. With pre-made components for integrating LLMs and LAMs, they let developers quickly create intelligent agents that perceive, reason and act with low manual coding. Acting as blueprints for building flexible, goal-driven solutions, these frameworks streamline workflows and tool integration.

To break it down, AI Agent frameworks can help developers with:

• Streamlined modules reduce development complexity, enabling developers to focus on innovation.
• Frameworks scale from single agents to complex multi-agent systems and offer seamless integration with APIs, databases, and tools.
• Pre-built memory and action management features accelerate deployment and boost performance, fostering rapid experimentation and breakthroughs in AI applications.

Best AI Agentic Frameworks You Should Know

Here are the best AI Agentic frameworks for building autonomous systems:

1. LangChain

LangChain is one of the top agentic AI frameworks designed to simplify building applications with large language models (LLMs). It excels in managing context, memory, and external tool integration, making it ideal for conversational agents and dynamic workflows. With a modular design, it connects LLMs to APIs, databases and memory systems for context-aware responses. Its robust community and extensive documentation make it accessible for beginners and experts. LangChain is perfect for prototyping and scaling LLM-powered applications, though it may require careful tuning for production stability.

2. LangGraph 

Built on LangChain, LangGraph extends its capabilities with a graph-based approach for stateful, multi-agent systems. It represents workflows as nodes and edges, offering precise control over complex processes and agent interactions. This framework shines in applications requiring advanced memory, error recovery, and human-in-the-loop features. LangGraph’s flexibility suits intricate, non-linear tasks like decision-making systems or simulations. While powerful, its complexity and dependency on LangChain can pose a learning curve, making it best for developers needing detailed orchestration and debugging in sophisticated agentic setups.

3. CrewAI

CrewAI is an intuitive framework focused on multi-agent collaboration, mimicking human team dynamics. It simplifies creating role-based AI agents that work together on tasks, with easy setup and minimal coding. Ideal for rapid prototyping, CrewAI excels in scenarios like logistics or resource planning, where agents coordinate seamlessly. Built on LangChain, it leverages a broad tool ecosystem but sacrifices some flexibility for simplicity. CrewAI suits beginners or projects needing quick deployment, though its opinionated design may limit customisation for advanced use cases.

4. Microsoft Semantic Kernel

Microsoft Semantic Kernel integrates AI into enterprise applications, emphasising semantic reasoning and context awareness. It combines LLMs with traditional programming, offering pre-built connectors for seamless business system integration. Designed for .NET and Python, it’s lightweight yet powerful and can improve decision-making in customer service or IT operations. Its strength lies in reusable components and memory retention, ideal for virtual assistants. While enterprise-friendly, it’s less feature-rich than LangChain, so it caters to developers prioritising security and adoption over extensive customisation.

5. Microsoft AutoGen v0.4

AutoGen from Microsoft, is an enterprise-grade framework for multi-agent systems, focusing on automation and scalability. It supports code generation, execution, and agent collaboration, with robust error handling and logging. Microsoft AutoGen v0.4 improves its modular design, making it suitable for complex workflows like cloud automation or IT management. AutoGen Studio’s no-code interface broadens accessibility, while its flexibility suits advanced users. It’s ideal for production environments needing reliability, though setup can be more involved than simpler frameworks like CrewAI, balancing power with a steeper learning curve.

Conclusion 

AI agents help in human decision-making and automation, making them essential for organisations focused on operational efficiency. Frameworks like LangChain, LangGraph, CrewAI, Microsoft Semantic Kernel, and AutoGen v0.4 enable rapid prototyping and enterprise-scale deployments, supporting smarter, more capable agents for complex automation needs. With our high-bandwidth GPU architecture and high-speed networking of up to 350 Gbps for NVIDIA A100, NVIDIA H100 SXM and the NVIDIA H100 PCIe GPUs, you get low latency and compute required for seamless performance.

New to Hyperstack? Try our cloud platform to power your AI projects.

Stay tuned for our upcoming series on AI agents and tutorials to explore this exciting technology. 

FAQs

What are AI Agentic frameworks?

Agentic AI frameworks are tools that help developers build autonomous AI agents by integrating LLMs, managing memory, and automating workflows with minimal manual coding.

Which is the best AI agentic framework for beginners?

CrewAI is beginner-friendly due to its simple setup and focus on multi-agent collaboration, making it ideal for quick prototyping and task automation.

How does LangChain differ from LangGraph?

LangChain focuses on modular LLM-based app development, while LangGraph extends it with graph-based state management for complex multi-agent workflows and decision-making.

Which AI Agentic framework is enterprise-friendly?

Microsoft Semantic Kernel integrates AI with business applications, providing semantic reasoning, memory retention, and security-focused features tailored for enterprise workflows.

Is AutoGen suitable for large-scale automation?

Yes, AutoGen v0.4 is designed for scalable, multi-agent automation with strong error handling, logging, and no-code options for enterprise AI solutions.

Where can I deploy AI agents built with these frameworks?

You can deploy AI agents on cloud platforms like Hyperstack to scale workloads efficiently and leverage our high-performance GPUs like the NVIDIA A100, NVIDIA H100 SXM and the NVIDIA H100 PCIe.