Top AI Agent Frameworks in 2025: Features, Comparisons & Use Cases

AI agents are helpful tools in today’s business world. They handle repetitive tasks, analyze large amounts of data, and interact with customers. They can even perform tasks independently without needing human intervention.

So, the hype around AI agents is real. But what about creating one? Well, that is a whole different story.

Building an AI agent is not just selecting the right model or platform. You also need to choose the right artificial intelligence agent framework. So, the real question that comes here is, how do you determine which framework best suits your business needs?

In this guide, we will explain the top AI agent frameworks, along with their features, benefits, and the types of agents you can create with them.

  Generate  Key Takeaways Generating...

• TensorFlow and PyTorch are popular for AI development. However, for building AI agents powered by LLMs, LangChain and Microsoft AutoGen are currently the top choices.

• For enterprise-level applications, frameworks like Microsoft AutoGen and Microsoft Semantic Kernel are excellent. They are scalable, secure, and integrate seamlessly with existing business systems.

• AI frameworks are making a significant impact in various sectors, including customer service, finance, healthcare, e-commerce, manufacturing, and IT/cybersecurity, by automating tasks and enhancing operational efficiency.

• The best choice of AI agent framework depends on your goals. Some popular options, like LangChain, AutoGen, CrewAI, and Microsoft Semantic Kernel, excel in different situations, so choose the one that best fits your project.

What are AI Agent Frameworks?

Before learning about the best frameworks for creating AI agents, it's important to understand the key components involved. Let's explore them below.

Agent Architecture

Agent architecture refers to the structure of an AI agent. This includes how it makes decisions, how it remembers information, and how it interacts with others.

Environment Interfaces

Environment interfaces are tools that connect agents to the environments in which they operate.

Task Management

Task management systems help define, assign, and monitor tasks that agents need to complete.

Communication Protocols

Communication protocols are methods that enable agents to interact with each other and with humans.

Learning Mechanisms

Learning mechanisms employ various machine learning models and draw from a knowledge base to enable agents to improve their skills over time.

Integration Tools

Integration tools connect agents to external data sources, APIs, and other software systems.

Monitoring and Debugging

Monitoring and debugging features of the agents enable developers to observe agent behavior, track their performance, and identify any issues.

Orchestration Layer

The orchestration layer manages how tasks flow, allocates resources, and coordinates agents. It is like a "traffic controller" that manages task flow and coordinates multiple agents in complex workflows.

What Are the Key Features of an Excellent AI Agent Framework?

In AI development, autonomous agents have shown significant advancements. However, when considering these intelligent agents, it is crucial to understand the features that distinguish these AI agent frameworks. So, what makes an AI agent framework excellent? Well, it is the features. Modern AI agent frameworks give language models the tools and features to:

• Plan complex tasks execution effectively  
• Use tools and connect to APIs  
• Retain information across interactions  
• Manage workflows, including error handling 
• Collaborate with other agents  
• Access data sources for enhanced responses  

Now that we understand what makes a great AI agent framework, we can proceed. We will move forward to explore the best AI agent frameworks that have these features.

Top 9 Artificial Intelligence Agent Frameworks

When building AI agents, there are vast framework options, but selecting the right platform requires knowing how each one meets specific business needs and technical requirements.

Let’s look at some of the best AI agent frameworks and gain insights from them.

1. Langchain

Langchain is a flexible framework that helps developers build applications using large language models. This framework has over 14,000 stars on GitHub and 4.2 million downloads each month, showing its popularity among businesses. It offers numerous tools for creating AI agents that can perform tasks, solve complex problems, and interact with APIs and other data sources.

Why LangChain?

Developers often face challenges when using language models, such as maintaining context in lengthy conversations, accessing external information, and managing multi-step projects. Langchain effectively addresses these challenges.

Key Features of LangChain

• LangChain helps you create complex workflows using chains and agents.

• It can connect with various large language models like OpenAI and Hugging Face.

• You can design and customize prompts easily.

• You can analyze the agent's behavior.

• LangChain also includes tools for web scraping, using APIs, and querying databases.

• It supports semantic search and vector storage.

• LangChain allows you to process different data types with multimodal agents.

• It can generate contextually aware outputs by reasoning across different domains.

Types of Applications that Can Be Built Using LangChain

• Conversational AI assistants

• Autonomous research and task completion systems

• Document analysis and Q&A agents

• Code generation and analysis tools

• Personalized recommendation systems

• Content summarization and generation

2. LangGraph

LangGraph is a graph-based framework for building agents. It helps you build applications with multiple users that use large language models. LangGraph adds new features to LangChain by allowing you to create and manage cyclical graphs. This is essential for developing advanced applications that use multiple and specialized agents. With LangGraph, developers can define and coordinate multiple LLM agents. This makes it easier for them to share information and execute tasks in the right order.

Why LangGraph?

It is perfect for scenarios that require cyclical or branching logic. This allows agents to revisit previous steps and adjust their actions based on new information.

Key Features of LangGraph

• LangGraph provides a simple API for AI agent development and the creation of user-friendly interfaces.

• LangGraph takes care of how agents work together and manage data, ensuring everything runs smoothly.

• It allows agents to revisit earlier steps and adjust to new situations.

• LangGraph maintains data availability across various application uses.

• It seamlessly integrates with LangChain, providing you access to many tools and models.

LangGraph Use Cases for Enterprises

• Customer Support Automation: An agent analyzes a customer's query, another agent checks their account status. And a third agent generates a personalized response.

• Legal Document Processing: Agents collaborate to review a legal document, identify key clauses, and summarize potential risks.

3. LlamaIndex

LlamaIndex, formerly known as GPT Index, is an open-source AI framework that facilitates the integration of private and public data for building collaborative AI systems using large language models. It provides tools for data collection, organization, and searching. This makes it a go-to choice for generative AI workflows.

LlamaIndex offers an efficient solution for connecting to and ingesting data from a diverse range of sources. These sources include APIs, PDFs, SQL and NoSQL databases, various document formats, online platforms such as Notion and Slack, as well as code repositories like GitHub.

According to a report by Gartner, over 80% of enterprise data is unstructured. This makes frameworks like LlamaIndex essential for leveraging that information.

Key features of LlamaIndex

• LlamaIndex simplifies the process of connecting and gathering data from different sources.

• It offers various indexing models to help with data exploration and organization.

• It provides an efficient data retrieval and query interface.

• Llama Index offers flexibility by providing high-level APIs for beginners and low-level APIs for experts.

Applications of LlamaIndex

• Corporate knowledge libraries

• HR and employee assistance portals

• Turning unstructured data into an LLM-powered workflow.

4. CrewAI, Inc.

CrewAI is a tool that helps manage AI agents that work together as a team. With this framework, developers can create a "crew" of AI agents, each with its own individual responsibilities, to work collaboratively on complicated tasks.

Key Features of CrewAIA

• CrewAI provides a role-based agent architecture with dynamic task planning and delegation capabilities.

• It provides sophisticated inter-agent collaboration protocols.

• It has built-in conflict resolution systems, performance monitoring, and optimization tools

• API integration for enhanced agent functionality

• It eases managing AI agents through communicating using natural language understanding

• It supports enhanced teamwork among multiple agents using role-based AI collaboration.

What AI agents can be built with CrewAI?

• AI-driven project management tools

• Automated data analysis and reporting assistants

• AI-enabled IT and business process automation

Recommended Read: A Guide to Agentic Process Automation

5. OpenAI Swarm

OpenAI Swarm framework focuses on multi-agent collaboration. It's a popular framework with 20.2k stars on GitHub. It enables AI agents to easily connect with external sources, coordinate, and tackle complex processes. This framework is especially useful for complex AI systems that need multiple AI agents to collaborate.

Key Features of OpenAI Swarm

• OpenAI Swarm has a multi-agent coordination system to allow intelligent agents to work together.

• It uses OpenAI's language models for advanced decision-making abilities.

• The system enhances data processing and communication through integrated natural language processing tools.

• It provides seamless API integrations that give access to external knowledge sources.

What AI agents can be built with OpenAI Swarm?

1. AI-powered research teams  

2. AI-driven security systems  

3. Automated content generators

6. Microsoft Semantic Kernel

Semantic Kernel is an open-source framework from Microsoft that helps integrate AI models, like LLMs, into applications. It provides a way to organize AI tasks into clear steps. This makes it useful for automating IT and customer support tasks such as sorting emails, summarizing meetings, and managing workflows.

Using Semantic Kernel helps organizations adopt AI gradually without needing to rewrite their code completely. Businesses that use Microsoft products can easily integrate tools like Dynamics 365 and Outlook.

Key Features of Microsoft Semantics Kernel

• Microsoft's semantics kernel offers seamless integration of AI capabilities into applications.

• It provides multi-language support and relevant context.

• The Semantic Kernel features robust security and compliance.

• Semantic Kernal has enterprise-grade AI SDKs for lightweight integration.

What AI agents can be built with Semantic Kernel?

• Enterprise customer support chatbots and virtual assistants

• Intelligent process automation

• AI-enhanced productivity tools

• Natural language interfaces for applications

7. AutoGPT

AutoGPT is a new, open-source AI tool that operates independently. It is based on the same technology as ChatGPT. With AutoGPT, you can give one prompt or set of instructions, and it will break down the task into smaller steps to achieve the main goal you have set.

This framework automates the process of asking multiple questions that you would typically need to manage when using a chatbot like ChatGPT. The code for AutoGPT is mostly written in Python and was created by Toran Bruce Richards.

Key Features of AutoGPT

• AutoGPT is different from traditional software development or developer tools because it can complete tasks on its own. It breaks tasks into logical steps without needing detailed prompts.

• The autonomous AI agents in AutoGPT can browse the web, analyze data, and retrieve real-time information to reach their goals.

• It integrates easily into existing workflows that makes it useful for developers and businesses.

What AI agents can be built with AutoGPT?

• Advanced conversational AI chatbots

• Website Development

• Content Automation

8. Microsoft AutoGen v0.4

AutoGen created by Microsoft, is an open-source tool for creating agents. It is designed for complex systems with multiple agents. It allows each agent to tackle complex tasks while working together in real-time. AutoGen is ideal for situations that require robust task automation, task assignment, and seamless integration with large language models.

Auto Gen emphasizes modularity, extensibility, and ease of use. It helps developers build advanced AI systems quickly and navigate through the development process efficiently.

Why AutoGen?

Its emphasis on modularity and extensibility makes it easy for developers to build advanced AI systems quickly. It’s perfect for creating a "human-in-the-loop" experience where a human can step in to guide the conversation.

Key Features of Microsoft AutoGen

• It is a multi-agent conversation framework

• AutoGen offers customizable agent roles and behaviors

• Microsoft AutoGen enhances conversational memory management

• AutoGen has built-in error handling and task recovery mechanisms

• It can seamlessly integrate with external tools and services

Microsoft AutoGen Use Cases for Enterprises

• Advanced conversational AI systems

• Automated coding assistants

• Intelligent tutoring platforms

• Automated customer support agents

• Creative writing systems

• Task planning agents

Recommended Read: Agentic AI vs Traditional Automation: Which Gives best Enterprise ROI?

9. Agno (by Phidata)

Agno is a minimalist framework that follows the “less is more” principle. It uses clean Python syntax and has built-in features for cloud deployment. This makes it great for quickly creating prototypes.

If you want to make a Twitter sentiment bot on AWS Lambda, Agno is a good starting point. It integrates seamlessly with LLMs and APIs, like DuckDuckGo or Yahoo Finance.

Using Agno, you can build basic AI agents and advanced ones using function calling, structured output, and fine-tuning. Agno also provides free, Pro, and enterprise pricing.

Key Features of Agno

• Agno includes a built-in agent UI for managing local and cloud-based agentic AI projects and handles sessions automatically.

• You can publish your agents to GitHub or any cloud service, or connect your AWS account for production deployment.

• Track important metrics by viewing your sessions, API calls, and tokens, and adjust settings to enhance your agents' performance.

• Use templates to accelerate the development and deployment of your AI agents with ready-made codebases.

• Agno integrates seamlessly with AWS, allowing you to run your entire application on an AWS account.

• With Agno, you can import your models and API keys from top providers like OpenAI, Anthropic, Groq, and Mistral.

• You can use Agno to create a team of agents that can share tasks and work together on complex projects.

• Agno manages the orchestration of these agents in the background.

What AI agents can be built with Agno?

• Agents with RAG Capabilities

• Advanced Agentic Systems

• Domain-Specific Agents

 Comparative Summary of the Top AI Agent Frameworks

Let’s look at the top AI agent frameworks. Below is a summary table of the leading AI agents. This will help you understand your options before making a decision.

How are AI Frameworks Transforming Enterprises?

AI agent frameworks are changing how businesses operate. They automate tasks, improve decision-making, and allow different agents to work together smoothly on complex projects.

Enhancing customer and employee experiences

When organizations leverage AI effectively, it enhances experiences for both employees and customers.

Using AI agents can enhance productivity, reduce wait times, and improve overall satisfaction. They deliver instant, accurate answers, offer personalized shopping suggestions, and support job onboarding processes.

According to McKinsey’s 2024 AI report, 65% of companies now regularly use generative AI, but many still face challenges in implementing practical use cases.

Streamlining High-volume workflows through automation

Gartner predicts that by 2026, 20% of organizations will use AI to automate management tasks. This shows that investing in generative AI is crucial for businesses to remain competitive. By automating repetitive tasks, companies can reduce errors and make quicker decisions. This helps them run more efficiently across various industries. For example, in finance, AI handles transactions and quickly detects fraud.

Improving security and ensuring compliance

AI agents are changing IT support, cybersecurity, and system monitoring by handling tasks more efficiently. They analyze network traffic, spot unusual activity, and find potential threats early on. Using machine learning, they adapt to new attack patterns. This proactive approach can lower the costs of data breaches, with some research showing an average savings of USD 2.2 million in certain cases.

Factors to Consider When Choosing an AI Agent Framework

Choosing the right AI agent framework can be challenging because there are many open-source platforms and services to choose from. To make this easier, focus on your workflow needs. Here is a simple checklist of important points to discuss with your team:

Ease of use

When choosing an AI agent framework for your project, consider these important questions:

• Is it easy to use?

• Is the framework simple to set up?

• Does your project need rapid development or require low/ no coding effort?

Scalability

It is essential to choose a framework that can help you scale as the business expands. So, that is why it is imperative to understand the queries like:

• Can your framework handle increased traffic?

• Does it grow with demand?

• Is it a cost-effective solution for scaling?

Integration

Every advancement you choose for your project must be seamlessly integrable with the existing applications and workflows. So, considering the integration capabilities with existing infrastructure is the real need.

Security

Data security is a top priority for businesses. It's crucial to select an autonomous agent framework that ensures data security.

Complexity of Workflow

If you need to create workflows with loops, conditions, or branching logic, LangGraph and LangChain are great options. LangGraph is especially good for managing complex, stateful processes. This makes it ideal for chatbots that need to retry failed steps or handle unresolved issues.

Deployment Environment

For apps that operate locally in fields like healthcare or banking, where protecting data is very important, OpenAI's Agent SDK keeps everything on your device or in your office, without needing cloud services. However, if you prefer a cloud-based approach that is accessible to non-technical users, Agno is ready to work with AWS, GCP, and serverless setups. This enables a quick and easy deployment to production with just one click.

Discuss with your team which features are most crucial for your organization. Working together in this discussion can help you gain valuable insights into what your workflows really need.

Bottom Line

AI agent frameworks are changing how teams build software. They allow you to focus on outcomes rather than infrastructure. We hope this blog has provided you with the best AI agent frameworks and insights into AI capabilities that can benefit your business. Still have a query in your mind? Our AI consulting experts can guide you with everything you need to get started.

With modular flows, built-in tools, and an LLM-native design, we help you develop AI agents that work in production with complete control over them. Build your personalized AI Agent by connecting with us today.