Top 11 AI agent frameworks to consider in 2025

Table of Contents

• What are AI agent frameworks?
• Key components of AI agent frameworks
• The importance of AI agent frameworks
• 1. CrewAI: Orchestrating collaboration among AI agents
• 2. Langchain: Streamlined development for LLM-powered applications
• 3. LangGraph: Advanced framework for stateful AI applications
• 4. Microsoft Semantic Kernel: Integrating AI with traditional development
• 5. Microsoft AutoGen: Building advanced AI agents
• 6. OpenAI Swarm: Lightweight multi-agent orchestration
• 7. LangFlow: Graph-based multi-agent framework
• 8. Phidata: Transforming Large Language Models into AI Agents
• 9. PromptFlow: Streamlining your AI development with visual precision
• 10. Rasa: A Leading AI agent framework for conversational AI applications
• 11. Chatbase: Build custom AI experiences with ease and versatility
• Want to build a custom AI agent for your workflow? Partner with High Peak

Feeling overwhelmed by the multitude of AI agent frameworks available? You’re not alone. With the AI agents market poised to skyrocket to USD 47.1 billion by 2030, choosing the right framework is crucial. In this blog, we aim to simplify your decision-making by presenting the top 10 frameworks to consider in 2025. Thus ensuring you make an informed choice for your needs. Let’s get started!

What are AI agent frameworks?

AI agent frameworks are essential tools for developing intelligent systems. They provide the structure and guidelines necessary for building autonomous agents that can perform tasks without human intervention. These frameworks offer developers various modules and libraries to create, test, and deploy AI agents efficiently. 

Key components of AI agent frameworks

An understanding of key components is vital for harnessing the full potential of AI agent frameworks. These components form the backbone of how intelligent systems are crafted and deployed. From architecture to learning mechanisms, each element plays a crucial role in the development process. Let’s see the details in the following sections.

Environment: The operating environment provides the context in which an AI agent operates. This includes the hardware, software, and network conditions that influence the agent’s actions and decisions.

Sensors: Tools that allow the agent to perceive its environment, such as cameras, microphones, or data collectors.

Actuators: Mechanisms that enable the agent to take action within its environment, ranging from motors in robots to commands in software.

Perception: The process of interpreting sensory data to understand the environment, which can involve image recognition, natural language processing, and data analysis.

Decision making: The logic that allows the agent to choose the best course of action based on its goals and the state of its environment, often using machine learning models or rule-based systems.

Learning: The ability of the agent to improve over time by learning from experiences, typically using techniques like supervised learning, reinforcement learning, or unsupervised learning.

Memory: Storage for the agent’s knowledge and experiences, enabling it to recall past events and use this information in decision making.

Communication: The ability for the agent to interact with other agents or humans through natural language processing, speech recognition, and other communication protocols.

Goals and objectives: The motivations that drive the agent’s behavior, which can be predefined or dynamically set based on learning and interactions.

Read more about AI agentic workflows

The importance of AI agent frameworks

AI agent frameworks hold significant value in the development of advanced technologies. They streamline the creation process and contribute to the scalability, efficiency, and robustness of AI systems. These frameworks are a cornerstone for teams working on complex projects. Let’s see the details in the following sections.

Accelerated development: Frameworks provide pre-built components and best practices. They significantly reduce the time and effort required to create sophisticated AI agents. This acceleration is invaluable in meeting stringent project timelines.

Standardization: AI agent frameworks promote consistent approaches to common challenges in AI development. This standardization facilitates collaboration and knowledge sharing within the AI community. It ensures that different teams can work together seamlessly.

Scalability: Many frameworks support the development of systems ranging from single-agent applications to complex multi-agent environments. This scalability is crucial for projects that anticipate growth or require high computational power.

Accessibility: Frameworks make advanced AI techniques more accessible to a wider range of developers and researchers. By abstracting away complex aspects of AI development, they lower the barrier to entry.

Innovation: By handling many foundational aspects of AI agent development, frameworks allow researchers and developers to focus on pushing the boundaries of what’s possible in AI. This fosters innovation and the creation of cutting-edge solutions.

Rapid prototyping: With AI agent frameworks, developers can build functional AI agents quickly. This ability to rapidly prototype shortens development time, making the process more agile and adaptive to changes.

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Now that you know what the AI agent framework is and its importance, let’s list the top 10. They are:

1. CrewAI: Orchestrating collaboration among AI agents

CrewAI is a framework for orchestrating role-playing AI agents. It allows developers to create a “crew” of AI agents that can work together on complex tasks. Each agent is assigned a specific role and responsibilities. Let’s see the details.

Features

• Role-based agent architecture.
• Dynamic task planning and delegation.
• Sophisticated inter-agent communication protocols.
• Flexible agent configuration for various roles.
• Real-time performance monitoring.

Benefits

• Facilitates complex task completion through role specialization.
• Scalable for different team sizes and task complexities.
• Promotes modular and reusable agent designs.
• Enhances overall efficiency with specialized agents.
• Supports rapid adaptation to changing task requirements.
• Provides robust error handling and recovery mechanisms.

CrewAI stands out among AI agent frameworks as one of the best AI agent frameworks available. It promotes efficient task management through collaborative, role-based specialization. 

Read more on using Crew AI’s multi agent AI systems

2. Langchain: Streamlined development for LLM-powered applications

Langchain is one of the AI agent frameworks and is a widely-used open-source framework that makes it easier to build applications powered by large language models (LLMs). Developers rely on Langchain to create AI agents capable of complex reasoning, task execution, and interaction with external data sources and APIs. Let’s see the details.

Features

• Extensive library of pre-built components for LLM-powered applications.
• Supports multiple LLM providers, including OpenAI and Hugging Face.
• Robust memory management and context handling.
• Modular architecture for customizable workflows.
• Advanced debugging and error handling tools.
• Seamless API integration for external data and services.

Benefits

• Large and active community that provides support and contributes to the framework’s development.
• Flexibility in designing complex agent behaviors.
• Easy integration with various data sources and external tools.
• Enhanced scalability for handling larger datasets and transactions.
• Rapid prototyping capabilities, reducing development time.
• High adaptability for evolving requirements and technologies.

Langchain is considered one of the best AI agent frameworks available today. It combines simplicity and versatility with powerful capabilities to create sophisticated AI agents.

3. LangGraph: Advanced framework for stateful AI applications

LangGraph is an extension of LangChain that uses large language models (LLMs) to create stateful, multi-actor applications. Developers find it especially useful for building complex, interactive AI systems that involve planning, reflection, and multi-agent coordination. Let’s see the details.

Features

• Stateful interactions and workflows.
• Multi-agent coordination and communication.
• Graph-based representation of agent interactions.
• Advanced scheduling and task management.
• Real-time data processing.
• Integration with external tools and APIs.

Benefits

• Enables the creation of more complex, stateful AI applications.
• Integrates seamlessly with the LangChain ecosystem.
• Supports building sophisticated multi-agent systems.
• Enhances scalability and efficiency for managing multiple agents.
• Provides robust tools for monitoring and debugging.
• Facilitates rapid development and prototyping.

LangGraph stands out among AI agent frameworks as one of the best AI agent frameworks available. It allows for the creation of advanced, multi-agent systems that are both versatile and efficient.

4. Microsoft Semantic Kernel: Integrating AI with traditional development

From an expert’s perspective, Microsoft Semantic Kernel exemplifies the next evolution in AI agent frameworks. This framework skillfully bridges traditional software development with cutting-edge AI capabilities, focusing on the seamless integration of large language models (LLMs) into existing applications. Let’s see the details.

Features

• Seamless integration of AI capabilities into applications.
• Multi-language support including C#, Python, and Java.
• Orchestrators for managing complex tasks.
• Customizable deployment options.
• Real-time data analytics integration.

Benefits

• Supports enterprise-grade applications.
• Flexible in AI model selection and combination.
• Has strong security and compliance capabilities.
• Enhances existing applications with advanced AI features.
• Reduces development time and complexity.
• Provides comprehensive support and documentation.

Microsoft Semantic Kernel stands out among AI agent frameworks as one of the best AI agent frameworks available. It provides a robust toolset for enhancing traditional applications with AI.

5. Microsoft AutoGen: Building advanced AI agents

Microsoft AutoGen represents a significant advancement in the realm of AI agent frameworks, particularly for those engaged in developing multi-agent systems. This open-source framework, courtesy of Microsoft Research, streamlines the creation of complex and specialized AI agents. Let’s see the details.

Features

• Multi-agent conversation framework.
• Supports large language models and conventional APIs.
• Customizable agent roles and behaviors.
• Scalable architecture for large deployments.
• Real-time monitoring and debugging tools.

Benefits

• Simplifies the development of complex multi-agent systems.
• Enables the creation of specialized agents for diverse tasks.
• Facilitates seamless integration of different AI models and services.
• Accelerates the implementation of AI-driven solutions.
• Ensures consistent performance across various tasks.
• Offers extensive community support and resources.

Microsoft AutoGen excels among AI agent frameworks as one of the best AI agent frameworks available. It simplifies the creation and management of sophisticated AI systems.

6. OpenAI Swarm: Lightweight multi-agent orchestration

OpenAI Swarm is an innovative and experimental open-source framework that excels in multi-agent orchestration. Designed to be lightweight, it provides a flexible and scalable solution for managing multiple AI agents. Let’s see the details.

Features

• Handoff conversations for seamless interaction between agents.
• Scalable architecture to accommodate growing demands.
• Extendability for easy integration of new functionalities.
• Lightweight and efficient, minimizing system resource usage.
• Support for a variety of AI models and APIs.

Benefits

• Choose a preferred LLM tailored to specific needs.
• Enables the addition of a knowledge base for contextual understanding.
• Built-in memory for maintaining continuity in conversations.
• Reduces operational complexity with seamless orchestration.
• Open-source nature encourages community-driven enhancements.
• Facilitates rapid deployment and experimentation.

OpenAI Swarm signifies a significant advancement in AI agent frameworks. Thus making it one of the best AI agent frameworks available for those seeking scalable and flexible solutions.

7. LangFlow: Graph-based multi-agent framework

LangFlow is a sophisticated graph-based agentic framework designed for building multi-agents capable of handling complex tasks. As part of the LangChain ecosystem, it leverages a node-based approach to simplify the development and deployment of multi-agent systems. Let’s see the details.

Features

• Node-based architecture for intuitive agent design.
• Token-by-token streaming support for real-time processing.
• Multiple deployment options to cater to diverse requirements.
• Performance monitoring with LangSmith to ensure optimal operation.
• Comprehensive support within the LangChain ecosystem.

Benefits

• Free and open-source, encouraging wide accessibility and collaboration.
• Choose a preferred LLM, offering flexibility and customization.
• Enables the addition of a knowledge base for enhanced information handling.
• Simplifies the creation of complex multi-agent workflows.
• Offers real-time insights with robust monitoring tools.
• Fosters community contributions and continuous improvement.

LangFlow stands out among AI agent frameworks as one of the best AI agent frameworks available. It provides an intuitive, scalable, and efficient solution for building advanced multi-agent systems.

Read this blog to know more about agentic AI

8. Phidata: Transforming Large Language Models into AI Agents

Phidata is a powerful Python-based framework designed to transform large language models (LLMs) into intelligent agents for AI-driven products. This framework empowers developers and organizations to efficiently deploy, monitor, and manage AI agents within various services. Thus making it an innovative AI agent framework for advancing AI solutions.

Features

• Built-in Agent UI for user-friendly agent management.
• Seamless deployment to platforms like GitHub.
• Performance monitoring with key metrics.
• Python-based for easy integration and customization.
• Integrates easily with existing workflows and ecosystems.

Benefits

• Choose a preferred LLM for flexibility and customization.
• Integrate a knowledge base for enhanced context.
• Memory retention for conversation continuity.
• Scalable and adaptable for various domains.
• Streamlined development for reduced time-to-market.
• Empowers organizations to develop advanced AI solutions efficiently.
• Phidata enables the creation of advanced AI solutions by transforming LLMs into adaptable agents.

Phidata enables the creation of advanced AI solutions by transforming large language models into adaptable agents. Thus making it an innovative AI agent framework for advancing AI applications.

9. PromptFlow: Streamlining your AI development with visual precision

PromptFlow is an advanced AI agent framework within Azure Machine Learning. It combines visual design with powerful integration and monitoring capabilities. This approach provides a streamlined solution for developing, deploying, and managing AI applications. PromptFlow’s versatility and user-friendly features make it a strong contender amongst the best AI agent frameworks available today.

Features

• Drag-and-drop interface for easy workflow configuration.
• Flexible prompt creation with Jinja templating.
• Connect diverse tools and services in one workflow.
• Built-in metrics for comprehensive performance evaluation.
• Team collaboration with version control and shared access.

Benefits

• Faster prototyping with a visual interface.
• Streamlined workflow with intuitive design tools.
• Better team coordination with shared workspace features.
• Robust deployment utilizing Azure’s infrastructure.
• Integration of various tools and custom code for tailored solutions.
• Continuous performance monitoring and optimization.

PromptFlow empowers organizations to rapidly develop sophisticated AI solutions. Thus making it an invaluable AI agent framework for efficiently deploying advanced LLM applications.

10. Rasa: A Leading AI agent framework for conversational AI applications

Rasa is an advanced framework for building conversational AI applications, combining open-source tools with enterprise-grade features. Its flexibility, extensive customization options, and robust performance make it one of the best AI agent frameworks, enabling scalable and reliable automation of customer interactions and support services.

Features

• Intent recognition for accurately identifying user intentions.
• Entity extraction to detect relevant information from user inputs.
• Conversation pathways to dynamically manage conversation flows.
• Custom actions for personalized response handling.
• Multiple input channels for integration with various communication platforms.
• Annotation for intent and entities to refine understanding of user inputs.
• Wide language support for interacting with a diverse audience.

Benefits

• Enhanced customer experience through tailored responses.
• Scalability to handle enterprise-scale applications.
• Flexibility and customization for advanced conversational AI development.
• Improved accuracy with continuous feedback and user studies.
• Seamless integration with existing systems and platforms.
• Rich conversational AI tools for comprehensive development and management.
• Collaboration, version control, and shared access for better team coordination.

Rasa provides flexible, scalable, and customizable tools for developing advanced conversational AI applications. Thus enabling reliable automation of customer interactions with its leading AI agent framework.

11. Chatbase: Build custom AI experiences with ease and versatility

Chatbase stands out as a user-friendly no-code AI agent framework that simplifies the process of creating and deploying AI-powered chatbots. While the provided sources don’t explicitly rank AI agent frameworks, they do highlight features that position Chatbase as a strong contender in the landscape of top frameworks

Features

• Create tailored chatbots using unique data across multiple platforms.
• Get a no-code interface and an Easy-to-use interface for users with limited technical skills.
• Train AI agents with data from diverse sources.
• Develop distinct AI personalities for enhanced engagement.
• Monitor and optimize chatbot performance.
• Collect leads through chatbot interactions.

Benefits

• Accessible AI creation without extensive coding
• Get 24/7 customer support and enhance response times and satisfaction
• Deliver interactions based on user preferences
• Increase efficiency through AI-driven automation and processes
• Offers a free trial for exploring AI capabilities
• Boost user interaction with AI personas

Chatbase offers an accessible, no-code platform for creating versatile AI chatbots. Thus enhancing customer support and engagement with its user-friendly AI agent framework.

Want to build a custom AI agent for your workflow? Partner with High Peak

High Peak is one of the leading AI agent development companies. We specialize in leveraging top AI agent frameworks to build custom solutions tailored to your workflow needs. 

Contact us today for a consultation on innovative and efficient AI agent development.