Top 5 AI Agent Frameworks to Explore in 2025

In today’s rapidly advancing AI technology, AI Agents have become a hot area of research and application. Both enterprises and individual developers hope to leverage AI Agents to enhance work efficiency, optimize business processes, or create entirely new user experiences. However, building an AI Agent from scratch is not an easy task. Fortunately, there are many excellent frameworks available on the market to help us quickly get started. Today, let’s take a look at the top five AI Agent frameworks worth exploring in 2025!

1. Understanding What an AI Agent Is

The most common translation I have seen for AI Agent is “Intelligent Agent“, but the direct translation is “Intelligent Proxy“.

It is an intelligent system capable of perceiving the environment, making autonomous decisions, and executing tasks to achieve specific goals. It is centered around large language models (LLMs), granting machines autonomy, adaptability, and interactivity, allowing them to operate independently in complex and changing environments. In simple terms, an AI Agent is like a smart assistant with independent thinking and action capabilities, able to understand your needs and complete a series of complex tasks by calling various tools and resources.

Just like a capable personal assistant, it can not only execute instructions but also understand the context of tasks, formulate execution plans, and flexibly adjust strategies when encountering problems.

The core of AI Agents lies in their autonomous learning and decision-making abilities, allowing them to optimize their behavior patterns through continuous experience accumulation.

AI Agents typically possess the following characteristics:

Autonomy: AI Agents can make decisions and execute tasks independently without human intervention.
Adaptability: They can adjust their behavior based on environmental changes.
Interactivity: They can communicate and collaborate with humans or other AI Agents.
Learning Ability: They continuously optimize their behavior and decisions through learning.

In today’s digital age, AI Agents are gradually becoming an important force driving the development of various industries. They are widely used in customer service, medical diagnosis, stock trading, intelligent transportation, educational tutoring, and many other fields, bringing great convenience and efficiency improvements to our lives and work. The AI Agent frameworks are key tools for building and developing these intelligent agents, providing comprehensive support for the creation, deployment, and management of AI Agents. Next, let’s delve into the five AI Agent frameworks worth exploring in 2025.

2. Framework One: AutoGen

AutoGen is an intelligent agent collaboration framework released by Microsoft, developed in collaboration with teams from Pennsylvania State University and the University of Washington, launched in October 2023. It is designed to help developers create complex applications based on large language models (LLMs) through collaboration and interaction among multiple agents to achieve smarter and more flexible task handling.

2.1 Main Features

Multi-Agent Collaboration: Supporting the integration of different large language models, AutoGen allows the creation of an agent network where each agent can work independently or collaborate with others.
Code Auto-Generation and Execution: Agents can automatically generate, execute, and debug code, which is very useful for software engineering and data analysis tasks.
External Tool Integration: Agents can interact with external tools, services, and APIs, significantly expanding their capabilities.
Pluggable Components: The system supports customization through pluggable components, including custom agents, tools, memory, and models.

2.2 Basic Usage

AutoGen supports Python and .NET, allowing developers to configure agents to request human user guidance or approval before executing specific tasks. Agents communicate asynchronously and support event-driven and request/response interaction modes.

1. For example, using Python, install the pyautogen library with the following command:

pip install pyautogen

2. The core component ConversableAgent: It manages the behavior of each role and is the base class for conversations. It is generally not used directly but serves as the parent class for other classes. It can maintain conversation states, histories, and call other tools. For example, to create an AssistantAgent (inheriting from ConversableAgent):

from autogen import AssistantAgent

# Create an AssistantAgent and set the system message
assistant = AssistantAgent(
    name="assistant",
    system_message="You are a helpful AI assistant"
)

3. UserProxyAgent: Used to simulate user input and execute code, acting as the user role. For instance:

from autogen import UserProxyAgent

# Create a UserProxyAgent and set code execution configuration
user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={"work_dir": "coding", "use_docker": False}
)

4. GroupChat: Used to manage collaboration among multiple agents, where all agents participate in the same conversation thread and share the same context. An example is as follows:

from autogen import GroupChat, GroupChatManager

# Create multiple agents
agent1 = AssistantAgent("agent1", system_message="Task description for agent1")
agent2 = AssistantAgent("agent2", system_message="Task description for agent2")

# Create a GroupChat and add agents
 groupchat = GroupChat(agents=[agent1, agent2], messages=[])
# Create GroupChatManager to manage GroupChat
manager = GroupChatManager(groupchat=groupchat)

5. Initiate a conversation: Use the initiate_chat method to start a conversation between agents, as shown below:

# Initiate a conversation between user proxy and assistant agent
user_proxy.initiate_chat(assistant, message="Please help me write an outline for an article on artificial intelligence development")

In the above code, an assistant agent assistant and a user agent user_proxy are first created, then the conversation is initiated through user_proxy with a request to generate an outline for an article on artificial intelligence development. In practical applications, you can adjust the configuration, number, and content of the agents according to specific needs to accomplish various complex tasks.

2.3 Application Scenarios

AutoGen has a wide range of application scenarios and can play an important role in multiple fields:

Software Development: It can automatically generate code, helping developers quickly implement various functions. Through collaboration among agents, it can complete a series of software development tasks from requirement analysis to code writing and testing, improving development efficiency and reducing development cycles.
Data Analysis: Assists users in automatically generating data analysis models and algorithms. Through the division of labor among multiple agents, it can quickly complete data preprocessing, feature selection, model training, and evaluation, providing strong support for data analysis.
Intelligent Customer Service: Multiple agents can work collaboratively to better understand user queries and provide accurate and quick responses. They can handle complex customer inquiries, offer personalized services, and enhance customer satisfaction.
Education Sector: Used to create intelligent tutoring systems, where agents collaborate based on students’ learning situations and questions to provide targeted tutoring and answers, helping students learn and master knowledge better.

3. Framework Two: LangGraph

LangGraph is a framework focused on natural language processing that helps developers build complex AI Agents through techniques such as loop control, state management, and human-computer interaction. It is suitable for various application scenarios, such as intelligent customer service and intelligent investment advisory.

3.1 Main Features

Loop Control: Capable of handling complex loop tasks, ensuring that the Agent maintains coherence and consistency in multi-turn dialogues.
State Management: Effectively manages the Agent’s state, allowing it to respond reasonably based on different contexts and user inputs.
Human-Computer Interaction: Provides rich human-computer interaction functions, enabling the Agent to better understand and meet user needs.
Multi-Agent Communication and Collaboration: Supports communication and collaboration among multiple agents to jointly complete complex tasks.
Highly Customizable: Can be customized according to specific needs, with rich plugin, tool, and third-party service support for easy function expansion and optimization.

3.2 Basic Usage

To use LangGraph, you first need to install the LangGraph framework by using pip to install the langgraph library with the following command:

pip install -U langgraph

During installation, you may also need to install other related dependencies, such as langchain, depending on the actual situation and error messages.

2. Then, define the Agent’s name, functions, language model, etc. By setting loop control and state management, you can create Agents capable of handling complex tasks. During development, you can use the tools and interfaces provided by LangGraph to test and optimize the Agent’s performance.

3.3 Application Scenarios

LangGraph has a wide range of application scenarios and can play an important role in multiple fields:

Interactive Narrative: Used to create interactive stories and games with rich plots and branching narratives. Through loops and branching capabilities, it dynamically generates different story developments based on user choices, providing a personalized narrative experience.
Intelligent Customer Service and After-Sales Support: When dealing with complex customer issues, it can flexibly adjust the dialogue process based on customer feedback and problem status, providing more accurate and thoughtful service. For example, it can loop back to ask for more information to resolve customer issues.
Automated Task Flow Optimization: For automated tasks that require multiple iterations and feedback, such as data cleaning, model training, and optimization in data analysis processes, LangGraph’s loop computing and state management capabilities can be utilized to achieve automation and optimization of processes.
Multi-Agent Collaboration System: Supports complex collaboration among multiple agents, clearly defining the interaction relationships and collaboration processes among agents through graph models. For example, in a smart city management system, multiple agents can be responsible for different fields such as traffic, environment, and energy, and LangGraph can coordinate their work.

4. Framework Three: Phidata

Phidata is a Python-based framework that can transform large language models (LLMs) into Agents in AI products.

Top 5 AI Agent Frameworks to Explore in 2025 It supports various mainstream closed-source and open-source LLMs from major companies, such as OpenAI, Anthropic, Cohere, Ollama, and Together AI. Through its support for databases and vector storage, we can easily connect AI systems to Postgres, PgVector, Pinecone, LanceDb, etc.

4.1 Main Features

Multi-LLM Support: Supports mainstream closed-source and open-source LLMs from major companies, such as OpenAI and Anthropic.
Database Support: Through its support for databases and vector storage, it can easily connect AI systems to Postgres, PgVector, etc.
Monitoring Key Metrics: Provides session snapshots, API calls, token usage, and supports adjustments and Agent improvements.
Built-in Agent UI: Phidata provides a ready-to-use user interface for running Agent projects locally or in the cloud, managing sessions in the background.
Template Support: Accelerates the development and production process of AI agents through pre-configured code library templates.
Flexible Deployment: You can publish the Agent to GitHub or any cloud service, or connect to AWS accounts to deploy it to the production environment.

4.2 Basic Usage

To use Phidata, you first need to install Phidata and its tools by running the following command:

pip install -U phidata

After installation, you can run commands to create a new Phidata project. In the project, you can define the Agent’s name, functions, language model, etc., and then create advanced Agents through function calls, structured outputs, and fine-tuning.

5. Framework Four: OpenAI Swarm

Swarm is an experimental multi-agent orchestration framework released by OpenAI, aimed at simplifying the construction, orchestration, and management of multi-agent systems. It is used to create and manage multiple AI Agents.

5.1 Main Features

Lightweight Design: The framework structure is simple and efficient, easy to understand and use.
Highly Controllable: Provides precise agent coordination and execution control, allowing developers to easily manage Agent behavior and task assignments.
Client-Side Execution: Almost entirely executed on the client side, reducing server-side load and improving response speed.
Stateless Design: Similar to the stateless features of the Chat Completions API, making interactions between Agents more flexible and efficient.

5.2 Basic Usage

1. Use the pip command to install the Swarm framework with the following command:

pip install git+ssh://[email protected]/openai/swarm.git

2. When using OpenAI Swarm, you need to create a client instance. First, import the Swarm class and instantiate a Swarm client, as shown below:

from swarm import Swarm

# Instantiate Swarm client
client = Swarm()

3. Define Agents, for example, create two Agents where Agent A receives user messages and forwards them to Agent B, and Agent B replies in a specific format:

from swarm import Agent

# Define a function to transfer the conversation to Agent B
def transfer_to_agent_b():
    return agent_b

# Define Agent A
agent_a = Agent(name="Agent A", instructions="You are a helpful agent, responsible for forwarding user messages to Agent B", functions=[transfer_to_agent_b])

# Define Agent B
agent_b = Agent(name="Agent B", instructions="Please answer the user's question in three sentences, each no more than 10 words")

4. Use the run function of the Swarm client, passing in the Agents and user messages to receive and process messages, as shown below:

# Run Swarm and pass in user messages
response = client.run(agent=agent_a, messages=[{"role": "user", "content": "Please recommend a good book"}])
print(response.messages[-1]["content"])

In the above code, a Swarm client client is first instantiated. Then, two Agents agent_a and agent_b are defined, where agent_a transfers the conversation to agent_b using the function transfer_to_agent_b. Finally, the client.run method is used to run Swarm, passing in the Agent agent_a and user messages, obtaining and printing the reply from Agent agent_b. In practical applications, you can increase the number of Agents, define more complex instructions and functions according to specific needs, and achieve various complex multi-agent collaborative tasks.

5.3 Application Scenarios

Quickly Build Demonstration Projects: Due to its simplified agent creation and lightweight characteristics, it is very suitable for quickly building demonstration projects to showcase multi-agent system concepts and functions, helping developers quickly validate ideas.
Education Sector: Can be used to create teaching cases and experimental projects, helping students better understand the principles and applications of multi-agent systems, enhancing students’ practical skills and innovative thinking.
Customer Service Scenarios: Build multi-agent systems, such as reception AI responsible for initial customer reception, technical support AI answering technical questions, and after-sales AI handling after-sales matters, all seamlessly cooperating through the Swarm framework to provide customers with comprehensive and efficient service experiences.
Content Creation: In content creation, different Agents can be responsible for creative conception, material collection, content writing, editing, and review, achieving automated generation of articles, video scripts, etc., improving the quality and efficiency of creation.
Data Analysis: Capable of handling a large number of independent functions and instructions that are difficult to encode into a single prompt. Swarm can help developers build multi-agent systems to achieve automated processing and analysis of data, such as data cleaning, feature extraction, and model training tasks that can be collaboratively completed by different Agents.

6. Framework Five: CrewAI

CrewAI is one of the most popular agent-based AI frameworks, capable of quickly building AI Agents and integrating them into the latest LLMs and codebases.

Released by OpenAI in August 2023, it allows developers to create a “Crew” consisting of AI Agents, each with specific roles and responsibilities, working together to complete complex tasks. This framework is especially suitable for building collaborative artificial intelligence systems to address issues requiring different expertise and coordinated work.

6.1 Main Features

Support for Multiple Models: Supports integration with various large language models (LLMs), including OpenAI and local open-source models like Ollama, providing developers with more options to choose the most suitable model based on project needs and budget.
Strong Scalability: Supports integration with over 700 applications, including Notion, Zoom, Stripe, Mailchimp, Airtable, etc.
Role Specialization: Adopts a role-based agent design, allowing developers to customize specific roles, goals, and tools for each agent.
Autonomous Delegation and Collaboration: Agents can autonomously delegate tasks and ask each other questions, improving problem-solving efficiency. When an agent encounters a problem it cannot solve alone, it can proactively seek help from other agents with relevant expertise for efficient collaboration.
Flexible Task Management: Uses customizable tools to define tasks and dynamically assign them to agents. Developers can flexibly adjust task assignments based on task requirements and agent capabilities to ensure tasks are handled appropriately.
Process-Driven: Currently mainly supports sequential task execution and hierarchical processes. For example, in a project management scenario, tasks can be completed sequentially by different agents, with the output of the previous task serving as the input for the next task.

6.2 Basic Usage

1. Install the crewai library using pip with the following command:

pip install crewai

If you want to install the full version with tools, including additional agent assistance tools, execute: pip install 'crewai[tools]'

2. Define agents with roles and goals. For example, create an agent responsible for market analysis:

from crewai import Agent
# Create a market analyst agent
market_analyst = Agent(
    role='Market Analyst',
    goal='Analyze market trends and provide valuable insights',
    backstory='An experienced market analyst with years of industry experience',
    tools=[],  # Add tools as needed
    verbose=True
)

3. Create tasks for the agents. For example, create a market trend analysis task for the above market analyst agent:

from crewai import Task
# Create a market trend analysis task
market_task = Task(
    description='Conduct an in-depth analysis of the main trends in the current market, including market size, growth trends, competitive landscape, etc.',
    agent=market_analyst
)

4. Instantiate a team and use sequential processing. For example, create a team consisting of a market analyst and a strategist, assigning tasks:

from crewai import Crew
# Create a strategist agent
strategy_maker = Agent(
    role='Strategist',
    goal='Develop corresponding market strategies based on market analysis results',
    backstory='Skilled in formulating effective strategies based on market conditions',
    tools=[],
    verbose=True
)
# Create a task requiring strategy formulation based on market analysis results
strategy_task = Task(
    description='Develop a feasible market strategy based on the market analysis report provided by the market analyst',
    agent=strategy_maker
)
# Instantiate a crew, setting agents and tasks, and specifying sequential processing
crew = Crew(
    agents=[market_analyst, strategy_maker],
    tasks=[market_task, strategy_task],
    verbose=2  # Set to 1 or 2 for different logging levels
)

5. Let the team start working and execute tasks:

# Start team tasks
result = crew.kickoff()
print(result)

In the above code, the market analyst and strategist agents are created first, with their roles, goals, background stories, etc., set. Then, corresponding tasks are created for each agent. Next, a crew is instantiated through Crew, adding agents and tasks to the team and setting the logging level. Finally, the kickoff method is used to start team tasks, executing tasks and outputting results. In practical applications, you can further expand and optimize the code based on specific needs, such as adding more agents and tasks, adjusting task assignments and execution order, etc., to achieve various complex collaborative tasks.

6.3 Application Scenarios

CrewAI has a rich and diverse range of application scenarios, especially suitable for the following:

Building Collaborative AI Systems: Such as intelligent customer service teams, where different agents can handle different areas of inquiry, collaborating to provide comprehensive service; multi-agent research teams simulate and optimize the collaboration process of research teams, improving research efficiency.
Project Management: Simulates project management teams, where different agents can handle scheduling, resource allocation, and risk assessment tasks, achieving efficient project management.
Content Creation: Multiple agents collaborate on content creation, including researchers collecting materials, writers drafting content, and editors reviewing and optimizing, improving the quality and efficiency of content creation.
Data Analysis: In data analysis scenarios, different agents can be responsible for data collection, cleaning, analysis, and visualization tasks, collaboratively completing complex data analysis work.

7. Conclusion

These are the top five AI Agent frameworks worth exploring in 2025. Each framework has its unique features and advantages, suitable for different development scenarios and needs. Whether you are a beginner or an experienced developer, you can choose the appropriate framework based on your project needs to quickly build efficient and intelligent AI Agents. I hope this article helps everyone better understand and apply AI Agent technology in the new year, jointly promoting the development of the artificial intelligence industry.