Top 5 AI Agent Frameworks to Explore in 2025

The following code snippet demonstrates the simplest AI Agent. The AI Agent solves problems using language models. The definition of an AI Agent may include large or small language models, memory, storage, external knowledge sources, vector databases, instructions, descriptions, names, etc.

For example, modern AI Agents like Windsurf can help anyone quickly generate, run, edit, build, and deploy full-stack web applications. It supports code generation and application building across various web technologies and databases such as Astro, Vite, Next.js, Superbase, etc.

Agentic AI systems have a wide range of applications in enterprise environments, especially in executing automation and repetitive tasks. Here are the key application scenarios where AI Agents are useful in the enterprise domain:

• Call and Other Analytics: Analyze participants’ video calls to gain insights into emotions, intentions, and satisfaction. Multi-Agent systems excel at analyzing and reporting user intentions, demographics, and interactions. Their analytical/reporting capabilities help businesses target potential customers or markets.

• Call Classification: Automatically classify calls based on participants’ network bandwidth and strength for efficient handling.

• Market Listening: Monitor and analyze customer sentiment in market applications.

• Survey and Review Analysis: Utilize customer feedback and survey data to gain insights and improve customer experience.

• Travel and Expense Management: Automate expense reporting, tracking, and approvals.

• Conversational Banking: Assist customers in banking transactions through AI-driven chat or voice assistants.

• General AI Supported Chatbots: Customer support agents can handle customer complaints, troubleshoot issues, and delegate complex tasks to other agents.

• Finance: Financial agents can be used to predict economic, stock, and market trends, providing actionable investment advice.

• Marketing: Marketing teams in enterprises can leverage AI Agents to create personalized content and ad copy for different target audiences, thus improving conversion rates.

• Sales: AI Agents can help analyze customer interaction patterns in systems, assisting sales teams to focus on potential customer conversions.

• Technology: In the tech industry, AI coding agents can help developers and engineers improve productivity by accelerating code completion, generation, automation, testing, and debugging.

You can use multiple Python frameworks to create and add agents to applications and services. These frameworks include no-code (visual AI agent builders), low-code, and mid-code tools. Now, I will introduce you to the top five Python-based agent builders for 2024, which you can choose based on your business needs.

1. Phidata

Phidata is a Python-based framework that converts LLMs into agents within AI products. It supports mainstream closed-source and open-source LLMs from major companies like OpenAI, Anthropic, Cohere, Ollama, and Together AI. With its support for databases and vector storage, we can easily connect AI systems to Postgres, PgVector, Pinecone, LanceDb, etc. Using Phidata, we can build basic agents or create advanced agents through function calls, structured outputs, and fine-tuning.

Main Features of Phidata

• 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.

• Deployment: You can publish agents to GitHub or any cloud service, or connect your AWS account to deploy them in production environments.

• Monitor Key Metrics: Provides session snapshots, API calls, token usage, and supports adjustments and improvements to agents.

• Template Support: Accelerates the development and production process of AI agents through pre-configured code library templates.

• Supports AWS: Phidata integrates seamlessly with AWS, allowing full applications to run on AWS accounts.

• Model Independence: Supports using advanced models and API keys from OpenAI, Anthropic, Groq, and Mistral.

• Build Multi-Agent: Using Phidata, you can create a team of agents that pass tasks to each other and collaborate to complete complex tasks. Phidata seamlessly handles the coordination of agents in the background.

Now, I will show you how to build a basic AI Agent that queries financial data from Yahoo Finance using the Phidata framework and OpenAI’s LLM. This agent is designed to summarize analysts’ recommendations for various companies through Yahoo Finance.

Install dependencies:

Create a new financial_agent.py:

import openaifrom phi.agent import Agentfrom phi.model.openai import OpenAIChatfrom phi.tools.yfinance import YFinanceToolsfrom dotenv import load_dotenvimport os
# Load environment variables from .env file
load_dotenv()
# Get API key from environment
openai.api_key = os.getenv("OPENAI_API_KEY")
# Initialize Agent
finance_agent = Agent(
    name="Finance AI Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[
        YFinanceTools(
            stock_price=True,
            analyst_recommendations=True,
            company_info=True,
            company_news=True,
        )
    ],
    instructions=["Use tables to display data"],
    show_tool_calls=True,
    markdown=True,
)
# Output summary of analyst recommendations for NVDA
finance_agent.print_response("Summarize analyst recommendations for NVDA", stream=True)

The above code:

1. Import Modules and Load API Key: First, import the required modules and packages, and load OpenAI’s API key through the .env file. This method of loading the API key also applies to other model providers such as Anthropic, Mistral, and Groq.

2. Create Agent: Use Phidata’s Agent class to create a new Agent and specify its unique features and characteristics, including model, tools, instructions, etc.

3. Print Response: Call the print_response method to output the agent’s response to a question and specify whether to display it in streaming mode (stream=True).

2. Swarm

Swarm is an open-source experimental agent framework recently released by OpenAI, designed as a lightweight Multi-Agent orchestration framework.

Note: Swarm is still in the experimental stage. It can be used for development and educational purposes but is not recommended for production environments. You can refer to the official repository for the latest information:

https://github.com/openai/swarm

Swarm uses Agents and Handoffs as abstract concepts for agent orchestration and coordination. It is a lightweight framework that facilitates testing and management. Swarm’s agents can configure tools, instructions, and other parameters to perform specific tasks.

In addition to its lightweight and simple architecture, Swarm also has the following key features:

1. Conversational Handoffs: Swarm supports building Multi-Agent systems where one agent can hand off conversations to other agents at any time.

2. Scalability: With its simplified handoff architecture, Swarm makes it easy to build agent systems that can support millions of users.

3. Customizability: Swarm is designed to be highly customizable, allowing for the creation of fully tailored agent experiences.

4. Built-in Retrieval System and Memory Handling: Swarm has built-in capabilities for storing and processing conversation content.

5. Data Privacy: Swarm primarily runs on the client side and does not retain state between calls, greatly enhancing data privacy.

6. Educational Resources: Swarm provides a range of basic to advanced Multi-Agent application examples for developers to test and learn.

Next, I will demonstrate how to use Swarm:

from swarm import Swarm, Agent
# Initialize Swarm client
client = Swarm()mini_model = "gpt-4o-mini"
# Define coordination function to hand over tasks 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 assistant.",
    functions=[transfer_to_agent_b],
)
# Define Agent B
agent_b = Agent(
    name="Agent B",
    model=mini_model,
    instructions="You speak only in Finnish.",
)
# Run agent system and get response
response = client.run(
    agent=agent_a,
    messages=[{"role": "user", "content": "I want to talk to Agent B."}],
    debug=False,
)
# Print Agent B's response
print(response.messages[-1]["content"])

The above code

1. Initialization

• Swarm is used to create a client instance.

• Agent defines the agent’s name, functions, and language model (e.g., gpt-4o-mini).

transfer_to_agent_b is a coordinator function that transfers tasks from agent_a to agent_b.

client.run() executes the agent system, passing in messages and debug parameters to track the task execution process.

If you change the language in agent_b’s instructions to another language (e.g., English, Swedish, Finnish), you will receive responses in the corresponding language.

3. CrewAI

CrewAI is one of the most popular agent-based AI frameworks, allowing for rapid construction of AI Agents and integration with the latest LLMs and codebases. Major companies like Oracle, Deloitte, and Accenture use and trust CrewAI.

Compared to other agent-based frameworks, CrewAI offers richer functionality and diverse features.

1. Scalability

   Supports integration with over 700 applications, including Notion, Zoom, Stripe, Mailchimp, Airtable, etc.

2. Tools

• Developers can build Multi-Agent automation systems from scratch using the CrewAI framework.

• Designers can create fully functional agents in a no-code environment using its UI Studio and template tools.

First, we need to install CrewAI:

The above command will install CrewAI and its tools, and verify the installation was successful.

After installation, you can run the following command to create a new CrewAI project:

After running this command, the system will prompt you to choose one from the list of model providers, such as OpenAI, Anthropic, xAI, Mistral, etc. After selecting a provider, you can also choose a specific model from the list, such as gpt-4o-mini.

The following command can be used to create a Multi-Agent system:

The complete CrewAI application has been uploaded to the GitHub repository, and you can download and run it using the following command:

https://github.com/GetStream/stream-tutorial-projects/tree/main/AI/Multi-Agent-AI

After running it, you will see a response similar to the following:

4. Autogen

Autogen is an open-source framework for building agent systems. With this framework, you can create Multi-Agent collaboration and LLM workflows.

Autogen has the following key features:

1. Cross-Language Support

   Build agents using programming languages like Python and .NET.

2. Local Agents

   Can run and experiment with agents locally to ensure higher privacy.

3. Asynchronous Messaging

   Use asynchronous messages for communication between agents.

4. Scalability

   Supports developers in building distributed agent networks suitable for collaboration between different organizations.

5. Customizability

   Customize and build fully personalized agent system experiences through its pluggable components.

The following code block builds a simple AI weather agent system:

import asynciofrom autogen_agentchat.agents import AssistantAgentfrom autogen_agentchat.task import Console, TextMentionTerminationfrom autogen_agentchat.teams import RoundRobinGroupChatfrom autogen_ext.models import OpenAIChatCompletionClient
import osfrom dotenv import load_dotenv
load_dotenv()
# Define tool
async def get_weather(city: str) -> str:    return f"The weather in {city} is 73 degrees and Sunny."
async def main() -> None:    # Define Agent    weather_agent = AssistantAgent(
        name="weather_agent",
        model_client=OpenAIChatCompletionClient(
            model="gpt-4o-mini",
            api_key=os.getenv("OPENAI_API_KEY"),
        ),
        tools=[get_weather],
    )
    # Define termination condition    termination = TextMentionTermination("TERMINATE")
    # Define Agent team    agent_team = RoundRobinGroupChat([weather_agent], termination_condition=termination)
    # Run team and stream messages to console    stream = agent_team.run_stream(task="What is the weather in New York?")    await Console(stream)
asyncio.run(main())

The above code

1. Tool Definition: get_weather is a sample tool function that returns weather information for a city.

2. Agent Definition: Define an agent using AssistantAgent and set the model client to OpenAI’s GPT-4o-mini. The API key is loaded from the .env file.

3. Termination Condition: Define a termination condition using TextMentionTermination, which terminates the task when “TERMINATE” is mentioned.

4. Agent Team: Create an agent team using RoundRobinGroupChat to assign tasks in a round-robin manner.

After running this code, the console will display output similar to the following:

5. LangGraph

LangGraph is a node-based AI framework designed for building Multi-Agent systems that handle complex tasks. As part of the LangChain ecosystem, LangGraph is a graph-structured agent framework. Users can build linear, hierarchical, and sequential workflows through nodes and edges, where nodes represent agent actions, edges represent transitions between actions, and states are another important component of LangGraph agents.

Advantages and Main Features of LangGraph

1. Free and Open Source

   LangGraph is a free library that follows the MIT open-source license.

2. Streaming Support

   Provides word-by-word streaming support, showcasing the intermediate steps and thought processes of Agents.

3. Deployment Options

   Supports various large-scale deployment methods, allowing monitoring of Agent performance through LangSmith. The enterprise version options allow for complete deployment on users’ own infrastructure.

4. Enterprise Adaptability

   Replit uses LangGraph to support its AI coding Agents, proving LangGraph’s enterprise applicability.

5. High Performance

   When handling complex Agent workflows, it does not add code overhead.

6. Loops and Control

   Easy to define Multi-Agent workflows that include loops and have complete control over Agent states.

7. Persistence

   LangGraph automatically saves the state of Agents after each operation in the graph and supports pausing and resuming execution at any point.

Visit the following address to download sample code:

https://langchain-ai.github.io/langgraph/

More AI news, please follow "PaiPai AI Academy"
https://www.pypyai.com/