10 best AI agent frameworks

Since the AI boom that kicked off with the public release of ChatGPT in 2022, the technology has advanced rapidly, leading to the release of other large language models (LLMs) like DeepSeek, Claude, and LLaMA.

While LLMs are great at reasoning, they were originally stuck inside a text-based box with no way to interact with the world. Over time, that started to change with new features like image generation, file handling, and code execution, making these models much more capable.

This shift is what gave birth to the concept of AI agents. Instead of just responding to text prompts, an AI agent can interact with its environment to complete user-defined tasks. By combining reasoning, planning, and action execution via external tools, these agents can automate more complex workflows.

That all sounds great, but how do you actually build one? The easiest way is by using an AI agent framework.

To help you pick the right one, we’ll go over some of the best AI agent frameworks and platforms available in 2025. The ranking will be split into two categories: the top five paid frameworks/platforms and the top five open-source options. But before we jump into the ranking, let’s take a moment to understand what exactly is an AI agent framework and what it does for us.

What is an AI agent framework?

In general, an AI agent framework is a tool that streamlines the process of developing AI agents. Most frameworks work with popular AI models like OpenAI’s GPT, Anthropic’s Claude, and open-source models like LLaMA and Mistral, where these LLMs essentially act as the “brains” of the AI agent.

AI frameworks might come in different forms. Some are commercial platforms that offer built-in infrastructure, integrations, and sometimes even monetization options for your Agents. Others are fully open-source, giving you more development freedom. That being said, it's important to note that these categories can, and often do, coexist, and large-scale projects tend to rely on both open-source and paid infrastructure in their builds.

At the end of the day, there's no one-size-fits-all solution for AI agents, especially since the technology is still in its early stages. Some frameworks are built for specific tasks, while others facilitate integration between multiple agents to handle more complex workflows.

How to choose the best AI agent framework

Now that you have a general idea of what an AI agent framework is and the different forms it can take, let’s make things clearer by putting the criteria you might use to select a framework for your agent into a reference table.

Best AI agent framework platforms

AI agent frameworks are just one piece of the puzzle when it comes to building a scalable, commercially viable AI application. Fully featured platforms do more than just offer tooling to facilitate agent development, they also make it easier to integrate with third-party tools, handle cloud hosting, monitor performance, and distribute your agents.

1. Apify

Apify is a full-stack development platform that was originally built for data extraction and automation. Today, it has evolved into one of the most flexible solutions for building, deploying, and monetizing AI agents.

Thanks to its origins, Apify provides instant access to thousands of ready-made data acquisition and automation tools that can be readily integrated into AI agents. It also offers a comprehensive developer suite with established open-source libraries, SDKs, and APIs.

Developers can easily build and deploy AI agents, called Actors, on the platform, making them available to Apify users while also monetizing them.

• A marketplace where developers can publish, monetize, and use AI-powered Actors
• Scalable cloud infrastructure for running AI agents without managing servers
• Built-in integrations with OpenAI, Zapier, LangChain, HuggingFace, and more
• Extensive collection of APIs, SDKs, and open-source libraries to aid in the development process.
• SOC 2 Type II Compliant
• Developer community on Discord with 10k+ users

2. CrewAI

CrewAI is a platform designed for orchestrating multi-agent AI workflows. Aside from its paid plans, CrewAI also has a strong open-source community built around its crewAI open-source agent orchestration framework. This framework enables developers to build multi-agent workflows where AI agents take on specialized roles and collaborate dynamically.

On the platform, you can choose from ready-to-use agent templates or deploy your own custom agents.

• Strong open-source environment
• Ready-made templates (crews)
• Integration with various LLMs, including OpenAI, Anthropic, and open-source models

3. Relevance AI

RelevanceAI is a no-code platform entry in our list. Similar to crewAI and Apify, you can build and run AI agents and multi-agent teams. The difference is its stronger focus on a no-code approach, which makes the agent-building process more friendly to non-technical users.

• No-code tools for building AI-powered workflows
• Vector database capabilities for advanced search and retrieval tasks
• Integration with OpenAI and other LLM providers
• Pre-built Agent templates for common business use cases
• Custom AI agent deployment with API support

4. OttoGrid AI

OttoGrid AI is an AI agent framework designed to help businesses automate workflows and analyze data, with a strong focus on non-technical users. Its tools require little to no technical knowledge, making it accessible to a wider audience.

Like other platforms, it offers ready-made agent templates, but what sets it apart is how users create and interact with agents. The platform features a table-based UI for its Agents and direct interaction through an AI chat, creating a quite unique experience.

• Native integrations with HubSpot, Salesforce, Slack and Airtable.
• Enable multi-agent workflows.
• No-code Agent development.
• Ready-made Agent template gallery

5. Beam AI

Beam AI is an enterprise-focused platform that requires your company to be whitelisted before gaining access. It's the only platform on the list that doesn’t feature a default free plan and doesn’t have different standard tiers of services available to the public.

• Library of pre-trained Agent templates
• Industry-specific Agents, like Healthcare, Insurance and Property Management
• Tools to tailor AI agents to specific company needs.
• GDPR and SOC 2 Compliant.

Beam AI does not publicly disclose its pricing information. Companies need to request access to the platform before accessing their services and getting pricing information.

Best open-source AI agent frameworks

If you’re looking for flexibility and customization while keeping costs low, open-source AI agent frameworks can be a great option. Some of the platforms we covered earlier have their own open-source tools, but for the sake of diversity, this list will focus only on projects that are primarily open-source and haven’t been mentioned yet. The ranking is based on adoption rates, popularity, and overall features.

6. AutoGen

AutoGen is an open-source framework developed by Microsoft for building multi-agent AI applications. Designed with flexibility in mind, it provides a layered architecture, allowing developers to work at different levels of complexity based on the project requirements.

• Adapts to different levels of complexity, from high-level prototyping to fine-grained agent control.
• Multi-Agent Orchestration
• Works with both .NET and Python, offering flexibility across different development environments.
• Allows easy integration with external tools and AI models through the Extensions API, including OpenAI, Azure OpenAI, and code execution.

# Create an assistant agent using OpenAI's GPT-4o model
# **Example taken from AutoGen's documentation**
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main() -> None:
    agent = AssistantAgent("assistant", OpenAIChatCompletionClient(model="gpt-4o"))
    print(await agent.run(task="Say 'Hello World!'"))

asyncio.run(main())

7. LlamaIndex

LlamaIndex is an open-source framework designed to give large language models (LLMs) better ways of accessing private or structured data.

It accomplishes that by providing a set of tools to ingest, parse, index, and process your data, enabling context augmentation for LLM applications. By making external data accessible to LLMs, developers can build advanced AI-driven workflows, from retrieval-augmented generation (RAG) to conversational chatbots, autonomous agents, and document understanding tools.

• Extract data from APIs, SQL databases, PDFs, and more.
• Structures data into efficient representations for LLM consumption.
• Enable natural language question-answering with retrieval-augmented generation (RAG).
• Provide conversational interfaces for interacting with structured data.
• Augment AI Agents with external tools and API integrations.
• Monitor, experiment, and refine AI applications with built-in analytics.

# Simple Agent example that can perform basic multiplication by calling a tool
# **Example taken from LlamaIndex documentation**
import asyncio
from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.llms.openai import OpenAI

# Define a simple calculator tool
def multiply(a: float, b: float) -> float:
    """Useful for multiplying two numbers."""
    return a * b

# Create an agent workflow with our calculator tool
agent = AgentWorkflow.from_tools_or_functions(
    [multiply],
    llm=OpenAI(model="gpt-4o-mini"),
    system_prompt="You are a helpful assistant that can multiply two numbers.",
)

async def main():
    # Run the agent
    response = await agent.run("What is 1234 * 4567?")
    print(str(response))

# Run the agent
if __name__ == "__main__":
    asyncio.run(main())

8. SmolAgents

SmolAgents is an open-source AI agent framework developed by Hugging Face, built to be lightweight, modular, and easy to use. It’s designed to help you create AI agents that can handle a variety of tasks while keeping things simple and efficient.

If you're new to AI agents, SmolAgents is a great place to start. Its self-explanatory syntax, clear documentation, and beginner-friendly courses make it easy to build and customize your first Agent.

• Designed with minimal abstractions, making it easy to understand and extend.
• Agents execute actions directly in code and can run securely in sandboxed environments like Docker or E2B.
• Easily share, discover, and pull tools from the Hugging Face community hub.
• Supports a range of input types, including text, vision, video, and audio.
• Works with various external tools, including LangChain, Anthropic's MCP, and Hugging Face Spaces.

# Example taken from the SmolAgents documentation
from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel

model = HfApiModel()
agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)

agent.run("How many seconds would it take for a leopard at full speed to run through Pont des Arts?")

9. LangGraph

LangGraph is an open-source framework for building stateful, multi-actor applications with LLMs. Developed by LangChain Inc., it extends LangChain by introducing a graph-based execution model that enables structured and dynamic workflows.

At its core, LangGraph features a central persistence layer that supports memory for conversational agents, human-in-the-loop interactions, and checkpointing for execution resumption. Additionally, LangGraph is trusted by big industry names like LinkedIn, Uber, and GitLab for production-grade AI agents.

• Retains context across interactions, allowing for long-term AI workflows.
• Supports manual human intervention and validation without disrupting the workflow.
• Works independently or alongside LangChain, LangGraph Platform and LangSmith.

# High-level implementation example taken from the LangGraph documentation
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool

# Define the tools for the agent to use
@tool
def search(query: str):
    """Call to surf the web."""
    # This is a placeholder, but don't tell the LLM that...
    if "sf" in query.lower() or "san francisco" in query.lower():
        return "It's 60 degrees and foggy."
    return "It's 90 degrees and sunny."

tools = [search]
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)

# Initialize memory to persist state between graph runs
checkpointer = MemorySaver()

app = create_react_agent(model, tools, checkpointer=checkpointer)

# Use the agent
final_state = app.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content

10. CAMEL AI

CAMEL AI is an open-source community-driven framework focused on studying the scaling laws of AI agents.

Unlike the other frameworks we discussed so far that are focused on Agent development and industry applications, CAMEL is primarily designed to facilitate research on multi-agent behaviors, capabilities, and risks. It provides tools for simulating agent interactions, generating research data, and advancing AI through reinforcement and supervised learning.

• Agents maintain memory across interactions, enabling complex, multi-step tasks.
• Uses clear, structured code and comments as prompts, making agent interactions more interpretable.
• Developed for large-scale studies on multi-agent behaviors, capabilities, and potential risks.
• Backed by a global network of over 100 researchers contributing to multi-agent AI advancements.

# Partial example taken from the CAMEL AI codebase.
from camel.agents import ChatAgent
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType

sys_msg = "You are a helpful assistant."
usr_msg = """Who is the best basketball player in the world?
Tell about his carrer.
"""

openai_model = ModelFactory.create(
    model_platform=ModelPlatformType.DEFAULT,
    model_type=ModelType.DEFAULT,
)

openai_agent = ChatAgent(
    system_message=sys_msg,
    model=openai_model,
)

# 1st run: the ordinary response

response = openai_agent.step(usr_msg)
print(response.msgs[0].content)

Conclusion: build agents with a platform or open-source framework

For businesses and enterprises, platforms like Apify, CrewAI, and Relevance AI offer a wide range of platform features with built-in integrations, scalability, and support. If you’re an Agent developer seeking monetization, Apify also provides a great marketplace to distribute your solutions.

For those seeking flexibility and customization, open-source frameworks like LangGraph, LlamaIndex, and SmolAgents offer modular solutions with greater control over agent behavior, execution, and integrations. These can be a great choice for hobbyists as well as large enterprises who have enough resources to build tailored internal solutions.

In the end, there’s no one-size-fits-all solution for AI Agents, it all comes down to your goals and the level of control you need. That said, the tools in this article are among the most reputable available, and many offer free plans. So don’t hesitate to try them out and see which one best fits your needs.

Note: This evaluation is based on our understanding of information available to us as of March 2025. Readers should conduct their own research for detailed comparisons. Product names, logos, and brands are used for identification only and remain the property of their respective owners. Their use does not imply affiliation or endorsement.

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