Recently, the topic of AI agents has gained immense popularity, with the rapid advancement of reasoning model performance igniting a heightened interest in the application prospects of agents. Although the concept of agents has existed for a long time, supported by the reasoning capabilities of large models, the value expectations of AI agents within business systems have shown exponential growth, promising to perceive the environment and execute more intelligent operations based on inputs.
The AI agent craze has also spawned a series of excellent framework solutions. This article will guide you through five different AI agent frameworks and provide a comprehensive review to explore which option best fits your use case needs.
LangGraph
As the name suggests, LangGraph comes from the LangChain development team and constructs AI agent systems using graphical technology. In other words, we can describe the operational steps and action directions taken by the agent in detail using a graphical format.
This framework is used to build agents with state attributes. It provides fine-grained control over applications and excels in handling specific workflows involving a large number of complex tasks, such as automated decision-making and multi-step processes. The LangGraph library is built on top of LangChain, allowing the use of various features provided by the latter, and it can also work with LangSmith to manage the complete lifecycle of applications.
LangGraph is suitable for scenarios involving dynamic decision-making and human intervention. For example, LangGraph can set human intervention times, effectively combining human experience with customer service agent systems.
LangGraph has strong detail handling capabilities, but it requires users to have an in-depth understanding of graph-based workflows, making the learning curve steeper compared to other frameworks. However, once the basic knowledge of AI agents is mastered, its outstanding performance is definitely worth it.
CrewAI
CrewAI is a Python framework for developing multi-agent systems. With a user-friendly API, this framework effectively promotes collaboration among agents to solve complex tasks.
With the assistance of CrewAI, we can develop role-playing AI agents with clear objectives, backgrounds, and accompanying tools. It can also assign tasks and describe the goals to be achieved in detail. Through collaboration among various combinations, the CrewAI framework can combine agents into a collaborative AI system, focusing on their specific tasks while achieving common goals through information sharing.
The CrewAI framework is suitable for systems that require multi-agent collaboration, such as research projects or project management. We can assign a task to each agent, such as data collection, data analysis, and report generation. CrewAI will create teams of agents focused on specific tasks and then integrate them into a multi-task agent system.
CrewAI is easier to use than other frameworks and provides a certain level of orchestration detail. However, its downside is the inconsistency of results when targeting specific use cases, and it requires deep performance optimization to run stably in complex tasks.
Smolagents
Smolagents is an AI agent framework released by the Hugging Face team, designed with a lightweight approach, allowing for the construction of any agent while inheriting various large models and tool resources from the Hugging Face Hub.
This framework is mainly used for the early development and prototyping of agents, focusing more on simplicity and rapid prototyping. We can even quickly build AI agents with just one line of code, making Smolagents the ideal option for testing ideas with clear use case validation.
Smolagents performs well in tasks that do not require complex orchestration, including simple chatbots or Q&A agents. Users can also integrate it with the Hugging Face Hub to greatly expand its usability and reusability.
However, simplicity can be a double-edged sword, making this framework unsuitable for large-scale and more complex interactive agents, especially multi-agent systems. If necessary, it can be used alongside other AI agent frameworks to improve stability.
Autogen
AutoGen is developed by the Microsoft team, using conversational agents to build multi-agent systems and is one of the earliest frameworks to focus on constructing AI agent systems.
This framework is suitable for developing multi-agent systems for scalable and distributed application scenarios, fully meeting the collaboration needs of agents in real-time environments. It also supports tool execution and function calling, allowing agent systems to independently perform complex tasks.
If you plan to build large applications that require complex systems and real-time data processing (such as financial trading systems or real-time monitoring systems), then AutoGen is a good choice. It is also the best solution for handling real-time use cases.
However, this is a complex framework with a steep learning curve, and improper operation can lead to skyrocketing computational costs. Therefore, if you are just starting to build AI agents, please use it cautiously.
Phidata
As the last contender, Phidata is a multimodal agent framework that can be used to develop collaborative execution agent systems. It can also work with memory and other tool components to achieve autonomous and consistent execution performance.
Phidata agents natively support multimodal data, including text, images, and audio, without relying on external tools. If you want to interact visually with the agents, the Phidata framework also offers Agentic UI. Additionally, it is a pioneer in the Agentic RAG field, allowing agents to search their knowledge bases.
Phidata is suitable for developing systems targeting specific domains, especially large-scale scenarios requiring collaboration among multiple dedicated agents, such as AI assistants for financial trading or research development.
Phidata is not difficult to get started with, but if you want the results to run stably in a production environment, the learning curve is not gentle. Once misconfigured, its resource consumption can be quite excessive, so please adjust agent settings carefully.
Conclusion
This article explored and compared five framework options for building AI agent systems. The summary conclusion is:
·LangGraph: Suitable for detail-rich, stateful systems requiring human intervention.
·CrewAI: Suitable for multi-agent scenarios emphasizing collaboration.
·Smolagents: Suitable for rapid prototyping and lightweight tasks.
·Autogen: Suitable for systems emphasizing real-time, scalable capabilities.
·Phidata: Suitable for multimodal, domain-specific collaborative scenarios.
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