Like most software developers, AI engineers use trusted development frameworks to build complex applications. They also use AI agent frameworks for advanced agentic systems to design, train, and manage intelligent AI agents.
These frameworks simplify the creation and management of AI agents by offering pre-built tools for tasks like workflow management and API integration.
The text highlights five popular frameworks Hugging Face Transformers, CrewAI, LangChain, OpenAI Gym, and TensorFlow Agents. Each framework caters to different needs, offering features such as pre-trained models, agent orchestration, and reinforcement learning environments. While each framework provides distinct advantages, limitations like computational resource requirements or complex setups exist.
These frameworks are crucial in developing advanced AI agentic systems, enabling developers to focus on problem-solving and efficient deployment.
By mid-2025, the landscape has further evolved – new AI agent frameworks have emerged (highlighted later in this article), offering even more options for autonomous systems development.
AI agent frameworks are software platforms, libraries, or environments that help AI developers build intelligent agents and autonomous agentic systems.
Simply put, these systems simplify the process of building and managing intelligent agents by providing ready-to-use tools and structures.
These frameworks handle varying tasks like managing workflows, integrating APIs, handling memory, and enabling complex decision-making. That way, you don’t have to reinvent the wheel, and you can focus on problem-solving features. Click this to learn more about AI agents use cases, especially for businesses.
Agentic systems (also known as autonomous AI agents) are advanced forms of AI that can perform complex planning, reasoning, and workflow orchestration with minimal human intervention.
Because they’re so autonomous and can simulate human decision-making as closely as possible, agentic systems can drastically improve productivity, support employees in gaining new skills, and enhance your company’s bottom line while automating routine or repetitive tasks.
The benefits continue; no wonder Salesforce has added two fully autonomous sales agents to help train and scale their sales teams.
With so many platforms and services available, it’s easy to be worried about selecting the best AI agent frameworks for your project.
Below, we’ve listed five that cover different needs.
Transformers Agents by Hugging Face is an open-source framework for deep learning that allows developers to download pre-trained transformer models like GPT, BERT, and T5 to build, test, and deploy AI agents that can handle complex NLP tasks like text classification, translation, summarization, and named-entity recognition.
These pre-trained models can also be fine-tuned to enhance performance through unified, user-friendly, and flexible API tools, Its Inference API allows developers to deploy AI models without needing extensive computational resources, making it more accessible to smaller teams.
Besides natural language processing, transformer models also support tasks in different modalities, such as:
However, a potential drawback is that transformer models require more computational resources and memory to train and run, especially compared to recurrent neural networks (RNNs).
Update : Hugging Face recently launched the SmolAgents library – a minimalist agent framework that allows developers to run powerful multi-step AI agents in just a few lines of code, with simple integration of any LLM and robust tool support.
CrewAI is an open-source Python-based framework designed to help developers build, manage, and deploy multi-agent AI systems. These autonomous AI agents work together like a “crew” to complete complex tasks.
With CrewAI, developers can assign specific roles (role-based architecture) and enable autonomous decision-making between agents. This allows them to solve complex problems faster and more effectively than individual agents that work alone.
Other key features of Crew AI include:
Update: CrewAI has rapidly evolved with frequent releases (reaching v0.119 by May 2025). New capabilities include multimodal agent support, improved collaborative workflows, and expanded integrations (e.g., support for additional cloud platforms and vector databases), making it even more enterprise-ready.
LangChain is an open-source framework for building complex applications with language models. Its structured prompt templates and integration with most LLMs make it ideal for handling text-heavy workflows like summarization or advanced research tools.
In addition, LangChain’s features, like the Model I/O module, allow it to interact with most language models, performing tasks like managing inputs and extracting information from outputs. It also has prompt templates that allow devs to create structured prompts for LLMs.
However, it has a steep learning curve. LangChain may require explicit configuration of memory and context windows, making its workflows difficult for new users to understand and set up.
Learn more about LangChain vs LlamaIndex
Update: The LangChain ecosystem introduced LangGraph (including a new “Swarm” capability) to better support multi-agent systems and large toolchains within LangChain workflows. This addition, along with continual improvements in memory handling and evaluation tools, reflects LangChain’s ongoing evolution to meet more complex application needs.
OpenAI Gym is an open-source Python library that provides a standardized interface for developing and comparing reinforcement learning (RL) algorithms.
For context, reinforcement learning allows an agent to interact with and learn behaviors from its environment through trial and error.
OpenAI Gym streamlines this process by providing features and tools like:
Perhaps one of OpenAI Gym’s limitations is its limited support for multi-agent environments. Many newbies have also reported it has a complex setup.
Like OpenAI Gym, TensorFlow Agents or TF-Agents is an open-source, versatile, and user-friendly library for developing Reinforcement Learning (RL) algorithms and applications.
TF-Agents streamlines this process by providing features and tools like:
Since TensorFlow builds it, TF-Agents also integrates with TensorFlow 2.x, helping developers access more powerful features and build complex models.
Perhaps one drawback to consider is the limited Windows support compared to platforms like Linux.
As generative AI evolves, AI agent frameworks have become crucial when building autonomous and advanced agentic systems. They provide the necessary tools and infrastructure so devs can focus on solving specific problems and deploying AI agents quickly.
AutoGen is an open-source framework developed by Microsoft Research’s AI Frontiers Lab for building advanced AI agent systems. In early 2025, AutoGen v0.4 introduced a complete redesign focused on improving code quality, robustness, usability, and the scalability of multi-agent workflows.
The framework simplifies the creation of event-driven, distributed agents and supports multiple large and small language models, integrated tools, and complex multi-agent design patterns – establishing a strong ecosystem for agentic AI development.
MetaGPT is an open-source multi-agent framework that transforms a single user prompt into a structured collaborative workflow, simulating a software development team. It assigns specialized AI agents to roles like Product Manager, Developer, and Tester, enabling the agents to plan, write, and review code collectively. Developed by DeepWisdom (led by Chenglin Wu), MetaGPT gained rapid popularity in 2025 for its straightforward approach to orchestrating LLM-powered agents using familiar software engineering processes.
OpenDevin is an open-source dual-agent system aimed at replicating an autonomous AI software engineer. It combines a Planner agent for strategic problem-solving with a CodeAct agent for precise code implementation. This architecture allows OpenDevin to tackle complex programming challenges while collaborating with human developers to ensure high-quality results and accelerated development timelines.
As generative AI evolves, AI agent frameworks have become crucial when building autonomous and advanced agentic systems. They provide the necessary tools and infrastructure so developers can focus on solving specific problems and deploying AI agents quickly.
If you’re looking for suitable AI agent Frameworks for any advanced agentic systems you plan on building, here are important factors to consider before making a choice:
Agentic systems can be single-agent or multi-agent, each with different features and applications.
In a single-agent system, a standalone AI entity performs specific tasks independently. It follows a set of rules and algorithms to reach its objectives without interacting with other agents.
For example, the robotic vacuum cleaner in your home is a single-agent system. It autonomously moves between spaces, decides on cleaning paths based on sensors, and can optimize performance without collaborating with other devices.
On the other hand, a multi-agent system (MAS) is a network of multiple AI entities cooperating, communicating, and coordinating their actions to solve complex problems or achieve common goals.
An example of a multi-agent application could be a traffic management system designed to optimize traffic flow and enhance security in a smart city. This may be implemented through autonomous vehicles, surveillance drones, traffic signals, and road sensors, all acting as agents working together in real-time to achieve coordinated traffic control and monitoring.
Some futuristic use cases, like autonomous drone coordination for logistics or AI-driven financial trading systems, also showcase the versatility of MAS and how they can tackle large-scale, complex challenges.
Integration is crucial when choosing the tools in your tech stack. So, think about this when selecting the best AI agent frameworks for advanced agentic systems; some aspects you should keep in mind:
An AI agent framework with strong integration capabilities keeps business operations running smoothly without disruption.
In addition, you should consider the ease of development. While some frameworks require advanced programming expertise (so you naturally need a proficient team), others have user-friendly interfaces, pre-built components, or low-code/no-code capabilities that make developing AI Agents easier and may even be done with minimal expertise or coding knowledge.
Ethical concerns are a notable issue when working in the artificial intelligence, and the same applies to AI agent frameworks for advanced agentic systems. Addressing concerns such as transparency, data privacy, bias mitigation, and responsible AI compliance is essential. Adhering to these standards helps to ensure that your systems work pretty and securely and that you stay compliant with regulations that may cause legal, financial, and reputational risks.
Finally, but no less critical, you need to consider the cost associated with any AI agent framework you choose. Some are free, open-source options, while others require licensing or subscription fees. Also, don’t be overly fixated on the upfront costs. Consider ongoing expenses like maintenance, scaling, and upgrades to understand better the investment required.
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There’s no single best AI agent framework. The ideal choice depends heavily on your specific project needs and goals.
AI Agents are tools made for specific task and may often have limited autonomy, while Agentic AI is a broader concept that allows systems to adapt, learn, and make decisions based on their environment, all within a defined scope.
When choosing an AI agent framework, factors like the complexity of your project, your team’s expertise, integration capabilities, ethical considerations, cost, and the desired level of control will guide you toward the most suitable option for your goals.
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