AI agent frameworks have become essential for developing applications that think, act, and adapt. These frameworks allow AI systems to handle tasks, process data in real time, and improve based on feedback.

As we approach 2025, choosing the right framework can help you build scalable, responsive solutions—whether you’re developing data-driven applications or memory-based conversational agents. Here’s a look at some popular frameworks that offer diverse features to meet today’s evolving project demands.

Autogen: The flexible multitasker

Autogen has earned its place as a robust, adaptable framework ideal for complex setups. Known for handling multiple agents and real-time data processing, it’s well-suited to scenarios requiring multitasking. Autogen’s planning agent feature is particularly helpful, allowing developers to map out complex workflows and adjust in real time. The active community on GitHub is a big plus, offering ample support for troubleshooting.

• Strengths: Highly adaptable and excellent for handling multi-agent and real-time environments.
• Drawbacks: Initial setup demands more coding effort, which may be challenging for newer developers.

Autogen is ideal for experienced developers aiming to build multifaceted AI applications and seeking flexibility in design.

Semantic Kernel: Optimized for memory and reusability

Semantic Kernel works especially well alongside Autogen, with features designed for continuous data management and built-in memory functions. This means it can “remember” past interactions, making it perfect for AI that needs to recall user preferences. Reusability is another standout feature, allowing developers to integrate components across various projects seamlessly.

• Strengths: Works smoothly with Autogen, has memory capabilities, and is great for building reusable components.
• Drawbacks: Primarily supports C#, with a slower rollout for Python, which may limit its accessibility.

For applications that require continuity and personalization, Semantic Kernel’s memory feature is particularly valuable.

Promptflow: Structured (but sometimes cumbersome)

• Strengths: Supports structured prompts and workflows for specialized projects.
• Drawbacks: Known for slower setup times and a less user-friendly interface, especially when working with Azure.

Promptflow includes features that are valuable for specific structured workflows, though users have reported challenges with its performance. Initial setup can feel slow, and it doesn’t integrate as smoothly as other frameworks, especially with Azure. It may not be as flexible as other frameworks for developers looking for an intuitive, easy-to-deploy option.

Promptflow can work well in cases where structured prompts are essential, but teams seeking ease of use might find it somewhat limited.

LangChain: Well-known for language models (but has some quirks)

LangChain has grown in popularity, especially within the large language model (LLM) community, thanks to its focus on language-based AI projects. It’s backed by a large user base and offers extensive features, though some developers have noted occasional reliability issues, particularly with larger projects.

• Strengths: Strong community support and a broad set of features for text-based applications.
• Drawbacks: Some users report stability challenges, making it potentially less suitable for enterprise-scale applications.

LangChain is an excellent choice for developers focused on LLM-based projects, though stability might need extra attention in larger deployments.

CrewAI: Simple setup, perfect for fast results

CrewAI is designed for simplicity, making it a beginner-friendly framework that minimizes the coding required for setup. It’s a great choice for developers who want to get an agent up and running quickly without delving into complex configurations. However, it lacks streaming functionality, which could be a limitation for real-time applications.

• Strengths: Minimal coding required for setup, which is helpful for quick deployment and prototyping.
• Drawbacks: Lacks support for streaming functions, limiting its use in high-speed or interactive applications.

CrewAI is ideal for new developers or teams looking to quickly create a basic agent without extensive configuration.

MemGPT: A Unique addition to AI memory

Though not a full agent framework, MemGPT enhances an AI’s memory, allowing it to store and recall information beyond the typical context window. This feature gives AI applications a more human-like feel by “remembering” past interactions, which is helpful for personalization. However, limited support from the creators may make troubleshooting difficult.

• Strengths: Expands memory for more human-like interactions and longer conversational continuity.
• Drawbacks: Limited direct support, which can complicate troubleshooting.

MemGPT works best for projects where conversational context and memory are essential, though the limited support could pose challenges for more complex implementations.

LangFlow: An accessible visual interface for LangChain

LangFlow offers a graphical interface for LangChain, making it ideal for those who prefer a no-code approach. With its drag-and-drop setup, LangFlow allows developers to quickly create workflows and experiment without heavy coding. While it’s highly accessible, it may not offer the same depth of customization as code-based frameworks.

• Strengths: No-code interface, ideal for quick prototyping and experimentation.
• Drawbacks: Limited customization options compared to full-featured frameworks, which may hinder more complex projects.

LangFlow is perfect for developers who want to prototype AI workflows quickly, especially if they are already familiar with LangChain

Framework	Best For	Strengths	Drawbacks	Unique Feature
Autogen	Complex, multitasking setups	Handles live data, flexible	Code-intensive setup	Planning agent for complex workflows
Semantic Kernel	Memory-based, reusable components	Built-in memory, integrates with Autogen	C#-oriented, slower Python rollout	Component reuse across projects
Promptflow	Structured prompts	Workflow support	Slow interface, challenging setup	Prompts for structured workflows
LangChain	Large language models (LLMs)	Strong community, LLM-focused features	Stability issues at scale	Popular in LLM development
CrewAI	Fast setup, beginner-friendly	Minimal code, easy start	Lacks streaming function support	Quick setup for basic agents
MemGPT	Long-term memory capabilities	Extended conversation memory	Limited support	Enhances memory for AI interactions
LangFlow	No-code prototyping with LangChain	Visual interface, easy experimentation	Limited customizability	Drag-and-drop for quick workflows

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Picking the Right Framework for Your Needs

Each of these frameworks offers unique advantages and is suited to different project requirements. Here’s a recap to help with your selection:

• Autogen is a flexible choice for projects that need to handle multiple agents in complex, real-time setups. It’s a strong fit for workflows where various agents work together in parallel, ideal for tasks that depend on dynamic, multi-agent coordination.
• Semantic Kernel is well-suited for applications that benefit from memory retention and reusable components. It is especially useful in projects that need continuity, like virtual assistants that remember user preferences.
• Promptflow is built to manage structured prompts but can be tricky to get started with. Its setup process and interface may feel slower or less intuitive, especially in environments like Azure. Although it offers tools for building and testing prompts, it’s generally better suited for projects with stable, well-defined workflows rather than highly dynamic applications.
• CrewAI is a solid choice for straightforward projects where developers need to get agents up and running fast. Its simplicity makes it great for prototyping without a lengthy setup process, giving teams a quick-start option when complexity isn’t required.
• LangChain is popular among developers working with language-based AI, especially those building applications around large language models. It’s known for its supportive community and plenty of features designed to handle text processing and language generation. Though it’s widely used for language tasks, some developers have encountered occasional stability issues in larger projects, which may require additional adjustments when scaling up.
• MemGPT gives AI agents an extended memory, allowing them to keep track of previous conversations for more personalized interactions. This feature is a strong asset in customer support or virtual assistants, where remembering past exchanges can significantly enhance the user experience.
• LangFlow provides a visual, drag-and-drop tool within Langchain for testing and designing workflows without heavy coding. It’s a great option for experimenting with AI applications quickly and easily, particularly for those who prefer a no-code setup.

Choosing the right AI agent framework depends on your project’s goals, the complexity of your applications, and the type of user experience you want to create. Each framework offers unique strengths that cater to different needs, from real-time data handling to memory and personalization.

If you’re ready to bring a future-ready AI solution to your business, Curotec can help. Our team has the experience and expertise to guide you through every step—from framework selection to full deployment. Connect with Curotec today to explore how we can turn your AI vision into reality.